COVID-19 Epidemic Forecasting and Cost-Effectiveness Analysis: A Case Study of Hong Kong

Wanying Tao, Hainan Guo, Qinneng Xu and Dandan Yu ()
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Wanying Tao: Shenzhen University
Hainan Guo: Shenzhen University
Qinneng Xu: Shenzhen Liangyi Information Technology Co., Ltd.
Dandan Yu: The First Affiliated Hospital of Dalian Medical University

A chapter in AI and Analytics for Smart Cities and Service Systems, 2021, pp 351-364 from Springer

Abstract: Abstract In December 2019, COVID-19 pneumonia was diagnosed and announced to the public for the first time in Wuhan, Hubei. The COVID-19 disease has spread worldwide. In the critical situation of lack of effective drug treatment in the early stage, Hong Kong, China has adopted various non-drug treatment measures to control the spread and spread of the epidemic, including isolating foreign tourists, closing public places, and compulsory wearing masks. This article divides the development of the Hong Kong epidemic into three stages, each of which has different scales, scope of influence and response measures. Therefore, this article establishes an individual-based stochastic simulation model of the spread of infectious diseases to fit and predict the future development trend of the Hong Kong epidemic. Based on the simulation results, it is predicted that the strict implementation of quarantine measures in the third stage can reduce the total number of patients with new coronary pneumonia in Hong Kong by 83.89%. Combined with the vaccination strategy, achieving 90% of the population's vaccination within a limited time can effectively control the epidemic in Hong Kong and have economic benefits, which will reduce the cost of 10.74% relative to non-vaccination.

Keywords: COVID-19; Stochastic model; Quarantine measures; Infectious disease prediction; Vaccination (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-90275-9_28

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