Bayesian spatial modeling of HIV mortality via zero‐inflated Poisson models

Musal, Muzaffer; Aktekin, Tevfik

doi:10.1002/sim.5457

Cited by 20 publications

(16 citation statements)

References 33 publications

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“…Though spatial models of infectious disease describe reported case counts, these models rarely apply zero-inflated Poisson and Poisson hurdle models to adjust for excess zeros [22]; however, these models, with and without a spatial component, have been used to analyze other ecological and public health processes [23-26]. Rarely have zero-inflated models been used to forecast future case counts.…”

Section: Introductionmentioning

confidence: 99%

Predictive spatial risk model of poliovirus to aid prioritization and hasten eradication in Nigeria

Upfill‐Brown

Lyons

Pate

et al. 2014

BMC Med

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BackgroundOne of the challenges facing the Global Polio Eradication Initiative is efficiently directing limited resources, such as specially trained personnel, community outreach activities, and satellite vaccinator tracking, to the most at-risk areas to maximize the impact of interventions. A validated predictive model of wild poliovirus circulation would greatly inform prioritization efforts by accurately forecasting areas at greatest risk, thus enabling the greatest effect of program interventions.MethodsUsing Nigerian acute flaccid paralysis surveillance data from 2004-2013, we developed a spatial hierarchical Poisson hurdle model fitted within a Bayesian framework to study historical polio caseload patterns and forecast future circulation of type 1 and 3 wild poliovirus within districts in Nigeria. A Bayesian temporal smoothing model was applied to address data sparsity underlying estimates of covariates at the district level.ResultsWe find that calculated vaccine-derived population immunity is significantly negatively associated with the probability and number of wild poliovirus case(s) within a district. Recent case information is significantly positively associated with probability of a case, but not the number of cases. We used lagged indicators and coefficients from the fitted models to forecast reported cases in the subsequent six-month periods. Over the past three years, the average predictive ability is 86 ± 2% and 85 ± 4% for wild poliovirus type 1 and 3, respectively. Interestingly, the predictive accuracy of historical transmission patterns alone is equivalent (86 ± 2% and 84 ± 4% for type 1 and 3, respectively). We calculate uncertainty in risk ranking to inform assessments of changes in rank between time periods.ConclusionsThe model developed in this study successfully predicts districts at risk for future wild poliovirus cases in Nigeria. The highest predicted district risk was 12.8 WPV1 cases in 2006, while the lowest district risk was 0.001 WPV1 cases in 2013. Model results have been used to direct the allocation of many different interventions, including political and religious advocacy visits. This modeling approach could be applied to other vaccine preventable diseases for use in other control and elimination programs.

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Section: Introductionmentioning

confidence: 99%

Predictive spatial risk model of poliovirus to aid prioritization and hasten eradication in Nigeria

Upfill‐Brown

Lyons

Pate

et al. 2014

BMC Med

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“…This was assumed to have a conditional autoregressive structure using the first-order spatial interaction neighbourhood based on contiguity between the hexagons (Besag et al, 1991). This type of CAR-ZIP regression has been used in various applications in ecology (Agarwal et al, 2002;Rathbun and Fei, 2006;Flores et al, 2009) and epidemiology (Musal and Aktekin, 2013;Vergne et al, 2014).…”

Section: A C C E P T E D Mmentioning

confidence: 99%

Modelling African swine fever presence and reported abundance in the Russian Federation using national surveillance data from 2007 to 2014

Vergne

Korennoy

Combelles

et al. 2016

Spatial and Spatio-temporal Epidemiology

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“…Lee et al () further extended the method to the domain of multilevel ZIP regression models, where a multilevel correlation structure is modelled. This family of models have become increasingly popular in recent years and have successfully been applied in different areas of scientific research, including quality control (Lambert, ), medical research (Xiang et al, ; Xiang et al, ), disease mapping (Musal & Aktekin, ), marketing (Rossi et al, ) and so on. Further theoretical development of the model can be found in Min & Agresti (), Liu & Chan () and Xiang et al (), among others.…”

Section: Introductionmentioning

confidence: 99%

Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects

2016

Scandinavian J Statistics

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Focusing on the model selection problems in the family of Poisson mixture models (including the Poisson mixture regression model with random effects and zero-inflated Poisson regression model with random effects), the current paper derives two conditional Akaike information criteria. The criteria are the unbiased estimators of the conditional Akaike information based on the conditional log-likelihood and the conditional Akaike information based on the joint loglikelihood, respectively. The derivation is free from the specific parametric assumptions about the conditional mean of the true data-generating model and applies to different types of estimation methods. Additionally, the derivation is not based on the asymptotic argument. Simulations show that the proposed criteria have promising estimation accuracy. In addition, it is found that the criterion based on the conditional log-likelihood demonstrates good model selection performance under different scenarios. Two sets of real data are used to illustrate the proposed method.

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Bayesian spatial modeling of HIV mortality via zero‐inflated Poisson models

Cited by 20 publications

References 33 publications

Predictive spatial risk model of poliovirus to aid prioritization and hasten eradication in Nigeria

Predictive spatial risk model of poliovirus to aid prioritization and hasten eradication in Nigeria

Modelling African swine fever presence and reported abundance in the Russian Federation using national surveillance data from 2007 to 2014

Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects

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