{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T06:10:29Z","timestamp":1785478229085,"version":"3.56.0"},"reference-count":279,"publisher":"Springer Science and Business Media LLC","issue":"2-3","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Compartmental models have gained significant attention not only in public health studies but also in fields such as Operations Research (OR), social sciences, and logistics, particularly following the COVID-19 pandemic. Their broad applicability in epidemiology\u00a0and their utility in understanding, predicting, and controlling the global spread of infectious diseases have made them indispensable across various disciplines. The appeal of these models lies in their simplicity yet effectiveness in capturing the essential dynamics of disease transmission. This paper provides a comprehensive review of compartmental models, focusing on the Susceptible-Infectious-Recovered (SIR) models and the key aspects of their structure. The primary objective of this review is to enhance the ability of researchers and practitioners to understand and manage infectious disease outbreaks through a twofold approach: (1) an evaluation of the assumptions, equations, and methodologies used for estimating critical parameters in SIR models, and (2) an exploration of the relationship between SIR models and optimization models. Additionally, a systematic micro-level review has identified the most significant research gaps in the literature on compartmental models, leading to recommendations for future research. A key finding emphasizes the need to revisit various assumptions to clarify the connection between SIR models and optimization approaches, which is expected to offer valuable insights for epidemic disease modeling.<\/jats:p>","DOI":"10.1007\/s10479-025-06893-1","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:36:01Z","timestamp":1760956561000},"page":"1021-1078","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Compartmental models in epidemiology: bridging the gap with operations research for enhanced epidemic control"],"prefix":"10.1007","volume":"357","author":[{"given":"Fatemeh","family":"Mirsaeedi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Sheikhalishahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3726-8356","authenticated-orcid":false,"given":"Mehrdad","family":"Mohammadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Pirayesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitry","family":"Ivanov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"6893_CR1","doi-asserted-by":"publisher","DOI":"10.1101\/2021.05.07.21256860","author":"FJ Aguilar-Canto","year":"2021","unstructured":"Aguilar-Canto, F. J., Ponce de Le\u00f3n, U. A., & Avila-Vales, E. (2021). SIR-based model with multiple imperfect vaccines. medRxiv. https:\/\/doi.org\/10.1101\/2021.05.07.21256860","journal-title":"medRxiv"},{"key":"6893_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2020.556366","volume":"7","author":"S Ahmetolan","year":"2020","unstructured":"Ahmetolan, S., Bilge, A. H., Demirci, A., Peker-Dobie, A., & Ergonul, O. (2020). What Can We Estimate From Fatality and Infectious Case Data Using the Susceptible-Infected-Removed (SIR) Model? A Case Study of Covid-19 Pandemic. Frontiers in Medicine, 7, Article 556366.","journal-title":"Frontiers in Medicine"},{"key":"6893_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2022.112964","volume":"166","author":"M Ahumada","year":"2023","unstructured":"Ahumada, M., Ledesma-Araujo, A., Gordillo, L., & Mar\u00edn, J. F. (2023). Mutation and SARS-CoV-2 strain competition under vaccination in a modified SIR model. Chaos, Solitons & Fractals, 166, Article 112964.","journal-title":"Chaos, Solitons & Fractals"},{"key":"6893_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2022.e12469","volume":"8","author":"H Ai","year":"2022","unstructured":"Ai, H., Wang, Q., & Liu, W. (2022). A mathematical prediction model of infectious diseases considering vaccine and temperature, and its prediction in Hong Kong. Heliyon, 8, Article e12469.","journal-title":"Heliyon"},{"key":"6893_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8857346","author":"SA Alanazi","year":"2020","unstructured":"Alanazi, S. A., Kamruzzaman, M. M., et al. (2020). Measuring and preventing COVID-19 using the SIR model and machine learning in smart health care. Journal of Healthcare Engineering. https:\/\/doi.org\/10.1155\/2020\/8857346","journal-title":"Journal of Healthcare Engineering"},{"key":"6893_CR6","doi-asserted-by":"publisher","first-page":"6088","DOI":"10.1016\/j.vaccine.2021.08.098","volume":"39","author":"V Albani","year":"2021","unstructured":"Albani, V., Loria, J., Massad, E., & Zubelli, J. P. (2021a). The impact of COVID-19 vaccination delay: A data-driven modeling analysis for Chicago and New York City. Vaccine, 39, 6088\u20136094.","journal-title":"Vaccine"},{"key":"6893_CR7","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1186\/s12879-021-06780-7","volume":"21","author":"V Albani","year":"2021","unstructured":"Albani, V., Loria, J., Massad, E., & Zubelli, J. (2021b). COVID-19 underreporting and its impact on vaccination strategies. Bmc Infectious Diseases, 21, 1111.","journal-title":"Bmc Infectious Diseases"},{"key":"6893_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110042","volume":"139","author":"D Aldila","year":"2020","unstructured":"Aldila, D., Khoshnaw, H. A., et al. (2020). A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment: The case of Jakarta, Indonesia. Chaos, Solitons & Fractals, 139, Article 110042.","journal-title":"Chaos, Solitons & Fractals"},{"key":"6893_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.138861","volume":"728","author":"I Ali","year":"2020","unstructured":"Ali, I., & Albahri, O. M. (2020). COVID-19: Disease, management, treatment, and social impact. Science of the Total Environment, 728, Article 138861.","journal-title":"Science of the Total Environment"},{"key":"6893_CR10","doi-asserted-by":"publisher","first-page":"3711","DOI":"10.3390\/electronics11223711","volume":"11","author":"AK Alkhamis","year":"2022","unstructured":"Alkhamis, A. K., & Hosny, M. (2022). A synthesis of pulse influenza vaccination policies using an efficient controlled elitism non-dominated sorting genetic algorithm (CENSGA). Electronics, 11, 3711.","journal-title":"Electronics"},{"key":"6893_CR11","first-page":"56","volume":"12","author":"SA Al-Sheikh","year":"2013","unstructured":"Al-Sheikh, S. A. (2013). Modeling and analysis of an SEIR epidemic model with a lim- ited resource for treatment resource for treatment. Global Journal of Science Frontier Research Mathematics and Decision Sciences, 12, 56\u201366.","journal-title":"Global Journal of Science Frontier Research Mathematics and Decision Sciences"},{"key":"6893_CR12","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1038\/280361a0","volume":"280","author":"RM Anderson","year":"1979","unstructured":"Anderson, R. M., & May, R. M. (1979). Population biology of infectious diseases: Part I. Nature, 280, 361\u2013367.","journal-title":"Nature"},{"key":"6893_CR13","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1016\/j.idm.2024.04.006","volume":"9","author":"C Andreu-Vilarroig","year":"2024","unstructured":"Andreu-Vilarroig, C., Villanueva, R. J., & Gonzalez-Parra, G. (2024). Mathematical modeling for estimating influenza vaccine efficacy: A case study of the Valencian Community, Spain. Infectious Disease Modelling, 9, 744\u2013762.","journal-title":"Infectious Disease Modelling"},{"key":"6893_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.111621","volume":"154","author":"M Angeli","year":"2022","unstructured":"Angeli, M., Neofotistos, G., Matthhekis, M., & Kaxiras, E. (2022). Modeling the effect of the vaccination campaign on the COVID-19 pandemic. Chaos, Solitons & Fractals, 154, Article 111621.","journal-title":"Chaos, Solitons & Fractals"},{"key":"6893_CR15","doi-asserted-by":"publisher","DOI":"10.1111\/deci.12443","author":"OM Araz","year":"2020","unstructured":"Araz, O. M., Choi, T. M., Olson, D., & Salman, F. (2020). Data analytics for operational risk management. Decision Sciences. https:\/\/doi.org\/10.1111\/deci.12443","journal-title":"Decision Sciences"},{"key":"6893_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.healthpol.2023.104769","volume":"132","author":"F Ardesch","year":"2023","unstructured":"Ardesch, F., et al. (2023). The introduction of a data-driven population health management approach in the Netherlands since 2019: The Extramural LUMC Academic Network data infrastructure. Health Policy, 132, Article 104769.","journal-title":"Health Policy"},{"key":"6893_CR17","first-page":"717","volume":"66","author":"MF Arefi","year":"2020","unstructured":"Arefi, M. F., & Poursadeqiyan, M. (2020). A review of studies on the COVID-19 epidemic crisis with a preventive approach. Work, 66, 717\u2013729.","journal-title":"Work"},{"issue":"1","key":"6893_CR18","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.aej.2020.09.011","volume":"60","author":"M Arfan","year":"2021","unstructured":"Arfan, M., Shah, K., Abdeljawad, T., Mlaiki, N., & Ullah, A. (2021). A caputo power law model predicting the spread of the COVID-19 outbreak in Pakistan. Alexandria Engineering Journal, 60(1), 447\u2013456.","journal-title":"Alexandria Engineering Journal"},{"key":"6893_CR19","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1016\/j.idm.2024.06.007","volume":"9","author":"MS Aronna","year":"2024","unstructured":"Aronna, M. S., & Moschen, L. M. (2024). Optimal vaccination strategies on networks and in metropolitan areas. Infectious Disease Modelling, 9, 1198\u20131222.","journal-title":"Infectious Disease Modelling"},{"issue":"12","key":"6893_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006638","volume":"14","author":"S Arregui","year":"2018","unstructured":"Arregui, S., Aleta, A., Sanz, J., & Moreno, Y. (2018). Projecting social contact matrices to different demographic structures. PLoS Computational Biology, 14(12), Article e1006638.","journal-title":"PLoS Computational Biology"},{"key":"6893_CR21","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-022-07486-0","volume":"22","author":"AM Arribas","year":"2022","unstructured":"Arribas, A. M., Aleta, A., & Moreno, Y. (2022). Impact of vaccine hesitancy on secondary COVID-19 outbreaks in the US: An age-structured SIR model. Bmc Infectious Diseases, 22, Article 511.","journal-title":"Bmc Infectious Diseases"},{"issue":"5","key":"6893_CR22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012096","volume":"20","author":"P Asplin","year":"2024","unstructured":"Asplin, P., Keeling, M. J., Mancy, R., & Hill, E. M. (2024). Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation. PLoS Computational Biology, 20(5), Article e1012096.","journal-title":"PLoS Computational Biology"},{"key":"6893_CR23","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1056\/NEJMoa1411100","volume":"371","author":"B Aylward","year":"2014","unstructured":"Aylward, B., Barboza, P., et al. (2014). Ebola virus disease in West Africa \u2013 The first 9 months of the epidemic and forward projections. The New England Journal of Medicine, 371, 1481\u20131495.","journal-title":"The New England Journal of Medicine"},{"key":"6893_CR24","doi-asserted-by":"publisher","first-page":"5845","DOI":"10.1016\/j.vaccine.2021.08.053","volume":"39","author":"JM Azam","year":"2021","unstructured":"Azam, J. M., Saitta, B., Bonner, K., Ferrari, M. J., & Pulliam, J. R. (2021). Modelling the relative benefits of using the measles vaccine outside cold chain for outbreak response. Vaccine, 39, 5845\u20135853.","journal-title":"Vaccine"},{"key":"6893_CR250","unstructured":"Bailey, N. T. (1975). The mathematical theory of infectious diseases and its applications (2nd ed.). London: Griffin."},{"key":"6893_CR25","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1056\/NEJMoa1404505","volume":"371","author":"S Baize","year":"2014","unstructured":"Baize, S., Pannetier, D., et al. (2014). Emergence of Zaire Ebola virus disease in Guinea. The New England Journal of Medicine, 371, 1418\u20131425.","journal-title":"The New England Journal of Medicine"},{"key":"6893_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2023.113339","volume":"170","author":"M Banerjee","year":"2023","unstructured":"Banerjee, M., Ghosh, S., Manfredi, P., & D\u2019Onofrio, A. (2023). Spatio-temporal chaos and clustering induced by nonlocal information and vaccine hesitancy in the SIR epidemic model. Chaos, Solitons & Fractals, 170, Article 113339.","journal-title":"Chaos, Solitons & Fractals"},{"key":"6893_CR27","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511791383","volume-title":"Dynamical processes on complex networks","author":"A Barrat","year":"2008","unstructured":"Barrat, A., Barthelem, M., & Vespignan, A. (2008). Dynamical processes on complex networks. Cambridge University Press."