Background
Since the commitment by the World Health Assembly to eradicate poliovirus in 1988, the disease burden has dramatically declined by more than 99% to 223 cases reported in 2012 [
1,
2]. Yet, in the face of growing financial and political investments, polio remains endemic in Nigeria, Pakistan, and Afghanistan and has been repeatedly exported to other previously polio-free countries—leading the 65th World Health Assembly to declare polio eradication a “programmatic emergency for global public health” in 2012 [
3].
Though substantial, the resources of the Global Polio Eradication Initiative (GPEI), including vaccines, specially trained personnel, and social mobilization campaigns, are limited and must be targeted to high-risk areas within endemic countries in order to maximize impact [
4]. The GPEI supports the surveillance and survey collection designed to monitor program performance in these areas [
5]. However, converting these data sources into operationally useful and scientifically valid measures of future risk can be challenging.
Furthermore, before 2010, wild poliovirus (WPV) epidemics affected large portions of northern Nigeria, and 80 to 90 different districts were reporting cases within a six-month period. Thus, the need for prioritization was low, as cases were scattered across the whole of northern Nigeria and outbreak response was the focus of the problem. The lull in reported WPV cases in 2010 coupled with the small number of WPV “infected” districts scattered across northern Nigeria in 2011 suggested that the epidemic in Nigeria had become more focused; a much smaller number of districts were contributing to WPV transmission in Nigeria. Increased focus by the program was needed to anticipate which high-risk districts were at highest risk for future cases in order to prevent further transmission. Programmatically, the need to identify the districts at highest risk was intensified by a planned surge in technical and administrative capacity, supported by the World Health Organization (WHO).
Currently, one systematic method for risk assessment at the district level has been described in the literature: the WHO’s method of regional polio risk assessment, which uses a weighted linear combination of available indicators [
6]. This approach is currently used by all WHO regional offices for supplementary immunization activity (SIA) campaign planning and outbreak risk assessment. As Lowther and colleagues recognize [
6], this approach has limitations: these weights are based on expert opinion, not statistical modeling, and its historical predictive accuracy has yet to be demonstrated.
Previous work has demonstrated the accuracy of WPV outbreak prediction using hierarchical statistical modeling in the African region at the country/province level [
7]. This model incorporated measures of connectedness between areas and population immunity and demonstrated good predictive accuracy. However, in endemic areas, operations are carried out at the district administrative level or below; at this spatial scale, underlying causal factors, such as migration, are poorly measured.
To better support modeling of spatial heterogeneity and correlation, recent work focusing on other infectious diseases has increasingly applied Bayesian spatial modeling methods [
8]. These methods have been used to map infection prevalence in unmeasured areas for malaria [
9,
10], schistosomiasis [
11‐
15], soil-transmitted helminths [
16‐
18], and filariasis [
19].
Only recently in infectious disease research have these methods been used to aid in forecasting disease prevalence or incidence in a future time period [
20,
21]. 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.
We present a predictive spatial hierarchical Poisson hurdle model for serotype 1 and serotype 3 WPV (WPV1 and WPV3) transmission. This model is currently incorporated in district-level prioritization planning in Nigeria. Using this modeling framework, we identify the most important risk factors of the presence of WPV and the historical number of WPV cases, and also highlight areas in which these indicators are weakly informative. Due to the sparsity of the data underlying the estimates of district-level covariates, we employ a two-part methodology in which these estimates are smoothed using a temporal hierarchical model prior to their use as inputs to the spatial Poisson hurdle models. We examine this model’s historical ability to forecast districts that will report one or more WPV cases six months in the future.
Discussion
The smoothing model handles missing data and smooths over unrealistic changes in indicators, such as rapid six-month fluctuations in the under-five immunity estimated from sparse NP-AFP samples. In the absence of any vaccination, the under-five OPV-induced population immunity should decrease by roughly 8-9% in a six-month period (see Figure
1B)
c. Though thresholds based on this or similar considerations are not explicit in our smoothing model, the smoothed variations between time periods are more realistic than those in empirical data. Smoothing models could be improved by limiting changes between time points to demographically constrained values.
Despite its limitations [
50], the OPV-based population immunity, calculated using efficacies derived from case-control population studies [
29,
33], is strongly associated with the presence and case count of WPV1 at the district level in Nigeria. It is the only covariate significantly associated with the number of cases in a district.
It is expected that the recent caseload is significantly associated only with the expected presence of WPV1, as recent circulation has a mixed effect on the future case count. While a large number of cases within a time period suggests potentially higher transmission (temporally and spatially) from a large infectious reservoir, WPV1 circulation will also boost natural immunity in an area, providing additional protection and reducing the expected caseload in the ensuing time period [
50]. Indeed, we find that both recent caseload within a district and in surrounding districts are significantly associated with the probability of a WPV1 case, but not the number of WPV1 cases.
Although increased population density is thought to increase poliovirus transmission potential [
51], we find no association in either portion of the model. Due to data limitations, population density was calculated at the district level, which may be a poor representation of the experienced population density on more functional scales of transmission, such as the population density immediately surrounding the household reporting a WPV case. A more fine-grained analysis is needed to better understand the impact of population density of WPV transmission.
