Tracking R of COVID-19: A new real-time estimation using the Kalman filter
| Autor: | Francisco Bullano, Francisco Arroyo-Marioli, Carlos Rondón-Moreno, Simas Kucinskas |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Viral Diseases
Time Factors Coronavirus disease 2019 (COVID-19) Computer science Epidemiology Dashboard (business) Social Sciences Sample (statistics) School Closures Tracking (particle physics) Research and Analysis Methods Geographical locations Education Bayes' theorem Medical Conditions Sociology Time estimation Diagnostic Medicine Germany Medicine and Health Sciences Humans European Union Pandemics Virus Testing Retrospective Studies Multidisciplinary Models Statistical Applied Mathematics Simulation and Modeling COVID-19 Covid 19 Bayes Theorem Kalman filter Europe Infectious Diseases Italy Physical Sciences People and places Epidemic model Algorithm Kalman Filter Mathematics Algorithms Research Article |
| Zdroj: | PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | We develop a new method for estimating the effective reproduction number of an infectious disease (R) and apply it to track the dynamics of COVID-19. The method is based on the fact that in the SIR model, R is linearly related to the growth rate of the number of infected individuals. This time-varying growth rate is estimated using the Kalman filter from data on new cases. The method is easy to implement in standard statistical software, and it performs well even when the number of infected individuals is imperfectly measured, or the infection does not follow the SIR model. Our estimates of R for COVID-19 for 124 countries across the world are provided in an interactive online dashboard, and they are used to assess the effectiveness of non-pharmaceutical interventions in a sample of 14 European countries. |
| Databáze: | OpenAIRE |
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