Preprint Article Version 1 This version is not peer-reviewed

Estimation of Time-Dependent Reproduction Number for Global COVID-19 Outbreak

Version 1 : Received: 22 June 2020 / Approved: 24 June 2020 / Online: 24 June 2020 (09:03:44 CEST)

How to cite: Petrova, T.; Soshnikov, D.; Grunin, A. Estimation of Time-Dependent Reproduction Number for Global COVID-19 Outbreak. Preprints 2020, 2020060289 (doi: 10.20944/preprints202006.0289.v1). Petrova, T.; Soshnikov, D.; Grunin, A. Estimation of Time-Dependent Reproduction Number for Global COVID-19 Outbreak. Preprints 2020, 2020060289 (doi: 10.20944/preprints202006.0289.v1).

Abstract

Real-time estimation of the parameters characterising infectious disease transmission is important for optimization quarantine interventions during outbreaks. One of the most significant parameters is the effective reproduction number - number of secondary cases produced by a single infection. The current study presents an approach for estimating the effective reproduction number and its application to COVID-19 outbreak. The method is based on fitting SIR epidemic model to observation data in a sliding time window and allows to show real-time dynamics of reproduction number at any phase of epidemic for countries globally. Online data on COVID-19 daily cases of infections, recoveries, deaths are used.Finally, time-dependent reproduction number is explored in connection with dynamics of peoples mobility. The method allows to assess the disease transmission potential and understand the effect of interventions on epidemics spread. It also can be easily adapted to future outbreaks of different pathogens. The tool is available online as Python code from the Github repository.

Supplementary and Associated Material

https://github.com/shwars/SlidingSir: A tool for estimating the time-dependent reproduction number during COVID-19 pandemic for countries worldwide is available online as Python code from the following Github repository

Subject Areas

reproduction number; infectious disease epidemiology; COVID-19; epidemic modelling; mobility index

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