COVID-19 in China: Risk Factors and R-0 Revisited

Irtesam Mahmud Khan, Ubydul Haque, Wenyi Zhang, Sumaira Zafar, Yong Wang, Junyu He, Hailong Sun, Jailos Lubinda, M Sohel Rahman

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The COVID-19 epidemic spread rapidly through China and subsequently proliferated globally leading to a pandemic situation around the globe. Human-to-human transmission, as well as asymptomatic transmission of the infection, have been confirmed. As of April 03, 2020, public health crisis in China due to COVID-19 was potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020. Understanding the characteristics of spatial clustering of the COVID-19 epidemic and R 0 is critical in effectively preventing and controlling the ongoing global pandemic. Considering this, the prefectures were grouped based on several relevant features using unsupervised machine learning techniques. Subsequently, we performed a computational analysis utilizing the reported cases in China to estimate the revised R 0 among different regions. Finally, our overall research indicates that the impact of temperature and demographic factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help in prevention planning in an ongoing global pandemic, prioritizing segments of a given community/region for action and providing a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

Original languageEnglish
Article number105731
Number of pages8
JournalActa tropica
Volume213
Early online date22 Oct 2020
DOIs
Publication statusPublished - 31 Jan 2021

Bibliographical note

Funding Information:
UH was supported by the Research Council of Norway (grant # 281077 ). Wenyi Zhang was partly supported by grants from the Chinese Major grant for the Prevention and Control of Infectious Diseases (No. 2018ZX10733402-001-004, 2018ZX10713003). Yong Wang was partly supported by the National Natural Science Foundation of China (No. 12031010 ).

Publisher Copyright:
© 2020 Elsevier B.V.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • COVID-19
  • Clustering
  • Stochastic Transmission Model

Fingerprint

Dive into the research topics of 'COVID-19 in China: Risk Factors and R-0 Revisited'. Together they form a unique fingerprint.

Cite this