Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion

The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as dynamic causal modeling (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.

Automated summary

The COVID-19 pandemic has led to a debate on the hidden factors driving the outbreak dynamics, with various computational models proposed to inform social and healthcare strategies. The dynamic causal modeling (DCM) framework has shown reliability in predicting and analyzing the factors governing the pandemic diffusion, particularly in northern Italy. This modeling tool has the potential to identify containment and control strategies against further waves of infection by understanding the mechanisms of the spread of SARS-CoV-2.