ArticleSpatial statistics2022
A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence.
Article in Spatial statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline 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
- Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru.Entropy (Basel, Switzerland) · 2024Article
- The role of the socio-economic context in the spread of the first wave of COVID-19 in the Marche Region (central Italy).International journal of disaster risk reduction : IJDRR · 2022Article
- Endemic-epidemic models to understand COVID-19 spatio-temporal evolution.Spatial statistics · 2022Article
- Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe.Spatial statistics · 2022Article
- Spatial clustering behaviour of Covid-19 conditioned by the development level: Case study for the administrative units in Romania.Spatial statistics · 2022Article
- Spatio-temporal modelling of COVID-19 incident cases using Richards' curve: An application to the Italian regions.Spatial statistics · 2022Article
- Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million.Chaos, solitons, and fractals · 2022Article
- A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy.Spatial statistics · 2022Article
- A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy.Statistical methods & applications · 2022Article
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2 authors.
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Abstract
We propose a model based on discrete latent variables, which are spatially associated and time specific, for the analysis of incident cases of SARS-CoV-2 infections. We assume that for each area the sequence of latent variables across time follows a Markov chain with initial and transition probabilities that also depend on latent variables in neighboring areas. The model is estimated by a Markov chain Monte Carlo algorithm based on a data augmentation scheme, in which the latent states are drawn together with the model parameters for each area and time. As an illustration we analyze incident cases of SARS-CoV-2 collected in Italy at regional level for the period from February 24, 2020, to January 17, 2021, corresponding to 48 weeks, where we use number of swabs as an offset. Our model identifies a common trend and, for every week, assigns each region to one among five distinct risk groups.
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