COVID-19 pandemic: a mobility-dependent SEIR model with undetected cases in Italy, Europe, and US

Nicola Picchiotti, Monica Salvioli*, Elena Zanardini, Francesco Missale

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Web of Science)


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 percenta- ge 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 estima- ted, which are likely to represent the main source of infec- tions in the near future. The model is applied to the Hubei case study, which is the first place to relax mobility restric- tions. Results show different possible scenarios. Mobility and the adoption of personal protective measures greatly influen- ce 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 in- sights for healthcare decision makers to determine the best strategy in case of future outbreaks.
Original languageEnglish
Pages (from-to)136-143
Number of pages8
JournalEpidemiologia & Prevenzione
Issue number5-6 (Suppl 2)
Publication statusPublished - 2020
Externally publishedYes


  • COVID-19
  • SEIR
  • epidemiology
  • lockdown
  • mathematical models
  • public health

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