ArticleBMC infectious diseases2020
Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy.
Article in BMC infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 4 of them syntheses that pooled it.
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Who cites it
78 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Pooled it
- What Makes Urban Communities More Resilient to COVID-19? A Systematic Review of Current Evidence.International journal of environmental research and public health · 2022Pooled it
- Regional Differences in Mortality Rates During the COVID-19 Epidemic in Italy.Disaster medicine and public health preparedness · 2022Pooled it
- Spatiotemporal modeling of regional short-term pertussis transmission risk using a propagation-enhanced prediction framework.Frontiers in public health · 2026Article
- Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.Journal of global health · 2025Article
- The impact of non-pharmaceutical interventions on COVID-19 transmission and its effect on life expectancy in two European regions.BMC public health · 2025Article
- Spatio-temporal forecasting of COVID-19 cases in the Netherlands for source and contact tracing.International journal of population data science · 2025Article
- Endemic-epidemic modelling of school closure to prevent spread of COVID-19 in Switzerland.BMC infectious diseases · 2024Article
- The Geographical Conditioning of Regional Differentiation Characterising the COVID-19 Pandemic in European Countries.International journal of environmental research and public health · 2024Article
- Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN.Scientific reports · 2024Article
- Dynamic hierarchical state space forecasting.Statistics in medicine · 2024Article
- Predicting subnational incidence of COVID-19 cases and deaths in EU countries.BMC infectious diseases · 2024Article
- Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China.PloS one · 2024Article
- Article
- An analysis of the spatio-temporal behavior of COVID-19 patients using activity trajectory data.Heliyon · 2023Article
- Comparison of Soft Indicator and Poisson Kriging for the Noise-Filtering and Downscaling of Areal Data: Application to Daily COVID-19 Incidence Rates.ISPRS international journal of geo-information · 2023Article
- The impact of national and international travel on spatio-temporal transmission of SARS-CoV-2 in Belgium in 2021.BMC infectious diseases · 2023Article
- Spatio-temporal model to investigate COVID-19 spread accounting for the mobility amongst municipalities.Spatial and spatio-temporal epidemiology · 2023Article
- Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda.BMC public health · 2023Article
18 more citing papers are in PubMed but not listed here.
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time.
methodsOfficial freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon.
resultsThree subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting.
conclusionsA strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.