Predicting new cases of COVID-19 and the application to population sustainability analysis

Chengcheng Bei; Shiping Liu; Yin Liao; Gaoliang Tian; Zichen Tian

doi:10.1111/acfi.12785

Predicting new cases of COVID-19 and the application to population sustainability analysis

Chengcheng Bei, Shiping Liu, Yin Liao, Gaoliang Tian^*, Zichen Tian

^*Corresponding author for this work

Department of Applied Finance

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

We propose a new spatio-temporal point process model to predict infectious cases of COVID-19. We illustrate its practical use with data from six key cities in China, and we analyse the effects of natural and social factors on the occurrence and spread of COVID-19. We show that large-scale testing and strict containment are key factors for the successful suppression of the COVID-19 contagion. This study provides an effective tool to develop early warning systems for major infectious diseases, offering insights on how to develop prevention and control strategies to reduce the impact of disease and maintain population sustainability.

Original language	English
Pages (from-to)	4859-4884
Number of pages	26
Journal	Accounting & Finance
Volume	61
Issue number	3
DOIs	https://doi.org/10.1111/acfi.12785
Publication status	Published - Sept 2021

Keywords

Control measures
COVID-19
Infectious cases
Spatio-temporal point process
Sustainability

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10.1111/acfi.12785

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abstract = "We propose a new spatio-temporal point process model to predict infectious cases of COVID-19. We illustrate its practical use with data from six key cities in China, and we analyse the effects of natural and social factors on the occurrence and spread of COVID-19. We show that large-scale testing and strict containment are key factors for the successful suppression of the COVID-19 contagion. This study provides an effective tool to develop early warning systems for major infectious diseases, offering insights on how to develop prevention and control strategies to reduce the impact of disease and maintain population sustainability.",

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AU - Bei, Chengcheng

AU - Liu, Shiping

AU - Liao, Yin

AU - Tian, Gaoliang

AU - Tian, Zichen

PY - 2021/9

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AB - We propose a new spatio-temporal point process model to predict infectious cases of COVID-19. We illustrate its practical use with data from six key cities in China, and we analyse the effects of natural and social factors on the occurrence and spread of COVID-19. We show that large-scale testing and strict containment are key factors for the successful suppression of the COVID-19 contagion. This study provides an effective tool to develop early warning systems for major infectious diseases, offering insights on how to develop prevention and control strategies to reduce the impact of disease and maintain population sustainability.

KW - Control measures

KW - COVID-19

KW - Infectious cases

KW - Spatio-temporal point process

KW - Sustainability

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Predicting new cases of COVID-19 and the application to population sustainability analysis

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Keywords

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