{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:21:16Z","timestamp":1755224476786,"version":"3.43.0"},"reference-count":59,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CBET 1802588"],"award-info":[{"award-number":["CBET 1802588"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>During the COVID-19 pandemic, the prevalence of asymptomatic cases challenged the reliability of epidemiological statistics in policymaking. To address this, we introduced <jats:italic>contagion potential<\/jats:italic> (CP) as a continuous metric derived from sociodemographic and epidemiological data to quantify the infection risk posed by the asymptomatic within a region. However, CP estimation is hindered by incomplete or biased incidence data, where underreporting and testing constraints make direct estimation infeasible. To overcome this limitation, we employ a hypothesis-testing approach to infer CP from sampled data, allowing for robust estimation despite missing information. Even within the sample collected from spatial contact data, individuals possess partial knowledge of their neighborhoods, as their awareness is restricted to interactions captured by available tracking data. We introduce an adjustment factor that calibrates the sample CPs so that the sample is a reasonable estimate of the population CP. Further complicating estimation, biases in epidemiological and mobility data arise from heterogeneous reporting rates and sampling inconsistencies, which we address through <jats:italic>inverse probability weighting<\/jats:italic> to enhance reliability. Using a spatial model for infection spread through social mixing and an optimization framework based on the SIRS epidemic model, we analyze real infection datasets from Italy, Germany, and Austria. Our findings demonstrate that statistical methods can achieve high-confidence CP estimates while accounting for variations in sample size, confidence level, mobility models, and viral strains. By assessing the effects of bias, social mixing, and sampling frequency, we propose statistical corrections to improve CP prediction accuracy. Finally, we discuss how reliable CP estimates can inform outbreak mitigation strategies despite the inherent uncertainties in epidemiological data.<\/jats:p>","DOI":"10.1371\/journal.pone.0329828","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T17:46:42Z","timestamp":1755020802000},"page":"e0329828","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing infection profiling under data uncertainty through contagion potential"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6767-266X","authenticated-orcid":true,"given":"Satyaki","family":"Roy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Preetom","family":"Biswas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3880-5886","authenticated-orcid":true,"given":"Preetam","family":"Ghosh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"issue":"6","key":"pone.0329828.ref001","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1080\/14760584.2021.1915140","article-title":"The emergence of new strains of SARS-CoV-2. What does it mean for COVID-19 vaccines?","volume":"20","author":"MK Hossain","year":"2021","journal-title":"Expert Rev Vaccines."},{"issue":"7873","key":"pone.0329828.ref002","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/s41586-021-03792-w","article-title":"After the pandemic: Perspectives on the future trajectory of COVID-19","volume":"596","author":"A Telenti","year":"2021","journal-title":"Nature."},{"key":"pone.0329828.ref003","doi-asserted-by":"crossref","first-page":"15110","DOI":"10.1109\/ACCESS.2021.3053268","article-title":"Optimal Time-Varying Vaccine Allocation Amid Pandemics With Uncertain Immunity Ratios","volume":"9","author":"S Roy","year":"2021","journal-title":"IEEE Access."},{"issue":"2","key":"pone.0329828.ref004","doi-asserted-by":"crossref","first-page":"160","DOI":"10.3390\/vaccines9020160","article-title":"COVID-19 vaccine hesitancy worldwide: A concise systematic review of vaccine acceptance rates","volume":"9","author":"M Sallam","year":"2021","journal-title":"Vaccines (Basel)."},{"key":"pone.0329828.ref005","first-page":"30226","article-title":"COVID-19 vaccines: The pandemic will not end overnight","volume":"2","author":"V Marco","year":"2020","journal-title":"Lancet Microbe."},{"key":"pone.0329828.ref006","article-title":"The Lancet Commission on lessons for the future from the covid-19 pandemic","author":"J Sachs","year":"2022","journal-title":"The Lancet."},{"key":"pone.0329828.ref007","doi-asserted-by":"crossref","unstructured":"Nguyen T, et al. Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions. arXiv preprint. 2020. https:\/\/doi.org\/10.48550\/arXiv.2008.07343","DOI":"10.36227\/techrxiv.12743933"},{"issue":"2","key":"pone.0329828.ref008","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3609324","article-title":"Towards a unified pandemic management architecture: Survey, challenges, and future directions","volume":"56","author":"S Roy","year":"2023","journal-title":"ACM Comput Surv."