Modeling Early Phases of Covid-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion
Bibliographic Data
| ID | 22078451 |
|---|---|
| Authors | Daniela Gandolfi (0000-0003-2315-2309, University of Modena and Reggio Emilia, corresponding author), Giuseppe Pagnoni (0000-0002-8272-8091, University of Modena and Reggio Emilia, corresponding author), Tommaso Filippini (0000-0003-2100-0344, University of Modena and Reggio Emilia), Alessia Goffi (0000-0001-5548-4661), Marco Vinceti (0000-0002-0551-2473, Boston University), Egidio D'Angelo, Egidio D’Angelo (0000-0002-6007-7187, University of Pavia), Jonathan Mapelli (0000-0002-0381-1576, University of Modena and Reggio Emilia, corresponding author) |
| Year | 2021 |
| Volume | 9 |
| Pages | 724362-724362 |
| Publication date | 2021-12-16 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2021.724362 |
| PMID | 34976909 |
| OpenAlex | W4200284380 |
| Language | EN |
| Citations received | 2 |
| References cited | 38 |
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
Data science · Diffusion · Disease · Econometrics · Economics · Geography · Operations research · Outbreak · Pandemic · Physics · Computer Science · COVID-19 epidemiological studies · Influenza Virus Research Studies · Mathematics · Medicine · SARS-CoV-2 and COVID-19 Research · Virology
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Economic and social consequences of human mobility restrictions under Covid-19
Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (Sars-CoV-2)
The Efficacy of Lockdown Against Covid-19
Projecting the transmission dynamics of Sars-CoV-2 through the postpandemic period
The effect of human mobility and control measures on the Covid-19 epidemic in China
Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of Sars-CoV-2 in different settings
Modeling the Novel Coronavirus (Sars-CoV-2) Outbreak in Sicily, Italy
Modelling the impact of testing, contact tracing and household quarantine on second waves of Covid-19
| Citations per year | 0.5 |
|---|---|
| Citation span | 2022 - 2025 (4) |
| Highly cited | No |