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Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis
Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spre...
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Published in: | Journal of infection in developing countries 2020-03, Vol.14 (3), p.246-253 |
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container_title | Journal of infection in developing countries |
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creator | Huang, Rui Liu, Miao Ding, Yongmei |
description | Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spread from Hubei Province in Central China to neighbouring areas. Logistic model was employed according to the trend of available data, which shows the difference between Hubei Province and outside of it. We also calculated the reproduction number R0 for the range of [2.23, 2.51] via SEIR model. The measures to reduce or prevent the virus spread should be implemented, and we expect our data-driven modeling analysis providing some insights to identify and prepare for the future virus control. |
doi_str_mv | 10.3855/jidc.12585 |
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subjects | Betacoronavirus China - epidemiology Cities Coronavirus Infections - epidemiology Coronaviruses COVID-19 Disease Outbreaks Disease transmission Forecasting Humans Logistic Models Pandemics Pneumonia, Viral - epidemiology SARS-CoV-2 Spatio-Temporal Analysis |
title | Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis |
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