},{"key":"6893_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2023.109059","volume":"364","author":"S Barua","year":"2023","unstructured":"Barua, S., & D\u00e9nes, A. (2023). Global dynamics of a compartmental model to assess the effect of transmission from deceased. Mathematical Biosciences, 364, Article 109059.","journal-title":"Mathematical Biosciences"},{"key":"6893_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2022.112286","volume":"160","author":"L Basnarkov","year":"2022","unstructured":"Basnarkov, L., Tomovski, I., Sandev, T., & Kocarev, L. (2022). Non-Markovian SIR epidemic spreading model of COVID-19. Chaos, Solitons & Fractals, 160, Article 112286.","journal-title":"Chaos, Solitons & Fractals"},{"issue":"15","key":"6893_CR30","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.ifacol.2021.10.247","volume":"54","author":"CM Batistela","year":"2021","unstructured":"Batistela, C. M., Romas, M. M., Cabrera, M. A., Dieguea, G. M., & Piqueira, J. R. (2021). Vaccination and social distance to prevent COVID-19. IFAC-PapersOnLine, 54(15), 151\u2013156.","journal-title":"IFAC-PapersOnLine"},{"issue":"31","key":"6893_CR31","doi-asserted-by":"publisher","first-page":"4090","DOI":"10.1016\/j.vaccine.2009.04.079","volume":"27","author":"C Bauch","year":"2009","unstructured":"Bauch, C., Szusz, E., & Garrison, L. (2009). Scheduling of measles vaccination in low- income countries: Projections of a dynamic model. Vaccine, 27(31), 4090\u20134098.","journal-title":"Vaccine"},{"key":"6893_CR32","doi-asserted-by":"publisher","unstructured":"Baveja, A., Kapoor, A., & Melamed, B. (2020). Stopping COVID-19: A pandemic- management service value chain approach (SSRN Scholarly Paper No. ID 3555280). https:\/\/doi.org\/10.2139\/ssrn.3555280","DOI":"10.2139\/ssrn.3555280"},{"key":"6893_CR33","doi-asserted-by":"publisher","DOI":"10.1111\/irv.13229","volume":"17","author":"SJ Bents","year":"2023","unstructured":"Bents, S. J., et al. (2023). Modeling the impact of COVID-19 nonpharmaceutical interventions on respiratory syncytial virus transmission in South Africa. Influenza and Other Respiratory Viruses, 17, Article e13229.","journal-title":"Influenza and Other Respiratory Viruses"},{"key":"6893_CR34","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s10729-020-09542-0","volume":"24","author":"D Bertsimas","year":"2021","unstructured":"Bertsimas, D., et al. (2021). From predictions to prescriptions: A data-driven response to COVID-19. Health Care Management Science, 24, 253\u2013272.","journal-title":"Health Care Management Science"},{"key":"6893_CR35","doi-asserted-by":"publisher","first-page":"20210009","DOI":"10.1098\/rsif.2021.0009","volume":"18","author":"M Betti","year":"2021","unstructured":"Betti, M., et al. (2021). Integrated vaccination and nonpharmaceutical interventions based strategies in Ontario, Canada, as a case study: A mathematical modelling study. Journal of the Royal Society Interface, 18, 20210009.","journal-title":"Journal of the Royal Society Interface"},{"key":"6893_CR350","doi-asserted-by":"crossref","unstructured":"Bhadauria, A., Pathak, R., & Chaudhary, M. (2021). A SIQ mathematical model on COVID-19 investigating the lockdown effect. Infectious Disease Modelling, 6, 244\u2013257.","DOI":"10.1016\/j.idm.2020.12.010"},{"key":"6893_CR36","doi-asserted-by":"publisher","unstructured":"Bicher, M. R., Rippinger, C., Urach, C., Brunmeir, D., Siebert, U., & Popper, N. (2020). Agent-based simulation for evaluation of contact-tracing policies against the spread of SARS-CoV-2. MedRxiv. R\u00e9cup\u00e9r\u00e9 sur https:\/\/doi.org\/10.1101\/2020.05.12.20098970","DOI":"10.1101\/2020.05.12.20098970"},{"issue":"2","key":"6893_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s43069-023-00194-8","volume":"4","author":"E Blasioli","year":"2023","unstructured":"Blasioli, E., Mansouri, B., Tamvada, S. S., & Hassini, E. (2023). Vaccine allocation and distribution: A review with a focus on quantitative methodologies and application to equity, hesitancy, and COVID-19 pandemic. Operations Research Forum, 4(2), Article 27.","journal-title":"Operations Research Forum"},{"issue":"6","key":"6893_CR38","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.2105\/AJPH.2020.306114","volume":"111","author":"DE Bloom","year":"2021","unstructured":"Bloom, D. E., Cadarette, D., & Ferranna, M. (2021). The societal value of vaccination in the age of COVID-19. American Journal of Public Health, 111(6), 1049\u20131054.","journal-title":"American Journal of Public Health"},{"key":"6893_CR39","volume":"11","author":"RD Booton","year":"2021","unstructured":"Booton, R. D., MacGregor, L., Vass, L., et al. (2021). Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: A mathematical modelling framework. British Medical Journal Open, 11, Article e041536.","journal-title":"British Medical Journal Open"},{"key":"6893_CR40","doi-asserted-by":"crossref","unstructured":"Brandeau, M. (2008). Resource allocation for epidemic control. Encyclopedia of Optimization, 3291\u20133295.","DOI":"10.1007\/978-0-387-74759-0_563"},{"issue":"3","key":"6893_CR41","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007679","volume":"16","author":"T Brett","year":"2020","unstructured":"Brett, T., Ajelli, M., Liu, Q. H., Krauland, M. G., et al. (2020). Detecting critical slowing down in high-dimensional epidemiological systems. PLoS Computational Biology, 16(3), Article e1007679.","journal-title":"PLoS Computational Biology"},{"issue":"8","key":"6893_CR42","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1080\/00207543.2022.2126021","volume":"61","author":"X Brusset","year":"2022","unstructured":"Brusset, X., Davari, M., Kinra, A., & Torre, D. L. (2022a). Modelling ripple effect propagation and global supply chain workforce productivity impacts in pandemic disruptions. International Journal of Production Research, 61(8), 2493\u20132512.","journal-title":"International Journal of Production Research"},{"key":"6893_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2023.108935","volume":"263","author":"X Brusset","year":"2023","unstructured":"Brusset, X., Ivanov, D., Jebali, A., Torre, D. L., & Repetto, M. (2023a). A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic. Internation Journal of Production Economics, 263, Article 108935.","journal-title":"Internation Journal of Production Economics"},{"issue":"5","key":"6893_CR44","doi-asserted-by":"publisher","first-page":"1642","DOI":"10.1080\/00207543.2022.2044535","volume":"61","author":"X Brusset","year":"2022","unstructured":"Brusset, X., Jebali, A., & Torre, D. L. (2022b). Production optimization in a pandemic context. International Journal of Production Research, 61(5), 1642\u20131663.","journal-title":"International Journal of Production Research"},{"key":"6893_CR45","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-023-05206-8","author":"X Brusset","year":"2023","unstructured":"Brusset, X., Jebali, A., Torre, D. L., & Liuzzi, D. (2023b). Production optimization in the time of pandemic: An SIS-based optimal control model with protection effort and cost minimization. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-023-05206-8","journal-title":"Annals of Operations Research"},{"key":"6893_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2020.105794","volume":"141","author":"G Buonanno","year":"2020","unstructured":"Buonanno, G., Stabile, L., & Morawska, L. (2020). Estimation of airborne viral emission: Quanta emission rate of SARS-CoV-2 for infection risk assessment. Environment International, 141, Article 205794.","journal-title":"Environment International"},{"issue":"2","key":"6893_CR47","volume":"15","author":"A Buratto","year":"2022","unstructured":"Buratto, A., Muttoni, M., Wrzaczek, S., & Freiberger, M. (2022). Should the COVID-19 lockdown be relaxed or intensified in case a vaccine becomes available? PLoS ONE, 15(2), Article E0243413.","journal-title":"PLoS ONE"},{"key":"6893_CR48","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.prrv.2021.07.002","volume":"39","author":"JM Caldwell","year":"2021","unstructured":"Caldwell, J. M., Le, X., McIntosh, L., Meehan, M. T., Ogunlade, S., Ragonnet, R., et al. (2021). Vaccines and variants: Modelling insights into emerging issues in COVID-19 epidemiology. Paediatric Respiratory Reviews, 39, 32\u201339.","journal-title":"Paediatric Respiratory Reviews"},{"key":"6893_CR49","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s41885-022-00106-w","volume":"6","author":"B Callegari","year":"2022","unstructured":"Callegari, B., & Feder, C. (2022). A literature review of pandemics and development: The long-term perspective. Economics of Disasters and Climate Change, 6, 183\u2013212.","journal-title":"Economics of Disasters and Climate Change"},{"issue":"7919","key":"6893_CR50","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41586-022-04788-w","volume":"607","author":"CJ Carlson","year":"2023","unstructured":"Carlson, C. J., Albery, G. F., Merow, C., et al. (2023). Climate change increases cross-species viral transmission risk. Nature, 607(7919), 555\u2013562.","journal-title":"Nature"},{"key":"6893_CR510","unstructured":"Castilho, C. (2006). Optimal control of an epidemic through educational campaigns. Electronic Journal of Differential Equations, (125), 1\u201311."},{"key":"6893_CR51","doi-asserted-by":"publisher","unstructured":"Castro, M. F., Duarte, J. B., & Brinca, P. (2020). Measuring sectoral supply and demand shocks during COVID-19. https:\/\/doi.org\/10.20955\/wp.2020.011.","DOI":"10.20955\/wp.2020.011"},{"issue":"5","key":"6893_CR52","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.pt.2019.01.009","volume":"35","author":"S Cauchemez","year":"2019","unstructured":"Cauchemez, S., Hoz\u00e9, N., Cousien, A., Nikolay, B., & Bosch, Q. T. (2019). How modelling can enhance the analysis of imperfect epidemic data. Trends in Parasitology, 35(5), 369\u2013379.","journal-title":"Trends in Parasitology"},{"key":"6893_CR53","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.ejor.2023.04.033","volume":"311","author":"JP Caulkins","year":"2023","unstructured":"Caulkins, J. P., Grass, D., Feichtinger, G., Hartl, Rf., Kort, P. M., Kuhn, M., et al. (2023). The hammer and the jab: Are COVID-19 lockdowns and vaccinations complements or substitutes? European Journal of Operational Research, 311, 233\u2013250.","journal-title":"European Journal of Operational Research"},{"key":"6893_CR54","doi-asserted-by":"crossref","unstructured":"Chang, S. L., Piraveenan, M., & Prokopenko, M. (2020). IMPACT OF NETWORK ASSORTATIVITY ON EPIDEMIC AND VACCINATION BEHAVIOUR. Chaos, Solitons & Fractals, 140.","DOI":"10.1016\/j.chaos.2020.110143"},{"key":"6893_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.meegid.2021.104834","volume":"92","author":"D Chaturvedi","year":"2021","unstructured":"Chaturvedi, D., & Chakravarty, U. (2021). Predictive analysis of COVID-19 eradication with vaccination in India, Brazil, and U.S.A. Infection, Genetics and Evolution, 92, Article 104834.","journal-title":"Infection, Genetics and Evolution"},{"issue":"36","key":"6893_CR56","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000027169","volume":"100","author":"CM Chen","year":"2021","unstructured":"Chen, C. M., & Stanciu, A. C. (2021). Simulating influenza epidemics with waning vaccine immunity. Medicine, 100(36), Article e27169.","journal-title":"Medicine"},{"key":"6893_CR57","doi-asserted-by":"publisher","first-page":"3059","DOI":"10.3390\/su15043059","volume":"15","author":"J Chen","year":"2023","unstructured":"Chen, J., & Yin, T. (2023). Transmission mechanism of post-COVID-19 emergency supply chain based on complex network: An improved SIR model. Sustainability, 15, 3059.","journal-title":"Sustainability"},{"issue":"5","key":"6893_CR58","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1016\/j.jinf.2020.03.004","volume":"80","author":"J Chen","year":"2020","unstructured":"Chen, J., Qi, T., Liu, L., Ling, Y., Qian, Z., Li, T., et al. (2020a). Clinical progression of patients with COVID-19 in Shanghai, China. Journal of Infection, 80(5), e1\u2013e6.","journal-title":"Journal of Infection"},{"key":"6893_CR59","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.inffus.2020.08.002","volume":"64","author":"M Chen","year":"2020","unstructured":"Chen, M., Li, M., Hao, Y., Liu, Z., Hu, L., & Wang, L. (2020b). The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy. Information Fusion, 64, 252\u2013258.","journal-title":"Information Fusion"},{"issue":"1","key":"6893_CR60","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.jinf.2020.04.024","volume":"81","author":"VC Cheng","year":"2020","unstructured":"Cheng, V. C., Wong, S. C., Chuang, V. M., et al. (2020). The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. Journal of Infection, 81(1), 107\u2013114.","journal-title":"Journal of Infection"},{"key":"6893_CR61","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.jmii.2020.04.004","volume":"53","author":"N Chintalapudi","year":"2020","unstructured":"Chintalapudi, N., Battineni, G., & Amenta, F. (2020). COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach. Journal of Microbiology, Immunology, and Infection, 53, 396\u2013403.","journal-title":"Journal of Microbiology, Immunology, and Infection"},{"key":"6893_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2020.110422","volume":"505","author":"W Choi","year":"2020","unstructured":"Choi, W., & Shim, E. (2020). Optimal strategies for vaccination and social distancing in a game-theoretic epidemiologic model. Journal of Theoretical Biology, 505, Article 110422.","journal-title":"Journal of Theoretical Biology"},{"issue":"5","key":"6893_CR63","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s10654-020-00649-w","volume":"35","author":"R Chowdhury","year":"2020","unstructured":"Chowdhury, R., Heng, K., Shawon, M., et al. (2020). Dynamic interventions to control COVID-19 pandemic: A multivariate prediction modelling study comparing 16 worldwide countries. European Journal of Epidemiology, 35(5), 389\u2013399.","journal-title":"European Journal of Epidemiology"},{"issue":"9","key":"6893_CR64","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1093\/aje\/kwt133","volume":"178","author":"A Cori","year":"2013","unstructured":"Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S. (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American J. of Epidemiology, 178(9), 1505\u20131512.","journal-title":"American J. of Epidemiology"},{"issue":"4","key":"6893_CR65","doi-asserted-by":"publisher","first-page":"217","DOI":"10.5694\/mja2.52400","volume":"221","author":"V Costantino","year":"2024","unstructured":"Costantino, V., et al. (2024). The public health and economic burden of long COVID in Australia, 2022\u201324: A modelling study. The Medical Journal of Australia, 221(4), 217\u2013223.","journal-title":"The Medical Journal of Australia"},{"key":"6893_CR66","unstructured":"Couronne, I. (2020). Catching coronavirus outside is rare but not impossible. MedicalXpress."