Background: The outbreak of Coronavirus disease 2019 (COVID-19) emerged in China at the end of 2019 and has spread worldwide within few weeks reaching a pandemic status. China and Italy, followed by other countries, have implemented strict control measures including complete lockdown. The effects of such measures and the optimal strategies to relax them are major Public Health concerns. Method: We introduce a SEIR compartmental model describing the COVID-19 pandemic , taking into account the region-specific fraction of undetected cases, the effects of mobility restrictions fitting Google COVID-19 Community Mobility Report data , and the personal protective measures adopted as frequent hand washing or use of masks. Results: The model is experimentally validated with data of  Italian regions, European countries and US, obtaining fitting parameters in good agreement with previous literature. The mean absolute percentage error analysis for forecasting accuracy judgement showed that our model was highly accurate in 12 regions (46\%), good in 7 (27\%) and reasonable in 7 (27\%). The estimation of the undetected cases allows to evaluate different post-lockdown scenarios which is crucial for public-health decision makers. Conclusions: The proposed model , calibrated and designed on measured or literature-based parameters, was successfully applied in all scenariosanalyzed and the estimation of the dynamic of the whole number of infectious individuals could help the planning of different strategies aimed at relaxing the lockdown measures .
OBJECTIVES: to describe the first wave of the COVID-19 pandemic with a focus on undetected cases and to evaluate different post-lockdown scenarios. DESIGN: the study introduces a SEIR compartmental model , taking into account the region-specific fraction of undetected cases, the effects of mobility restrictions , and the personal protective measures adopted , such as wearing a mask and washing hands frequently. SETTING AND PARTICIPANTS: the model is experimentally validated with data of all the Italian regions, some European countries, and the US. MAIN OUTCOME MEASURES: the accuracy of the model results is measured through the mean absolute percentage error (MAPE) and Lewis criteria; fitting parameters are in good agreement with previous literature. RESULTS: the epidemic curves for different countries and the amount of undetected and asymptomatic cases are estimated, which are likely to represent the main source of infections in the near future. The model is applied to the Hubei case study, which is the first place to relax mobility restrictions. Results show different possible scenarios. Mobility and the adoption of personal protective measures greatly influence the dynamics of the infection, determining either a huge and rapid secondary epidemic peak or a more delayed and manageable one. CONCLUSIONS: mathematical models can provide useful insights for healthcare decision makers to determine the best strategy in case of future outbreaks .
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Background: The outbreak of Coronavirus disease 2019 (COVID-19) emerged in China at the end of 2019 and has spread worldwide within few weeks reaching a pandemic status. China and Italy, followed by other countries, have implemented strict control measures including complete lockdown. The effects of such measures and the optimal strategies to relax them are major Public Health concerns. Method: We introduce
OBJECTIVES: to describe the first wave of the COVID-19 pandemic with a focus on undetected cases and to evaluate different post-lockdown scenarios. DESIGN: the study introduces
D
describing the COVID-19 pandemic
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fitting Google COVID-19 Community Mobility Report data
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as frequent hand washing or use of masks. Results: The
, such as wearing a mask and washing hands frequently. SETTING AND PARTICIPANTS: the
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all the
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European countries and US, obtaining fitting parameters
some European countries, and the US. MAIN OUTCOME MEASURES: the accuracy of the model results is measured through the mean absolute percentage error (MAPE) and Lewis criteria; fitting parameters are
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The mean absolute percentage error analysis for forecasting accuracy judgement showed that our model was highly accurate in 12 regions (46\%), good in 7 (27\%) and reasonable in 7 (27\%). The estimation of the undetected cases allows to evaluate different post-lockdown scenarios which is crucial for public-health decision makers. Conclusions: The proposed model , calibrated and designed on measured or literature-based parameters, was successfully applied in all scenariosanalyzed and the estimation of
RESULTS: the epidemic curves for different countries and
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dynamic of the whole number of infectious individuals could help the planning of different strategies aimed at relaxing the lockdown measures
amount of undetected and asymptomatic cases are estimated, which are likely to represent the main source of infections in the near future. The model is applied to the Hubei case study, which is the first place to relax mobility restrictions. Results show different possible scenarios. Mobility and the adoption of personal protective measures greatly influence the dynamics of the infection, determining either a huge and rapid secondary epidemic peak or a more delayed and manageable one. CONCLUSIONS: mathematical models can provide useful insights for healthcare decision makers to determine the best strategy in case of future outbreaks