Evidence map›Paper›PMID 32967639›Full record

ArticleBMC infectious diseases2020

Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy.

Diego Giuliani, Maria Michela Dickson, Giuseppe Espa, Flavio Santi

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

78 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  3. What Makes Urban Communities More Resilient to COVID-19? A Systematic Review of Current Evidence.International journal of environmental research and public health · 2022
    Pooled it
  4. Regional Differences in Mortality Rates During the COVID-19 Epidemic in Italy.Disaster medicine and public health preparedness · 2022
    Pooled it
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  12. Dynamic hierarchical state space forecasting.Statistics in medicine · 2024
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18 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Diego GiulianiDepartment of Economics and Management, University of Trento, Via V. Inama 5, Trento, 38122, Italy.
Maria Michela DicksonDepartment of Economics and Management, University of Trento, Via V. Inama 5, Trento, 38122, Italy.
Giuseppe EspaDepartment of Economics and Management, University of Trento, Via V. Inama 5, Trento, 38122, Italy.
Flavio SantiDepartment of Economics, University of Verona, Via Cantarane 24, Verona, 37129, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

BetacoronavirusCommunicable Disease ControlCoronavirus InfectionsCOVID-19GeographyHumansItalyLinear ModelsPandemicsPneumonia, ViralPublic HealthSARS-CoV-2Spatio-Temporal AnalysisTime FactorsCOVID-19ItalySpatio-temporal model

Identifiers

PMID32967639
PMCPMC7509829

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.