},{"key":"6893_CR67","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.jtbi.2008.05.015","volume":"254","author":"J Cui","year":"2008","unstructured":"Cui, J., Mu, X., & Wang, H. (2008). Saturation recovery leads to multiple endemic equilibria and backward bifurcation. Journal of Theoretical Biology, 254, 275\u2013283.","journal-title":"Journal of Theoretical Biology"},{"issue":"6629","key":"6893_CR68","first-page":"1","volume":"11","author":"RP Curiel","year":"2021","unstructured":"Curiel, R. P., & Ramirez, H. G. (2021). Vaccination strategies against COVID-19 and the diffusion of anti-vaccination views. Scientific Reports, 11(6629), 1\u201313.","journal-title":"Scientific Reports"},{"key":"6893_CR69","doi-asserted-by":"publisher","first-page":"3230","DOI":"10.3390\/ijerph19063230","volume":"19","author":"T Daghiri","year":"2022","unstructured":"Daghiri, T., Proctor, M., & Matthews, S. (2022). Evolution of select epidemiological modeling and the rise of population sentiment analysis: A literature review and COVID-19 sentiment illustration. International Journal of Environmental Research and Public Health, 19, 3230.","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"6893_CR70","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.221277","volume":"10","author":"J Dagpunar","year":"2023","unstructured":"Dagpunar, J., & Wu, C. (2023). Sensitivity of endemic behaviour of COVID-19 under a multi-dose vaccination regime, to various biological parameters and control variables. Royal Society Open Science, 10, Article 221277.","journal-title":"Royal Society Open Science"},{"issue":"8","key":"6893_CR71","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1038\/s41591-020-0962-9","volume":"26","author":"NG Davies","year":"2020","unstructured":"Davies, N. G., Klepac, P., Liu, Y., Prem, K., Jit, M., Pearson, C. A. B., Quilty, B. J., Kucharski, A. J., Gibbs, H., Clifford, S., Gimma, A., van Zandvoort, K., Munday, J. D., Diamond, C., Edmunds, W. J., Houben, R. M. G. J., Hellewell, J., Russell, T. W., Abbott, S., \u2026 Eggo, R. M. (2020). Age-dependent effects in the transmission and control of COVID-19 epidemics. Nature Medicine, 26(8), 1205\u20131211.","journal-title":"Nature Medicine"},{"key":"6893_CR72","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s11538-020-00817-9","volume":"82","author":"A Deka","year":"2020","unstructured":"Deka, A., Pantha, B., & Bhattacharyya, S. (2020). Optimal management of public perceptions during a flu outbreak: A game-theoretic perspective. Bulletin of Mathematical Biology, 82, 139.","journal-title":"Bulletin of Mathematical Biology"},{"key":"6893_CR73","doi-asserted-by":"publisher","first-page":"345","DOI":"10.3390\/biology11030345","volume":"11","author":"J Demongeot","year":"2022","unstructured":"Demongeot, J., Griette, Q., Magal, P., & Webb, G. (2022). Modeling vaccine efficacy for COVID-19 outbreak in New York City. Biology, 11, 345.","journal-title":"Biology"},{"key":"6893_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2021.110698","volume":"523","author":"J Deng","year":"2021","unstructured":"Deng, J., Tang, S., & Shu, H. (2021). Joint impacts of media, vaccination and treatment on an epidemic Filippov model with application to COVID-19. Journal of Theoretical Biology, 523, Article 110698.","journal-title":"Journal of Theoretical Biology"},{"key":"6893_CR75","unstructured":"Deo, V., & Grover, G. (2022). Method Based on State-Space Epidemiological Model for Cost-Effectiveness Analysis of Non-Medical Interventions- A Study on COVID-19 in California and Florida. (pp. 69\u201382). 24th Annual Conference. Consult\u00e9 le February 23\u201327, 2022"},{"key":"6893_CR76","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1002\/nav.22181","volume":"71","author":"S Dey","year":"2024","unstructured":"Dey, S., et al. (2024). Optimization modeling for pandemic vaccine supply chain management: A review and future research opportunitiesv. Naval Research Logistics, 71, 976\u20131016.","journal-title":"Naval Research Logistics"},{"key":"6893_CR77","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.isatra.2021.05.004","volume":"124","author":"S Dias","year":"2022","unstructured":"Dias, S., Queiroz, K., & Araujo, A. (2022). Controlling epidemic diseases based only on social distancing level: General case. ISA Transactions, 124, 21\u201330.","journal-title":"ISA Transactions"},{"key":"6893_CR78","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1016\/j.idm.2024.07.004","volume":"9","author":"C Dings","year":"2024","unstructured":"Dings, C., et al. (2024). Effect of vaccinations and school restrictions on the spread of COVID-19 in different age groups in Germany. Infectious Disease Modelling, 9, 1250\u20131264.","journal-title":"Infectious Disease Modelling"},{"key":"6893_CR79","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.medj.2023.02.001","volume":"4","author":"Z Du","year":"2023","unstructured":"Du, Z., Wang, L., et al. (2023). Cost effectiveness of fractional doses of COVID-19 vaccine boosters in India. Med, 4, 182\u2013190.","journal-title":"Med"},{"key":"6893_CR80","doi-asserted-by":"publisher","DOI":"10.1016\/j.meegid.2023.105479","volume":"113","author":"A Duan","year":"2023","unstructured":"Duan, A., Li, J., Yang, Z., & He, Y. (2023). The defense of Shangri-La: Protecting isolated communities by periodic infection screening in the worst future pandemic. Infection, Genetics and Evolution, 113, Article 105479.","journal-title":"Infection, Genetics and Evolution"},{"issue":"1","key":"6893_CR81","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.ejor.2018.01.015","volume":"268","author":"LE Duijzer","year":"2018","unstructured":"Duijzer, L. E., van Jaarsveld, W., & Dekker, R. (2018). Literature review: The vaccine supply chain. European Journal of Operational Research, 268(1), 174\u2013192.","journal-title":"European Journal of Operational Research"},{"key":"6893_CR82","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1016\/j.ejor.2019.11.025","volume":"283","author":"S Enayati","year":"2020","unstructured":"Enayati, S., & Ozaltin, O. Y. (2020). Optimal influenza vaccine distribution with equity. European Journal of Operational Research, 283, 714\u2013725.","journal-title":"European Journal of Operational Research"},{"key":"6893_CR83","doi-asserted-by":"publisher","DOI":"10.1016\/j.onehlt.2020.100202","volume":"12","author":"FA Engelbrecht","year":"2021","unstructured":"Engelbrecht, F. A., & Scholes, R. J. (2021). Test for covid-19 seasonality and the risk of second waves. One Health, 12, Article 100202.","journal-title":"One Health"},{"key":"6893_CR84","doi-asserted-by":"publisher","DOI":"10.1016\/j.seps.2024.101895","volume":"93","author":"G Erdo\u011fan","year":"2024","unstructured":"Erdo\u011fan, G., Y\u00fccel, E., Kiavash, P., & Salman, F. S. (2024). Fair and effective vaccine allocation during a pandemic. Socio-Economic Planning Sciences, 93, Article 101895.","journal-title":"Socio-Economic Planning Sciences"},{"issue":"2","key":"6893_CR85","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1177\/00375497221120018","volume":"99","author":"B Erkayman","year":"2023","unstructured":"Erkayman, B., Ak, F., & Codur, S. (2023). A simulation approach for COVID-19 pandemic assessment based on vaccine logistics, SARS-CoV-2 variants, and spread rate. SIMULATION, 99(2), 127\u2013135.","journal-title":"SIMULATION"},{"issue":"4","key":"6893_CR86","doi-asserted-by":"publisher","DOI":"10.1007\/s11538-020-00726-x","volume":"82","author":"S Eubank","year":"2020","unstructured":"Eubank, S., Eckstrand, I., Lewis, B., Venkatramanan, S., Marathe, M., & Barrett, C. L. (2020). Commentary on Ferguson, & et al., Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Bulletin of Mathematical Biology, 82(4), Article 52.","journal-title":"Bulletin of Mathematical Biology"},{"key":"6893_CR87","doi-asserted-by":"crossref","unstructured":"Farhat, F., et al. (2023). COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control. Frontiers in Artificial Intelligence, 6.","DOI":"10.3389\/frai.2023.1266560"},{"key":"6893_CR88","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.idm.2024.01.004","volume":"9","author":"IS Fauzi","year":"2024","unstructured":"Fauzi, I. S., et al. (2024). Assessing the impact of booster vaccination on diphtheria transmission: Mathematical modeling and risk zone mapping. Infectious Disease Modelling, 9, 245\u2013262.","journal-title":"Infectious Disease Modelling"},{"key":"6893_CR89","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s00199-022-01475-9","volume":"77","author":"S Federico","year":"2024","unstructured":"Federico, S., Ferrari, G., & Torrente, M. L. (2024). Optimal vaccination in a SIRS epidemic model. Economic Theory, 77, 49\u201374.","journal-title":"Economic Theory"},{"key":"6893_CR90","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.tpb.2020.01.005","volume":"132","author":"Z Feng","year":"2020","unstructured":"Feng, Z., Feng, Y., & Glasser, J. W. (2020). Influence of demographically-realistic mortality schedules on vaccination strategies in age-structured models. Theoretical Population Biology, 132, 24\u201332.","journal-title":"Theoretical Population Biology"},{"key":"6893_CR91","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.111359","volume":"152","author":"TR Filho","year":"2021","unstructured":"Filho, T. R., Moret, M. A., Chow, C. C., Phillips, J. C., Cordeiro, J. J., Scorza, F. A., et al. (2021). A data-driven model for COVID-19 pandemic \u2013Evolution of the attack rate and prognosis for Brazil. Chaos, Solitons and Fractals, 152, Article 111359.","journal-title":"Chaos, Solitons and Fractals"},{"issue":"4","key":"6893_CR92","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0297093","volume":"19","author":"DN Fisman","year":"2024","unstructured":"Fisman, D. N., Amoako, A., Simmons, A., & Tuite, A. R. (2024). Impact of immune evasion, waning and boosting on dynamics of population mixing between a vaccinated majority and unvaccinated minority. PLoS ONE, 19(4), Article e0297093.","journal-title":"PLoS ONE"},{"key":"6893_CR93","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108136","volume":"249","author":"J Flaig","year":"2024","unstructured":"Flaig, J., & Houy, N. (2024). Disease x epidemic control using a stochastic model and a deterministic approximation: Performance comparison with and without parameter uncertainties. Computer Methods and Programs in Biomedicine, 249, Article 108136.