},{"issue":"4","key":"pone.0329828.ref009","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1080\/09603123.2020.1793921","article-title":"Climatic factors influence the spread of COVID-19 in Russia","volume":"32","author":"M Pramanik","year":"2022","journal-title":"Int J Environ Health Res."},{"issue":"1","key":"pone.0329828.ref010","doi-asserted-by":"crossref","DOI":"10.1038\/s41612-020-00142-x","article-title":"Exploring dependence of COVID-19 on environmental factors and spread prediction in India","volume":"3","author":"H Bherwani","year":"2020","journal-title":"npj Clim Atmos Sci."},{"key":"pone.0329828.ref011","doi-asserted-by":"crossref","unstructured":"Roy S, Ghosh P. Examining post-pandemic behaviors influencing human mobility trends. In: Proceedings of the 13th ACM international conference on bioinformatics, computational biology and health informatics; 2022. p. 1\u201310. https:\/\/doi.org\/10.1145\/3535508.3545552","DOI":"10.1145\/3535508.3545552"},{"key":"pone.0329828.ref012","doi-asserted-by":"crossref","first-page":"109946","DOI":"10.1016\/j.chaos.2020.109946","article-title":"Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models","volume":"138","author":"O Torrealba-Rodriguez","year":"2020","journal-title":"Chaos Solitons Fractals."},{"issue":"1","key":"pone.0329828.ref013","doi-asserted-by":"crossref","first-page":"16522","DOI":"10.1038\/s41598-021-95919-2","article-title":"A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD","volume":"11","author":"S Roy","year":"2021","journal-title":"Sci Rep."},{"issue":"2","key":"pone.0329828.ref014","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1093\/cid\/ciaa863","article-title":"COVID-19 in immunocompromised hosts: What we know so far","volume":"72","author":"M Fung","year":"2021","journal-title":"Clin Infect Dis."},{"issue":"1","key":"pone.0329828.ref015","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jmii.2020.05.001","article-title":"A systematic review of asymptomatic infections with COVID-19","volume":"54","author":"Z Gao","year":"2021","journal-title":"J Microbiol Immunol Infect."},{"key":"pone.0329828.ref016","doi-asserted-by":"crossref","first-page":"134577","DOI":"10.1109\/ACCESS.2020.3010226","article-title":"A survey of COVID-19 contact tracing apps","volume":"8","author":"N Ahmed","year":"2020","journal-title":"IEEE Access."},{"issue":"1","key":"pone.0329828.ref017","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s41109-021-00437-9","article-title":"Scalable and distributed strategies for socially distanced human mobility","volume":"6","author":"S Roy","year":"2021","journal-title":"Appl Netw Sci."},{"issue":"8","key":"pone.0329828.ref018","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1002\/phar.2439","article-title":"COVID-19 clinical diagnostics and testing technology","volume":"40","author":"CH Chau","year":"2020","journal-title":"Pharmacotherapy."},{"key":"pone.0329828.ref019","doi-asserted-by":"crossref","first-page":"593176","DOI":"10.3389\/fpubh.2021.593176","article-title":"Pitfalls in modeling asymptomatic COVID-19 infection","volume":"9","author":"S Yang","year":"2021","journal-title":"Front Public Health."},{"issue":"2","key":"pone.0329828.ref020","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.pulmoe.2020.05.015","article-title":"Evaluating the massive underreporting and undertesting of COVID-19 cases in multiple global epicenters","volume":"27","author":"H Lau","year":"2021","journal-title":"Pulmonology."},{"issue":"1","key":"pone.0329828.ref021","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1186\/s12879-021-06780-7","article-title":"COVID-19 underreporting and its impact on vaccination strategies","volume":"21","author":"V Albani","year":"2021","journal-title":"BMC Infect Dis."},{"key":"pone.0329828.ref022","doi-asserted-by":"crossref","unstructured":"Meadows A, et al. Estimating infectious disease underreporting at the country level: a model and application to the COVID-19 pandemic. Available at SSRN 3706 059; 2020.","DOI":"10.2139\/ssrn.3706059"},{"issue":"10293","key":"pone.0329828.ref023","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1016\/S0140-6736(21)01219-8","article-title":"The complex causes of India\u2019s 2021 COVID-19 surge","volume":"397","author":"A Visaria","year":"2021","journal-title":"Lancet."},{"issue":"9","key":"pone.0329828.ref024","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1111\/tmi.13628","article-title":"How many more? Under-reporting of the COVID-19 deaths in Brazil in 2020","volume":"26","author":"E Kupek","year":"2021","journal-title":"Trop Med Int Health."},{"issue":"2","key":"pone.0329828.ref025","doi-asserted-by":"crossref","DOI":"10.2196\/19509","article-title":"Machine learning to detect self-reporting of symptoms, testing access, and recovery associated with COVID-19 on Twitter: Retrospective big data infoveillance study","volume":"6","author":"T Mackey","year":"2020","journal-title":"JMIR Public Health Surveill."