{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T20:10:30Z","timestamp":1782331830355,"version":"3.54.5"},"reference-count":198,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>Infectious diseases occur when pathogens from other individuals or animals infect a person, causing harm to both individuals and society. Outbreaks of such diseases can pose a significant threat to human health. However, early detection and tracking of these outbreaks have the potential to reduce mortality rates. To address these threats, public health authorities have endeavored to establish comprehensive mechanisms for collecting disease data. Many countries have implemented infectious disease surveillance systems, with epidemic detection as a primary objective. The clinical healthcare system, local\/state health agencies, federal agencies, academic\/professional groups, and collaborating governmental entities all play pivotal roles within this system. Moreover, search engines and social media platforms can serve as valuable tools for monitoring disease trends. The Internet and social media have become significant platforms where users share information about their preferences and relationships. This real-time information can be harnessed to gauge the influence of ideas and societal opinions, proving highly useful across various domains and research areas, such as marketing campaigns, financial predictions, and public health. This article provides a review of the existing standard methods developed by researchers for detecting outbreaks using time series data. These methods leverage various data sources, including conventional data sources and social media data or Internet data sources. The review particularly concentrates on works published within the timeframe of 2015 to 2022.<\/jats:p>","DOI":"10.1145\/3708549","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T13:53:00Z","timestamp":1734097980000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Disease Outbreak Detection and Forecasting: A Review of Methods and Data Sources"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4715-5371","authenticated-orcid":false,"given":"Ghazaleh","family":"Babanejaddehaki","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1765-5751","authenticated-orcid":false,"given":"Aijun","family":"An","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0138-2541","authenticated-orcid":false,"given":"Manos","family":"Papagelis","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1002\/9781119785620.ch12","volume-title":"Data Analytics in Bioinformatics: A Machine Learning Perspective","author":"Aakash V.","year":"2021","unstructured":"V. Aakash, S. Sridevi, G. Ananthi, and S. Rajaram. 2021. Forecasting of novel corona virus disease (Covid-19) using LSTM and XG boosting algorithms. In Data Analytics in Bioinformatics: A Machine Learning Perspective. Rabinarayan Satpathy, Tanupriya Choudhury, Suneeta Satpathy, Sachi Nandan Mohanty, Xiaobo Zhang (Eds.), John Wiley & Sons, 293\u2013311."},{"key":"e_1_3_1_3_2","volume-title":"PLoS Neglected Tropical Diseases","author":"Chan Emily H.","year":"2011","unstructured":"Emily H. Chan, Vikram Sahai, Corrie Conrad, and John S. Brownstein. 2011. Using web search query data to monitor dengue epidemics: A new model for neglected tropical disease surveillance. PLoS Neglected Tropical Diseases 5, 5 (May 2011), e1206."},{"issue":"1","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"jphr.2013.e4","DOI":"10.4081\/jphr.2013.e4","article-title":"Mining social media and web searches for disease detection","volume":"2","author":"Yang Y. Tony","year":"2013","unstructured":"Y. Tony Yang, Michael Horneffer, and Nicole DiLisio. 2013. Mining social media and web searches for disease detection. Journal of Public Health Research 2, 1 (Mar. 2013), jphr.2013.e4.","journal-title":"Journal of Public Health Research"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.ijid.2016.04.021","article-title":"Traditional and syndromic surveillance of infectious diseases and pathogens","volume":"48","author":"Abat C\u00e9dric","year":"2016","unstructured":"C\u00e9dric Abat, Herv\u00e9 Chaudet, Jean-Marc Rolain, Philippe Colson, and Didier Raoult. 2016. Traditional and syndromic surveillance of infectious diseases and pathogens. International Journal of Infectious Diseases 48 (2016), 22\u201328.","journal-title":"International Journal of Infectious Diseases"},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"100117","DOI":"10.1016\/j.sintl.2021.100117","article-title":"Telemedicine for healthcare: Capabilities, features, barriers, and applications","volume":"2","author":"Haleem Abid","year":"2021","unstructured":"Abid Haleem, Mohd Javaid, Ravi Pratap Singh, and Rajiv Suman. 2021. Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors International 2 (2021), 100117.","journal-title":"Sensors International"},{"key":"e_1_3_1_7_2","first-page":"1","article-title":"The role of telehealth during COVID-19 outbreak: A systematic review based on current evidence","volume":"20","author":"Monaghesh Elham","year":"2020","unstructured":"Elham Monaghesh and Alireza Hajizadeh. 2020. The role of telehealth during COVID-19 outbreak: A systematic review based on current evidence. BMC Public Health 20 (2020), 1\u20139.","journal-title":"BMC Public Health"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/21505594.2015.1040975"},{"key":"e_1_3_1_9_2","volume-title":"PLoS Computational Biology","author":"Santillana Mauricio","year":"2015","unstructured":"Mauricio Santillana, Andr\u00e9 T. Nguyen, Mark Dredze, Michael J. Paul, Elaine O. Nsoesie, and John S. Brownstein. 2015. Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Computational Biology 11, 10 (Oct. 2015), e1004513."},{"key":"e_1_3_1_10_2","first-page":"21","volume-title":"International Conference on Electronic Healthcare","author":"De Quincey Ed","year":"2009","unstructured":"Ed De Quincey and Patty Kostkova. 2009. Early warning and outbreak detection using social networking websites: The potential of Twitter. In International Conference on Electronic Healthcare. Springer, 21\u201324."},{"key":"e_1_3_1_11_2","first-page":"131","volume-title":"Emerging and Re-emerging Viral Infections: Advances in Microbiology, Infectious Diseases and Public Health","author":"Al-Surimi Khaled","year":"2017","unstructured":"Khaled Al-Surimi, Mohammed Khalifa, Salwa Bahkali, Ashraf El-Metwally, and Mowafa Househ. 2017. The potential of social media and internet-based data in preventing and fighting infectious diseases: from internet to twitter. In Emerging and Re-emerging Viral Infections: Advances in Microbiology, Infectious Diseases and Public Health. Giovanni Rezza, Giuseppe Ippolito (Eds.), Vol. 6, Springer, 131\u2013139."},{"key":"e_1_3_1_12_2","first-page":"3","volume-title":"23rd International Joint Conference on Artificial Intelligence","author":"Xie Yusheng","year":"2013","unstructured":"Yusheng Xie, Zhengzhang Chen, Yu Cheng, Kunpeng Zhang, Ankit Agrawal, Wei-keng Liao, and Alok Choudhary. 2013. Detecting and tracking disease outbreaks by mining social media data. In 23rd International Joint Conference on Artificial Intelligence, 3."},{"key":"e_1_3_1_13_2","unstructured":"Erik Bohlin. 2012. Tracking the Outbreak of Diseases Using Twitter: A Machine Learning Approach. Digitala Vetenskapliga Arkivet. Retrieved from https:\/\/uu.diva-portal.org\/smash\/record.jsf?pid=diva2%3A548652&dswid=7904"},{"issue":"1","key":"e_1_3_1_14_2","first-page":"1","article-title":"Advances in nowcasting influenza-like illness rates using search query logs","volume":"5","author":"Lampos Vasileios","year":"2015","unstructured":"Vasileios Lampos, Andrew C. Miller, Steve Crossan, and Christian Stefansen. 2015. Advances in nowcasting influenza-like illness rates using search query logs. Scientific Reports 5, 1 (2015), 1\u201310.","journal-title":"Scientific Reports"},{"issue":"13","key":"e_1_3_1_15_2","first-page":"19162","article-title":"Internet surveillance systems for early alerting of health threats","volume":"14","author":"Linge Jens P.","year":"2009","unstructured":"Jens P. Linge, Ralf Steinberger, T. P. Weber, Roman Yangarber, Erik van der Goot, D. H. Al Khudhairy, and N. I. Stilianakis. 2009. Internet surveillance systems for early alerting of health threats. Eurosurveillance 14, 13 (2009), 19162.","journal-title":"Eurosurveillance"},{"key":"e_1_3_1_16_2","first-page":"21","volume-title":"2nd International ICST Conference on Electronic Healthcare","author":"De Quincey Ed","year":"2010","unstructured":"Ed De Quincey and Patty Kostkova. 2010. Early warning and outbreak detection using social networking websites: The potential of Twitter. In 2nd International ICST Conference on Electronic Healthcare. Springer, 21\u201324."},{"issue":"7232","key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1038\/nature07634","article-title":"Detecting influenza epidemics using search engine query data","volume":"457","author":"Ginsberg Jeremy","year":"2009","unstructured":"Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant. 2009. Detecting influenza epidemics using search engine query data. Nature 457, 7232 (2009), 1012\u20131014.","journal-title":"Nature"},{"issue":"11","key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"e14118","DOI":"10.1371\/journal.pone.0014118","article-title":"Pandemics in the age of Twitter: Content analysis of tweets during the 2009 H1N1 outbreak","volume":"5","author":"Chew Cynthia","year":"2010","unstructured":"Cynthia Chew and Gunther Eysenbach. 2010. Pandemics in the age of Twitter: Content analysis of tweets during the 2009 H1N1 outbreak. PLoS One 5, 11 (2010), e14118.","journal-title":"PLoS One"},{"issue":"6","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.arcmed.2005.06.005","article-title":"The Internet and the global monitoring of emerging diseases: Lessons from the first 10 years of ProMED-mail","volume":"36","author":"Madoff Lawrence C.","year":"2005","unstructured":"Lawrence C. Madoff and John P. Woodall. 2005. The Internet and the global monitoring of emerging diseases: Lessons from the first 10 years of ProMED-mail. Archives of Medical Research 36, 6 (2005), 724\u2013730.","journal-title":"Archives of Medical Research"},{"key":"e_1_3_1_20_2","first-page":"345","volume-title":"Intelligent Distributed Computing VIII (Studies in Computational Intelligence)","author":"Bello-Orgaz Gema","year":"2015","unstructured":"Gema Bello-Orgaz, Julio Hernandez-Castro, and David Camacho. 2015. A survey of social web mining applications for disease outbreak detection. In Intelligent Distributed Computing VIII (Studies in Computational Intelligence). David Camacho, Lars Braubach, Salvatore Venticinque, and Costin Badica (Eds.), Springer International Publishing, Cham, 345\u2013356."},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbi.2016.05.005","article-title":"Using online social networks to track a pandemic: A systematic review","volume":"62","author":"Al-garadi Mohammed Ali","year":"2016","unstructured":"Mohammed Ali Al-garadi, Muhammad Sadiq Khan, Kasturi Dewi Varathan, Ghulam Mujtaba, and Abdelkodose M. Al-Kabsi. 2016. Using online social networks to track a pandemic: A systematic review. Journal of Biomedical Informatics 62 (Aug. 2016), 1\u201311.","journal-title":"Journal of Biomedical Informatics"},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1108\/978-1-78756-269-120181016","volume-title":"Social Media Use in Crisis and Risk Communication","author":"Hornmoen Harald","year":"2018","unstructured":"Harald Hornmoen and Colin McInnes. 2018. Social media communication during disease outbreaks: Findings and recommendations. In Social Media Use in Crisis and Risk Communication. Harald Hornmoen and Klas Backholm (Eds.), Emerald Publishing Limited, 255\u2013275."