Modeling COVID-19 pandemic in northern Italy predicts second wave scenarios

Daniela Gandolfi, Giuseppe Pagnoni, Tommaso Filippini, Alessia Goffi, Marco Vinceti, Egidio D'Angelo, Jonathan Mapelli

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Received date: 30th September 2020

The COVID-19 pandemic has sparked an intense debate about the factors underlying the dynamics of the outbreak. Mitigating virus spread could benefit from reliable predictive models informing effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. The analytic framework known as “Dynamic Causal Modeling” (DCM) has recently been applied to the COVID-19 pandemic, shedding new light on the mechanisms and latent factors driving its evolution. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak. The model was able to accurately predict the evolution of the pandemic, revealing the potential factors underlying regional heterogeneity. Importantly, the model predicts that a second wave could arise due to a loss of effective immunity after about 6 months. The DCM appears to be a reliable tool to shape and predict the spread of the COVID-19, and to identify the containment and control strategies that could efficiently counteract its second wave, until effective vaccines become available.

Read in full at medRxiv.

This is an abstract of a preprint hosted on a preprint server, which is currently undergoing peer review at Scientific Reports. The findings have yet to be thoroughly evaluated, nor has a decision on ultimate publication been made. Therefore, the results reported should not be considered conclusive, and these findings should not be used to inform clinical practice, or public health policy, or be promoted as verified information.

Scientific Reports

Nature Research, Springer Nature