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"6893_CR94","doi-asserted-by":"publisher","first-page":"860","DOI":"10.3390\/microorganisms11040860","volume":"11","author":"M Fudolig","year":"2023","unstructured":"Fudolig, M. (2023). Effect of Transmission and Vaccination on Time to Dominance of Emerging Viral Strains: A Simulation-Based Study. Microorganisms, 11, 860.","journal-title":"Microorganisms"},{"issue":"12","key":"6893_CR95","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0243408","volume":"15","author":"M Fudolig","year":"2020","unstructured":"Fudolig, M., & Howard, R. (2020). The local stability of a modified multi-strain SIR model for emerging viral strains. PLoS ONE, 15(12), Article e0243408.","journal-title":"PLoS ONE"},{"key":"6893_CR96","doi-asserted-by":"publisher","DOI":"10.3390\/vaccines12040434","volume":"12","author":"K Fust","year":"2024","unstructured":"Fust, K., et al. (2024). The potential economic impact of the updated COVID-19 mRNA fall 2023 vaccines in Japan. Vaccines, 12, Article 434.","journal-title":"Vaccines"},{"key":"6893_CR960","doi-asserted-by":"crossref","unstructured":"Gaff, H., & Schaefer, E. (2009). Optimal control applied to vaccination and treatment strategies for various epidemiological models. Mathematical Biosciences and Engineering, 6(3), 469.","DOI":"10.3934\/mbe.2009.6.469"},{"key":"6893_CR97","doi-asserted-by":"publisher","DOI":"10.1155\/2007\/64870","author":"S Gao","year":"2007","unstructured":"Gao, S., Teng, Z., Nieto, J. J., & Torres, A. (2007). Analysis of an SIR epidemic model with pulse vaccination and distributed time delay. Journal of Biomedicine & Biotechnology. https:\/\/doi.org\/10.1155\/2007\/64870","journal-title":"Journal of Biomedicine & Biotechnology"},{"issue":"191","key":"6893_CR98","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2022.0045","volume":"19","author":"F Geoffroy","year":"2022","unstructured":"Geoffroy, F., Traulsen, A., & Uecker, H. (2022). Vaccination strategies when vaccines are scarce: On conflicts between reducing the burden and avoiding the evolution of escape mutants. Journal of the Royal Society Interface, 19(191), Article 20220045.","journal-title":"Journal of the Royal Society Interface"},{"key":"6893_CR99","doi-asserted-by":"publisher","DOI":"10.3390\/math9060636","volume":"9","author":"R Ghostine","year":"2021","unstructured":"Ghostine, R., Gharamti, M., Hassrouny, S., & Hoteit, I. (2021). An extended SEIR model with vaccination for forecasting the COVID-19 pandemic in Saudi Arabia using an ensemble Kalman filter. Mathematics, 9, Article 636.","journal-title":"Mathematics"},{"key":"6893_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2024.100341","volume":"5","author":"ED Ginting","year":"2024","unstructured":"Ginting, E. D., Aldila, D., & Febiriana, I. H. (2024). A deterministic compartment model for analyzing tuberculosis dynamics considering vaccination and reinfection. Healthcare Analytics, 5, Article 100341.","journal-title":"Healthcare Analytics"},{"key":"6893_CR101","doi-asserted-by":"publisher","DOI":"10.1016\/j.nonrwa.2024.104097","volume":"78","author":"A G\u00f6k\u00e7e","year":"2024","unstructured":"G\u00f6k\u00e7e, A., G\u00fcrb\u00fcz, B., & Rendall, A. D. (2024). Dynamics of a mathematical model of virus spreading incorporating the effect of a vaccine. Nonlinear Anal. RWA, 78, Article 104097.","journal-title":"Nonlinear Anal. RWA"},{"issue":"2","key":"6893_CR102","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1017\/bca.2021.4","volume":"12","author":"C Gollier","year":"2021","unstructured":"Gollier, C. (2021). The welfare cost of vaccine misallocation, delays and nationalism. Journal of Benefit-Cost Analysis, 12(2), 199\u2013226.","journal-title":"Journal of Benefit-Cost Analysis"},{"key":"6893_CR103","doi-asserted-by":"publisher","DOI":"10.1016\/j.sca.2023.100022","volume":"3","author":"KR Gomes","year":"2023","unstructured":"Gomes, K. R., Perera, H. N., Thibbotuwawa, A., & Sunil-Chandra, N. P. (2023). Comparative analysis of lean and agile supply chain strategies for effective vaccine distribution in pandemics: A case study of COVID-19 in a densely populated developing region. Supply Chain Analytics, 3, Article 100022.","journal-title":"Supply Chain Analytics"},{"key":"6893_CR104","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijindorg.2022.102840","volume":"84","author":"M Goodkin-Gold","year":"2022","unstructured":"Goodkin-Gold, M., Kremer, M., Snyder, C. M., & Williams, H. (2022). Optimal vaccine subsidies for endemic diseases. International Journal of Industrial Organization, 84, Article 102840.","journal-title":"International Journal of Industrial Organization"},{"key":"6893_CR105","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2023.100680","volume":"43","author":"H Gorji","year":"2023","unstructured":"Gorji, H., Stauffer, N., Lunati, I., Caduff, A., B\u00fchler, M., Engel, D., et al. (2023). Projection of healthcare demand in Germany and Switzerland urged by Omicron wave (January\u2013March 2022). Epidemics, 43, Article 100680.","journal-title":"Epidemics"},{"key":"6893_CR106","doi-asserted-by":"crossref","unstructured":"Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 101967.","DOI":"10.1016\/j.tre.2020.101967"},{"key":"6893_CR107","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.tpb.2024.02.002","volume":"156","author":"D Grass","year":"2024","unstructured":"Grass, D., et al. (2024). Riding the waves from epidemic to endemic: Viral mutations, immunological change and policy responses. Theoretical Population Biology, 156, 46\u201365.","journal-title":"Theoretical Population Biology"},{"key":"6893_CR108","doi-asserted-by":"publisher","DOI":"10.1016\/j.seps.2021.101196","volume":"81","author":"C Gros","year":"2022","unstructured":"Gros, C., & Gros, D. (2022). The economics of stop-and-go epidemic control. Socio-Economic Planning Sciences, 81, Article 101196.","journal-title":"Socio-Economic Planning Sciences"},{"issue":"1","key":"6893_CR109","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1093\/abt\/tbab006","volume":"4","author":"D Guttieres","year":"2021","unstructured":"Guttieres, D., Sinskey, A. J., & Springs, S. L. (2021). Models to inform neutralizing antibody therapy strategies during pandemics: The case of SARS-CoV-2. Antibody Therapeutics, 4(1), 60\u201371.","journal-title":"Antibody Therapeutics"},{"key":"6893_CR110","doi-asserted-by":"publisher","DOI":"10.7189\/jogh.13.06038","volume":"13","author":"Z Han","year":"2023","unstructured":"Han, Z., et al. (2023). How enlightened self-interest guided global vaccine sharing benefits all: A modeling study. Journal of Global Health, 13, Article 06038.","journal-title":"Journal of Global Health"},{"key":"6893_CR111","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8570089","author":"I Haq","year":"2022","unstructured":"Haq, I., Hossain, M. I., Saleheen, A. A., Nayan, M. I., & Mila, M. S. (2022). Prediction of COVID-19 pandemic in Bangladesh: Dual application of Susceptible-Infective-Recovered (SIR) and machine learning approach. Interdisciplinary Perspectives on Infectious Diseases. https:\/\/doi.org\/10.1155\/2022\/8570089","journal-title":"Interdisciplinary Perspectives on Infectious Diseases"},{"key":"6893_CR1120","doi-asserted-by":"crossref","unstructured":"Harari, G. S., & Monteiro, H. A. (2022). An Epidemic Model with Pro and Anti-vaccine Groups. Acta Biotheor, 70(3), 20.","DOI":"10.1007\/s10441-022-09443-5"},{"key":"6893_CR112","unstructured":"Harris, R. (2020). Covid-19 and productivity in the UK . Durham University Busi- ness School. R\u00e9cup\u00e9r\u00e9 sur https:\/\/www.dur.ac.uk\/research\/news\/item\/?itemno=41707"},{"issue":"1","key":"6893_CR113","doi-asserted-by":"publisher","DOI":"10.1128\/mbio.03789-21","volume":"13","author":"MT Hawkes","year":"2022","unstructured":"Hawkes, M. T., & Good, M. (2022). Vaccinating children against COVID-19: Commentary and mathematical modeling. Mbio, 13(1), Article e0378921.","journal-title":"Mbio"},{"key":"6893_CR114","doi-asserted-by":"publisher","first-page":"834","DOI":"10.3390\/math11040834","volume":"11","author":"C Hazard-Vald\u00e9s","year":"2023","unstructured":"Hazard-Vald\u00e9s, C., & Montero, E. (2023). A heuristic approach for determining efficient vaccination plans under a SARS-CoV-2 epidemic model. Mathematics, 11, 834.","journal-title":"Mathematics"},{"key":"6893_CR115","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1126\/science.1092002","volume":"303","author":"J He","year":"2004","unstructured":"He, J., Peng, G., et al. (2004). Molecular evolution of the SARS coronavirus, during the course of the SARS epidemic in China. Science, 303, 1666\u20131669.","journal-title":"Science"},{"issue":"4","key":"6893_CR116","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1137\/S0036144500371907","volume":"42","author":"HW Hethcote","year":"2000","unstructured":"Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599\u2013653.","journal-title":"SIAM Review"},{"key":"6893_CR117","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1016\/j.jfranklin.2023.12.053","volume":"361","author":"NT Hieu","year":"2024","unstructured":"Hieu, N. T., Nguyen, D. H., Nguyen, N. N., & Yin, G. (2024). Analyzing a class of stochastic SIRS models under imperfect vaccination. Journal of the Franklin Institute, 361, 1284\u20131302.","journal-title":"Journal of the Franklin Institute"},{"key":"6893_CR118","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.puhe.2024.05.011","volume":"233","author":"A Holleran","year":"2024","unstructured":"Holleran, A., Martonosi, S. E., & Veatch, M. (2024). To give or not to give? Pandemic vaccine donation policy. Public Health, 233, 164\u2013169.","journal-title":"Public Health"},{"issue":"4","key":"6893_CR119","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1056\/NEJMp2016822","volume":"383","author":"I Holmdahl","year":"2020","unstructured":"Holmdahl, I., & Buckee, C. (2020). Wrong but useful\u2014What Covid-19 epidemiologic models can and cannot tell us. New England Journal of Medicine, 383(4), 303\u2013305.","journal-title":"New England Journal of Medicine"},{"key":"6893_CR120","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s10479-022-05037-z","volume":"339","author":"Z Hong","year":"2024","unstructured":"Hong, Z., Li, Y., Gong, Y., & Chen, W. (2024). A data-driven spatially-specific vaccine allocation framework for COVID-19. Annals of Operations Research, 339, 203\u2013226.","journal-title":"Annals of Operations Research"},{"key":"6893_CR121","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang, C., Wang, Y., et al. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 395, 497\u2013506.","journal-title":"Lancet"},{"key":"6893_CR122","doi-asserted-by":"publisher","first-page":"7594","DOI":"10.3390\/ijerph18147594","volume":"18","author":"D Huang","year":"2021","unstructured":"Huang, D., Tao, H., Wuq, Q., Huang, S. Y., & Xiao, Y. (2021). Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States. International Journal of Environmental Research and Public Health, 18, 7594.","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"6893_CR123","doi-asserted-by":"crossref","unstructured":"Huang, Y., & Li, C. (2019). Backward bifurcation and stability analysis of a networkbased SIS epidemic model with saturated treatment function. Physica A, 527.","DOI":"10.1016\/j.physa.2019.121407"},{"key":"6893_CR124","first-page":"012022","volume":"2084","author":"H Husniah","year":"2021","unstructured":"Husniah, H., Ruhanda, & Supriatna, A. K. (2021). SIR mathematical model of convalescent plasma transfusion applied to the COVID-19 pandemic data in Indonesia to control the spread of the disease. Journal of Physics: Conference Series, 2084, 012022.","journal-title":"Journal of Physics: Conference Series"},{"issue":"7","key":"6893_CR125","first-page":"3050","volume":"20","author":"AM Hussien","year":"2022","unstructured":"Hussien, A. M., & Mohammad, A. A. (2022). New SIR model and vaccine rate with application. NeuroQuantology, 20(7), 3050\u20133059.","journal-title":"NeuroQuantology"},{"issue":"3","key":"6893_CR126","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.3934\/mbe.2020121","volume":"17","author":"Y Hwang","year":"2020","unstructured":"Hwang, Y., Kwon, H., & Lee, J. (2020). Feedback control problem of an SIR epidemic model based on the Hamilton-Jacobi-Bellman equation. Mathematical Biosciences and Engineering, 17(3), 2284\u20132301.","journal-title":"Mathematical Biosciences and Engineering"},{"key":"6893_CR127","doi-asserted-by":"crossref","unstructured":"Iranzo, V., & P\u00e9rez-Gonz\u00e1lez, S. (2021). Epidemiological models and COVID-19: A comparative view. History and Philosophy of the Life Sciences, 43(104).","DOI":"10.1007\/s40656-021-00457-9"},{"key":"6893_CR1270","doi-asserted-by":"crossref","unstructured":"Isidori, A. (1995). Nonlinear control systems. In Communications and control engineering. Berlin, Heidelberg: Springer.","DOI":"10.1007\/978-1-84628-615-5"},{"key":"6893_CR128","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2020.101922","volume":"136","author":"D Ivanov","year":"2020","unstructured":"Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19\/SARS-CoV-2) case. Transportation Research Part E, 136, Article 101922.","journal-title":"Transportation Research Part E"},{"issue":"10","key":"6893_CR129","doi-asserted-by":"publisher","first-page":"2904","DOI":"10.1080\/00207543.2020.1750727","volume":"58","author":"D Ivanov","year":"2020","unstructured":"Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability: A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904\u20132915.","journal-title":"International Journal of Production Research"},{"issue":"9","key":"6893_CR130","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1080\/09537287.2020.1768450","volume":"32","author":"D Ivanov","year":"2021","unstructured":"Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruptions risks and resilience in the era of Industry 4.0. Production Planning and Control, 32(9), 775\u2013788.","journal-title":"Production Planning and Control"},{"key":"6893_CR131","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1007\/s10479-020-03685-7","volume":"319","author":"MM Queiroz","year":"2020","unstructured":"Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2020). Impacts of epidemic out-breaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 319, 1159\u20131196.","journal-title":"Annals of Operations Research"},{"key":"6893_CR132","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108031","volume":"167","author":"P Jarumaneeroj","year":"2022","unstructured":"Jarumaneeroj, P., Dusadeerungsikul, P. O., Chotivanich, T., Nopsopon, T., & Pongpirul, K. (2022). An epidemiology-based model for the operational allocation of COVID-19 vaccines: A case study of Thailand. Computers and Industrial Engineering, 167, Article 108031.","journal-title":"Computers and Industrial Engineering"},{"key":"6893_CR133","doi-asserted-by":"publisher","first-page":"2849","DOI":"10.3390\/math9222849","volume":"9","author":"Q