TY - JOUR
T1 - COVID-19 in China
T2 - Risk Factors and R0 Revisited
AU - Khan, Irtesam Mahmud
AU - Haque, Ubydul
AU - Zhang, Wenyi
AU - Zafar, Sumaira
AU - Wang, Yong
AU - He, Junyu
AU - Sun, Hailong
AU - Lubinda, Jailos
AU - Rahman, M. Sohel
N1 - 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.
PY - 2021/1
Y1 - 2021/1
N2 - 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 R0 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 R0 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.
AB - 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 R0 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 R0 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.
KW - COVID-19
KW - Clustering
KW - Stochastic Transmission Model
UR - http://www.scopus.com/inward/record.url?scp=85095426312&partnerID=8YFLogxK
U2 - 10.1016/j.actatropica.2020.105731
DO - 10.1016/j.actatropica.2020.105731
M3 - Article
C2 - 33164890
AN - SCOPUS:85095426312
SN - 0001-706X
VL - 213
JO - Acta Tropica
JF - Acta Tropica
M1 - 105731
ER -