title: Predictive spatial risk model of poliovirus to aid prioritization and hasten eradication in Nigeria.
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.
authors.affiliation: Institute for Disease Modeling, Intellectual Ventures, 1555 132nd Ave NE, Bellevue, USA. aupfillbrown@intven.com.
authors.forename: Alexander M
authors.initials: AM
authors.lastname: Upfill-Brown
authors.forename: Hil M
authors.initials: HM
authors.lastname: Lyons
authors.forename: Muhammad A
authors.initials: MA
authors.lastname: Pate
authors.forename: Faisal
authors.initials: F
authors.lastname: Shuaib
authors.forename: Shahzad
authors.initials: S
authors.lastname: Baig
authors.forename: Hao
authors.initials: H
authors.lastname: Hu
authors.forename: Philip A
authors.initials: PA
authors.lastname: Eckhoff
authors.forename: Guillaume
authors.initials: G
authors.lastname: Chabot-Couture
mesh_terms: D001499:Bayes Theorem; D060740:Disease Eradication / Q000706:statistics & numerical data*; D005260:Female; D062306:Geography, Medical; D006295:Health Resources / Q000458:organization & administration*; D006801:Humans; D015233:Models, Statistical; D009549:Nigeria / Q000453:epidemiology; D016012:Poisson Distribution; D011051:Poliomyelitis / Q000453:epidemiology / Q000276:immunology / Q000517:prevention & control* / Q000635:transmission / Q000821:virology*; D017955:Poliovirus; D012306:Risk; D013997:Time Factors; D035501:Uncertainty