ArticleFrontiers in public health2021
Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Dynamic causal models in infectious disease epidemiology-an assessment of their predictive validity based on the COVID-19 epidemic in the UK 2020 to 2024.Frontiers in public health · 2025Article
- Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling.Computational psychiatry (Cambridge, Mass.) · 2023Article
- A 12-month projection to September 2022 of the COVID-19 epidemic in the UK using a dynamic causal model.Frontiers in public health · 2022Article
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Authors and funding
7 authors.
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Abstract
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.
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