Bayesian negative binomial regression with spatially varying dispersion: Modeling COVID-19 incidence in Georgia

Mutiso, Fedelis; Pearce, John L.; Benjamin‐Neelon, Sara E.; Mueller, Noel T.; Li, Hong; Neelon, Brian

doi:10.1016/j.spasta.2022.100703

Cited by 8 publications

(10 citation statements)

References 22 publications

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“…We assume the model with constant dispersion in modeling spatio-temporal count data in this article. However, we may consider non-constant dispersion to capture additional source of heterogeneity (Mutiso et al, 2022). Finally, some counties within the state respond differently to the covariates of interest (e.g., drought exposure) after adjusting for other potential confounders such as baseline characteristic of the counties.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Spatiotemporal Modeling of Drought-related Respiratory Mortality

Gwon,

Nagaya,

Bell

2023

Preprint

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Drought is a complex climate hazard that is increasing in frequency and severity in parts of the world due to climate change. More research is needed to identify the relationship of drought on human health. In this study, we present a statistical model to evaluate the effect of drought exposure on health outcomes. Specifically, we introduce a Bayesian negative binomial regression model with spatial and temporal variabilities to examine the association between respiratory mortality and drought exposure. To conduct a Bayesian computation, an efficient computational algorithm was developed. The deviance information criteria (DIC) was computed to determine which of three drought indices (USDM, 6-month SPEI, 12-month SPEI) is the most suitable drought indicator. The proposed method is applied to a study of the health effects of drought in two selected states, Arizona and California, from 2000 to 2018.

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Section: Discussionmentioning

confidence: 99%

Bayesian Spatiotemporal Modeling of Drought-related Respiratory Mortality

Gwon,

Nagaya,

Bell

2023

Preprint

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“…However, we observe from the COVID-19 data that the degree of over-dispersion is different depending on specific time and areas. Modeling this varied dispersion parameter to specify the characteristics of the site was well established in the traffic accident literature [ 29 , 30 ] and also recently used for the COVID-19 data analysis [ 15 ]. We assumed that dispersion of the infectious disease outbreak can be influenced by the area-specific characteristics and used the spatial correlated and uncorrelated random components from the mean model to specify the dispersion parameter.…”

Section: Methodsmentioning

confidence: 99%

“…The negative binomial likelihood has been used to model over-dispersed count data in many application areas, such as genetics and traffic accident literature [11][12][13]. It has also been used for infectious disease surveillance [8,14,15] but not used widely in a Bayesian spatiotemporal analysis with the prospective surveillance setting. Several nowcasting papers used negative binomial likelihood for Bayesian spatiotemporal modeling to deal with the over-dispersed data [8,16].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis

Kim

Lawson

Neelon

et al. 2023

BMC Med Res Methodol

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Background COVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance. Methods We evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic. Results The simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods. Conclusion We evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results.

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“…For scenarios characterized by rapidly rising disease cases and over-dispersed infectious outbreaks, such as the initial wave of COVID-19 in New England regions in 2020, the negative binomial likelihood, incorporating an additional dispersion parameter, proves more adaptable. Several studies show the efficacy of this model in capturing the heightened variability observed during such outbreaks [1,[44][45][46]. In spatial epidemiology, overdispersion is common due to the presence of often undisclosed factors influencing disease prevalence in each location.…”

Section: Probability Distribution In Spatial Epidemiologymentioning

confidence: 99%

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

Wang,

Chen,

Xue

2024

IJGI

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Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies.

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Bayesian negative binomial regression with spatially varying dispersion: Modeling COVID-19 incidence in Georgia

Cited by 8 publications

References 22 publications

Bayesian Spatiotemporal Modeling of Drought-related Respiratory Mortality

Bayesian Spatiotemporal Modeling of Drought-related Respiratory Mortality

Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

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