},{"issue":"5","key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"966","DOI":"10.3390\/ijerph15050966","article-title":"A simulation-based study on the comparison of statistical and time series forecasting methods for early detection of infectious disease outbreaks","volume":"15","author":"Yang Eunjoo","year":"2018","unstructured":"Eunjoo Yang, Hyun Park, Yeon Choi, Jusim Kim, Lkhagvadorj Munkhdalai, Ibrahim Musa, and Keun Ryu. 2018. A simulation-based study on the comparison of statistical and time series forecasting methods for early detection of infectious disease outbreaks. IJERPH 15, 5 (May 2018), 966.","journal-title":"IJERPH"},{"key":"e_1_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Chris Chatfield and Mohammad Yar. 1988. Holt-Winters forecasting: Some practical issues. Journal of the Royal Statistical Society: Series D (The Statistician) 37 2 (1988) 129\u2013140. Retrieved from https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.2307\/2348687","DOI":"10.2307\/2348687"},{"issue":"1","key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.20965\/jaciii.2021.p0138","article-title":"Forecasting influenza based on autoregressive moving average and Holt-Winters exponential smoothing models","volume":"25","author":"Zhu Guohun","year":"2021","unstructured":"Guohun Zhu, Liping Li, Yuebin Zheng, Xiaowei Zhang, and Hui Zou. 2021. Forecasting influenza based on autoregressive moving average and Holt-Winters exponential smoothing models. Journal of Advanced Computational Intelligence and Intelligent Informatics 25, 1 (2021), 138\u2013144.","journal-title":"Journal of Advanced Computational Intelligence and Intelligent Informatics"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","unstructured":"Mrutyunjaya Panda. 2020. Application of ARIMA and Holt-Winters forecasting model to predict the spreading of COVID-19 for India and its states. medRxiv. DOI: 10.1101\/2020.07.14.20153908","DOI":"10.1101\/2020.07.14.20153908"},{"key":"e_1_3_1_27_2","first-page":"241","volume-title":"International Journal of Management and Decision Making","author":"Hansun Seng","year":"2021","unstructured":"Seng Hansun, Vincent Charles, Tatiana Gherman, Subanar, and Christiana Rini Indrati. 2021. A tuned Holt-Winters white-box model for COVID-19 prediction. International Journal of Management and Decision Making 20, 3 (Jan. 2021), 241\u2013262."},{"key":"e_1_3_1_28_2","first-page":"101","volume-title":"ESTEEM Academic Journal","author":"Mahmud Norwaziah","year":"2021","unstructured":"Norwaziah Mahmud, Nur Syuhada Muhammat Pazil, Hafawati Jamaluddin, and Nur Aqilah Ali. 2021. Prediction of dengue outbreak: A comparison between ARIMA and Holt-Winters methods. ESTEEM Academic Journal 17 (Aug. 2021), 101\u2013111."},{"issue":"1","key":"e_1_3_1_29_2","first-page":"1","article-title":"Monitoring pertussis infections using Internet search queries","volume":"7","author":"Zhang Yuzhou","year":"2017","unstructured":"Yuzhou Zhang, Gabriel Milinovich, Zhiwei Xu, Hilary Bambrick, Kerrie Mengersen, Shilu Tong, and Wenbiao Hu. 2017. Monitoring pertussis infections using Internet search queries. Scientific Reports 7, 1 (2017), 1\u20137.","journal-title":"Scientific Reports"},{"key":"e_1_3_1_30_2","volume-title":"Journal of Physics: Conference Series","volume":"1882","author":"Djakaria I.","year":"2021","unstructured":"I. Djakaria and S. E. Saleh. 2021. Covid-19 forecast using Holt-Winters exponential smoothing. In Journal of Physics: Conference Series, Vol. 1882. IOP Publishing, 012033."},{"key":"e_1_3_1_31_2","first-page":"1997","volume-title":"International Journal of Nonlinear Analysis and Applications","author":"Shukur Sarab D.","year":"2021","unstructured":"Sarab D. Shukur and Tasnim Hasan Kadhim. 2021. Time series analysis of the number of Covid-19 deaths in Iraq. International Journal of Nonlinear Analysis and Applications 12, 2 (July 2021), 1997\u20132007."},{"key":"e_1_3_1_32_2","first-page":"35","volume-title":"Journal of the University of Ruhuna","author":"Wickramasinghe S. S.","year":"2023","unstructured":"S. S. Wickramasinghe and K. M. U. B. Konarasinghe. 2023. Forecasting COVID-19 daily infected cases in Sri Lanka by Holt-Winters exponential smoothing method. Journal of the University of Ruhuna 11, 2 (Dec. 2023), 35\u201341."},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"132","DOI":"10.9781\/ijimai.2020.02.002","article-title":"Finding an accurate early forecasting model from small dataset: A case of 2019-NCoV novel coronavirus outbreak","volume":"6","author":"Fong Simon James","year":"2020","unstructured":"Simon James Fong, Gloria Li, Nilanjan Dey, Rub\u00e9n Gonz\u00e1lez Crespo, and Enrique Herrera-Viedma. 2020. Finding an accurate early forecasting model from small dataset: A case of 2019-NCoV novel coronavirus outbreak. International Journal of Interactive Multimedia and Artificial Intelligence 6 (Mar. 2020), 132\u2013140.","journal-title":"International Journal of Interactive Multimedia and Artificial Intelligence"},{"issue":"1","key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/1472-6963-5-36","article-title":"Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore","volume":"5","author":"Earnest Arul","year":"2005","unstructured":"Arul Earnest, Mark I. Chen, Donald Ng, and Leo Yee Sin. 2005. Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore. BMC Health Services Research 5, 1 (May 2005), 36.","journal-title":"BMC Health Services Research"},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"580327","DOI":"10.3389\/fpubh.2020.580327","article-title":"Global forecasting confirmed and fatal cases of COVID-19 outbreak using autoregressive integrated moving average model","volume":"8","author":"Dansana Debabrata","year":"2020","unstructured":"Debabrata Dansana, Raghvendra Kumar, Janmejoy Das Adhikari, Mans Mohapatra, Rohit Sharma, Ishaani Priyadarshini, and Dac-Nhuong Le. 2020. Global forecasting confirmed and fatal cases of COVID-19 outbreak using autoregressive integrated moving average model. Frontiers in Public Health 8, 580327.","journal-title":"Frontiers in Public Health"},{"key":"e_1_3_1_36_2","volume-title":"PLoS One","author":"Pourghasemi Hamid Reza","year":"2020","unstructured":"Hamid Reza Pourghasemi, Soheila Pouyan, Zakariya Farajzadeh, Nitheshnirmal Sadhasivam, Bahram Heidari, Sedigheh Babaei, and John P. Tiefenbacher. 2020. Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models. PLoS One 15, 7 (Jul. 2020), e0236238."},{"issue":"1","key":"e_1_3_1_37_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/1472-6947-10-37","article-title":"Detecting the start of an influenza outbreak using exponentially weighted moving average charts","volume":"10","author":"Steiner Stefan H.","year":"2010","unstructured":"Stefan H. Steiner, Kristina Grant, Michael Coory, and Heath A. Kelly. 2010. Detecting the start of an influenza outbreak using exponentially weighted moving average charts. BMC Medical Informatics and Decision Making 10, 1 (Jun. 2010), 37.","journal-title":"BMC Medical Informatics and Decision Making"},{"issue":"4","key":"e_1_3_1_38_2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1002\/j.2161-4296.2004.tb00357.x","article-title":"Modeling inertial sensor errors using autoregressive (AR) models","volume":"51","author":"Nassar Sameh","year":"2004","unstructured":"Sameh Nassar, Klaus-Peter Schwarz, Naser El-Sheimy, and Aboelmagd Noureldin. 2004. Modeling inertial sensor errors using autoregressive (AR) models. Navigation 51, 4 (2004), 259\u2013268. Retrieved from https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/j.2161-4296.2004.tb00357.x","journal-title":"Navigation"},{"issue":"1","key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40745-020-00314-9","article-title":"Outbreak prediction of COVID-19 for dense and populated countries using machine learning","volume":"8","author":"Khakharia Aman","year":"2021","unstructured":"Aman Khakharia, Vruddhi Shah, Sankalp Jain, Jash Shah, Amanshu Tiwari, Prathamesh Daphal, Mahesh Warang, and Ninad Mehendale. 2021. Outbreak prediction of COVID-19 for dense and populated countries using machine learning. Annals of Data Science 8, 1 (Mar. 2021), 1\u201319.","journal-title":"Annals of Data Science"},{"key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.envint.2018.05.016","article-title":"Using Google trends and ambient temperature to predict seasonal influenza outbreaks","volume":"117","author":"Zhang Yuzhou","year":"2018","unstructured":"Yuzhou Zhang, Hilary Bambrick, Kerrie Mengersen, Shilu Tong, and Wenbiao Hu. 2018. Using Google trends and ambient temperature to predict seasonal influenza outbreaks. Environment International 117 (Aug. 2018), 284\u2013291.","journal-title":"Environment International"},{"issue":"3","key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.jmii.2020.04.004","article-title":"COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach","volume":"53","author":"Chintalapudi Nalini","year":"2020","unstructured":"Nalini Chintalapudi, Gopi Battineni, and Francesco Amenta. 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, 3 (Jun. 2020), 396\u2013403.","journal-title":"Journal of Microbiology, Immunology and Infection"},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","first-page":"550602","DOI":"10.3389\/fpubh.2020.550602","article-title":"COVID-19 infection process in Italy and Spain: Are data talking? Evidence from ARMA and vector autoregression models","volume":"8","author":"Monllor Paloma","year":"2020","unstructured":"Paloma Monllor, Zhenyu Su, Laura Gabrielli, and Paloma Taltavull de La Paz. 2020. COVID-19 infection process in Italy and Spain: Are data talking? Evidence from ARMA and vector autoregression models. Frontiers in Public Health 8, 550602.","journal-title":"Frontiers in Public Health"},{"issue":"6","key":"e_1_3_1_43_2","doi-asserted-by":"crossref","first-page":"6851","DOI":"10.1007\/s13369-021-06526-2","article-title":"Forecasting COVID-19: Vector autoregression-based model","volume":"47","author":"Rajab Khairan","year":"2022","unstructured":"Khairan Rajab, Firuz Kamalov, and Aswani Kumar Cherukuri. 2022. Forecasting COVID-19: Vector autoregression-based model. Arabian Journal for Science and Engineering 47, 6 (Jun. 2022), 6851\u20136860.","journal-title":"Arabian Journal for Science and Engineering"},{"issue":"17","key":"e_1_3_1_44_2","doi-asserted-by":"crossref","first-page":"5458","DOI":"10.3390\/en14175458","article-title":"Projection of post-pandemic Italian industrial production through vector AutoRegressive models","volume":"14","author":"Oliva Antonio","year":"2021","unstructured":"Antonio Oliva, Francesco Gracceva, Daniele Lerede, Matteo Nicoli, and Laura Savoldi. 2021. Projection of post-pandemic Italian industrial production through vector AutoRegressive models. Energies 14, 17 (Jan. 2021), 5458.","journal-title":"Energies"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","unstructured":"Qinan Wang Yaomu Zhou and Xiaofei Chen. 2021. A vector autoregression prediction model for COVID-19 outbreak. arXiv:2102.04843. DOI: 10.48550\/arXiv.2102.04843","DOI":"10.48550\/arXiv.2102.04843"},{"key":"e_1_3_1_46_2","first-page":"139","article-title":"Vector autoregressive models","author":"L\u00fctkepohl Helmut","year":"2013","unstructured":"Helmut L\u00fctkepohl. 2013. Vector autoregressive models. In Handbook of Research Methods and Applications in Empirical Macroeconomics. Nigar Hashimzade and Michael A. Thornton (Eds.), Edward Elgar, 139\u2013164.","journal-title":"Handbook of Research Methods and Applications in Empirical Macroeconomics"},{"issue":"1","key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/1472-6947-8-37","article-title":"Applying CUSUM-based methods for the detection of outbreaks of Ross River virus disease in western Australia","volume":"8","author":"Watkins Rochelle E.","year":"2008","unstructured":"Rochelle E. Watkins, Serryn Eagleson, Bert Veenendaal, Graeme Wright, and Aileen J. Plant. 2008. Applying CUSUM-based methods for the detection of outbreaks of Ross River virus disease in western Australia. BMC Medical Informatics and Decision Making 8, 1 (Aug. 2008), 37.","journal-title":"BMC Medical Informatics and Decision Making"},{"issue":"4","key":"e_1_3_1_48_2","first-page":"276","article-title":"Early detection of dysentery outbreaks by cumulative sum method based on National Surveillance System data in 1393-1396","volume":"16","author":"Sharifolkashani K.","year":"2021","unstructured":"K. Sharifolkashani, P. Yavari, R. Shekarriz, F. Tajdini, and N. Aghili. 