COVID-19 pandemic has changed and challenged nearly every aspect of modern life. In the absence of a cure, non-pharmaceutical intervention and behavioral changes have played a crucial role in controlling its spread . Joint efforts of the scientific community have aimed to evaluate through models the effectiveness of different actions and to forecast the detrimental effects of this disease. In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region. Then, we assessed the effectiveness of different governmental interventions on the spread of COVID-19 . A comparison between the reproductive numbers (Rt) separately obtained from raw data and fitted parameters .
Non-pharmaceutical interventions (NPIs) have played a crucial role in controlling the spread of COVID-19. Nevertheless, NPI efficacy varies enormously between and within countries, mainly because of population and behavioral heterogeneity. In this work, we adapted a multi-group SEIRA model to study the spreading dynamics of COVID-19 in Chile, representing geographically separated regions of the country by different groups. We use national mobilization statistics to estimate the connectivity between regions and data from governmental repositories to obtain each region's spreading and infection fatality rate. We then assessed the effectiveness of different NPIs by studying the temporal evolution of the reproduction number Rt. Analyzing data-driven and model-based estimates of Rt, we found a strong coupling of different regions, highlighting the necessity URLanized, coordinated actions to control the spread of SARS-CoV-2. Finally, we evaluated different scenarios to forecast the evolution of COVID-19 in the most densely populated regions, finding that the early lifting of restriction probably will lead to novel outbreaks .
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COVID-19 pandemic has changed and challenged nearly every aspect of modern life. In the absence of a cure, non-pharmaceutical intervention and behavioral changes
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its spread . Joint efforts of the scientific community have aimed to evaluate through models the effectiveness of different actions and to forecast the detrimental effects of this disease. In the present
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among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region. Then, we
between regions and data from governmental repositories to obtain each region's spreading and infection fatality rate. We then
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governmental interventions on the spread of
NPIs by studying the temporal evolution of the reproduction number Rt. Analyzing data-driven and model-based estimates of Rt, we found a strong coupling of different regions, highlighting the necessity URLanized, coordinated actions to control the spread of SARS-CoV-2. Finally, we evaluated different scenarios to forecast the evolution of
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. A comparison between the reproductive numbers (Rt) separately obtained from raw data and fitted parameters
in the most densely populated regions, finding that the early lifting of restriction probably will lead to novel outbreaks