ArticleApplied network science2020
Network-based prediction of COVID-19 epidemic spreading in Italy.
Article in Applied network science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Networked SIRS model with Kalman filter state estimation for epidemic monitoring in Europe.Communications medicine · 2026Article
- Remarks on a Scaling Theory of Spread of COVID-19 with an Application to the Case of Bulgaria.Entropy (Basel, Switzerland) · 2026Article
- From Agent-Based Markov Dynamics to Hierarchical Closures on Networks: Emergent Complexity and Epidemic Applications.Entropy (Basel, Switzerland) · 2026Article
- Inference on spatiotemporal dynamics for coupled biological populations.Journal of the Royal Society, Interface · 2024Article
- Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context.Scientific reports · 2024Article
- Emergent encoding of dispersal network topologies in spatial metapopulation models.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach.Healthcare (Basel, Switzerland) · 2023Article
- Country-specific optimization strategy for testing through contact tracing can help maintain a low reproduction number ([Formula: see text]) during unlock.Scientific reports · 2022Article
- Prediction of COVID-19 Infected Population for Indian States through a State Interaction Network-based SEIR Epidemic Model.IFAC-PapersOnLine · 2022Article
- Dynamical analysis of novel COVID-19 epidemic model with non-monotonic incidence function.Journal of public affairs · 2021Article
- Comparing the accuracy of several network-based COVID-19 prediction algorithms.International journal of forecastingArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Initially emerged in the Chinese city Wuhan and subsequently spread almost worldwide causing a pandemic, the SARS-CoV-2 virus follows reasonably well the Susceptible-Infectious-Recovered (SIR) epidemic model on contact networks in the Chinese case. In this paper, we investigate the prediction accuracy of the SIR model on networks also for Italy. Specifically, the Italian regions are a metapopulation represented by network nodes and the network links are the interactions between those regions. Then, we modify the network-based SIR model in order to take into account the different lockdown measures adopted by the Italian Government in the various phases of the spreading of the COVID-19. Our results indicate that the network-based model better predicts the daily cumulative infected individuals when time-varying lockdown protocols are incorporated in the classical SIR model.
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