2021. Early detection of dysentery outbreaks by cumulative sum method based on National Surveillance System data in 1393-1396. Iranian Journal of Epidemiology 16, 4 (Mar. 2021), 276\u2013284.","journal-title":"Iranian Journal of Epidemiology"},{"issue":"10","key":"e_1_3_1_49_2","first-page":"1366","article-title":"Early detection of meningitis outbreaks: Application of limited-baseline data","volume":"46","author":"Karami Manoochehr","year":"2017","unstructured":"Manoochehr Karami, Maryam Ghalandari, Jalal Poorolajal, and Javad Faradmal. 2017. Early detection of meningitis outbreaks: Application of limited-baseline data. Iranian Journal of Public Health 46, 10 (Oct. 2017), 1366\u20131373.","journal-title":"Iranian Journal of Public Health"},{"key":"e_1_3_1_50_2","first-page":"1","volume-title":"2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)","author":"Buendia Richard John M.","year":"2015","unstructured":"Richard John M. Buendia and Geoffrey A. Solano. 2015. A disease outbreak detection system using autoregressive moving average in time series analysis. In 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), 1\u20135."},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"106759","DOI":"10.1016\/j.dib.2021.106759","article-title":"Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and Na\u00efve forecasting methods","volume":"35","author":"Lynch Christopher J.","year":"2021","unstructured":"Christopher J. Lynch and Ross Gore. 2021. Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and Na\u00efve forecasting methods. Data in Brief 35 (Apr. 2021), 106759.","journal-title":"Data in Brief"},{"key":"e_1_3_1_52_2","volume-title":"JMIR Public Health and Surveillance","author":"Singh Ram Kumar","year":"2020","unstructured":"Ram Kumar Singh, Meenu Rani, Akshaya Srikanth Bhagavathula, Ranjit Sah, Alfonso J. Rodriguez-Morales, Himangshu Kalita, Chintan Nanda, Shashi Sharma, Yagya Datt Sharma, Ali A. Rabaan, et al. 2020. Prediction of the COVID-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health and Surveillance 6, 2 (May 2020), e19115."},{"issue":"7","key":"e_1_3_1_53_2","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1016\/j.jiph.2021.04.015","article-title":"COVID-19 prevalence forecasting using autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN): Case of Turkey","volume":"14","author":"To\u011fa G\u00fclhan","year":"2021","unstructured":"G\u00fclhan To\u011fa, Berrin Atalay, and M. Duran Toksari. 2021. COVID-19 prevalence forecasting using autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN): Case of Turkey. Journal of Infection and Public Health 14, 7 (Jul. 2021), 811\u2013816.","journal-title":"Journal of Infection and Public Health"},{"key":"e_1_3_1_54_2","doi-asserted-by":"crossref","first-page":"107161","DOI":"10.1016\/j.asoc.2021.107161","article-title":"Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-regressive integrated moving average (ARIMA) and seasonal auto-regressive integrated moving average (SARIMA)","volume":"103","author":"ArunKumar K. E.","year":"2021","unstructured":"K. E. ArunKumar, Dinesh V. Kalaga, Ch. Mohan Sai Kumar, Govinda Chilkoor, Masahiro Kawaji, and Timothy M. Brenza. 2021. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-regressive integrated moving average (ARIMA) and seasonal auto-regressive integrated moving average (SARIMA). Applied Soft Computing 103 (May 2021), 107161.","journal-title":"Applied Soft Computing"},{"issue":"2","key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"83","DOI":"10.4103\/1995-7645.306739","article-title":"Predicting cutaneous leishmaniasis using SARIMA and Markov switching models in Isfahan, Iran: A time-series study","volume":"14","author":"Rahmanian Vahid","year":"2021","unstructured":"Vahid Rahmanian, Saied Bokaie, Aliakbar Haghdoost, and Mohsen Barouni. 2021. Predicting cutaneous leishmaniasis using SARIMA and Markov switching models in Isfahan, Iran: A time-series study. Asian Pacific Journal of Tropical Medicine 14, 2 (2021), 83\u201393.","journal-title":"Asian Pacific Journal of Tropical Medicine"},{"issue":"2","key":"e_1_3_1_56_2","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s00477-020-01773-5","article-title":"A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks","author":"Amor\u00f3s Rub\u00e9n","year":"2020","unstructured":"Rub\u00e9n Amor\u00f3s, David Conesa, Antonio L\u00f3pez-Qu\u00edlez, and Miguel-Angel Martinez-Beneito. 2020. A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks. Stochastic Environmental Research and Risk Assessment 34, 2 (Feb. 2020), 275\u2013292.","journal-title":"Stochastic Environmental Research and Risk Assessment 34"},{"key":"e_1_3_1_57_2","author":"Salvador Rub\u00e9n Amor\u00f3s","year":"2017","unstructured":"Rub\u00e9n Amor\u00f3s Salvador. 2017. Bayesian Temporal and Spatio-Temporal Markov Switching Models for the Detection of Influenza Outbreaks. Universitat de Val\u00e8ncia. Retrieved from http:\/\/purl.org\/dc\/dcmitype\/Text","journal-title":"Bayesian Temporal and Spatio-Temporal Markov Switching Models for the Detection of Influenza Outbreaks"},{"key":"e_1_3_1_58_2","first-page":"76","volume-title":"2008 IEEE International Conference on Intelligence and Security Informatics","author":"Lu Hsin-Min","year":"2008","unstructured":"Hsin-Min Lu, Daniel Zeng, and Hsinchun Chen. 2008. Bioterrorism event detection based on the Markov switching model: A simulated anthrax outbreak study. In 2008 IEEE International Conference on Intelligence and Security Informatics, 76\u201381."},{"key":"e_1_3_1_59_2","doi-asserted-by":"crossref","first-page":"100504","DOI":"10.1016\/j.spasta.2021.100504","article-title":"A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence","volume":"49","author":"Bartolucci Francesco","year":"2022","unstructured":"Francesco Bartolucci and Alessio Farcomeni. 2022. A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence. Spatial Statistics 49 (2022), 100504.","journal-title":"Spatial Statistics"},{"key":"e_1_3_1_60_2","doi-asserted-by":"crossref","first-page":"e11537","DOI":"10.7717\/peerj.11537","article-title":"Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models","volume":"9","author":"Feroze Navid","year":"2021","unstructured":"Navid Feroze, Kamran Abbas, Farzana Noor, and Amjad Ali. 2021. Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models. PeerJ 9 (2021), e11537.","journal-title":"PeerJ"},{"key":"e_1_3_1_61_2","first-page":"97","volume-title":"American Journal of Public Health","author":"Costagliola D.","year":"1991","unstructured":"D. Costagliola, A. Flahault, D. Galinec, P. Garnerin, J. Menares, and A. J. Valleron. 1991. A routine tool for detection and assessment of epidemics of influenza-like syndromes in France. American Journal of Public Health 81, 1 (Jan. 1991), 97\u201399."},{"issue":"4","key":"e_1_3_1_62_2","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/BF00146364","article-title":"Five years of sentinel surveillance of acute respiratory infections (1985\u20131990): The benefits of an influenza early warning system","volume":"8","author":"Snacken R.","year":"1992","unstructured":"R. Snacken, J. Lion, V. Van Casteren, R. Cornelis, F. Yane, M. Mombaerts, W. Aelvoet, and A. Stroobant. 1992. Five years of sentinel surveillance of acute respiratory infections (1985\u20131990): The benefits of an influenza early warning system. European Journal of Epidemiology 8, 4 (Jul. 1992), 485\u2013490.","journal-title":"European Journal of Epidemiology"},{"key":"e_1_3_1_63_2","first-page":"435","volume-title":"Epidemiology","author":"Stroup Donna F.","year":"1993","unstructured":"Donna F. Stroup and Stephen B. Thacker. 1993. A Bayesian approach to the detection of aberrations in public health surveillance data. Epidemiology 4, 5 (1993), 435\u2013443."},{"issue":"3","key":"e_1_3_1_64_2","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1093\/oxfordjournals.aje.a113090","article-title":"An evaluation of influenza mortality surveillance, 1962\u20131979: I. Time series forecasts of expected pneumonia and influenza deaths","volume":"113","author":"Choi Keewhan","year":"1981","unstructured":"Keewhan Choi and Stephen B. Thacker. 1981. An evaluation of influenza mortality surveillance, 1962\u20131979: I. Time series forecasts of expected pneumonia and influenza deaths. American Journal of Epidemiology 113, 3 (Mar. 1981), 215\u2013226.","journal-title":"American Journal of Epidemiology"},{"issue":"3","key":"e_1_3_1_65_2","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1093\/oxfordjournals.aje.a116684","article-title":"Evaluation of a method for detecting aberrations in public health surveillance data","volume":"137","author":"Stroup Donna F.","year":"1993","unstructured":"Donna F. Stroup, Melinda Wharton, Karen Kafadar, and Andrew G. Dean. 1993. Evaluation of a method for detecting aberrations in public health surveillance data. American Journal of Epidemiology 137, 3 (Feb. 1993), 373\u2013380.","journal-title":"American Journal of Epidemiology"},{"issue":"11","key":"e_1_3_1_66_2","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1002\/sim.4780111102","article-title":"The changing geographical coherence of measles morbidity in the United States, 1962\u201388","volume":"11","author":"Cliff Andrew D.","year":"1992","unstructured":"Andrew D. Cliff, Peter Haggett, Donna F. Stroup, and Elizabeth Cheney. 1992. The changing geographical coherence of measles morbidity in the United States, 1962\u201388. Statistics in Medicine 11, 11 (1992), 1409\u20131424. Retrieved from https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/sim.4780","journal-title":"Statistics in Medicine"},{"issue":"2","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1093\/ije\/23.2.408","article-title":"A monitoring system to detect changes in public health surveillance data","volume":"23","author":"Nobre Flavio F.","year":"1994","unstructured":"Flavio F. Nobre and Donna F. Stroup. 1994. A monitoring system to detect changes in public health surveillance data. International Journal of Epidemiology 23, 2 (Apr. 1994), 408\u2013418.","journal-title":"International Journal of Epidemiology"},{"key":"e_1_3_1_68_2","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1007\/s40808-020-00890-y","article-title":"Spatial prediction of COVID-19 epidemic using ARIMA techniques in India","volume":"7","author":"Roy Santanu","year":"2021","unstructured":"Santanu Roy, Gouri Sankar Bhunia, and Pravat Kumar Shit. 2021. Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Modeling Earth Systems and Environment 7 (2021), 1385\u20131391.","journal-title":"Modeling Earth Systems and Environment"},{"key":"e_1_3_1_69_2","first-page":"707","volume-title":"Annals of Internal Medicine","author":"Thaker Samir I.","year":"2011","unstructured":"Samir I. Thaker, Amy S. Nowacki, Neil B. Mehta, and Ashley R. Edwards. 2011. How U.S. hospitals use social media. Annals of Internal Medicine 154, 10 (May 2011), 707\u2013708."},{"issue":"1","key":"e_1_3_1_70_2","doi-asserted-by":"crossref","first-page":"10434","DOI":"10.1038\/s41598-019-46898-y","article-title":"Avian influenza A (H7N9) and related Internet search query data in China","volume":"9","author":"Chen Ying","year":"2019","unstructured":"Ying Chen, Yuzhou Zhang, Zhiwei Xu, Xuanzhuo Wang, Jiahai Lu, and Wenbiao Hu. 2019. Avian influenza A (H7N9) and related Internet search query data in China. Scientific Reports 9, 1 (2019), 10434.","journal-title":"Scientific Reports"},{"issue":"1","key":"e_1_3_1_71_2","doi-asserted-by":"crossref","first-page":"4747","DOI":"10.1038\/s41598-020-61686-9","article-title":"Comparing social media and Google to detect and predict severe epidemics","volume":"10","author":"Samaras Loukas","year":"2020","unstructured":"Loukas Samaras, Elena Garc\u00eda-Barriocanal, and Miguel-Angel Sicilia. 