{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T17:43:37Z","timestamp":1751305417534,"version":"3.41.0"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T00:00:00Z","timestamp":1606953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Digit. Gov.: Res. Pract."],"published-print":{"date-parts":[[2021,1,31]]},"abstract":"<jats:p>The novel coronavirus that causes the now well-known COVID-19 illness has affected the whole world as the number of daily reported cases has exceeded 250,000. The spread trend of the virus across the globe has varied from one country to another, mainly because of each government\u2019s response. In this article, we discuss the statistics of the cases in Iraq and provide a projection model based on the current situation and government interventions. Using daily reported cases data from February 24, 2020--July 31, 2020, we report 6-week-ahead projections of daily cases, critical cases, and deaths. Specifically, we propose a simple machine learning-based model based on Gaussian Process\u00a0regression to project the future trend of COVID-19 in Iraq. This work aims to aid the Iraqi government by providing a statistical tool to provide future estimates of the suspected and death cases resulting from COVID-19 and make strategic decisions.<\/jats:p>","DOI":"10.1145\/3431769","type":"journal-article","created":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T17:09:25Z","timestamp":1604077765000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Projecting the Short-Term Trend of COVID-19 in Iraq"],"prefix":"10.1145","volume":"2","author":[{"given":"Murtadha","family":"Aldeer","sequence":"first","affiliation":[{"name":"WINLAB, Rutgers University, North Brunswick, NJ, USA"}]},{"given":"Ahmed Al","family":"Hilli","sequence":"additional","affiliation":[{"name":"Al-Furat Al-Awsat Technical University, Najaf, Iraq"}]},{"given":"Issam S.","family":"Ismail","sequence":"additional","affiliation":[{"name":"Wassit Health Directorate, Wassit, Iraq"}]}],"member":"320","published-online":{"date-parts":[[2020,12,3]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Kosar Muhammad Ali, Kamaran Amin Karadakhy, Safeen Othman Mahmood, Zana Hameed Mahmood, Karmand Qadir Hamad Amin, Peshnyar Muhammad Atta, Bryar Ezadeen Nuradeen, et al.","author":"Abdullah Hadi Mohammed","year":"2020","unstructured":"Hadi Mohammed Abdullah , Hersh H. Hama-Ali , Sabah Nasraddin Ahmed , Kosar Muhammad Ali, Kamaran Amin Karadakhy, Safeen Othman Mahmood, Zana Hameed Mahmood, Karmand Qadir Hamad Amin, Peshnyar Muhammad Atta, Bryar Ezadeen Nuradeen, et al. 2020 . Severe refractory COVID-19 patients responding to convalescent plasma; A case series. Ann. Med. Surg . 56 (August 2020), 125--127. Hadi Mohammed Abdullah, Hersh H. Hama-Ali, Sabah Nasraddin Ahmed, Kosar Muhammad Ali, Kamaran Amin Karadakhy, Safeen Othman Mahmood, Zana Hameed Mahmood, Karmand Qadir Hamad Amin, Peshnyar Muhammad Atta, Bryar Ezadeen Nuradeen, et al. 2020. Severe refractory COVID-19 patients responding to convalescent plasma; A case series. Ann. Med. Surg. 56 (August 2020), 125--127."},{"volume-title":"Proceedings of the 2016 IEEE Conference on Open Systems (ICOS\u201916)","author":"Aldeer Murtadha M. N.","key":"e_1_2_1_2_1","unstructured":"Murtadha M. N. Aldeer , Richard E. Howard , Richard P. Martin , Khalil Alkadhimi , and Latifah M. Kamarudin . 2016. Towards harnessing Wireless Sensor Networks for supporting the development process in Iraq . In Proceedings of the 2016 IEEE Conference on Open Systems (ICOS\u201916) . 81--86. Murtadha M. N. Aldeer, Richard E. Howard, Richard P. Martin, Khalil Alkadhimi, and Latifah M. Kamarudin. 2016. Towards harnessing Wireless Sensor Networks for supporting the development process in Iraq. In Proceedings of the 2016 IEEE Conference on Open Systems (ICOS\u201916). 81--86."},{"key":"e_1_2_1_3_1","volume-title":"Rodriguez-Morales","author":"Arab-Mazar Zahra","year":"2020","unstructured":"Zahra Arab-Mazar , Ranjit Sah , Ali A. Rabaan , Kuldeep Dhama , and Alfonso J . Rodriguez-Morales . 2020 . Mapping the incidence of the COVID-19 hotspot in Iran--Implications for Travellers. Travel Med. Infect. Dis. 34 (March\u2013April 2020), 101630. Zahra Arab-Mazar, Ranjit Sah, Ali A. Rabaan, Kuldeep Dhama, and Alfonso J. Rodriguez-Morales. 2020. Mapping the incidence of the COVID-19 hotspot in Iran--Implications for Travellers. Travel Med. Infect. Dis. 34 (March\u2013April 2020), 101630."},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 2018 IEEE Conference on Decision and Control (CDC\u201918)","author":"Beckers Thomas","year":"2018","unstructured":"Thomas Beckers , Jonas Umlauft , and Sandra Hirche . 2018 . Mean square prediction error of misspecified Gaussian process models . In Proceedings of the 2018 IEEE Conference on Decision and Control (CDC\u201918) . 1162--1167. Thomas Beckers, Jonas Umlauft, and Sandra Hirche. 2018. Mean square prediction error of misspecified Gaussian process models. In Proceedings of the 2018 IEEE Conference on Decision and Control (CDC\u201918). 1162--1167."},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1016\/S0140-6736(20)31097-7","article-title":"New Zealand eliminates COVID-19","volume":"395","author":"Cousins Sophie","year":"2020","unstructured":"Sophie Cousins . 2020 . New Zealand eliminates COVID-19 . The Lancet 395 , 10235 (2020), 1474 . Sophie Cousins. 2020. New Zealand eliminates COVID-19. 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Virol."},{"key":"e_1_2_1_8_1","volume-title":"Retrieved","author":"JHU.","year":"2020","unstructured":"JHU. 2020 . Johns Hopkins Coronavirus Resource Center-Global Map . Retrieved July 25, 2020 from https:\/\/coronavirus.jhu.edu\/. JHU. 2020. Johns Hopkins Coronavirus Resource Center-Global Map. Retrieved July 25, 2020 from https:\/\/coronavirus.jhu.edu\/."},{"key":"e_1_2_1_9_1","volume-title":"Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fract. 139 (October","author":"Lalmuanawma Samuel","year":"2020","unstructured":"Samuel Lalmuanawma , Jamal Hussain , and Lalrinfela Chhakchhuak . 2020. Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fract. 139 (October 2020 ), 110059. Samuel Lalmuanawma, Jamal Hussain, and Lalrinfela Chhakchhuak. 2020. 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