Nicola Picchiotti1,2,3,4, Monica Salvioli5, Elena Zanardini6, Francesco Missale7,8,3. 1. Department of Mathematics, University of Pavia (Italy). 2. Internal Model Validation, Banco BPM spa, Verona (Italy). 3. These authors equally contributed to the work. 4. The views, thoughts and opinions expressed in this report are those of the authors in their individual capacity and should not be attributed to Banco BPM or to the authors as representatives or employees of Banco BPM. 5. Department of Mathematics, University of Trento (Italy); monica.salvioli@polimi.it. 6. Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia (Italy). 7. Department of Molecular and Translational Medicine, University of Brescia (Italy). 8. IRCCS Ospedale Policlinico San Martino, Genova (Italy).
Abstract
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.
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.
Entities:
Keywords:
COVID-19; SEIR; epidemiology lockdown.; mathematical models; public health
Authors: Philip Gerlee; Julia Karlsson; Ingrid Fritzell; Thomas Brezicka; Armin Spreco; Toomas Timpka; Anna Jöud; Torbjörn Lundh Journal: Sci Rep Date: 2021-12-17 Impact factor: 4.379