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.