Ji","year":"2021","unstructured":"Ji, Q., Zhao, X., Ma, H., Liu, Q., Liu, Y., & Guan, Q. (2021). Estimation of COVID-19 transmission and advice on public health interventions. Mathematics, 9, 2849.","journal-title":"Mathematics"},{"key":"6893_CR134","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2023.111522","volume":"570","author":"S Jing","year":"2023","unstructured":"Jing, S., Milne, R., Wang, H., & Xuo, L. (2023). Vaccine hesitancy promotes emergence of new SARS-CoV-2 variants. Journal of Theoretical Biology, 570, Article 111522.","journal-title":"Journal of Theoretical Biology"},{"key":"6893_CR135","doi-asserted-by":"crossref","unstructured":"Jones, J. H., & Salathe, M. (2009). Early assessment of anxiety and behavioral response to novel Swine-Origin influenza A(H1N1). PLoS One, 4.","DOI":"10.1371\/journal.pone.0008032"},{"key":"6893_CR136","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.cnsns.2019.01.020","volume":"72","author":"KM Kabir","year":"2019","unstructured":"Kabir, K. M., & Tanimoto, J. (2019). Analysis of epidemic outbreaks in two-layer networks with different structures for information spreading and disease diffusion. Communications in Nonlinear Science and Numerical Simulation, 72, 565\u2013574.","journal-title":"Communications in Nonlinear Science and Numerical Simulation"},{"key":"6893_CR137","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2020.110379","volume":"503","author":"KM Kabir","year":"2020","unstructured":"Kabir, K. M., & Tanimoto, J. (2020). Cost-efficiency analysis of voluntary vaccination against n-serovar diseases using antibody-dependent enhancement: A game approach. Journal of Theoretical Biology, 503, Article 110379.","journal-title":"Journal of Theoretical Biology"},{"key":"6893_CR138","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2020.100430","volume":"34","author":"MP Kain","year":"2021","unstructured":"Kain, M. P., Childs, M. L., Becker, A. D., & Mordecai, E. A. (2021). Chopping the tail: How preventing superspreading can help to maintain COVID-19 control. Epidemics, 34, Article 100430.","journal-title":"Epidemics"},{"key":"6893_CR139","volume-title":"Modeling Infectious Diseases in Humans and Animals","author":"MJ Keeling","year":"2007","unstructured":"Keeling, M. J., & Rohani, P. (2007). Modeling Infectious Diseases in Humans and Animals. Princeton University Press."},{"key":"6893_CR140","unstructured":"Keimer, A., & Pflug, L. (2020). Modeling Infectious Diseases Using Integro-Differential Equations: Optimal Control Strategies for Policy Decisions and Applications in COVID-19. Friedrich-Alexander-Universit\u00e4t Erlangen-Nrnberg."},{"key":"6893_CR141","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2021.110874","volume":"530","author":"F Kemp","year":"2021","unstructured":"Kemp, F., Proverbio, D., Aalto, A., Mombaerts, L., et al. (2021). Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden. Journal of Theoretical Biology, 530, Article 110874.","journal-title":"Journal of Theoretical Biology"},{"key":"6893_CR142","first-page":"700","volume":"115","author":"WO Kermack","year":"1927","unstructured":"Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society a: Mathematical, Physical and Engineering Sciences, 115, 700\u2013721.","journal-title":"Proceedings of the Royal Society a: Mathematical, Physical and Engineering Sciences"},{"key":"6893_CR143","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2024.111815","volume":"587","author":"ME Khalifi","year":"2024","unstructured":"Khalifi, M. E., & Britton, T. (2024). Sirs epidemics with individual heterogeneity of immunity waning. Journal of Theoretical Biology, 587, Article 111815.","journal-title":"Journal of Theoretical Biology"},{"key":"6893_CR144","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1056\/NEJMc0904559","volume":"361","author":"K Khan","year":"2009","unstructured":"Khan, K., Arino, J., et al. (2009). Spread of a novel influenza A (H1N1) virus via global airline transportation. The New England Journal of Medicine, 361, 212\u2013214.","journal-title":"The New England Journal of Medicine"},{"key":"6893_CR145","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109069","volume":"181","author":"MA Khan","year":"2024","unstructured":"Khan, M. A., DarAssi, M. H., Ahmad, I., Seyam, N. M., & Alzahrani, E. (2024). The transmission dynamics of an infectious disease model in fractional derivative with vaccination under real data. Computers in Biology and Medicine, 181, Article 109069.","journal-title":"Computers in Biology and Medicine"},{"key":"6893_CR146","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1016\/j.idm.2024.03.004","volume":"9","author":"MM Khan","year":"2024","unstructured":"Khan, M. M., & Tanimoto, J. (2024). Influence of waning immunity on vaccination decisionmaking: A multi-strain epidemic model with an evolutionary approach analyzing cost and efficacy. Infectious Disease Modelling, 9, 657\u2013672.","journal-title":"Infectious Disease Modelling"},{"issue":"1","key":"6893_CR147","doi-asserted-by":"publisher","first-page":"2313318","DOI":"10.1080\/00219592.2024.2313318","volume":"57","author":"J Kim","year":"2024","unstructured":"Kim, J., et al. (2024). Scenario analysis of vaccine supply for COVID-19 in Japan using mathematical models of infectious diseases. Journal of Chemical Engineering of Japan, 57(1), 2313318.","journal-title":"Journal of Chemical Engineering of Japan"},{"issue":"11","key":"6893_CR148","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.3390\/ijerph15112369","volume":"15","author":"Y Kim","year":"2018","unstructured":"Kim, Y., Ryu, H., & Lee, S. (2018). Agent-based modeling for super-spreading events: A case study of MERS-CoV transmission dynamics in the Republic of Korea. International Journal of Environmental Research and Public Health, 15(11), 2369.","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"6893_CR149","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1186\/s40249-022-01001-y","volume":"11","author":"L Kong","year":"2022","unstructured":"Kong, L., et al. (2022). Compartmental structures used in modeling COVID-19: A scoping review. Infectious Diseases of Poverty, 11, 72.","journal-title":"Infectious Diseases of Poverty"},{"key":"6893_CR150","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1016\/j.idm.2021.01.006","volume":"6","author":"G Kozyreff","year":"2021","unstructured":"Kozyreff, G. (2021). Hospitalization dynamics during the first COVID-19 pandemic wave: SIR modelling compared to Belgium, France, Italy, Switzerland and New York City data. Infectious Disease Modelling, 6, 398\u2013404.","journal-title":"Infectious Disease Modelling"},{"key":"6893_CR151","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2021.108648","volume":"339","author":"MJ K\u00fchn","year":"2021","unstructured":"K\u00fchn, M. J., Abele, D., Mitra, T., Koslow, W., Abedi, M., Rack, K., et al. (2021). Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution. Mathematical Biosciences, 339, Article 108648.","journal-title":"Mathematical Biosciences"},{"key":"6893_CR152","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.eng.2021.03.017","volume":"7","author":"S Lai","year":"2021","unstructured":"Lai, S., et al. (2021). Assessing the effect of global travel and contact restrictions on mitigating the COVID-19 pandemic. Engineering, 7, 914\u2013923.","journal-title":"Engineering"},{"key":"6893_CR153","doi-asserted-by":"crossref","unstructured":"Le\u00f3n, U., P.d, Avila-Vales, E., & Huang, K. (2022). Modeling the Transmission of the SARS-CoV-2 Delta Variant in a Partially Vaccinated Population. Viruses, 14, 158.","DOI":"10.3390\/v14010158"},{"issue":"13","key":"6893_CR154","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1056\/NEJMoa2001316","volume":"382","author":"Q Li","year":"2020","unstructured":"Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., et al. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus infected pneumonia. New England Journal of Medicine, 382(13), 1199\u20131207.","journal-title":"New England Journal of Medicine"},{"key":"6893_CR155","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2021.801763","volume":"9","author":"R Li","year":"2021","unstructured":"Li, R., Li, Y., Zou, Z., Liu, Y., Li, X., Zhuang, G., et al. (2021). Evaluating the impact of SARS-CoV-2 variants on the COVID-19 epidemic and social restoration in the United States: A mathematical modelling study. Frontiers in Public Health, 9, Article 801763.","journal-title":"Frontiers in Public Health"},{"key":"6893_CR156","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104571","volume":"149","author":"R Li","year":"2024","unstructured":"Li, R., Song, Y., Qu, H., Li, M., & Jiang, G. P. (2024). A data-driven epidemic model with human mobility and vaccination protection for COVID-19 prediction. Journal of Biomedical Informatics, 149, Article 104571.","journal-title":"Journal of Biomedical Informatics"},{"key":"6893_CR157","doi-asserted-by":"publisher","DOI":"10.3390\/math10163008","volume":"10","author":"D Liang","year":"2022","unstructured":"Liang, D., Bhamra, R., Liu, Z., & Pan, Y. (2022). Risk propagation and supply chain health control based on the SIR epidemic model. Mathematics, 10, Article 3008.","journal-title":"Mathematics"},{"key":"6893_CR158","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105664","volume":"196","author":"GB Libotte","year":"2020","unstructured":"Libotte, G. B., Lobato, F. S., Platt, G. M., & Silva Neto, A. J. (2020). Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment. Computer Methods and Programs in Biomedicine, 196, Article 105664.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"6893_CR159","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2020.112915","volume":"287","author":"CK Lima","year":"2020","unstructured":"Lima, C. K., Carvalho, P. M., Lima, I. A., Nunes, J. V., Saraiva, J. S., Souza, R. I., et al. (2020). The emotional impact of Coronavirus 2019-nCoV (new Coronavirus disease). Psychiatry Research, 287, Article 112915.","journal-title":"Psychiatry Research"},{"key":"6893_CR160","doi-asserted-by":"publisher","DOI":"10.1016\/j.actatropica.2024.107159","volume":"253","author":"K Liu","year":"2024","unstructured":"Liu, K., Fang, S., Li, Q., & Lou, Y. (2024). Effectiveness evaluation of mosquito suppression strategies on dengue transmission under changing temperature and precipitation. Acta Tropica, 253, Article 10759.","journal-title":"Acta Tropica"},{"issue":"1","key":"6893_CR161","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jtbi.2007.10.014","volume":"253","author":"X Liu","year":"2008","unstructured":"Liu, X., Takeuchi, Y., & Iwami, S. (2008). SVIR epidemic models with vaccination strategies. Journal of Theoretical Biology, 253(1), 1\u201311.","journal-title":"Journal of Theoretical Biology"},{"key":"6893_CR162","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmateco.2021.102482","volume":"93","author":"S Loertscher","year":"2021","unstructured":"Loertscher, S., & Muir, E. V. (2021). Road to recovery: Managing an epidemic. Journal of Mathematical Economics, 93, Article 102482.","journal-title":"Journal of Mathematical Economics"},{"key":"6893_CR163","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2022.100630","volume":"41","author":"L L\u00f3pez","year":"2022","unstructured":"L\u00f3pez, L., Paul, R. E., Cao-Lormeau, V., & Rod\u00f3, X. (2022). Considering waning immunity to better explain dengue dynamics. Epidemics, 41, Article 100630.","journal-title":"Epidemics"},{"key":"6893_CR164","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.221656","volume":"10","author":"J Malinzi","year":"2023","unstructured":"Malinzi, J., & Juma, V. O. (2023). COVID-19 transmission dynamics and the impact of vaccination: Modelling, analysis and simulations. Royal Society Open Science, 10, Article 221656.","journal-title":"Royal Society Open Science"},{"key":"6893_CR165","doi-asserted-by":"publisher","first-page":"4137","DOI":"10.1038\/s41467-024-48332-y","volume":"15","author":"A Manna","year":"2024","unstructured":"Manna, A., Koltai, J., & Karsai, M. (2024). Importance of social inequalities to contact patterns, vaccine uptake, and epidemic dynamics. Nature Communications, 15, 4137.","journal-title":"Nature Communications"},{"key":"6893_CR166","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2023.109109","volume":"367","author":"JC Maraver","year":"2024","unstructured":"Maraver, J. C., Kevrekidis, P. G., Chen, Q. Y., Kevrekidis, G. A., & Drossinos, Y. (2024). Vaccination compartmental epidemiological models for the delta and omicron SARS-CoV-2 variants. Mathematical Biosciences, 367, Article 109109.","journal-title":"Mathematical Biosciences"},{"key":"6893_CR167","doi-asserted-by":"publisher","DOI":"10.1007\/s11587-023-00827-4","author":"RD Marca","year":"2024","unstructured":"Marca, R. D., & Menale, M. (2024). Modelling the impact of opinion flexibility on the vaccination choices during epidemics. Ricerche di Matematica. https:\/\/doi.org\/10.1007\/s11587-023-00827-4","journal-title":"Ricerche di Matematica"},{"key":"6893_CR168","volume":"75","author":"RD Marca","year":"2024","unstructured":"Marca, R. D., Onofrio, A. D., Sensi, M., & Sottile, S. (2024). A geometric analysis of the impact of large but finite switching rates on vaccination evolutionary games. Nonlinear Analysis: Real World Applications, 75, Article 103986.","journal-title":"Nonlinear Analysis: Real World Applications"},{"key":"6893_CR169","doi-asserted-by":"crossref","unstructured":"Marinov, T. T., & Marinova, R. S. (2020, Sep 20). Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19. Scientific reports, 12(1).","DOI":"10.1038\/s41598-022-20276-7"},{"key":"6893_CR170","doi-asserted-by":"publisher","DOI":"10.1016\/j.rinp.2021.104433","volume":"26","author":"R Markovic","year":"2021","unstructured":"Markovic, R., Sterk, M., Marhl, M., Perc, M., & Gosak, M. (2021). Socio-demographic and health factors drive the epidemic progression and should guide vaccination strategies for best COVID-19 containment. Results in Physics, 26, Article 104433.","journal-title":"Results in Physics"},{"key":"6893_CR171","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.prrv.2020.06.013","volume":"35","author":"ES McBryde","year":"2020","unstructured":"McBryde, E. S., Meehan, M. T., Adegboye, O. A., Adekunle, A. I., Caldwell, J. M., Pak, A., et al. (2020). Role of modelling in COVID-19 policy development. Paediatric Respiratory Reviews, 35, 57\u201360.","journal-title":"Paediatric Respiratory Reviews"},{"key":"6893_CR172","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2021.129809","volume":"272","author":"K Mehmood","year":"2021","unstructured":"Mehmood, K., Bao, Y., Petropoulos, G. P., Abbas, R., et al. (2021). Investigating connections between COVID-19 pandemic, air pollution and community interventions for Pakistan employing geoinformation technologies. Chemosphere, 272, Article 129809.","journal-title":"Chemosphere"},{"key":"6893_CR173","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1007\/s40121-024-00965-8","volume":"13","author":"D Mendes","year":"2024","unstructured":"Mendes, D., et al. (2024). Modelling COVID-19 vaccination in the UK: Impact of the Autumn 2022 and Spring 2023 booster campaigns. Infectious Diseases and Therapy, 13, 1127\u20131146.","journal-title":"Infectious Diseases and Therapy"},{"key":"6893_CR174","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.ijid.2021.01.010","volume":"104","author":"M Melis","year":"2021","unstructured":"Melis, M., & Littera, R. (2021). Undetected infectives in the covid-19 pandemic. International Journal of Infectious Diseases, 104, 262\u2013268.","journal-title":"International Journal of Infectious Diseases"},{"key":"6893_CR175","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-022-00075-x","volume":"2","author":"DO Mesa","year":"2022","unstructured":"Mesa, D. O., et al. (2022). Modelling the impact of vaccine hesitancy in prolonging the need for non-pharmaceutical interventions to control the COVID-19 pandemic. Communications Medicine, 2, Article 14.","journal-title":"Communications Medicine"},{"key":"6893_CR176","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s12530-023-09509-w","volume":"15","author":"S Mishra","year":"2024","unstructured":"Mishra, S., Singh, T., Kumar, M., & Satakshi. (2024). Multivariate time series short term forecasting using cumulative data of coronavirus. Evolving Systems, 15, 811\u2013828.","journal-title":"Evolving Systems"},{"issue":"1","key":"6893_CR177","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-20544-y","volume":"12","author":"D Mistry","year":"2021","unstructured":"Mistry, D., Litvinova, M., Piontti, A. P., et al. (2021). Inferring high-resolution human mixing patterns for disease modelling. Nature Communications, 12(1), 1\u201312.","journal-title":"Nature Communications"},{"key":"6893_CR178","doi-asserted-by":"publisher","first-page":"230621","DOI":"10.1098\/rsos.230621","volume":"10","author":"J Molla","year":"2023","unstructured":"Molla, J., Moyles, I. R., & Heffernan, J. M. (2023). Pharmaceutical and non-pharmaceutical interventions for controlling the COVID-19 pandemic. Royal Society Open Science, 10, 230621.","journal-title":"Royal Society Open Science"},{"key":"6893_CR179","doi-asserted-by":"crossref","unstructured":"Moret, M., et al. (2021). WHO vaccination protocol can be improved to save more lives. Research square. Consult\u00e9 le Preprint Available at https:\/\/www.researchsquare.com\/paper\/rs-148826\/v1","DOI":"10.21203\/rs.3.rs-148826\/v1"},{"issue":"4","key":"6893_CR180","first-page":"e116","volume":"22","author":"DM Morens","year":"2022","unstructured":"Morens, D. M., Folkers, G. K., & Fauci, A. S. (2022). Emerging infections: A perpetual challenge. The Lancet Infectious Diseases, 22(4), e116\u2013e125.","journal-title":"The Lancet Infectious Diseases"},{"issue":"9","key":"6893_CR181","doi-asserted-by":"publisher","first-page":"3068","DOI":"10.18203\/2394-6040.ijcmph20173814","volume":"4","author":"AV Mutalik","year":"2017","unstructured":"Mutalik, A. V. (2017). Models to predict H1N1 outbreaks: A literature review. International Journal of Community Medicine and Public Health, 4(9), 3068\u20133075.","journal-title":"International Journal of Community Medicine and Public Health"},{"key":"6893_CR182","doi-asserted-by":"publisher","first-page":"20","DOI":"10.46298\/ocnmp.7463","volume":"1","author":"G Nakamura","year":"2021","unstructured":"Nakamura, G., Grammaticos, B., & Badoual, M. (2021). Vaccination strategies for a seasonal epidemic: A simple SIR model. Open Communications in Nonlinear Mathematical Physics, 1, 20\u201340.","journal-title":"Open Communications in Nonlinear Mathematical Physics"},{"key":"6893_CR183","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104727","volume":"84","author":"SK Nanda","year":"2023","unstructured":"Nanda, S. K., Kumar, G., Bhatia, V., & Singh, A. K. (2023). Kalman-based compartmental estimation forc ovid-19 pandemic using. Biomedical Signal Processing and Control, 84, Article 104727.","journal-title":"Biomedical Signal Processing and Control"},{"key":"6893_CR184","volume":"1819","author":"H Nasution","year":"2021","unstructured":"Nasution, H., Sitompul, P., & Sinaga, L. P. (2021). Effect of the vaccine on the dynamics of speread of tuberculosis SIR models. Journal of Physics: Conference Series, 1819, Article 012062.","journal-title":"Journal of Physics: Conference Series"},{"key":"6893_CR185","doi-asserted-by":"publisher","DOI":"10.3390\/v14102237","volume":"14","author":"G Neofotistos","year":"2022","unstructured":"Neofotistos, G., et al. (2022). Susceptibility to resurgent COVID-19 outbreaks following vaccine rollouts: A modeling study. Viruses, 14, Article 2237.","journal-title":"Viruses"},{"key":"6893_CR186","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00794-3","volume":"2","author":"EG Nepomuceno","year":"2021","unstructured":"Nepomuceno, E. G., Peixoto, M. L., et al. (2021). Application of optimal control of infectious diseases in a model-free scenario. SN Computer Science, 2, Article 405.","journal-title":"SN Computer Science"},{"key":"6893_CR1870","doi-asserted-by":"crossref","unstructured":"Ng, W. L. (2020). To lockdown? When to peak? Will there be an end? A macroeconomic analysis on COVID-19 epidemic in the United States. Journal of Macroeconomics, 65, 103230.","DOI":"10.1016\/j.jmacro.2020.103230"},{"key":"6893_CR187","first-page":"186","volume":"161","author":"TK Nguyen","year":"2022","unstructured":"Nguyen, T. K., Hoang, N. H., Currie, G., & Vu, H. L. (2022). Enhancing covid-19 virus spread modeling using an activity travel model. Transportation Research Part a: Policy and Practice, 161, 186\u2013199.","journal-title":"Transportation Research Part a: Policy and Practice"},{"issue":"1","key":"6893_CR188","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.ejor.2020.08.001","volume":"290","author":"K Nikolopoulos","year":"2021","unstructured":"Nikolopoulos, K., Punia, S., Sch\u00e4fers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99\u2013115.","journal-title":"European Journal of Operational Research"},{"issue":"170","key":"6893_CR189","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2020.0094","volume":"17","author":"SM O\u2019Regan","year":"2020","unstructured":"O\u2019Regan, S. M., et al. (2020). Transient indicators of tipping points in infectious diseases. Journal of the Royal Society Interface, 17(170), Article 20200094.","journal-title":"Journal of the Royal Society Interface"},{"issue":"187","key":"6893_CR190","doi-asserted-by":"publisher","first-page":"20210702","DOI":"10.1098\/rsif.2021.0702","volume":"19","author":"EB O'Dea","year":"2022","unstructured":"O\u2019Dea, E. B., & Drake, J. M. (2022). A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations and deaths. Journal of the Royal Society Interface, 19(187), 20210702.","journal-title":"Journal of the Royal Society Interface"},{"issue":"1","key":"6893_CR191","first-page":"179","volume":"8","author":"N Ortiz-Robinson","year":"2021","unstructured":"Ortiz-Robinson, N., & Foster-Bey, C. (2021). Control strategies to contain SARS-CoV-2 in a data driven SIR model for the State of Michigan, USA. Letters in Biomathematics, 8(1), 179\u2013189.","journal-title":"Letters in Biomathematics"},{"key":"6893_CR192","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.idm.2021.11.003","volume":"7","author":"W Pang","year":"2022","unstructured":"Pang, W., Chehaitli, H., & Hurd, T. R. (2022). Impact of asymptomatic COVID-19 carriers on pandemic policy outcomes. Infectious Disease Modelling, 7, 16\u201329.","journal-title":"Infectious Disease Modelling"},{"key":"6893_CR193","doi-asserted-by":"publisher","first-page":"6504","DOI":"10.1002\/mma.9934","volume":"47","author":"VE Papageorgiou","year":"2024","unstructured":"Papageorgiou, V. E., & Tsaklidis, G. (2024). A stochastic particle extended SEIRS model with repeated vaccination: Application to real data of COVID-19 in Italy. Mathematical Methods in the Applied Sciences, 47, 6504\u20136538.","journal-title":"Mathematical Methods in the Applied Sciences"},{"key":"6893_CR194","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2023.100736","volume":"46","author":"SW Park","year":"2024","unstructured":"Park, S. W., et al. (2024). Predicting the impact of COVID-19 non-pharmaceutical intervention on short- and medium-term dynamics of enterovirus D68 in the US. Epidemics, 46, Article 100736.","journal-title":"Epidemics"},{"key":"6893_CR195","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.idm.2022.03.002","volume":"7","author":"N Parolini","year":"2022","unstructured":"Parolini, N., Dede\u2019, L., Ardenghi, G., & Quarteroni, A. (2022). Modelling the COVID-19 epidemic and the vaccination campaign in Italy by the SUIHTER model. Infectious Disease Modelling, 7, 45\u201363.","journal-title":"Infectious Disease Modelling"},{"issue":"3","key":"6893_CR196","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1103\/RevModPhys.87.925","volume":"87","author":"R Pastor-Satorra","year":"2015","unstructured":"Pastor-Satorra, R., Castellan, C., Van Mieghe, P., & Vespignan, A. (2015). Epidemic processes in complex network. Reviews of Modern Physics, 87(3), 925\u2013979.","journal-title":"Reviews of Modern Physics"},{"key":"6893_CR197","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29225-4","volume":"13","author":"L P\u00e9rez-Al\u00f3s","year":"2022","unstructured":"P\u00e9rez-Al\u00f3s, L., et al. (2022). Modeling of waning immunity after SARS-CoV-2 vaccination and influencing factors. Nature Communications, 13, Article 1614.","journal-title":"Nature Communications"},{"key":"6893_CR198","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.828691","volume":"9","author":"Y Pey","year":"2022","unstructured":"Pey, Y., Li, J., Xu, S., & Xu, Y. (2022). Adaptive multi-factor quantitative analysis and prediction models: Vaccination, virus mutation and social isolation on COVID-19. Frontier in Medicine, 9, Article 828691.","journal-title":"Frontier in Medicine"},{"key":"6893_CR199","doi-asserted-by":"publisher","DOI":"10.1016\/j.watres.2023.120098","volume":"241","author":"P Polcz","year":"2023","unstructured":"Polcz, P., Tornai, K., Juh\u00e1sz, J., Cserey, G., et al. (2023). Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants. Water Research, 241, Article 120098.","journal-title":"Water Research"},{"issue":"12","key":"6893_CR200","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.1093\/humrep\/deac215","volume":"37","author":"L Pomar","year":"2022","unstructured":"Pomar, L., Favre, G., et al. (2022). Impact of the first wave of the COVID-19 pandemic on birth rates in Europe: A time series analysis in 24 countries. Human Reproduction, 37(12), 2921\u20132931.","journal-title":"Human Reproduction"},{"key":"6893_CR201","doi-asserted-by":"publisher","DOI":"10.1016\/S2468-2667(20)30073-6","author":"K Prem","year":"2020","unstructured":"Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., Davies, N., et al. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study. The Lancet Public Health. https:\/\/doi.org\/10.1016\/S2468-2667(20)30073-6","journal-title":"The Lancet Public Health"},{"issue":"2","key":"6893_CR202","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s40171-023-00337-0","volume":"24","author":"JA Qundus","year":"2023","unstructured":"Qundus, J. A., Gupta, S., Abusaimeh, H., Peikert, S., & Paschke, A. (2023). Prescriptive analytics-based SIRM model for predicting Covid-19 outbreak. Global Journal of Flexible Systems Management, 24(2), 235\u2013246.","journal-title":"Global Journal of Flexible Systems Management"},{"issue":"4","key":"6893_CR203","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0267388","volume":"17","author":"MJ Rabil","year":"2022","unstructured":"Rabil, M. J., Tunc, S., Bish, D. R., & Bish, E. K. (2022). Benefits of integrated screening and vaccination for infection control. PLoS ONE, 17(4), Article e0267388.","journal-title":"PLoS ONE"},{"key":"6893_CR204","doi-asserted-by":"publisher","first-page":"2185","DOI":"10.3390\/v13112185","volume":"2021","author":"A Rahman","year":"2021","unstructured":"Rahman, A., Kuddus, M. A., Ip, R. H., & Bewong, M. (2021). A review of COVID-19 modelling strategies in three countries to develop a research framework for regional areas. Viruses, 2021, 2185.","journal-title":"Viruses"},{"key":"6893_CR205","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jprocont.2021.03.008","volume":"102","author":"A Rajaei","year":"2021","unstructured":"Rajaei, A., Raeiszadeh, M., Azimi, V., & Sharifi, M. (2021). State estimation-based control of COVID-19 epidemic before and after vaccine development. Journal of Process Control, 102, 1\u201314.","journal-title":"Journal of Process Control"},{"key":"6893_CR206","doi-asserted-by":"publisher","DOI":"10.1016\/j.gene.2024.148608","volume":"926","author":"SP Rajasekar","year":"2024","unstructured":"Rajasekar, S. P., Ramesh, R., & Sabbar, Y. (2024). Based on epidemiological parameter data, probe into a stochastically perturbed dominant variant of the COVID-19 pandemic model. Gene, 926, Article 148608.","journal-title":"Gene"},{"key":"6893_CR207","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2021.108621","volume":"337","author":"IJ Rao","year":"2021","unstructured":"Rao, I. J., & Brandeau, M. L. (2021a). Optimal allocation of limited vaccine to control an infectious disease: Simple analytical conditions. Mathematical Biosciences, 337, Article 108621.","journal-title":"Mathematical Biosciences"},{"key":"6893_CR208","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2021.108654","volume":"339","author":"IJ Rao","year":"2021","unstructured":"Rao, I. J., & Brandeau, M. L. (2021b). Optimal allocation of limited vaccine to minimize the effective reproduction number. Mathematical Biosciences, 339, Article 108654.","journal-title":"Mathematical Biosciences"},{"key":"6893_CR209","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2022.108879","volume":"351","author":"IJ Rao","year":"2022","unstructured":"Rao, I. J., & Brandeau, M. L. (2022). Sequential allocation of vaccine to control an infectious disease. Mathematical Biosciences, 351, Article 108879.","journal-title":"Mathematical Biosciences"},{"key":"6893_CR210","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.ijsu.2020.11.015","volume":"85","author":"A Rezapour","year":"2021","unstructured":"Rezapour, A., Souresrafil, A., Peighambari, M. M., Heidarali, M., & Tashakori-Miyanroudi, M. (2021). Economic evaluation of programs against COVID-19: A systematic review. International Journal of Surgery, 85, 10\u201318.","journal-title":"International Journal of Surgery"},{"issue":"6","key":"6893_CR211","doi-asserted-by":"publisher","DOI":"10.1136\/bmjgh-2020-002914","volume":"5","author":"T Rhodes","year":"2020","unstructured":"Rhodes, T., Lancaster, K., Lees, S., & Parker, M. (2020). Modelling the pandemic: Attuning models to their contexts. BMJ Global Health, 5(6), Article e002914.","journal-title":"BMJ Global Health"},{"issue":"20","key":"6893_CR212","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.1001\/jama.2020.6775","volume":"323","author":"S Richardson","year":"2020","unstructured":"Richardson, S., Hirsch, J. S., Narasimhan, M., Crawford, J. M., McGinn, T., &, et al. (2020). Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York city area. Journal of the American Medical Association, 323(20), 2052.","journal-title":"Journal of the American Medical Association"},{"key":"6893_CR2120","unstructured":"Rizvi, F. W. (2016). Mathematical Modeling of Two-DoseVaccines. Ohio State University."},{"key":"6893_CR213","volume":"12","author":"B Robinson","year":"2022","unstructured":"Robinson, B., et al. (2022). Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: A study protocol. British Medical Journal Open, 12, Article e052681.","journal-title":"British Medical Journal Open"},{"issue":"2","key":"6893_CR214","doi-asserted-by":"publisher","DOI":"10.1088\/0034-4885\/77\/2\/026602","volume":"77","author":"K Rock","year":"2014","unstructured":"Rock, K., Brand, S., Moir, J., & Keeling, M. J. (2014). Dynamics of infectious diseased. Reports on Progress in Physic, 77(2), Article 026602.","journal-title":"Reports on Progress in Physic"},{"key":"6893_CR215","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s10654-022-00938-6","volume":"38","author":"I Rodiah","year":"2023","unstructured":"Rodiah, I., Vanella, P., Kuhlmann, A., Jaeger, V. K., Harries, M., Krause, G., et al. (2023). Age-specific contribution of contacts to transmission of SARS-CoV-2 in Germany. European Journal of Epidemiology, 38, 39\u201358.","journal-title":"European Journal of Epidemiology"},{"key":"6893_CR216","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1038\/s41443-021-00434-7","volume":"34","author":"AF Rose","year":"2022","unstructured":"Rose, A. F., Mantica, G., et al. (2022). COVID-19 impact on birth rates: first data from Metropolitan City of Genoa Northern Italy. International Journal of Impotence Research, 34, 111\u2013112.","journal-title":"International Journal of Impotence Research"},{"key":"6893_CR217","unstructured":"Roser, M., Ritchie, H., Ortiz-Ospina, E., & Hasell, J. (2020). Coronavirus pandemic (COVID-19). Our World in Data. https:\/\/www.ourworldindata.org\/coronavirus"},{"issue":"3","key":"6893_CR218","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgph.0001693","volume":"3","author":"FJ Ruiz","year":"2023","unstructured":"Ruiz, F. J., Torres-Rueda, S., et al. (2023). What, how and who: Cost-effectiveness analyses of COVID-19 vaccination to inform key policies in Nigeria. PLOS Global Public Health, 3(3), Article e0001693.","journal-title":"PLOS Global Public Health"},{"issue":"11","key":"6893_CR219","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0259969","volume":"16","author":"AC Rusu","year":"2021","unstructured":"Rusu, A. C., Emonet, R., & Farrahi, K. (2021). Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers? PLoS ONE, 16(11), Article e0259969.","journal-title":"PLoS ONE"},{"key":"6893_CR220","doi-asserted-by":"crossref","unstructured":"Saad-Roy, C., M, Morris, S. E., & Metcalf, C. J. (2021). Epidemiological and evolutionary considerations of SARS-CoV-2 vaccine dosing regimes. Science, 372.","DOI":"10.1101\/2021.02.01.21250944"},{"issue":"4","key":"6893_CR221","doi-asserted-by":"publisher","first-page":"2613","DOI":"10.3934\/mbe.2019131","volume":"16","author":"M Safan","year":"2019","unstructured":"Safan, M. (2019). Mathematical analysis of an SIR respiratory infection model with sex and gender disparity: Special reference to influenza A. Mathematical Biosciences and Engineering, 16(4), 2613\u20132649.","journal-title":"Mathematical Biosciences and Engineering"},{"key":"6893_CR222","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s40435-020-00721-z","volume":"9","author":"S Saha","year":"2021","unstructured":"Saha, S., & Samanta, G. P. (2021). Modelling the role of optimal social distancing on disease prevalence of COVID-19 epidemic. International Journal of Dynamics and Control, 9, 1053\u20131077.","journal-title":"International Journal of Dynamics and Control"},{"key":"6893_CR223","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.matcom.2022.04.025","volume":"200","author":"S Saha","year":"2022","unstructured":"Saha, S., Samanta, G., & Nieto, J. J. (2022). Impact of optimal vaccination and social distancing on COVID-19 pandemic. Mathematics and Computers in Simulation, 200, 285\u2013314.","journal-title":"Mathematics and Computers in Simulation"},{"key":"6893_CR224","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100269","volume":"4","author":"S Saharan","year":"2023","unstructured":"Saharan, S., & Tee, C. (2023). A COVID-19 vaccine effectiveness model using the susceptible-exposed-infectious-recovered model. Healthcare Analytics, 4, Article 100269.","journal-title":"Healthcare Analytics"},{"key":"6893_CR225","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1016\/S0140-6736(03)13168-6","volume":"361","author":"W Seto","year":"2003","unstructured":"Seto, W., Tsang, D., Yung, R., Ching, T., Ng, T., Ho, M., et al. (2003). Effectiveness of precautions against droplets and contact in prevention of nosocomial transmission of severe acute respiratory syndrome (SARS). Lancet, 361, 1519\u20131520.","journal-title":"Lancet"},{"key":"6893_CR226","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdrr.2022.103327","volume":"82","author":"SV Sharif","year":"2022","unstructured":"Sharif, S. V., Moshfegh, P. H., Morshedi, M. A., & Kashani, H. (2022). Modeling the impact of mitigation policies in a pandemic: A system dynamics approach. International Journal of Disaster Risk Reduction, 82, Article 103327.","journal-title":"International Journal of Disaster Risk Reduction"},{"key":"6893_CR227","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110295","volume":"141","author":"KS Sharov","year":"2020","unstructured":"Sharov, K. S. (2020). Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency. Chaos, Solitons & Fractals, 141, Article 110295.","journal-title":"Chaos, Solitons & Fractals"},{"key":"6893_CR228","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.111039","volume":"148","author":"S Shringi","year":"2021","unstructured":"Shringi, S., Sharma, H., Rathie, P. N., Bansal, J. C., & Nagar, A. (2021). Modified SIRD model for COVID-19 spread prediction for Northern and Southern States of India. Chaos, Solitons & Fractals, 148, Article 111039.","journal-title":"Chaos, Solitons & Fractals"},{"key":"6893_CR229","first-page":"238","volume":"1084","author":"X Song","year":"2020","unstructured":"Song, X., Liu, M., Song, H., & Ren, J. (2020). Dynamical behavior of an SVIR epidemiological model with two stage characteristics of vaccine effectiveness and numerical simulation. Advances in Intelligent Systems and Interactive Applications, 1084, 238\u2013242.","journal-title":"Advances in Intelligent Systems and Interactive Applications"},{"issue":"12","key":"6893_CR230","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0225838","volume":"14","author":"O Stojanovic","year":"2019","unstructured":"Stojanovic, O., Leugering, J., Pipa, G., Ghozzi, S., & Ullrich, A. (2019). A bayesian monte carlo approach for predicting the spread of infectious diseases. PLoS ONE, 14(12), Article e225838.","journal-title":"PLoS ONE"},{"issue":"7","key":"6893_CR231","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1016\/j.mayocp.2021.04.012","volume":"96","author":"CB Storlie","year":"2021","unstructured":"Storlie, C. B., Pollock, B. D., Rojas, R. L., et al. (2021a). Quantifying the importance of COVID-19 vaccination to our future outlook. Mayo Clinic Proceedings, 96(7), 1890\u20131895.","journal-title":"Mayo Clinic Proceedings"},{"key":"6893_CR232","unstructured":"Storlie, C. B., Rojas, R. L., Demuth, G. O., et al. (2021b). A hierarchical Bayesian model for stochastic spatiotemporal SIR modeling and prediction of COVID-19 cases and hospitalizations. arXiv."},{"key":"6893_CR233","doi-asserted-by":"publisher","first-page":"727274","DOI":"10.3389\/fpubh.2021.727274","volume":"9","author":"T \u0160u\u0161ter\u0161ic","year":"2021","unstructured":"\u0160u\u0161ter\u0161ic, T., Blagojevic, A., et al. (2021). Epidemiological predictive modeling of COVID-19 infection: Development, testing, and implementation on the population of the Benelux Union. Frontier in Public Health, 9, 727274.","journal-title":"Frontier in Public Health"},{"issue":"6","key":"6893_CR234","doi-asserted-by":"publisher","first-page":"2721","DOI":"10.1097\/MS9.0000000000000627","volume":"85","author":"P Tamasiga","year":"2023","unstructured":"Tamasiga, P., Onyeaka, H., Umenweke, G. C., & Uwishema, O. (2023). An extended SEIRDV compartmental model: Case studies of the spread of COVID-19 and vaccination in Tunisia and South Africa. Annals of Medicine and Surgery, 85(6), 2721\u20132730.","journal-title":"Annals of Medicine and Surgery"},{"key":"6893_CR235","first-page":"248","volume":"5","author":"B Tang","year":"2020","unstructured":"Tang, B., Bragazzi, N. L., Li, Q., Tang, S., Xiao, Y., & Wu, J. (2020a). An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). Infectious Diseases, 5, 248\u2013255.","journal-title":"Infectious Diseases"},{"issue":"2","key":"6893_CR236","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1111\/insr.12402","volume":"88","author":"L Tang","year":"2020","unstructured":"Tang, L., Zhou, Y., Wang, L., et al. (2020b). A review of multi-compartment infectious disease models. International Statistical Review, 88(2), 462\u2013513.","journal-title":"International Statistical Review"},{"key":"6893_CR237","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0010911","author":"S Tang","year":"2010","unstructured":"Tang, S., Xiao, Y., Yang, Y., Zhou, Y., Wu, J., & Ma, Z. (2010). Community-based measures for mitigating the 2009 H1N1 pandemic in China. PLoS ONE. https:\/\/doi.org\/10.1371\/journal.pone.0010911","journal-title":"PLoS ONE"},{"key":"6893_CR238","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2458-11-S1-S5","author":"JM Tchuenche","year":"2011","unstructured":"Tchuenche, J. M., Dube, N., Bhunu, C. P., Smith, R. J., & Bauch, C. T. (2011). The impact of media coverage on the transmission dynamics of human influenza. BMC Public Health. https:\/\/doi.org\/10.1186\/1471-2458-11-S1-S5","journal-title":"BMC Public Health"},{"key":"6893_CR239","doi-asserted-by":"publisher","unstructured":"Team, I. C.