2020. Comparing social media and Google to detect and predict severe epidemics. Scientific Reports 10, 1 (Dec. 2020), 4747.","journal-title":"Scientific Reports"},{"issue":"7","key":"e_1_3_1_72_2","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.3390\/ijerph17072365","article-title":"Prediction of number of cases of 2019 novel coronavirus (COVID-19) using social media search index","volume":"17","author":"Qin Lei","year":"2020","unstructured":"Lei Qin, Qiang Sun, Yidan Wang, Ke-Fei Wu, Mingchih Chen, Ben-Chang Shia, and Szu-Yuan Wu. 2020. Prediction of number of cases of 2019 novel coronavirus (COVID-19) using social media search index. International Journal of Environmental Research and Public Health 17, 7 (Jan. 2020), 2365.","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"5","key":"e_1_3_1_73_2","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1007\/s40745-022-00418-4","article-title":"Forecasting the trends of Covid-19 and causal impact of vaccines using Bayesian structural time series and ARIMA","volume":"9","author":"Thorakkattle Muhammed Navas","year":"2022","unstructured":"Muhammed Navas Thorakkattle, Shazia Farhin, and Athar Ali Khan. 2022. Forecasting the trends of Covid-19 and causal impact of vaccines using Bayesian structural time series and ARIMA. Annals of Data Science 9, 5 (2022), 1025\u20131047.","journal-title":"Annals of Data Science"},{"issue":"11","key":"e_1_3_1_74_2","doi-asserted-by":"crossref","first-page":"e40866","DOI":"10.2196\/40866","article-title":"New surveillance metrics for alerting community-acquired outbreaks of emerging SARS-CoV-2 variants using imported case data: Bayesian Markov Chain Monte Carlo approach","volume":"8","author":"Yen Amy Ming-Fang","year":"2022","unstructured":"Amy Ming-Fang Yen, Tony Hsiu-Hsi Chen, Wei-Jung Chang, Ting-Yu Lin, Grace Hsiao-Hsuan Jen, Chen-Yang Hsu, Sen-Te Wang, Huong Dang, and Sam Li-Sheng Chen. 2022. New surveillance metrics for alerting community-acquired outbreaks of emerging SARS-CoV-2 variants using imported case data: Bayesian Markov Chain Monte Carlo approach. JMIR Public Health and Surveillance 8, 11 (2022), e40866.","journal-title":"JMIR Public Health and Surveillance"},{"key":"e_1_3_1_75_2","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s10796-018-9893-0","article-title":"Social media for nowcasting flu activity: Spatio-temporal big data analysis","volume":"21","author":"Zadeh Amir Hassan","year":"2019","unstructured":"Amir Hassan Zadeh, Hamed M. Zolbanin, Ramesh Sharda, and Dursun Delen. 2019. Social media for nowcasting flu activity: Spatio-temporal big data analysis. Information Systems Frontiers 21 (2019), 743\u2013760.","journal-title":"Information Systems Frontiers"},{"issue":"2","key":"e_1_3_1_76_2","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s00477-020-01773-5","article-title":"A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks","volume":"34","author":"Amor\u00f3s Rub\u00e9n","year":"2020","unstructured":"Rub\u00e9n Amor\u00f3s, David Conesa, Antonio L\u00f3pez-Qu\u00edlez, and Miguel-Angel Martinez-Beneito. 2020. A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks. Stochastic Environmental Research and Risk Assessment 34, 2 (2020), 275\u2013292.","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"issue":"1","key":"e_1_3_1_77_2","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1080\/13658816.2020.1798967","article-title":"A Bayesian spatio-temporal model to analyzing the stability of patterns of population distribution in an urban space using mobile phone data","volume":"35","author":"Wang Zhensheng","year":"2021","unstructured":"Zhensheng Wang, Yang Yue, Biao He, Ke Nie, Wei Tu, Qingyun Du, and Qingquan Li. 2021. A Bayesian spatio-temporal model to analyzing the stability of patterns of population distribution in an urban space using mobile phone data. International Journal of Geographical Information Science 35, 1 (2021), 116\u2013134.","journal-title":"International Journal of Geographical Information Science"},{"key":"e_1_3_1_78_2","volume-title":"Journal of Physics: Conference Series","volume":"1722","author":"Suryaningrat W.","year":"2021","unstructured":"W. Suryaningrat, D. Munandar, A. Maryati, A. S. Abdullah, and B. N. Ruchjana. 2021. Posted prediction in social media base on Markov chain model: Twitter dataset with Covid-19 trends. In Journal of Physics: Conference Series, Vol. 1722. IOP Publishing, 012001."},{"key":"e_1_3_1_79_2","doi-asserted-by":"crossref","first-page":"11738","DOI":"10.1007\/s11227-021-03726-3","article-title":"Epidemic zone of COVID-19 from social media using hypergraph with weighting factor (HWF)","volume":"77","author":"Pradeepa S.","year":"2021","unstructured":"S. Pradeepa and K. R. Manjula. 2021. Epidemic zone of COVID-19 from social media using hypergraph with weighting factor (HWF). The Journal of Supercomputing 77 (2021), 11738\u201311755.","journal-title":"The Journal of Supercomputing"},{"key":"e_1_3_1_80_2","first-page":"1369","volume-title":"Environmental Health Perspectives","author":"Shi Yuan","year":"2016","unstructured":"Yuan Shi, Xu Liu, Suet-Yheng Kok, Jayanthi Rajarethinam, Shaohong Liang, Grace Yap, Chee-Seng Chong, Kim-Sung Lee, Sharon S. Y. Tan, Christopher Kuan Yew Chin, et al. 2016. Three-month real-time dengue forecast models: An early warning system for outbreak alerts and policy decision support in Singapore. Environmental Health Perspectives 124, 9 (Sept. 2016), 1369\u20131375."},{"issue":"2","key":"e_1_3_1_81_2","doi-asserted-by":"crossref","first-page":"608","DOI":"10.21533\/pen.v7i2.442","article-title":"Review on nowcasting using least absolute shrinkage selector operator (LASSO) to predict dengue occurrence in San Juan and Iquitos as part of disease surveillance system","volume":"7","author":"Tang Sui Lan","year":"2019","unstructured":"Sui Lan Tang and Preethi Subramanian. 2019. Review on nowcasting using least absolute shrinkage selector operator (LASSO) to predict dengue occurrence in San Juan and Iquitos as part of disease surveillance system. Periodicals of Engineering and Natural Sciences 7, 2 (Jul. 2019), 608\u2013617.","journal-title":"Periodicals of Engineering and Natural Sciences"},{"key":"e_1_3_1_82_2","first-page":"101489","volume-title":"IEEE Access","volume":"8","author":"Rustam Furqan","year":"2020","unstructured":"Furqan Rustam, Aijaz Ahmad Reshi, Arif Mehmood, Saleem Ullah, Byung-Won On, Waqar Aslam, and Gyu Sang Choi. 2020. COVID-19 future forecasting using supervised machine learning models. IEEE Access 8 (2020), 101489\u2013101499."},{"key":"e_1_3_1_83_2","volume-title":"PLoS Neglected Tropical Diseases","author":"Guo Pi","year":"2017","unstructured":"Pi Guo, Tao Liu, Qin Zhang, Li Wang, Jianpeng Xiao, Qingying Zhang, Ganfeng Luo, Zhihao Li, Jianfeng He, Yonghui Zhang, et al. 2017. Developing a dengue forecast model using machine learning: A case study in China. PLoS Neglected Tropical Diseases 11, 10 (Oct. 2017), e0005973."},{"key":"e_1_3_1_84_2","volume-title":"PLoS One","author":"Lee Tsair-Fwu","year":"2014","unstructured":"Tsair-Fwu Lee, Pei-Ju Chao, Hui-Min Ting, Liyun Chang, Yu-Jie Huang, Jia-Ming Wu, Hung-Yu Wang, Mong-Fong Horng, Chun-Ming Chang, Jen-Hong Lan, et al. 2014. Using multivariate regression model with least absolute shrinkage and selection operator (LASSO) to predict the incidence of xerostomia after intensity-modulated radiotherapy for head and neck cancer. PLoS One 9, 2 (Feb. 2014), e89700."},{"key":"e_1_3_1_85_2","first-page":"407","volume-title":"The Annals of Statistics","author":"Efron Bradley","year":"2004","unstructured":"Bradley Efron, Trevor Hastie, Iain Johnstone, and Robert Tibshirani. 2004. Least angle regression. The Annals of Statistics 32, 2 (Apr. 2004), 407\u2013499."},{"key":"e_1_3_1_86_2","doi-asserted-by":"crossref","first-page":"679475","DOI":"10.3389\/fpubh.2021.679475","article-title":"Empirical examination on the drivers of the U.S. equity returns in the during the COVID-19 crisis","volume":"9","author":"Wang Qing","year":"2021","unstructured":"Qing Wang, Mo Bai, and Mai Huang. 2021. Empirical examination on the drivers of the U.S. equity returns in the during the COVID-19 crisis. Frontiers in Public Health 9, 679475.","journal-title":"Frontiers in Public Health"},{"issue":"4","key":"e_1_3_1_87_2","first-page":"1","article-title":"Xgboost: Extreme gradient boosting","volume":"1","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, Rory Mitchell, Ignacio Cano, Tianyi Zhou, et al. 2015. Xgboost: Extreme gradient boosting. R Package Version 0.4-2 1, 4 (2015), 1\u20134.","journal-title":"R Package Version 0.4-2"},{"key":"e_1_3_1_88_2","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","volume-title":"22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916)","author":"Chen Tianqi","year":"2016","unstructured":"Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A scalable tree boosting system. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916). ACM, New York, NY, 785\u2013794."},{"issue":"1","key":"e_1_3_1_89_2","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s41666-019-00063-2","article-title":"Predicting glycaemia in type 1 diabetes patients: Experiments in feature engineering and data imputation","volume":"4","author":"Jeon Jouhyun","year":"2020","unstructured":"Jouhyun Jeon, Peter J. Leimbigler, Gaurav Baruah, Michael H. Li, Yan Fossat, and Alfred J. Whitehead. 2020. Predicting glycaemia in type 1 diabetes patients: Experiments in feature engineering and data imputation. Journal of Healthcare Informatics Research 4, 1 (Mar. 2020), 71\u201390.","journal-title":"Journal of Healthcare Informatics Research"},{"key":"e_1_3_1_90_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/978-981-15-3125-5_2","volume-title":"Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies","author":"Shashvat Kumar","year":"2020","unstructured":"Kumar Shashvat, Rikmantra Basu, Amol P. Bhondekar, and Arshpreet Kaur. 2020. Epidemiology and forecasting of cholera incidence in North India. In Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies. Vinit Kumar Gunjan, Sabrina Senatore, Amit Kumar, Xiao-Zhi Gao, and Suresh Merugu (Eds.), Springer, Singapore, 9\u201317."},{"key":"e_1_3_1_91_2","first-page":"1","volume-title":"2022 International Conference on Recent Advances in Electrical Engineering & Computer Sciences (RAEE & CS)","author":"Saif Mahrukh","year":"2022","unstructured":"Mahrukh Saif, Muhammad Asif Zahoor Raja, and Aneela Zameer. 2022. Analysis of Covid-19 literature evolution via NLP and machine learning. In 2022 International Conference on Recent Advances in Electrical Engineering & Computer Sciences (RAEE & CS), 1\u20138."},{"issue":"2","key":"e_1_3_1_92_2","first-page":"6","article-title":"Optimized deep learning based ensemble model for forecasting of Covid-19","volume":"13","author":"Lalli M.","year":"2021","unstructured":"M. Lalli. 2021. Optimized deep learning based ensemble model for forecasting of Covid-19. International Journal of Computational Intelligence in Control 13, 2 (Dec. 2021), 6.","journal-title":"International Journal of Computational Intelligence in Control"},{"key":"e_1_3_1_93_2","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/ICIT.2018.00019","volume-title":"2018 International Conference on Information Technology (ICIT)","author":"Kalipe Godson","year":"2018","unstructured":"Godson Kalipe, Vikas Gautham, and Rajat Kumar Behera. 2018. Predicting malarial outbreak using machine learning and deep learning approach: A review and analysis. In 2018 International Conference on Information Technology (ICIT), 33\u201338."},{"issue":"7","key":"e_1_3_1_94_2","doi-asserted-by":"crossref","first-page":"e056685","DOI":"10.1136\/bmjopen-2021-056685","article-title":"Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: A time-series study","volume":"12","author":"Fang Zheng-gang","year":"2022","unstructured":"Zheng-gang Fang, Shu-qin Yang, Cai-xia Lv, Shu-yi An, and Wei Wu. 2022. Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: A time-series study. BMJ Open 12, 7 (Jul. 2022), e056685.","journal-title":"BMJ Open"},{"key":"e_1_3_1_95_2","unstructured":"Rohil Badkundri Victor Valbuena Srikusmanjali Pinnamareddy Brittney Cantrell and Janet Standeven. 