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Mathematical modelling on Dissemination and Control of COVID--19
  • M Veera Krishna
M Veera Krishna
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Abstract

Background: An outbreak of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2 known as COVID-19) has led to more than 10,000 confirmed cases as of April 3rd 2020. Understanding the early spread dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe COVID-19 spread with four datasets from within and outside Wuhan, China; we estimated how to spread in Wuhan varied between January and February 2020. We used these estimates to assess the potential for sustained human-to-human spread to occur in locations outside Wuhan if diseases holders were introduced. Methodology: We united SEIR frame work model with data on cases of COVID-19 in China and International cases that originated in Wuhan to estimate how spread had varied over time during January and February 2020. Based on these estimates, we calculated the probability that freshly introduced cases might produce outbreaks in other regions. Results: We calculated approximately the median day by day basic reproduction number ( ) in Wuhan, refused from 2•45 (95% CI: 1•16–4•87) 1 week before travel restrictions were introduced on Jan 23rd, 2020, to 1.05 (0•42–2•40) one week after. Based on our estimates of , presumptuous SARS approximating disparity, we computed that in locations with similar spread potential to Wuhan in near the beginning January, some time ago there are at least four independently set up cases, there is a more than fifty percent chance the infection will found within that inhabitants. Conclusions: COVID-19 spreading probably refused in Wuhan during delayed January, 2020, corresponding with the prologue of voyage control channels. As more cases arrive in international locations with similar spread potential to Wuhan before these organize measures, it is likely many chains of spread will fail to create initially, but might lead to innovative outbreaks ultimately.