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

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

doi:10.1007/s11071-020-05853-7

Cited by 43 publications

(34 citation statements)

References 22 publications

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“…This could indicate that quarantines are important to control the spread. This is also seen in the literature, where in countries such as Italy [50] , China [33] , and Canada [51] , similar conclusions are drawn. Other studies such as Nussbaumer-Streit et al [52] , Chowdhury et al [53] also find evidence of the importance of dynamic quarantine.…”

Section: Discussionsupporting

confidence: 84%

A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19

Kristjanpoller

Michell

Minutolo

2021

Applied Soft Computing

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Since the start of the pandemic caused by the novel coronavirus, COVID-19, more than 106 million people have been infected and global deaths have surpassed 2.4 million. In Chile, the government restricted the activities and movement of people, organizations, and companies, under the concept of dynamic quarantine across municipalities for a predefined period of time. Chile is an interesting context to study because reports to have a higher quantity of infections per million people as well as a higher number of polymerise chain reaction (PCR) tests per million people. The higher testing rate means that Chile has good measurement of the contagious compared to other countries. Further, the heterogeneity of the social, economic, and demographic variables collected of each Chilean municipality provides a robust set of control data to better explain the contagious rate for each city. In this paper, we propose a framework to determine the effectiveness of the dynamic quarantine policy by analyzing different causal models (meta-learners and causal forest) including a time series pattern related to effective reproductive number. Additionally, we test the ability of the proposed framework to understand and explain the spread over benchmark traditional models and to interpret the Shapley Additive Explanations (SHAP) plots. The conclusions derived from the proposed framework provide important scientific information for government policymakers in disease control strategies, not only to analyze COVID-19 but to have a better model to determine social interventions for future outbreaks.

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Section: Discussionsupporting

confidence: 84%

A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19

Kristjanpoller

Michell

Minutolo

2021

Applied Soft Computing

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“…al., 2020;D'Arienzo, et al, 2020;Gatto et al, 2020;Yuan et al, 2020). Dickson et al (2020) used statistical models to predict outbreaks that are based on both spatial and temporal attributes. They find that also spatial dimension is important to better predict trends over time, as for other others related to viral contagion.…”

Section: Background On Regional Analysis Of Covid-19 Outbreakmentioning

confidence: 99%

“…Others have focused on the epidemiological characteristics of the disease in Italy, as the reproduction number and the serial interval of the disease (Cereda et al, 2020; D'Arienzo & Coniglio, 2020; Gatto et al, 2020; Yuan, Li, Lu, & Lu, 2020). Dickson, Espa, Giuliani, Santi, and Savadori (2020) used statistical models to predict outbreaks that are based on both spatial and temporal attributes. They find that also spatial dimension is important to better predict trends over time, as for other others related to viral contagion.…”

Section: Background On Regional Analysis Of Covid‐19 Outbreakmentioning

confidence: 99%

A Spatio‐temporal analysis of COVID‐19 outbreak in Italy

Ghosh

Cartone

2020

Regional Science Policy & Practice

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Within two weeks from the first detection of the SARS-CoV-2 positive patient on 21st February, from Lombardy the disease has spread over every region in Italy. The main objective of this study is to identify spatial effects and spatiotemporal patterns of the outbreak of COVID-19 in different regions of Italy. Spatial indicators for different periods, as Moran's I, local Moran, LISA clusters, Getis and Ord G, and scatterplots are used for this purpose. Results confirm the great presence of spatial effects as well as changes in spatial regimes between the quarantine and the easing phase. The evidence could be of help for policymakers to a proper assessments of health strategies aware of local characteristics.

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“…Motivated by the HHH model, Paul et al [ 12 ] and Paul and Held [ 13 ] developed a spatio-temporal framework to jointly model several epidemics by considering the spatial interaction effect, as well as the time autoregressive effect. Their models have been applied to analyze the transmission of dengue fever in Guandong Province in China in 2014 [ 14 ], malaria and cutaneous leishmaniasis analysis in Afghanistan [ 15 ], hemorrhagic fever with renal syndrome in Zhejiang Province of China [ 16 ], and the effect of containment measures for COVID-19 in Italy [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model

Rui

Tian

Tang

et al. 2021

IJERPH

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With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.

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

Cited by 43 publications

References 22 publications

A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19

A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19

A Spatio‐temporal analysis of COVID‐19 outbreak in Italy

Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model

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