2019. Forecasting the 2017-2018 Yemen cholera outbreak with machine learning. arXiv:1902.06739. Retrieved from https:\/\/arxiv.org\/abs\/1902.06739"},{"key":"e_1_3_1_96_2","doi-asserted-by":"publisher","DOI":"10.1080\/09720529.2020.1784535"},{"key":"e_1_3_1_97_2","doi-asserted-by":"publisher","DOI":"10.1080\/09720502.2020.1833458"},{"issue":"2","key":"e_1_3_1_98_2","first-page":"3","article-title":"Tutorial on support vector machine (SVM)","volume":"37","author":"Jakkula Vikramaditya","year":"2006","unstructured":"Vikramaditya Jakkula. 2006. Tutorial on support vector machine (SVM). School of EECS, Washington State University 37, 2.5 (2006), 3.","journal-title":"School of EECS, Washington State University"},{"key":"e_1_3_1_99_2","doi-asserted-by":"publisher","unstructured":"Gurcan Comert Negash Begashaw and Ayse Turhan-Comert. 2020. Malaria outbreak detection with machine learning methods. BioRxiv 2020.07. DOI: 10.1101\/2020.07.21.214213","DOI":"10.1101\/2020.07.21.214213"},{"issue":"3","key":"e_1_3_1_100_2","doi-asserted-by":"crossref","first-page":"597","DOI":"10.30630\/joiv.6.2.788","article-title":"Predicting dengue outbreak based on meteorological data using artificial neural network and decision tree models","volume":"6","author":"Krishnan Nor Farisha Muhamad","year":"2022","unstructured":"Nor Farisha Muhamad Krishnan, Zuriani Ahmad Zukarnain, Azlin Ahmad, and Marhainis Jamaludin. 2022. Predicting dengue outbreak based on meteorological data using artificial neural network and decision tree models. International Journal on Informatics Visualization 6, 3 (Sept. 2022), 597\u2013603.","journal-title":"International Journal on Informatics Visualization"},{"issue":"1","key":"e_1_3_1_101_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s13755-022-00202-x","article-title":"Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree","volume":"10","author":"Khan Md. Ashikur Rahman","year":"2022","unstructured":"Md. Ashikur Rahman Khan, Jony Akter, Ishtiaq Ahammad, Sabbir Ejaz, and Tanvir Jaman Khan. 2022. Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree. Health Information Science and Systems 10, 1 (Nov. 2022), 32.","journal-title":"Health Information Science and Systems"},{"issue":"9","key":"e_1_3_1_102_2","first-page":"1","article-title":"A survey and analysis on classification and regression data mining techniques for diseases outbreak prediction in datasets","volume":"5","author":"Leopord Hakizimana","year":"2016","unstructured":"Hakizimana Leopord, W. Kipruto Cheruiyot, and Stephen Kimani. 2016. A survey and analysis on classification and regression data mining techniques for diseases outbreak prediction in datasets. International Journal of Engineering Science 5, 9 (2016), 1\u201311.","journal-title":"International Journal of Engineering Science"},{"key":"e_1_3_1_103_2","doi-asserted-by":"crossref","first-page":"105469","DOI":"10.1016\/j.prevetmed.2021.105469","article-title":"Decision tree analysis for pathogen identification based on circumstantial factors in outbreaks of bovine respiratory disease in calves","volume":"196","author":"Lowie T.","year":"2021","unstructured":"T. Lowie, J. Callens, J. Maris, S. Ribbens, and B. Pardon. 2021. Decision tree analysis for pathogen identification based on circumstantial factors in outbreaks of bovine respiratory disease in calves. Preventive Veterinary Medicine 196 (Nov. 2021), 105469.","journal-title":"Preventive Veterinary Medicine"},{"issue":"5","key":"e_1_3_1_104_2","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1023\/A:1016409317640","article-title":"Decision trees: An overview and their use in medicine","volume":"26","author":"Podgorelec Vili","year":"2002","unstructured":"Vili Podgorelec, Peter Kokol, Bruno Stiglic, and Ivan Rozman. 2002. Decision trees: An overview and their use in medicine. Journal of Medical Systems 26, 5 (Oct. 2002), 445\u2013463.","journal-title":"Journal of Medical Systems"},{"key":"e_1_3_1_105_2","volume-title":"Computational and Mathematical Methods in Medicine","author":"Ali Liaqat","year":"2019","unstructured":"Liaqat Ali, Shafqat Ullah Khan, Noorbakhsh Amiri Golilarz, Imrana Yakubu, Iqbal Qasim, Adeeb Noor, and Redhwan Nour. 2019. A feature-driven decision support system for heart failure prediction based on statistical model and Gaussian Naive Bayes. Computational and Mathematical Methods in Medicine 2019 (Nov. 2019), e6314328."},{"key":"e_1_3_1_106_2","volume-title":"Journal of Physics: Conference Series 1844","volume":"1","author":"Zakiyyah H.","year":"2021","unstructured":"H. Zakiyyah and S. Suyanto. 2021. Prediction of Covid-19 infection in Indonesia using machine learning methods. Journal of Physics: Conference Series 1844, 1 (Mar. 2021), 012002."},{"issue":"4","key":"e_1_3_1_107_2","doi-asserted-by":"crossref","first-page":"728","DOI":"10.3390\/covid1040058","article-title":"Combining international survey datasets to identify indicators of stress during the COVID-19 pandemic: A machine learning approach to improve generalization","volume":"1","author":"Zhao Eric Yunan","year":"2021","unstructured":"Eric Yunan Zhao, Daniel Xia, Mark Greenhalgh, Elena Colicino, Merylin Monaro, Rita Hitching, Odette A. Harris, and Maheen M. Adamson. 2021. Combining international survey datasets to identify indicators of stress during the COVID-19 pandemic: A machine learning approach to improve generalization. COVID 1, 4 (Dec. 2021), 728\u2013738.","journal-title":"COVID"},{"key":"e_1_3_1_108_2","first-page":"229","volume-title":"Journal of X-Ray Science and Technology","author":"Rezaeijo Seyed Masoud","year":"2021","unstructured":"Seyed Masoud Rezaeijo, Razzagh Abedi-Firouzjah, Mohammadreza Ghorvei, and Samad Sarnameh. 2021. Screening of COVID-19 based on the extracted radiomics features from chest CT images. Journal of X-Ray Science and Technology 29, 2 (Jan. 2021), 229\u2013243."},{"key":"e_1_3_1_109_2","volume-title":"AIP Conference Proceedings","volume":"2472","author":"Omadlao Zanya Reubenne D.","year":"2022","unstructured":"Zanya Reubenne D. Omadlao, Johanna Marie A. Cabrales, Samuel Christian M. Cristobal, Margaret Vianey A. Dee, Jim Reinier V. Tadeo, Joseph Ludwin D. C. Marigmen, and Romsto R. Pajarillo. 2022. Machine learning-based dengue forecasting system for Irisan, Baguio City, Philippines. AIP Conference Proceedings 2472, 1 (Aug. 2022), 040019."},{"issue":"1","key":"e_1_3_1_110_2","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1186\/1471-2105-15-276","article-title":"Comparison of ARIMA and random forest time series models for prediction of avian influenza H5N1 outbreaks","volume":"15","author":"Kane Michael J.","year":"2014","unstructured":"Michael J. Kane, Natalie Price, Matthew Scotch, and Peter Rabinowitz. 2014. Comparison of ARIMA and random forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinformatics 15, 1 (Aug. 2014), 276.","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"e_1_3_1_111_2","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1111\/tbed.13424","article-title":"Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data","volume":"67","author":"Liang Ruirui","year":"2020","unstructured":"Ruirui Liang, Yi Lu, Xiaosheng Qu, Qiang Su, Chunxia Li, Sijing Xia, Yongxin Liu, Qiang Zhang, Xin Cao, Qin Chen, and Bing Niu. 2020. Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data. Transboundary and Emerging Diseases 67, 2 (2020), 935\u2013946. Retrieved from https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/tbed.13424","journal-title":"Transboundary and Emerging Diseases"},{"key":"e_1_3_1_112_2","first-page":"1","volume-title":"IJRQEH","author":"Dansana Debabrata","year":"2022","unstructured":"Debabrata Dansana, Raghvendra Kumar, Aishik Bhattacharjee, and Chandrakanta Mahanty. 2022. COVID-19 outbreak prediction and analysis of E-healthcare data using random forest algorithms. IJRQEH 11, 1 (Jan. 2022), 1\u201313."},{"key":"e_1_3_1_113_2","doi-asserted-by":"crossref","first-page":"110210","DOI":"10.1016\/j.chaos.2020.110210","article-title":"Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm","volume":"140","author":"Ye\u015filkanat Cafer Mert","year":"2020","unstructured":"Cafer Mert Ye\u015filkanat. 2020. Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals 140 (Nov. 2020), 110210.","journal-title":"Chaos, Solitons & Fractals"},{"key":"e_1_3_1_114_2","volume-title":"PLoS Neglected Tropical Diseases","author":"Ong Janet","year":"2018","unstructured":"Janet Ong, Xu Liu, Jayanthi Rajarethinam, Suet Yheng Kok, Shaohong Liang, Choon Siang Tang, Alex R. Cook, Lee Ching Ng, and Grace Yap. 2018. Mapping dengue risk in Singapore using random forest. PLoS Neglected Tropical Diseases 12, 6 (Jun. 2018), e0006587."},{"key":"e_1_3_1_115_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jbi.2018.02.014","article-title":"The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison","volume":"81","author":"Chen Yirong","year":"2018","unstructured":"Yirong Chen, Collins Wenhan Chu, Mark I. C. Chen, and Alex R. Cook. 2018. The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison. Journal of Biomedical Informatics 81 (May 2018), 16\u201330.","journal-title":"Journal of Biomedical Informatics"},{"key":"e_1_3_1_116_2","doi-asserted-by":"crossref","first-page":"103144","DOI":"10.1016\/j.jbi.2019.103144","article-title":"Predicting temporal propagation of seasonal influenza using improved Gaussian process model","volume":"93","author":"Chen Shanen","year":"2019","unstructured":"Shanen Chen, Jian Xu, Yongsheng Wu, Xin Wang, Shisong Fang, Jinquan Cheng, Hanwu Ma, Renli Zhang, Yachuan Liu, Li Zhang, et al. 2019. Predicting temporal propagation of seasonal influenza using improved Gaussian process model. Journal of Biomedical Informatics 93 (2019), 103144.","journal-title":"Journal of Biomedical Informatics"},{"issue":"13","key":"e_1_3_1_117_2","doi-asserted-by":"crossref","first-page":"19162","DOI":"10.2807\/ese.14.13.19162-en","article-title":"Internet surveillance systems for early alerting of health threats","volume":"14","author":"Linge J. P.","year":"2009","unstructured":"J. P. Linge, R. Steinberger, T. P. Weber, R. Yangarber, E. van der Goot, D. H. Al Khudhairy, and N. I. Stilianakis. 2009. Internet surveillance systems for early alerting of health threats. Eurosurveillance 14, 13 (Apr. 2009), 19162.","journal-title":"Eurosurveillance"},{"key":"e_1_3_1_118_2","first-page":"2948","volume-title":"Weekly Releases (1997\u20132007)","author":"Kaiser R.","year":"2006","unstructured":"R. Kaiser and D. Coulombier. 2006. Different approaches to gathering epidemic intelligence in Europe. Weekly Releases (1997\u20132007) 11, 17 (Apr. 2006), 2948."},{"key":"e_1_3_1_119_2","first-page":"5","volume-title":"Eurosurveillance","author":"Paquet C.","year":"2006","unstructured":"C. Paquet, D. Coulombier, R. Kaiser, and M. Ciotti. 2006. Epidemic intelligence: A new framework for strengthening disease surveillance in Europe. Eurosurveillance 11, 12 (Dec. 2006), 5\u20136."},{"issue":"4","key":"e_1_3_1_120_2","first-page":"391","article-title":"Epidemiological surveillance during humanitarian emergencies","volume":"62","author":"Coulombier D.","year":"2002","unstructured":"D. Coulombier, A. Pinto, and M. Valenciano. 2002. Epidemiological surveillance during humanitarian emergencies. Medecine Tropicale (Mars) 62, 4 (Jan. 2002), 391\u2013395.","journal-title":"Medecine Tropicale (Mars)"},{"issue":"20","key":"e_1_3_1_121_2","first-page":"267","article-title":"The epidemic intelligence from open sources initiative: A collaboration to harmonize and standardize early detection and epidemic intelligence among public health organizations","volume":"93","author":"Abdelmalik P.","year":"2018","unstructured":"P. Abdelmalik, E. Peron, J. Schnitzler, J. Fontaine, E. Elfenkampera, and P. Barbozaa. 