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

BMC Medicine

Research Article

Muhammad A Pate¹  Guillaume Chabot-Couture²  Hil M Lyons²  Hao Hu²  Alexander M Upfill-Brown²  Philip A Eckhoff²  Faisal Shuaib³  Shahzad Baig⁴ 

[1] Duke Institute for Global Health, Duke University, Durham, USA;Institute for Disease Modeling, Intellectual Ventures, 1555 132nd Ave NE, Bellevue, USA;National Polio Emergency Operations Center, Abuja, Nigeria;National Primary Health Care Development Agency, Abuja, Nigeria;University of Alabama at Birmingham, Birmingham, USA;National Primary Health Care Development Agency, Abuja, Nigeria;Kano Polio Emergency Operations Center, Kano, Nigeria;

关键词: Polio eradication;    Spatial epidemiology;    Risk modeling;    Disease mapping;   

DOI  :  10.1186/1741-7015-12-92

 received in 2014-03-04, accepted in 2014-05-09,  发布年份 2014

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Upfill-Brown et al.; licensee BioMed Central Ltd. 2014