Prediction of global spread of COVID-19 pandemic: a review and research challenges

被引:0
作者
Saloni Shah
Aos Mulahuwaish
Kayhan Zrar Ghafoor
Halgurd S. Maghdid
机构
[1] Saginaw Valley State University,Department of Computer Science and Information Systems
[2] Knowledge University,Department of Computer Science
[3] Salahaddin University,Department of Software Engineering
[4] Koya University,Department of Software Engineering, Faculty of Engineering
来源
Artificial Intelligence Review | 2022年 / 55卷
关键词
COVID-19; Machine learning; Deep learning; Prediction methods;
D O I
暂无
中图分类号
学科分类号
摘要
Since the initial reports of the Coronavirus surfacing in Wuhan, China, the novel virus currently without a cure has spread like wildfire across the globe, the virus spread exponentially across all inhabited continent, catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyze and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modeling tools to the latest artificial intelligence technology, the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response—whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance, including lockdowns. On the one hand, this paper shows what accuracy of research brings to enable fighting this disease; while on the other hand, it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt to compile different research methods and comparing their accuracy in predicting the spread of COVID-19.
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页码:1607 / 1628
页数:21
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