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

期刊

ARTIFICIAL INTELLIGENCE REVIEW
卷 55, 期 3, 页码 1607-1628

出版社

SPRINGER
DOI: 10.1007/s10462-021-09988-w

关键词

COVID-19; Machine learning; Deep learning; Prediction methods

资金

  1. Saginaw Valley State University

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Since the outbreak of the COVID-19 pandemic, collaboration between the scientific community and governments has led to the use of various research methods to predict the spread of the virus and coordinate response measures.
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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