Predictive spatial risk model of polio virus to aid prioritization and hasten eradication in endemic countries

Abstract

Background: Polio eradication was declared a “programmatic emergency for global public health” by the world health assembly in 2012. Efficient prioritization of high risk areas would greatly improve the impact of the substantial resources of the Global Polio Eradication Initiative. Currently, one method for polio risk assessment has been described in the litterature, however this method has not been historically validated and fails to address uncertainty. Methods & Materials: Data, including vaccination history, collected from individuals through Acute Flaccid Paralysis (AFP) surveillence in Nigeria and Pakistan, used to calculate district level immunity and relevant covariates. We constructed heirachical temporal bayesian models to reduce measurement error in data and spatially smoothed Poisson hurdle models to assess predictors of wild polio virus (WPV) tranmission. Best fitting models used to forecast future district infection, predictive accuracy assessed with reciever operating characteristic (ROC) analysis. Results: Measurement error models greatly reduce changes in indicators from one time period to another. Calculated population immunity is a strongly associated with the presence of WPV and number of WPV cases within a district in Nigeria. In Pakistan, routine immunization dose history (not available through Nigerian AFP data) and zero-dose fraction are strongly associated with both aspects of WPV tranmission. A district risk list ordered according to model outputs demonstrates high forecast accuracy historically, with mean area under the ROC curve greater than 0.80 over the past three years. Model uncertainty propogated through to rank uncertainty, and the probability of a district belonging in the highest risk N district list can be calcuated. Conclusion: The measurement error smoothing model is a useful tool for estimation of important variables at small adminsitrative areas. With the Poisson hurdle model, we can study differential relatitionships between predictors and two aspects of WPV tranmsission. Historically forecast accuracy confirms the usefulness of this modeling approach in a disease control and elimination application. The quantification of uncertainty is essential to inform objectives descisions regarding changes in prioritization status of a district from one time period to another. These models can be naturally extended to other vaccine-preventable diseases, such as measles. In Nigeria, these models have been incorporated into national program planning.