2018. The epidemic intelligence from open sources initiative: A collaboration to harmonize and standardize early detection and epidemic intelligence among public health organizations. Weekly Epidemiological Record 93, 20 (2018), 267\u2013270.","journal-title":"Weekly Epidemiological Record"},{"key":"e_1_3_1_122_2","unstructured":"Google.org. (2024). Philanthropy Programs for Underserved Communities. Retrieved from https:\/\/www.google.org"},{"key":"e_1_3_1_123_2","unstructured":"X (formerly Twitter). (2024) Crunchbase Company Profile & Funding. Retrieved from https:\/\/www.crunchbase.com\/organization\/twitter"},{"key":"e_1_3_1_124_2","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.procs.2015.10.120","article-title":"An effective approach to track levels of influenza-a (H1N1) pandemic in India using Twitter","volume":"70","author":"Jain Vinay Kumar","year":"2015","unstructured":"Vinay Kumar Jain and Shishir Kumar. 2015. An effective approach to track levels of influenza-a (H1N1) pandemic in India using Twitter. Procedia Computer Science 70 (2015), 801\u2013807.","journal-title":"Procedia Computer Science"},{"key":"e_1_3_1_125_2","volume-title":"JMIR Public Health and Surveillance","author":"Alessa Ali","year":"2019","unstructured":"Ali Alessa and Miad Faezipour. 2019. Preliminary flu outbreak prediction using Twitter posts classification and linear regression with historical Centers for Disease Control and Prevention reports: Prediction framework study. JMIR Public Health and Surveillance 5, 2 (Jun. 2019), e12383."},{"issue":"3","key":"e_1_3_1_126_2","doi-asserted-by":"crossref","first-page":"e0230322","DOI":"10.1371\/journal.pone.0230322","article-title":"Automated monitoring of tweets for early detection of the 2014 Ebola epidemic","volume":"15","author":"Joshi Aditya","year":"2020","unstructured":"Aditya Joshi, Ross Sparks, Sarvnaz Karimi, Sheng-Lun Jason Yan, Abrar Ahmad Chughtai, Cecile Paris, and C. Raina MacIntyre. 2020. Automated monitoring of tweets for early detection of the 2014 Ebola epidemic. PLoS One 15, 3 (Mar. 2020), e0230322.","journal-title":"PLoS One"},{"issue":"10","key":"e_1_3_1_127_2","article-title":"Tracking coronavirus pandemic diseases using social media: A machine learning approach","volume":"11","author":"Fakhry Nuha Noha","year":"2020","unstructured":"Nuha Noha Fakhry, Evan Asfoura, and Gamal Kassam. 2020. Tracking coronavirus pandemic diseases using social media: A machine learning approach. International Journal of Advanced Computer Science and Applications 11, 10 (2020).","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"e_1_3_1_128_2","volume-title":"Complexity","author":"Amin Samina","year":"2021","unstructured":"Samina Amin, Muhammad Irfan Uddin, Duaa H. alSaeed, Atif Khan, and Muhammad Adnan. 2021. Early detection of seasonal outbreaks from Twitter data using machine learning approaches. Complexity 2021 (Mar. 2021), e5520366."},{"key":"e_1_3_1_129_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/978-1-4471-0219-9_20","volume-title":"Neural Nets WIRN Vietri-01 (Perspectives in Neural Computing)","author":"Gers Felix A.","year":"2002","unstructured":"Felix A. Gers, Douglas Eck, and J\u00fcrgen Schmidhuber. 2002. Applying LSTM to time series predictable through time-window approaches. In Neural Nets WIRN Vietri-01 (Perspectives in Neural Computing). Roberto Tagliaferri and Maria Marinaro (Eds.), Springer, London, 193\u2013200."},{"key":"e_1_3_1_130_2","doi-asserted-by":"crossref","first-page":"115153","DOI":"10.1016\/j.eswa.2021.115153","article-title":"Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm","volume":"180","author":"Kara Ahmet","year":"2021","unstructured":"Ahmet Kara. 2021. Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm. Expert Systems with Applications 180 (Oct. 2021), 115153.","journal-title":"Expert Systems with Applications"},{"issue":"21","key":"e_1_3_1_131_2","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.3390\/electronics10212668","article-title":"Predicting regional outbreaks of hepatitis a using 3D LSTM and open data in Korea","volume":"10","author":"Lee Kwangok","year":"2021","unstructured":"Kwangok Lee, Munkyu Lee, and Inseop Na. 2021. Predicting regional outbreaks of hepatitis a using 3D LSTM and open data in Korea. Electronics 10, 21 (Oct. 2021), 2668.","journal-title":"Electronics"},{"issue":"1","key":"e_1_3_1_132_2","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.idm.2021.12.005","article-title":"The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases","volume":"7","author":"Absar Nurul","year":"2022","unstructured":"Nurul Absar, Nazim Uddin, Mayeen Uddin Khandaker, and Habib Ullah. 2022. The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases. Infectious Disease Modelling 7, 1 (Mar. 2022), 170\u2013183.","journal-title":"Infectious Disease Modelling"},{"key":"e_1_3_1_133_2","first-page":"1","volume-title":"2019 IEEE International Conference on Healthcare Informatics (ICHI)","author":"Jia Wenxiao","year":"2019","unstructured":"Wenxiao Jia, Xiang Li, Kewei Tan, and Guotong Xie. 2019. Predicting the outbreak of the hand-foot-mouth diseases in China using recurrent neural network. In 2019 IEEE International Conference on Healthcare Informatics (ICHI), 1\u20134."},{"key":"e_1_3_1_134_2","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/B978-0-323-99031-8.00003-X","article-title":"Chapter 21 - Convolutional bi-directional long-short-term-memory based model to forecast COVID-19 in Algeria","author":"Shastri Sourabh","year":"2022","unstructured":"Sourabh Shastri, Kuljeet Singh, Astha Sharma, Mohamed Lounis, Sachin Kumar, and Vibhakar Mansotra. 2022. Chapter 21 - Convolutional bi-directional long-short-term-memory based model to forecast COVID-19 in Algeria. In Computational Intelligence in Healthcare Applications. Rajeev Agrawal, M. A. Ansari, R. S. Anand, Sweta Sneha, and Rajat Mehrotra (Eds.), Academic Press, 331\u2013343.","journal-title":"Computational Intelligence in Healthcare Applications"},{"key":"e_1_3_1_135_2","doi-asserted-by":"crossref","first-page":"110227","DOI":"10.1016\/j.chaos.2020.110227","article-title":"Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study","volume":"140","author":"Shastri Sourabh","year":"2020","unstructured":"Sourabh Shastri, Kuljeet Singh, Sachin Kumar, Paramjit Kour, and Vibhakar Mansotra. 2020. Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study. Chaos, Solitons & Fractals 140 (Nov. 2020), 110227.","journal-title":"Chaos, Solitons & Fractals"},{"key":"e_1_3_1_136_2","doi-asserted-by":"crossref","first-page":"110017","DOI":"10.1016\/j.chaos.2020.110017","article-title":"Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India","volume":"139","author":"Arora Parul","year":"2020","unstructured":"Parul Arora, Himanshu Kumar, and Bijaya Ketan Panigrahi. 2020. Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. Chaos, Solitons & Fractals 139 (Oct. 2020), 110017.","journal-title":"Chaos, Solitons & Fractals"},{"issue":"2","key":"e_1_3_1_137_2","first-page":"1","article-title":"Time-series forecasting and analysis of COVID-19 outbreak in highly populated countries: A data-driven approach","volume":"13","author":"Lakshmana Kumar Ramasamy P. M.","year":"2021","unstructured":"P. M. Lakshmana Kumar Ramasamy and Amala Jayanthi M. 2021. Time-series forecasting and analysis of COVID-19 outbreak in highly populated countries: A data-driven approach. International Journal of E-Health and Medical Communications 13, 2 (2021), 1\u201317.","journal-title":"International Journal of E-Health and Medical Communications"},{"issue":"1","key":"e_1_3_1_138_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15353\/jcvis.v6i1.3551","article-title":"Why can\u2019t neural networks forecast pandemics better","volume":"6","author":"Zelek Joshua D.","year":"2020","unstructured":"Joshua D. Zelek, John S. Zelek, and Alexander Wong. 2020. Why can\u2019t neural networks forecast pandemics better. Journal of Computational Vision and Imaging Systems 6, 1 (2020), 1\u20135.","journal-title":"Journal of Computational Vision and Imaging Systems"},{"key":"e_1_3_1_139_2","first-page":"1","volume-title":"International Journal of E-Health and Medical Communications","author":"Folorunso Sakinat Oluwabukonla","year":"2021","unstructured":"Sakinat Oluwabukonla Folorunso, Joseph Bamidele Awotunde, Oluwatobi Oluwaseyi Banjo, Ezekiel Adebayo Ogundepo, and Nureni Olawale Adeboye. 2021. Comparison of active COVID-19 cases per population using time-series models. International Journal of E-Health and Medical Communications 13, 2 (Jul. 2021), 1\u201321."},{"issue":"11","key":"e_1_3_1_140_2","doi-asserted-by":"crossref","first-page":"3880","DOI":"10.3390\/app10113880","article-title":"COVID-19: A comparison of time series methods to forecast percentage of active cases per population","volume":"10","author":"Papastefanopoulos Vasilis","year":"2020","unstructured":"Vasilis Papastefanopoulos, Pantelis Linardatos, and Sotiris Kotsiantis. 2020. COVID-19: A comparison of time series methods to forecast percentage of active cases per population. Applied Sciences 10, 11 (Jan. 2020), 3880.","journal-title":"Applied Sciences"},{"key":"e_1_3_1_141_2","doi-asserted-by":"crossref","first-page":"105560","DOI":"10.1016\/j.compbiomed.2022.105560","article-title":"A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning","volume":"146","author":"Jin Weiqiu","year":"2022","unstructured":"Weiqiu Jin, Shuqing Dong, Chengqing Yu, and Qingquan Luo. 2022. A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning. Computers in Biology and Medicine 146 (Jul. 2022), 105560.","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_3_1_142_2","volume-title":"PLoS One","author":"Ai Yuehan","year":"2022","unstructured":"Yuehan Ai, Fan He, Emma Lancaster, and Jiyoung Lee. 2022. Application of machine learning for multi-community COVID-19 outbreak predictions with wastewater surveillance. PLoS One 17, 11 (Nov. 2022), e0277154."},{"key":"e_1_3_1_143_2","first-page":"18138","volume-title":"Scientific Reports","author":"Zhao Daren","year":"2022","unstructured":"Daren Zhao, Ruihua Zhang, Huiwu Zhang, and Sizhang He. 2022. Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models. Scientific Reports 12, 1 (Oct. 2022), 18138."},{"key":"e_1_3_1_144_2","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.procs.2021.01.036","article-title":"Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET","volume":"179","author":"Satrio Christophorus Beneditto Aditya","year":"2021","unstructured":"Christophorus Beneditto Aditya Satrio, William Darmawan, Bellatasya Unrica Nadia, and Novita Hanafiah. 2021. Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Computer Science 179 (Jan. 2021), 524\u2013532.","journal-title":"Procedia Computer Science"},{"key":"e_1_3_1_145_2","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.patrec.2021.07.027","article-title":"Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics","volume":"151","author":"Dash Sujata","year":"2021","unstructured":"Sujata Dash, Chinmay Chakraborty, Sourav K. Giri, and Subhendu Kumar Pani. 2021. Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics. Pattern Recognition Letters 151 (Nov. 2021), 69\u201375.","journal-title":"Pattern Recognition Letters"},{"key":"e_1_3_1_146_2","article-title":"Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model","author":"Battineni Gopi","year":"2020","unstructured":"Gopi Battineni, Nalini Chintalapudi, and Francesco Amenta. 2020. Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model. Applied Computing and Informatics (Dec. 2020).","journal-title":"Applied Computing and Informatics"},{"key":"e_1_3_1_147_2","volume-title":"IOP Conference Series: Materials Science and Engineering","volume":"981","author":"Shaik Mohammed Ali","year":"2020","unstructured":"Mohammed Ali Shaik and Dhanraj Verma. 2020. Deep learning time series to forecast COVID-19 active cases in INDIA: A comparative study. IOP Conference Series: Materials Science and Engineering 981, 2 (Dec. 2020), 022041."},{"key":"e_1_3_1_148_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.9734\/jammr\/2020\/v32i2030675","article-title":"Growth models for Covid-19 death figures of Turkey","volume":"32","author":"Balaban Muzaffer","year":"2020","unstructured":"Muzaffer Balaban. 2020. Growth models for Covid-19 death figures of Turkey. Journal of Advances in Medicine and Medical Research 32 (Nov. 2020), 1\u201311.","journal-title":"Journal of Advances in Medicine and Medical Research"},{"key":"e_1_3_1_149_2","first-page":"97505","volume-title":"IEEE Access","volume":"9","author":"Dash Sujata","year":"2021","unstructured":"Sujata Dash, Chinmay Chakraborty, Sourav Kumar Giri, Subhendu Kumar Pani, and Jaroslav Frnda. 