Dataset Information

A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.

ABSTRACT: To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

SUBMITTER: De Witte D

PROVIDER: S-EPMC10170878 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.

De Witte Dries D Abad Ariel Alonso AA Molenberghs Geert G Verbeke Geert G Sanchez Lizet L Mas-Bermejo Pedro P Neyens Thomas T

Spatial and spatio-temporal epidemiology 20230510

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between mult ...[more]

PMID: 37301587

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Project description:BackgroundCOVID-19 is a highly transmittable respiratory illness induced by SARS-CoV-2, a novel coronavirus. The spatio-temporal analysis considers interactions between space and time is essential for understanding the virus's transmission pattern and developing efficient mitigation strategies.ObjectiveThis study explicitly examines how meteorological, demographic, and vaccination with all doses of risk factors are interrelated with COVID-19's complex evolution and dynamics in 64 Bangladeshi districts over space and time.MethodsThe study employed Bayesian spatio-temporal Poisson modeling to determine the most suitable model, including linear trend, analysis of variance (ANOVA), separable models, and Poisson temporal model for spatiotemporal effects. The study employed the Deviance Information Criterion (DIC) and Watanabe-Akaike information criterion (WAIC) for model selection. The Markov Chain Monte Carlo approach also provided information regarding both prior and posterior realizations.ResultsThe results of our study indicate that the spatio-temporal Poisson ANOVA model outperformed all other models when considering various criteria for model selection and validation. This finding underscores the significant relationship between spatial and temporal variations and the number of cases. Additionally, our analysis reveals that maximum temperature does not appear to have a significant association with infected cases. On the other hand, factors such as humidity (%), population density, urban population, aging index, literacy rate (%), households with internet users (%), and complete vaccination coverage all play vital roles in correlating with the number of affected cases in Bangladesh.ConclusionsThe research has demonstrated that demographic, meteorological, and vaccination variables possess significant potential to be associated with COVID-19-affected cases in Bangladesh. These data show that there are interconnections between space and time, which shows how important it is to use integrated modeling in pandemic management. An assessment of the risks particular to an area allows government agencies and communities to concentrate their efforts to mitigate those risks.

Project description:BackgroundRecurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models.MethodsWe used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios.ResultsAt a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics.DiscussionOur model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.

Dataset Information

A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.

Publications

A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.

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