-1., & Murray, C. J. (2020a). Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months. MedRxiv. https:\/\/doi.org\/10.1101\/2020.03.27.20043752","DOI":"10.1101\/2020.03.27.20043752"},{"key":"6893_CR240","doi-asserted-by":"publisher","unstructured":"Team, I. C.-1., & Murray, C. J. (2020a). Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months. MedRxiv. https:\/\/doi.org\/10.1101\/2020.03.27.20043752","DOI":"10.1101\/2020.03.27.20043752"},{"issue":"19","key":"6893_CR241","doi-asserted-by":"publisher","first-page":"e497","DOI":"10.1503\/cmaj.200476","volume":"192","author":"AR Tuite","year":"2020","unstructured":"Tuite, A. R., Fisman, D. N., & Greer, A. L. (2020). Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada. Canadian Medical Association Journal, 192(19), e497\u2013e505.","journal-title":"Canadian Medical Association Journal"},{"key":"6893_CR242","doi-asserted-by":"publisher","DOI":"10.1016\/j.cnsns.2023.107588","volume":"128","author":"TD Tuong","year":"2024","unstructured":"Tuong, T. D., Nguyen, D. H., & Nguyen, N. N. (2024). Stochastic multi-group epidemic SVIR models: Degenerate case. Communications in Nonlinear Science and Numerical Simulation, 128, Article 107588.","journal-title":"Communications in Nonlinear Science and Numerical Simulation"},{"key":"6893_CR243","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2022.127429","volume":"598","author":"M Turkyilmazoglu","year":"2022","unstructured":"Turkyilmazoglu, M. (2022). An extended epidemic model with vaccination: Weak-immune SIRVI. Physica a: Statistical Mechanics and Its Applications, 598, Article 127429.","journal-title":"Physica a: Statistical Mechanics and Its Applications"},{"key":"6893_CR244","doi-asserted-by":"publisher","DOI":"10.3390\/vaccines11040722","volume":"11","author":"E Tzamali","year":"2023","unstructured":"Tzamali, E., et al. (2023). Mathematical modeling evaluates how vaccinations affected the course of COVID-19 disease progression. Vaccines, 11, Article 722.","journal-title":"Vaccines"},{"key":"6893_CR245","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1016\/j.ejor.2023.03.032","volume":"310","author":"B Vahdani","year":"2023","unstructured":"Vahdani, B., Mohammadi, M., Thevenin, S., Gendreau, M., Dolgui, A., & Meyer, P. (2023). Fair-split distribution of multi-dose vaccines with prioritized age groups and dynamic demand: The case study of COVID-19. European Journal of Operational Research, 310, 1249\u20131272.","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"6893_CR246","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011832","volume":"20","author":"M van Boven","year":"2024","unstructured":"van Boven, M., et al. (2024). Estimation of introduction and transmission rates of SARS-CoV-2 in a prospective household study. PLoS Computational Biology, 20(1), Article e1011832.","journal-title":"PLoS Computational Biology"},{"key":"6893_CR247","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2022.100657","volume":"41","author":"G Vattiatio","year":"2022","unstructured":"Vattiatio, G., Lustig, A., Maclaren, O. J., & Plank, M. J. (2022). Modelling the dynamics of infection, waning of immunity and re-infection with the Omicron variant of SARS-CoV-2 in Aotearoa New Zealand. Epidemics, 41, Article 100657.","journal-title":"Epidemics"},{"key":"6893_CR248","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s10754-024-09367-2","volume":"24","author":"J Villota-Miranda","year":"2024","unstructured":"Villota-Miranda, J., & Odr\u00edguez-Ibeas, R. (2024). Simple economics of vaccination: Public policies and incentives. International Journal of Health Economics and Management, 24, 155\u2013172.","journal-title":"International Journal of Health Economics and Management"},{"key":"6893_CR249","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2023.100706","volume":"44","author":"IG Violaris","year":"2023","unstructured":"Violaris, I. G., et al. (2023). Modelling the COVID-19 pandemic: Focusing on the case of Greece. Epidemics, 44, Article 100706.","journal-title":"Epidemics"},{"key":"6893_CR2500","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3754051","author":"M Wali","year":"2022","unstructured":"Wali, M., Arshad, S., & Huang, J. (2022). Stability analysis of an extended SEIR COVID-19 fractional model with vaccination efficiency. Computational and Mathematical Methods in Medicine. https:\/\/doi.org\/10.1155\/2022\/3754051","journal-title":"Computational and Mathematical Methods in Medicine"},{"key":"6893_CR251","doi-asserted-by":"publisher","DOI":"10.1142\/S0218127413501447","author":"A Wang","year":"2013","unstructured":"Wang, A., & Xiao, Y. (2013). Sliding bifurcation and global dynamics of a Filippov epidemic model with vaccination. International Journal of Bifurcation and Chaos. https:\/\/doi.org\/10.1142\/S0218127413501447","journal-title":"International Journal of Bifurcation and Chaos"},{"issue":"2","key":"6893_CR252","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/JSTSP.2022.3152375","volume":"16","author":"H Wang","year":"2022","unstructured":"Wang, H., Tao, G., Ma, J., Jia, S., Chi, L., Yang, H., et al. (2022). Predicting the epidemics trend of COVID-19 using epidemiological-based generative adversarial networks. IEEE Journal of Selected Topics in Signal Processing, 16(2), 276\u2013288.","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"key":"6893_CR253","doi-asserted-by":"publisher","DOI":"10.1016\/j.rinp.2023.106889","volume":"52","author":"Y Wang","year":"2023","unstructured":"Wang, Y., et al. (2023). Numerical assessment of multiple vaccinations to mitigate the transmission of COVID-19 via a new epidemiological modeling approach. Results in Physics, 52, Article 106889.","journal-title":"Results in Physics"},{"issue":"5","key":"6893_CR254","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1001\/jama.2020.11787","volume":"324","author":"SH Woolf","year":"2020","unstructured":"Woolf, S. H., Chapman, D. A., Sabo, R. T., Weinberger, D. M., & Hill, L. (2020). Excess deaths from COVID-19 and other causes. JAMA, 324(5), 510\u2013513.","journal-title":"JAMA"},{"key":"6893_CR255","unstructured":"World Health Organization (WHO). (2023).\u00a0COVID-19 Situation Report \u2013 Global Status Update."},{"issue":"4","key":"6893_CR256","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1038\/s41591-020-0822-7","volume":"26","author":"JT Wu","year":"2020","unstructured":"Wu, J. T., Leung, K., Bushman, M., Kishore, N., Niehus, R., et al. (2020). Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nature Medicine, 26(4), 506\u2013510.","journal-title":"Nature Medicine"},{"key":"6893_CR257","doi-asserted-by":"publisher","DOI":"10.1016\/j.cegh.2022.101052","volume":"15","author":"N Yaladanda","year":"2022","unstructured":"Yaladanda, N., Mopuri, R., Vavilala, H. P., & Mutheneni, S. R. (2022). Modelling the impact of perfect and imperfect vaccination strategy against SARS CoV-2 by assuming varied vaccine efficacy over India. Clinical Epidemiology and Global Health, 15, Article 101052.","journal-title":"Clinical Epidemiology and Global Health"},{"issue":"3","key":"6893_CR258","doi-asserted-by":"publisher","first-page":"165","DOI":"10.21037\/jtd.2020.02.64","volume":"12","author":"Z Yang","year":"2020","unstructured":"Yang, Z., Zeng, Z., Wang, K., Wong, S. S., et al. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3), 165.","journal-title":"Journal of Thoracic Disease"},{"key":"6893_CR2590","doi-asserted-by":"crossref","unstructured":"Yu, P., Wang, P., Wang, Z., & Wang, J. (2022b). Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision. Sustainability, 14, 8420.","DOI":"10.3390\/su14148420"},{"key":"6893_CR2591","doi-asserted-by":"crossref","unstructured":"Yu, P., Wang, Z., Sun, Y., & Wang, P. (2022a). Risk Diffusion and Control under Uncertain Information Based. Mathematics, 10, 4344.","DOI":"10.3390\/math10224344"},{"issue":"1","key":"6893_CR259","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/S0025-5564(01)00050-5","volume":"171","author":"GS Zaric","year":"2001","unstructured":"Zaric, G. S., & Brandeau, M. L. (2001). Resource allocation for epidemic control over short time horizons. Mathematical Biosciences, 171(1), 33\u201358.","journal-title":"Mathematical Biosciences"},{"key":"6893_CR260","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.ins.2022.05.093","volume":"607","author":"C Zhan","year":"2022","unstructured":"Zhan, C., et al. (2022). Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model. Information Sciences, 607, 418\u2013439.","journal-title":"Information Sciences"},{"key":"6893_CR261","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.ins.2023.02.001","volume":"628","author":"C Zhan","year":"2023","unstructured":"Zhan, C., Zheng, Y., Shao, L., Chen, G., & Zhang, H. (2023). Modeling the spread dynamics of multiple-variant coronavirus disease under public health interventions: A general framework. Information Sciences, 628, 469\u2013487.","journal-title":"Information Sciences"},{"key":"6893_CR262","doi-asserted-by":"publisher","DOI":"10.1208\/s12248-022-00743-9","volume":"24","author":"P Zhang","year":"2022","unstructured":"Zhang, P., Feng, K., & Gong, Y. (2022a). Usage of compartmental models in predicting COVID-19 outbreaks. The AAPS Journal, 24, Article 98.","journal-title":"The AAPS Journal"},{"key":"6893_CR263","doi-asserted-by":"publisher","first-page":"9239","DOI":"10.3390\/ijerph19159239","volume":"19","author":"W Zhang","year":"2022","unstructured":"Zhang, W., Hugginst, Zheng, W., Liu, S., Du, Z., Zhu, H., et al. (2022b). Assessing the dynamic outcomes of containment strategies against COVID-19 under different public health governance structures: A comparison between Pakistan and Bangladesh. International Journal of Environmental Research and Public Health, 19, 9239.","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"6893_CR264","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106046","volume":"149","author":"W Zhang","year":"2022","unstructured":"Zhang, W., Xie, R., Dong, X., Li, J., Peng, P., & Gonzalez, E. S. (2022c). SEIR-fmi: A coronavirus disease epidemiological model based on intra-city movement, inter-city movement and medical resource investment. Computers in Biology and Medicine, 149, Article 106046.","journal-title":"Computers in Biology and Medicine"},{"key":"6893_CR265","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdrr.2023.103685","volume":"91","author":"Z Zhang","year":"2023","unstructured":"Zhang, Z., Fu, D., & Wang, J. (2023). How containment policy and medical service impact COVID-19 transmission: A cross-national comparison among China, the USA, and Sweden. International Journal of Disaster Risk Reduction, 91, Article 103685.","journal-title":"International Journal of Disaster Risk Reduction"},{"issue":"10229","key":"6893_CR266","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1016\/S0140-6736(20)30566-3","volume":"395","author":"F Zhou","year":"2020","unstructured":"Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., et al. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. The Lancet, 395(10229), 1054\u20131062.","journal-title":"The Lancet"},{"key":"6893_CR267","first-page":"312","volume":"13","author":"L Zhou","year":"2012","unstructured":"Zhou, L., & Fan, M. (2012). Dynamics of an SIR epidemic model with limited medical resources revisited. Nonlinear Analysis: Real World Applications, 13, 312\u2013324.","journal-title":"Nonlinear Analysis: Real World Applications"},{"key":"6893_CR268","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.jobb.2024.02.002","volume":"6","author":"R Zhou","year":"2024","unstructured":"Zhou, R., et al. (2024). Dynamic evolution of an SVEIR model with variants and non-pharmaceutical interventions for controlling COVID-19. Journal of Biosafety and Biosecurity, 6, 67\u201375.","journal-title":"Journal of Biosafety and Biosecurity"},{"key":"6893_CR269","doi-asserted-by":"publisher","first-page":"286","DOI":"10.3390\/systems12080286","volume":"12","author":"J Zhu","year":"2024","unstructured":"Zhu, J., Wang, Q., & Huang, M. (2024). Optimal allocation of multi-type vaccines in a two-dose vaccination campaign for epidemic control: A case study of COVID-19. Systems, 12, 286.","journal-title":"Systems"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-025-06893-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-025-06893-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-025-06893-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T11:02:48Z","timestamp":1770202968000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-025-06893-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,20]]},"references-count":279,"journal-issue":{"issue":"2-3","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["6893"],"URL":"https:\/\/doi.org\/10.1007\/s10479-025-06893-1","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,20]]},"assertion":[{"value":"31 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}