2021. BIFM: Big-data driven intelligent forecasting model for COVID-19. IEEE Access 9 (2021), 97505\u201397517."},{"issue":"1","key":"e_1_3_1_150_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s12911-018-0616-8","article-title":"Time series model for forecasting the number of new admission inpatients","volume":"18","author":"Zhou Lingling","year":"2018","unstructured":"Lingling Zhou, Ping Zhao, Dongdong Wu, Cheng Cheng, and Hao Huang. 2018. Time series model for forecasting the number of new admission inpatients. BMC Medical Informatics and Decision Making 18, 1 (Jun. 2018), 39.","journal-title":"BMC Medical Informatics and Decision Making"},{"key":"e_1_3_1_151_2","first-page":"277","volume-title":"Open Journal of Statistics","author":"Saliaj Lorena","year":"2022","unstructured":"Lorena Saliaj and Eugenia Nissi. 2022. Artificial neural networks for COVID-19 time series forecasting. Open Journal of Statistics 12, 2 (Mar. 2022), 277\u2013290."},{"key":"e_1_3_1_152_2","doi-asserted-by":"crossref","first-page":"110015","DOI":"10.1016\/j.chaos.2020.110015","article-title":"Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches","volume":"138","author":"Ki\u0307rba\u015f \u0130smail","year":"2020","unstructured":"\u0130smail Ki\u0307rba\u015f, Adnan S\u00f6zen, Azim Do\u011fu\u015f Tuncer, and Fikret \u015einasi Kazanci\u0307o\u011flu. 2020. Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches. Chaos, Solitons & Fractals 138 (Sept. 2020), 110015.","journal-title":"Chaos, Solitons & Fractals"},{"issue":"1","key":"e_1_3_1_153_2","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol Eric J.","year":"2019","unstructured":"Eric J. Topol. 2019. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine 25, 1 (Jan. 2019), 44\u201356.","journal-title":"Nature Medicine"},{"issue":"7767","key":"e_1_3_1_154_2","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41586-019-1390-1","article-title":"A clinically applicable approach to continuous prediction of future acute kidney injury","volume":"572","author":"Toma\u0161ev Nenad","year":"2019","unstructured":"Nenad Toma\u0161ev, Xavier Glorot, Jack W. Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, et al. 2019. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 7767 (Aug. 2019), 116\u2013119.","journal-title":"Nature"},{"issue":"8","key":"e_1_3_1_155_2","doi-asserted-by":"crossref","first-page":"6096","DOI":"10.1007\/s00330-021-07715-1","article-title":"A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)","volume":"31","author":"Wang Shuai","year":"2020","unstructured":"Shuai Wang, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, Mengjiao Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng, et al. 2020. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European Radiology 31, 8 (Aug. 2020), 6096\u20136104.","journal-title":"European Radiology"},{"key":"e_1_3_1_156_2","doi-asserted-by":"publisher","unstructured":"Chuansheng Zheng Xianbo Deng Qiang Fu Qiang Zhou Jiapei Feng Hui Ma Wenyu Liu and Xinggang Wang. 2020. Deep learning-based detection for COVID-19 from chest CT using weak label. DOI: 10.1101\/2020.03.12.20027185","DOI":"10.1101\/2020.03.12.20027185"},{"key":"e_1_3_1_157_2","doi-asserted-by":"crossref","first-page":"110212","DOI":"10.1016\/j.chaos.2020.110212","article-title":"Predictions for COVID-19 with deep learning models of LSTM, GRU and bi-LSTM","volume":"140","author":"Shahid Farah","year":"2020","unstructured":"Farah Shahid, Aneela Zameer, and Muhammad Muneeb. 2020. Predictions for COVID-19 with deep learning models of LSTM, GRU and bi-LSTM. Chaos, Solitons & Fractals 140 (Nov. 2020), 110212.","journal-title":"Chaos, Solitons & Fractals"},{"issue":"40","key":"e_1_3_1_158_2","doi-asserted-by":"crossref","first-page":"56043","DOI":"10.1007\/s11356-021-14286-7","article-title":"Predicting COVID-19 cases using bidirectional LSTM on multivariate time series","volume":"28","author":"Said Ahmed Ben","year":"2021","unstructured":"Ahmed Ben Said. 2021. Predicting COVID-19 cases using bidirectional LSTM on multivariate time series. Environmental Science and Pollution Research 28, 40 (2021), 56043\u201356052.","journal-title":"Environmental Science and Pollution Research"},{"key":"e_1_3_1_159_2","doi-asserted-by":"crossref","first-page":"104462","DOI":"10.1016\/j.rinp.2021.104462","article-title":"Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms","volume":"27","author":"Luo Junling","year":"2021","unstructured":"Junling Luo, Zhongliang Zhang, Yao Fu, and Feng Rao. 2021. Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms. Results in Physics 27 (Aug. 2021), 104462.","journal-title":"Results in Physics"},{"issue":"4","key":"e_1_3_1_160_2","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1007\/s00521-021-06548-9","article-title":"A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting","volume":"34","author":"Abbasimehr Hossein","year":"2022","unstructured":"Hossein Abbasimehr, Reza Paki, and Aram Bahrini. 2022. A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting. Neural Computing & Applications 34, 4 (Feb. 2022), 3135\u20133149.","journal-title":"Neural Computing & Applications"},{"key":"e_1_3_1_161_2","first-page":"2022","article-title":"A deep learning approach to forecast short-term COVID-19 cases and deaths in the US","author":"Du Hongru","year":"2022","unstructured":"Hongru Du, Ensheng Dong, Hamada S. Badr, Mary E. Petrone, Nathan D. Grubaugh, and Lauren M. Gardner. 2022. A deep learning approach to forecast short-term COVID-19 cases and deaths in the US. medRxiv (2022), 2022\u201308.","journal-title":"medRxiv"},{"key":"e_1_3_1_162_2","unstructured":"Novel Corona Virus 2019 Dataset. 2019. Retrieved from https:\/\/redivis.com\/datasets\/yn0q-4ff57142y"},{"key":"e_1_3_1_163_2","unstructured":"Population by Country 2020. 2020. Retrieved from https:\/\/www.populationpyramid.net\/population-size-per-country\/2020\/"},{"issue":"8","key":"e_1_3_1_164_2","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.3390\/ijerph15081596","article-title":"Predicting infectious disease using deep learning and big data","volume":"15","author":"Chae Sangwon","year":"2018","unstructured":"Sangwon Chae, Sungjun Kwon, and Donghyun Lee. 2018. Predicting infectious disease using deep learning and big data. International Journal of Environmental Research and Public Health 15, 8 (2018), 1596.","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"3","key":"e_1_3_1_165_2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.21037\/jtd.2020.02.64","article-title":"Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions","author":"Yang Zifeng","year":"2020","unstructured":"Zifeng Yang, Zhiqi Zeng, Ke Wang, Sook-San Wong, Wenhua Liang, Mark Zanin, Peng Liu, Xudong Cao, Zhongqiang Gao, Zhitong Mai, 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 (Mar. 2020), 165\u2013174.","journal-title":"Journal of Thoracic Disease 12"},{"key":"e_1_3_1_166_2","first-page":"4413","volume-title":"Scientific Reports","author":"Kim Juhyeon","year":"2021","unstructured":"Juhyeon Kim and Insung Ahn. 2021. Infectious disease outbreak prediction using media articles with machine learning models. Scientific Reports 11, 1 (Feb. 2021), 4413."},{"key":"e_1_3_1_167_2","unstructured":"Elham Afzali Adeola Adegoke Zhiyong Jin Woming Qiu and Liqun Wang. 2020. Hybrid VAR-LSTM networks modeling and forecasting COVID-19 data in Canada."},{"issue":"2","key":"e_1_3_1_168_2","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00500-021-06490-x","article-title":"India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability","volume":"26","author":"Ketu Shwet","year":"2022","unstructured":"Shwet Ketu and Pramod Kumar Mishra. 2022. India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability. Soft Computing 26, 2 (Jan. 2022), 645\u2013664.","journal-title":"Soft Computing"},{"key":"e_1_3_1_169_2","article-title":"An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound","volume":"132","author":"Dastider Ankan Ghosh","year":"2021","unstructured":"Ankan Ghosh Dastider, Farhan Sadik, and Shaikh Anowarul Fattah. 2021. An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound. Computers in Biology and Medicine 132 (May 2021), 104296.","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_3_1_170_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/8785636","article-title":"COVID-19 pandemic forecasting using CNN-LSTM: A hybrid approach","author":"Zain Zuhaira M.","year":"2021","unstructured":"Zuhaira M. Zain and Nazik M. Alturki. 2021. COVID-19 pandemic forecasting using CNN-LSTM: A hybrid approach. Journal of Control Science and Engineering 2021 (Jul. 2021), 1\u201323.","journal-title":"Journal of Control Science and Engineering"},{"key":"e_1_3_1_171_2","doi-asserted-by":"crossref","first-page":"103791","DOI":"10.1016\/j.jbi.2021.103791","article-title":"Comparative study of machine learning methods for COVID-19 transmission forecasting","volume":"118","author":"Dairi Abdelkader","year":"2021","unstructured":"Abdelkader Dairi, Fouzi Harrou, Abdelhafid Zeroual, Mohamad Mazen Hittawe, and Ying Sun. 2021. Comparative study of machine learning methods for COVID-19 transmission forecasting. Journal of Biomedical Informatics 118 (Jun. 2021), 103791.","journal-title":"Journal of Biomedical Informatics"},{"key":"e_1_3_1_172_2","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/978-3-030-79753-9_14","volume-title":"Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis","author":"Biswas Sitanath","year":"2022","unstructured":"Sitanath Biswas and Sujata Dash. 2022. LSTM-CNN deep learning\u2013based hybrid system for real-time COVID-19 data analysis and prediction using Twitter data. In Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis. Subhendu Kumar Pani, Sujata Dash, Wellington P. dos Santos, Syed Ahmad Chan Bukhari, and Francesco Flammini (Eds.), Springer International Publishing, Cham, 239\u2013257."},{"issue":"6","key":"e_1_3_1_173_2","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1007\/s12553-022-00711-5","article-title":"CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana","volume":"12","author":"Muhammad L. J.","year":"2022","unstructured":"L. J. Muhammad, Ahmed Abba Haruna, Usman Sani Sharif, and Mohammed Bappah Mohammed. 2022. CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana. Health and Technology 12, 6 (Nov. 2022), 1259\u20131276.","journal-title":"Health and Technology"},{"key":"e_1_3_1_174_2","first-page":"257","volume-title":"Neurocomputing","author":"Xiao Sun","year":"2016","unstructured":"Sun Xiao, Ye Jiaqi, and Ren Fuji. 2016. Detecting influenza states based on hybrid model with personal emotional factors from social networks. Neurocomputing 210 (2016), 257\u2013268."},{"issue":"3","key":"e_1_3_1_175_2","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1002\/tee.22389","article-title":"Trends detection of flu based on ensemble models with emotional factors from social networks","volume":"12","author":"Sun Xiao","year":"2017","unstructured":"Xiao Sun, Fuji Ren, and Jiaqi Ye. 2017. Trends detection of flu based on ensemble models with emotional factors from social networks. IEEJ Transactions on Electrical and Electronic Engineering 12, 3 (2017), 388\u2013396. Retrieved from https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/tee.22389","journal-title":"IEEJ Transactions on Electrical and Electronic Engineering"},{"issue":"10","key":"e_1_3_1_176_2","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","article-title":"A deep learning system to screen novel coronavirus disease 2019 pneumonia","volume":"6","author":"Xu Xiaowei","year":"2020","unstructured":"Xiaowei Xu, Xiangao Jiang, Chunlian Ma, Peng Du, Xukun Li, Shuangzhi Lv, Liang Yu, Qin Ni, Yanfei Chen, Junwei Su, et al. 2020. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6, 10 (Oct. 2020), 1122\u20131129.","journal-title":"Engineering"},{"key":"e_1_3_1_177_2","first-page":"1","article-title":"An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19","author":"Pustokhin Denis A.","year":"2020","unstructured":"Denis A. Pustokhin, Irina V. Pustokhina, Phuoc Nguyen Dinh, Son Van Phan, Gia Nhu Nguyen, Gyanendra Prasad Joshi, and Shankar K. 2020. An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19. Journal of Applied Statistics (Nov. 2020), 1\u201318.","journal-title":"Journal of Applied Statistics"},{"key":"e_1_3_1_178_2","article-title":"Ensemble forecasts of coronavirus disease 2019 (COVID-19) in the US","author":"Ray Evan L.","year":"2020","unstructured":"Evan L. Ray, Nutcha Wattanachit, Jarad Niemi, Abdul Hannan Kanji, Katie House, Estee Y. Cramer, Johannes Bracher, Andrew Zheng, Teresa K. Yamana, Xinyue Xiong, et al. 2020. Ensemble forecasts of coronavirus disease 2019 (COVID-19) in the US. MedRXiv (2020), 2020\u201308.","journal-title":"MedRXiv"},{"issue":"10","key":"e_1_3_1_179_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.3390\/a13100249","article-title":"Covid-19 outbreak prediction with machine learning","volume":"13","author":"Ardabili Sina F.","year":"2020","unstructured":"Sina F. Ardabili, Amir Mosavi, Pedram Ghamisi, Filip Ferdinand, Annamaria R. Varkonyi-Koczy, Uwe Reuter, Timon Rabczuk, and Peter M. Atkinson. 2020. Covid-19 outbreak prediction with machine learning. Algorithms 13, 10 (2020), 249.","journal-title":"Algorithms"},{"key":"e_1_3_1_180_2","first-page":"1","article-title":"Forecasting COVID-19 pandemic using Prophet, ARIMA, and hybrid stacked LSTM-GRU models in India","volume":"2022","author":"Sah Sweeti","year":"2022","unstructured":"Sweeti Sah, B. Surendiran, R. Dhanalakshmi, Sachi Nandan Mohanty, Fayadh Alenezi, and Kemal Polat. 2022. Forecasting COVID-19 pandemic using Prophet, ARIMA, and hybrid stacked LSTM-GRU models in India. Computational and Mathematical Methods in Medicine 2022 (May 2022), 1\u201319.","journal-title":"Computational and Mathematical Methods in Medicine"},{"key":"e_1_3_1_181_2","first-page":"1","volume-title":"IEEE Transactions on Intelligent Transportation Systems","author":"Guo Kehua","year":"2022","unstructured":"Kehua Guo, Changchun Shen, Xiaokang Zhou, Sheng Ren, Min Hu, Minxue Shen, Xiang Chen, and Haifu Guo. 2022. Traffic data-empowered XGBoost-LSTM framework for infectious disease prediction. IEEE Transactions on Intelligent Transportation Systems (2022), 1\u201312."},{"key":"e_1_3_1_182_2","first-page":"21484","volume-title":"Proceedings of the National Academy of Sciences","volume":"106","author":"Balcan Duygu","year":"2009","unstructured":"Duygu Balcan, Vittoria Colizza, Bruno Gon\u00e7alves, Hao Hu, Jos\u00e9 J. Ramasco, and Alessandro Vespignani. 2009. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences 106, 51 (2009), 21484\u201321489."},{"issue":"1","key":"e_1_3_1_183_2","doi-asserted-by":"crossref","first-page":"e13","DOI":"10.1371\/journal.pmed.0040013","article-title":"Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions","volume":"4","author":"Colizza Vittoria","year":"2007","unstructured":"Vittoria Colizza, Alain Barrat, Marc Barthelemy, Alain-Jacques Valleron, and Alessandro Vespignani. 2007. Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions. PLoS Medicine 4, 1 (2007), e13.","journal-title":"PLoS Medicine"},{"issue":"1","key":"e_1_3_1_184_2","first-page":"1","article-title":"Seasonal transmission potential and activity peaks of the new influenza a (H1N1): A Monte Carlo likelihood analysis based on human mobility","volume":"7","author":"Balcan Duygu","year":"2009","unstructured":"Duygu Balcan, Hao Hu, Bruno Goncalves, Paolo Bajardi, Chiara Poletto, Jose J. Ramasco, Daniela Paolotti, Nicola Perra, Michele Tizzoni, Wouter Van den Broeck, et al. 2009. Seasonal transmission potential and activity peaks of the new influenza a (H1N1): A Monte Carlo likelihood analysis based on human mobility. BMC Medicine 7, 1 (2009), 1\u201312.","journal-title":"BMC Medicine"},{"issue":"6988","key":"e_1_3_1_185_2","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1038\/nature02541","article-title":"Modelling disease outbreaks in realistic urban social networks","volume":"429","author":"Eubank Stephen","year":"2004","unstructured":"Stephen Eubank, Hasan Guclu, V. S. Anil Kumar, Madhav V. Marathe, Aravind Srinivasan, Zoltan Toroczkai, and Nan Wang. 2004. Modelling disease outbreaks in realistic urban social networks. Nature 429, 6988 (2004), 180\u2013184.","journal-title":"Nature"},{"issue":"7101","key":"e_1_3_1_186_2","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1038\/nature04795","article-title":"Strategies for mitigating an influenza pandemic","volume":"442","author":"Ferguson Neil M.","year":"2006","unstructured":"Neil M. Ferguson, Derek A. T. Cummings, Christophe Fraser, James C. Cajka, Philip C. Cooley, and Donald S. Burke. 2006. Strategies for mitigating an influenza pandemic. Nature 442, 7101 (2006), 448\u2013452.","journal-title":"Nature"},{"issue":"5","key":"e_1_3_1_187_2","doi-asserted-by":"crossref","first-page":"e401","DOI":"10.1371\/journal.pone.0000401","article-title":"Controlling pandemic flu: The value of international air travel restrictions","volume":"2","author":"Epstein Joshua M.","year":"2007","unstructured":"Joshua M. Epstein, D. Michael Goedecke, Feng Yu, Robert J. Morris, Diane K. Wagener, and Georgiy V. Bobashev. 2007. Controlling pandemic flu: The value of international air travel restrictions. PLoS One 2, 5 (2007), e401.","journal-title":"PLoS One"},{"issue":"3","key":"e_1_3_1_188_2","doi-asserted-by":"crossref","first-page":"e1790","DOI":"10.1371\/journal.pone.0001790","article-title":"Mitigation measures for pandemic influenza in Italy: An individual based model considering different scenarios","volume":"3","author":"degli Atti Marta Luisa Ciofi","year":"2008","unstructured":"Marta Luisa Ciofi degli Atti, Stefano Merler, Caterina Rizzo, Marco Ajelli, Marco Massari, Piero Manfredi, Cesare Furlanello, Gianpaolo Scalia Tomba, and Mimmo Iannelli. 2008. Mitigation measures for pandemic influenza in Italy: An individual based model considering different scenarios. PLoS One 3, 3 (2008), e1790.","journal-title":"PLoS One"},{"issue":"1","key":"e_1_3_1_189_2","first-page":"1","article-title":"Disease surveillance based on Internet-based linear models: An Australian case study of previously unmodeled infection diseases","volume":"6","author":"Rohart Florian","year":"2016","unstructured":"Florian Rohart, Gabriel J. Milinovich, Simon M. R. Avril, Kim-Anh L\u00ea Cao, Shilu Tong, and Wenbiao Hu. 2016. Disease surveillance based on Internet-based linear models: An Australian case study of previously unmodeled infection diseases. Scientific Reports 6, 1 (2016), 1\u201311.","journal-title":"Scientific Reports"},{"issue":"12","key":"e_1_3_1_190_2","doi-asserted-by":"crossref","first-page":"e81422","DOI":"10.1371\/journal.pone.0081422","article-title":"Correlation between national influenza surveillance data and Google trends in South Korea","volume":"8","author":"Cho Sungjin","year":"2013","unstructured":"Sungjin Cho, Chang Hwan Sohn, Min Woo Jo, Soo-Yong Shin, Jae Ho Lee, Seoung Mok Ryoo, Won Young Kim, and Dong-Woo Seo. 2013. Correlation between national influenza surveillance data and Google trends in South Korea. PLoS One 8, 12 (2013), e81422.","journal-title":"PLoS One"},{"issue":"1","key":"e_1_3_1_191_2","doi-asserted-by":"crossref","first-page":"e0165085","DOI":"10.1371\/journal.pone.0165085","article-title":"Dynamic forecasting of Zika epidemics using Google trends","volume":"12","author":"Teng Yue","year":"2017","unstructured":"Yue Teng, Dehua Bi, Guigang Xie, Yuan Jin, Yong Huang, Baihan Lin, Xiaoping An, Dan Feng, and Yigang Tong. 2017. Dynamic forecasting of Zika epidemics using Google trends. PLoS One 12, 1 (2017), e0165085.","journal-title":"PLoS One"},{"issue":"2","key":"e_1_3_1_192_2","doi-asserted-by":"crossref","first-page":"e56176","DOI":"10.1371\/journal.pone.0056176","article-title":"Influenza forecasting with Google flu trends","volume":"8","author":"Dugas Andrea Freyer","year":"2013","unstructured":"Andrea Freyer Dugas, Mehdi Jalalpour, Yulia Gel, Scott Levin, Fred Torcaso, Takeru Igusa, and Richard E. Rothman. 2013. Influenza forecasting with Google flu trends. PLoS One 8, 2 (2013), e56176.","journal-title":"PLoS One"},{"issue":"4","key":"e_1_3_1_193_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.5121\/ijcsit.2018.10405","article-title":"Epidemic outbreak prediction using artificial intelligence","volume":"10","author":"Das Adhikari Nimai Chand","year":"2018","unstructured":"Nimai Chand Das Adhikari, Arpana Alka, Vamshi Kumar Kurva, Suhas S., Hitesh Nayak, Kumar Rishav, Ashish Kumar Nayak, Sankalp Kumar Nayak, Vaisakh Shaj, and Karthikeyan. 2018. Epidemic outbreak prediction using artificial intelligence. IJCSIT 10, 4 (Aug. 2018), 49\u201364.","journal-title":"IJCSIT"},{"key":"e_1_3_1_194_2","unstructured":"Prosper Yeng Ashenafi Zebene Woldaregay and Gunnar Hartvigsen. 2019. K-CUSUM: Cluster detection mechanism in Edmon. Retrieved from https:\/\/hdl.handle.net\/10037\/18060"},{"key":"e_1_3_1_195_2","doi-asserted-by":"crossref","first-page":"107417","DOI":"10.1016\/j.knosys.2021.107417","article-title":"Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission","volume":"233","author":"Chew Alvin Wei Ze","year":"2021","unstructured":"Alvin Wei Ze Chew, Yue Pan, Ying Wang, and Limao Zhang. 2021. Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission. Knowledge-Based Systems 233 (2021), 107417.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_1_196_2","first-page":"1","article-title":"Security issues and recommendations for online social networks","volume":"1","author":"Hogben Giles","year":"2007","unstructured":"Giles Hogben. 2007. Security issues and recommendations for online social networks. ENISA Position Paper 1 (2007), 1\u201336.","journal-title":"ENISA Position Paper"},{"issue":"1","key":"e_1_3_1_197_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40504-017-0065-7","article-title":"Digital epidemiology: What is it, and where is it going?","volume":"14","author":"Salath\u00e9 Marcel","year":"2018","unstructured":"Marcel Salath\u00e9. (2018). Digital epidemiology: What is it, and where is it going? Life Sciences, Society and Policy 14, 1 (2018), 1.","journal-title":"Life Sciences, Society and Policy"},{"issue":"8","key":"e_1_3_1_198_2","first-page":"XI","article-title":"A public health role for Internet search engine query data?","volume":"174","author":"Pattie David C.","year":"2009","unstructured":"David C. Pattie, Kenneth L. Cox, Howard S. Burkom, Joseph S. Lombardo, and Joel C. Gaydos. 2009. A public health role for Internet search engine query data? Military Medicine 174, 8 (2009), XI.","journal-title":"Military Medicine"},{"issue":"2","key":"e_1_3_1_199_2","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/S1473-3099(13)70244-5","article-title":"Internet-based surveillance systems for monitoring emerging infectious diseases","volume":"14","author":"Milinovich Gabriel J.","year":"2014","unstructured":"Gabriel J. Milinovich, Gail M. Williams, Archie C. A. Clements, and Wenbiao Hu. 2014. Internet-based surveillance systems for monitoring emerging infectious diseases. The Lancet Infectious Diseases 14, 2 (2014), 160\u2013168.","journal-title":"The Lancet Infectious Diseases"}],"container-title":["ACM Transactions on Computing for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708549","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3708549","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:46Z","timestamp":1750295866000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708549"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,17]]},"references-count":198,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4,30]]}},"alternative-id":["10.1145\/3708549"],"URL":"https:\/\/doi.org\/10.1145\/3708549","relation":{},"ISSN":["2637-8051"],"issn-type":[{"value":"2637-8051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,17]]},"assertion":[{"value":"2023-01-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}