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Open Access Highly Accessed Research article

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

Alexander M Upfill-Brown1*, Hil M Lyons1, Muhammad A Pate2, Faisal Shuaib345, Shahzad Baig46, Hao Hu1, Philip A Eckhoff1 and Guillaume Chabot-Couture1

Author Affiliations

1 Institute for Disease Modeling, Intellectual Ventures, 1555 132nd Ave NE, Bellevue, USA

2 Duke Institute for Global Health, Duke University, Durham, USA

3 National Polio Emergency Operations Center, Abuja, Nigeria

4 National Primary Health Care Development Agency, Abuja, Nigeria

5 University of Alabama at Birmingham, Birmingham, USA

6 Kano Polio Emergency Operations Center, Kano, Nigeria

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BMC Medicine 2014, 12:92  doi:10.1186/1741-7015-12-92

Published: 4 June 2014

Abstract

Background

One 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.

Methods

Using 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.

Results

We 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.

Conclusions

The 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.

Keywords:
Polio eradication; Spatial epidemiology; Risk modeling; Disease mapping