},{"key":"pone.0329828.ref026","doi-asserted-by":"crossref","DOI":"10.1590\/1980-549720210047","article-title":"Estimating underdiagnosis of COVID-19 with nowcasting and machine learning","volume":"24","author":"LP Garcia","year":"2021","journal-title":"Rev Bras Epidemiol."},{"issue":"3","key":"pone.0329828.ref027","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/JBHI.2021.3052134","article-title":"Machine learning model for computational tracking and forecasting the COVID-19 dynamic propagation","volume":"25","author":"DCDS Gomes","year":"2021","journal-title":"IEEE J Biomed Health Inform."},{"issue":"3","key":"pone.0329828.ref028","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1111\/rssa.12826","article-title":"COVID-19 severity: A new approach to quantifying global cases and deaths","volume":"185","author":"D Millimet","year":"2022","journal-title":"J R Stat Soc Ser A: Stat Soc."},{"key":"pone.0329828.ref029","doi-asserted-by":"crossref","first-page":"289","DOI":"10.3389\/fphy.2020.00289","article-title":"Accounting for underreporting in mathematical modeling of transmission and control of COVID-19 in Iran","volume":"8","author":"M Saberi","year":"2020","journal-title":"Front Phys."},{"key":"pone.0329828.ref030","doi-asserted-by":"crossref","first-page":"104182","DOI":"10.1016\/j.rinp.2021.104182","article-title":"A new extension of state-space SIR model to account for Underreporting\u2014An application to the COVID-19 transmission in California and Florida","volume":"24","author":"V Deo","year":"2021","journal-title":"Results Phys."},{"issue":"6","key":"pone.0329828.ref031","doi-asserted-by":"crossref","first-page":"3327","DOI":"10.3390\/ijerph19063327","article-title":"A Bayesian hierarchical spatial model to correct for misreporting in count data: Application to state-level COVID-19 data in the United States","volume":"19","author":"J Chen","year":"2022","journal-title":"Int J Environ Res Public Health."},{"key":"pone.0329828.ref032","doi-asserted-by":"crossref","first-page":"26196","DOI":"10.1109\/ACCESS.2021.3058206","article-title":"Leveraging network science for social distancing to curb pandemic spread","volume":"9","author":"S Roy","year":"2021","journal-title":"IEEE Access."},{"issue":"8","key":"pone.0329828.ref033","article-title":"Determining the rate of infectious disease testing through contagion potential","volume":"3","author":"S Roy","year":"2023","journal-title":"PLOS Glob Public Health."},{"issue":"1","key":"pone.0329828.ref034","first-page":"20190012","article-title":"Contact network uncertainty in individual level models of infectious disease transmission","volume":"13","author":"W Almutiry","year":"2021","journal-title":"Stat Commun Infect Dis."},{"issue":"1","key":"pone.0329828.ref035","doi-asserted-by":"crossref","first-page":"22665","DOI":"10.1038\/s41598-021-01522-w","article-title":"Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas","volume":"11","author":"N Kumar","year":"2021","journal-title":"Sci Rep."},{"issue":"8","key":"pone.0329828.ref036","article-title":"Simulation of the COVID-19 epidemic on the social network of Slovenia: Estimating the intrinsic forecast uncertainty","volume":"15","author":"\u00c51\/2 Zaplotnik","year":"2020","journal-title":"PLoS One."},{"key":"pone.0329828.ref037","unstructured":"Andersson H, Britton T. Stochastic epidemic models and their statistical analysis. Springer Science & Business Media. 2012."},{"issue":"191","key":"pone.0329828.ref038","doi-asserted-by":"crossref","first-page":"20220124","DOI":"10.1098\/rsif.2022.0124","article-title":"Dynamic survival analysis for non-Markovian epidemic models","volume":"19","author":"F Di Lauro","year":"2022","journal-title":"J R Soc Interface."},{"key":"pone.0329828.ref039","doi-asserted-by":"crossref","unstructured":"Brauer F. Compartmental models in epidemiology. Mathematical epidemiology. Springer. 2008. p. 19\u201379.","DOI":"10.1007\/978-3-540-78911-6_2"},{"issue":"3","key":"pone.0329828.ref040","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s10884-008-9111-8","article-title":"Threshold dynamics for compartmental epidemic models in periodic environments","volume":"20","author":"W Wang","year":"2008","journal-title":"J Dyn Diff Equat."},{"issue":"4","key":"pone.0329828.ref041","doi-asserted-by":"crossref","first-page":"2755","DOI":"10.1109\/TNSE.2020.2987771","article-title":"Network reconstruction and prediction of epidemic outbreaks for general group-based compartmental epidemic models","volume":"7","author":"B Prasse","year":"2020","journal-title":"IEEE Trans Netw Sci Eng."},{"issue":"1","key":"pone.0329828.ref042","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.jeconom.2020.07.038","article-title":"Identification and estimation of the SEIRD epidemic model for COVID-19","volume":"220","author":"I Korolev","year":"2021","journal-title":"J Econom."