Modelling and Predicting the Spatio-Temporal Spread of Coronavirus Disease 2019 (COVID-19) in Italy

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

doi:10.2139/ssrn.3559569

Cited by 84 publications

(75 citation statements)

References 20 publications

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“…(1) correspond to the three components of the model: the endemic, epidemic-within and epidemic-between. From a terminological point of view, terms epidemic and endemic, when referring to the components of the model, have been inherited from Paul and Held [19], although terms within and between introduced in Giuliani et al [9] are adopted in this paper in order to distinguish between the temporal and spatial terms which Paul and Held [19] jointly refer to as the epidemic component. In this regard, it is worth pointing out that the term endemic originates from the role that the component plays in the model, and it does not imply any epidemiological qualification of COVID-19 in the population of the Italian provinces.…”

Section: Model Specificationmentioning

confidence: 99%

“…In light of the above, there is no doubt that in order to study the outbreak of COVID-19, it is essential to consider both the temporal and spatial components of its diffusion [9]. Due to the strong local differences in the effects of the disease and to the different moments chosen to impose lockdowns throughout Italy, there is now great interest in studying the effect of lockdown on the diffusion of the disease.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the effect of containment measures on the spatio-temporal dynamic of COVID-19 in Italy

et al. 2020

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This paper aims at investigating empirically whether and to what extent the containment measures adopted in Italy had an impact in reducing the diffusion of the COVID-19 disease across provinces. For this purpose, we extend the multivariate time-series model for infection counts proposed in Paul and Held (Stat Med 30(10):118–1136, 2011) by augmenting the model specification with B-spline regressors in order to account for complex nonlinear spatio-temporal dynamics in the propagation of the disease. The results of the model estimated on the time series of the number of infections for the Italian provinces show that the containment measures, despite being globally effective in reducing both the spread of contagion and its self-sustaining dynamics, have had nonlinear impacts across provinces. The impact has been relatively stronger in the northern local areas, where the disease occurred earlier and with a greater incidence. This evidence may be explained by the shared popular belief that the contagion was not a close-to-home problem but rather restricted to a few distant northern areas, which, in turn, might have led individuals to adhere less strictly to containment measures and lockdown rules.

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Section: Model Specificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the effect of containment measures on the spatio-temporal dynamic of COVID-19 in Italy

et al. 2020

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“…[22] fused an SEIR metapopulation model to simulate epidemic. [5] incorporated daily movements in an SEIR model, while [8] proposed a statistical model to handle the di usion of covid-19 in Italy.…”

Section: Introductionmentioning

confidence: 99%

A reaction-diffusion system to better comprehend the unlockdown: Application of SEIR-type model with diffusion to the spatial spread of COVID-19 in France

Mammeri

2020

Computational and Mathematical Biophysics

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We wondered that if a reaction-diffusion model considering only the mean daily movement of susceptible, exposed and asymptomatic individuals was enough to describe the spread of the COVID-19 virus. The model was calibrated using data on the confirmed infection and death from France as well as their initial spatial distribution. First, the system of partial differential equations is studied, then the basic reproduction number, 𝒭0 is derived. Second, numerical simulations, based on a combination of level-set and finite differences, shown the spatial spread of COVID-19 from March 16 to June 16. Finally, scenarios of unlockdown are compared according to variation of distancing, or partially spatial lockdown.

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“…Besides, spatiotemporal heterogeneity exists in the distribution of COVID-19 in worldwide [27][28]. Several studies confirm that the spread of COVID-19 is location dependent [29][30]. At different stages of resuming work, population flow is different driving spatiotemporal distribution of COVID-19 [31][32][33].…”

Section: Investigating the Impacts Of The Different Business Resumptimentioning

confidence: 99%

Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study

Zhang

Wang

et al. 2020

Preprint

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The effect of the COVID-19 outbreak has led policymakers around the world to attempt transmission control. However, lockdown and shutdown interventions have caused new social problems and designating policy resumption for infection control when reopening society remains a crucial issue. We investigated the effects of different resumption strategies on COVID-19 transmission using a modeling study setting. We employed a susceptible-exposed-infectious-removed model to simulate COVID-19 outbreaks under five reopening strategies based on the business resumption progress in China. The effect of each strategy was evaluated using the peak values of the epidemic curves with respect to confirmed active cases and cumulative cases. We found that a hierarchy-based reopen strategy performed best when current epidemic prevention measures were maintained save for lockdown, reducing the peak number of active cases and cumulative cases by 50% and 44%, respectively. However, the modeled effect of each strategy decreased when the current intervention was lifted somewhat. Additional attention should be given to regions with significant numbers of migrants, as the potential risk of COVID-19 outbreaks amid society reopening is intrinsically high. Business resumption strategies have the potential to eliminate COVID-19 outbreaks amid society reopening without special control measures. The proposed resumption strategies focused mainly on decreasing the number of imported exposure cases, guaranteeing medical support for epidemic control, or decreasing active cases.

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Modelling and Predicting the Spatio-Temporal Spread of Coronavirus Disease 2019 (COVID-19) in Italy

Cited by 84 publications

References 20 publications

Assessing the effect of containment measures on the spatio-temporal dynamic of COVID-19 in Italy

Assessing the effect of containment measures on the spatio-temporal dynamic of COVID-19 in Italy

A reaction-diffusion system to better comprehend the unlockdown: Application of SEIR-type model with diffusion to the spatial spread of COVID-19 in France

Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study

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