},{"issue":"2","key":"pone.0329828.ref043","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.mbs.2013.04.013","article-title":"The scaling of contact rates with population density for the infectious disease models","volume":"244","author":"H Hu","year":"2013","journal-title":"Math Biosci."},{"issue":"2","key":"pone.0329828.ref044","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/TNET.2011.2172984","article-title":"SLAW: Self-similar least-action human walk","volume":"20","author":"K Lee","year":"2011","journal-title":"IEEE\/ACM Trans Netw."},{"key":"pone.0329828.ref045","doi-asserted-by":"crossref","first-page":"125711","DOI":"10.1109\/ACCESS.2019.2939203","article-title":"A survey of human mobility models","volume":"7","author":"G Solmaz","year":"2019","journal-title":"IEEE Access."},{"issue":"9","key":"pone.0329828.ref046","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1016\/j.comcom.2010.01.013","article-title":"HCMM: Modelling spatial and temporal properties of human mobility driven by users\u2019 social relationships","volume":"33","author":"C Boldrini","year":"2010","journal-title":"Comput Commun."},{"issue":"532","key":"pone.0329828.ref047","doi-asserted-by":"crossref","first-page":"1851","DOI":"10.1080\/01621459.2019.1660173","article-title":"Robust inference using inverse probability weighting","volume":"115","author":"X Ma","year":"2019","journal-title":"J Am Stat Assoc."},{"key":"pone.0329828.ref048","unstructured":"Our World in Data. Our World in Data. https:\/\/ourworldindata.org\/covid-cases; 2022."},{"issue":"10","key":"pone.0329828.ref049","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0256971","article-title":"A hybrid approach to forecast the COVID-19 epidemic trend","volume":"16","author":"SA Nawaz","year":"2021","journal-title":"PLoS One."},{"issue":"8","key":"pone.0329828.ref050","doi-asserted-by":"crossref","first-page":"4541","DOI":"10.3390\/ijerph19084541","article-title":"Projecting the pandemic trajectory through modeling the transmission dynamics of COVID-19","volume":"19","author":"V Vakil","year":"2022","journal-title":"Int J Environ Res Public Health."},{"issue":"1","key":"pone.0329828.ref051","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.42210","article-title":"SARS-CoV-2 reinfection rate and estimated effectiveness of the inactivated whole virion vaccine BBV152 against reinfection among health care workers in New Delhi, India","volume":"5","author":"S Malhotra","year":"2022","journal-title":"JAMA Netw Open."},{"issue":"6538","key":"pone.0329828.ref052","doi-asserted-by":"crossref","DOI":"10.1126\/science.abg3055","article-title":"Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England","volume":"372","author":"NG Davies","year":"2021","journal-title":"Science."},{"issue":"1","key":"pone.0329828.ref053","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s12985-023-02018-x","article-title":"A comparison of transmissibility of SARS-CoV-2 variants of concern","volume":"20","author":"SS Manathunga","year":"2023","journal-title":"Virol J."},{"issue":"7","key":"pone.0329828.ref054","article-title":"The reproductive number of the Delta variant of SARS-CoV-2 is far higher compared to the ancestral SARS-CoV-2 virus","volume":"28","author":"Y Liu","year":"2021","journal-title":"J Travel Med."},{"issue":"3","key":"pone.0329828.ref055","article-title":"The effective reproductive number of the Omicron variant of SARS-CoV-2 is several times relative to Delta","volume":"29","author":"Y Liu","year":"2022","journal-title":"J Travel Med."},{"issue":"4","key":"pone.0329828.ref056","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1080\/1059924X.2020.1815622","article-title":"Sleeping within six feet: Challenging Oregon\u2019s labor housing COVID-19 guidelines","volume":"25","author":"EK Accorsi","year":"2020","journal-title":"J Agromedicine."},{"key":"pone.0329828.ref057","doi-asserted-by":"crossref","unstructured":"Nelson S, Ciaranello A. Six feet and the classroom; 2021.","DOI":"10.1093\/cid\/ciab274"},{"issue":"1","key":"pone.0329828.ref058","doi-asserted-by":"crossref","first-page":"280","DOI":"10.3390\/s22010280","article-title":"Non-pharmaceutical interventions against COVID-19 pandemic: Review of contact tracing and social distancing technologies, protocols, apps, security and open research directions","volume":"22","author":"UR Alo","year":"2021","journal-title":"Sensors (Basel)."},{"issue":"3","key":"pone.0329828.ref059","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"P Virtanen","year":"2020","journal-title":"Nat Methods."}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0329828","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T17:46:50Z","timestamp":1755020810000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0329828"}},"subtitle":[],"editor":[{"given":"Viswanathan","family":"Arunachalam","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,8,12]]},"references-count":59,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8,12]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0329828","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,12]]}}}