The ability to detect an association between zero-dose fraction, an indicator of possible clustering of vulnerability within a district, and the presence and number of WPV1 cases was compromised by a strong correlation between population immunity and zero-dose fraction. The collinearity between these indicators resulted in a negative association between zero-dose fraction and the number of WPV1 cases in a district, while in a model without population immunity, zero-dose fraction has a strong positive association with the number of cases in a district.
Although the historical predictive accuracy is high, covariates in the selected model do not substantially improve the forecasting ability of the model over the null model. In a null model, the random effects capture the mean historical frequency and number of WPV1 cases within a district. This result suggests that although dynamic indicators are associated with WPV1 transmission, historical transmission patterns are stronger predictors of future transmission than available model covariates.
The decrease in performance in 2009 is caused by a flare-up in WPV1 cases in southern districts with no history of WPV1 transmission in our dataset; because the spatial random effects alone are strong predictors of future caseloads, this modeling approach places very little risk in areas with no historical cases in the dataset. The selected model performed as poorly as the null model during this time period, indicating that factors other than calculated OPV-derived population immunity and local transmission contributed to this outbreak.
The outputs from the predictive models are well suited for use in prioritization by public health organizations. In Nigeria, model forecasts are used to inform sub-national SIA planning and allocation of specialized personnel. Prioritization analysis played a critical role in the distribution of technical and administrative field staff—supported by both WHO and the Nigerian government—to the highest risk Local Government Areas across the north during the capacity surge in June 2012. WHO alone grew an initial staff of 744 to over 2,900, an increase of nearly 300%. In addition to supporting SIA planning, monitoring, and evaluation, these personnel have supported household-based micro planning, intensified AFP surveillance activities, and helped strengthen routine immunization activities. Outreach from the federal government to state governors and district chairmen to increase local political buy-in was also heavily increased around this time; prioritizations based on the predictive risk model results were also used to direct this surge in advocacy. These efforts can be partly credited for the absence of cases in 2013 from the northwest of Nigeria, the area of focus for political engagement in the latter half of 2012. F Since June 2013, model outputs have been combined with results and input from the National Primary Health Care Development Agency (NPHCDA), Centers for Disease Control and Prevention (CDC), and WHO to categorize a subset of districts as “high risk” and “highest risk”. This categorization has been used to direct a number of interventions across partner organizations in Nigeria. Management support teams, comprising high level personnel from NPHCDA and other GPEI partner agencies, are deployed to prioritized districts seven to ten days before a SIA campaign to address challenges limiting vaccination coverage. Prioritized districts are selected for advocacy visits targeted at local political, traditional, and religious leaders. Additionally, supplemental logistics funds are provided to prioritized districts to enhance the ability of teams to vaccinate hard-to-reach and scattered settlements. The tracking of vaccinators using GPS-enabled smart phones has been targeted to prioritized districts. Often, these districts receive the first implementation of an intervention planned to eventually deploy across northern Nigeria.
Outside of national program planning, district prioritization categorizations are closely monitored by state governors and district chairmen. Because of additional support, a prioritized district reporting a WPV case is held more accountable than a non-prioritized district.
The quantification of uncertainty in risk rank can also be a useful tool for policy makers. As changes in prioritization are an additional strain on resources (due to required reallocation of people and materials), the quantification of uncertainty in rank can enable objective decisions regarding changes to the prioritization of an area. If a district is newly ranked in the top 100 or 200 highest risk areas, policy makers may only want to prioritize if there is a high level of certainty that the district belongs in the highest risk group.
One aspect not considered in the model was the migration and transport structures connecting non-neighboring districts. With data to inform such structures, we may be better able to predict infection in naive southern districts. WPV1 case information was only available beginning in 2004 for this study. More historical data, with instances of transmission in these areas, could also have improved the forecasting accuracy of the model during these time periods. Seasonal variations and trends, such as those used in predictive models for meningitis in France and Mali [
20,
21], could be incorporated, though the six-month time scale of model predictions required due to data sparsity may be too large for this technique to be useful.
Additional covariates or more representative data are needed to more fully understand WPV transmission in Nigeria. In the selected model, a substantial amount of residual spatial variation remains, as is demonstrated in Figure
4. This variation may be due to other factors such as poverty, malnutrition, sanitation, and level of health services, which influence WPV transmission potential and population vaccine efficacy [
52,
53]. In addition, the inclusion of a number of operational factors could greatly improve the model. Indicators capturing district management performance, training quality, vaccinator selection, population accessibility (the presence of hard-to-reach areas within districts, which may be seasonal), and non-compliance are currently missing from the model. Such indicators are an important part of regular program operations. Furthermore, these indicators could be more dynamic than current smoothed indicators, thus improving the responsiveness of model outputs to short-term changes in performance.
Based on the magnitude of district-level random effects, we find that in large portions of northern Nigeria covariates underestimate the probability of the presence of WPV1 in a district, while in southern Nigeria, these covariates overestimate the probability of WPV1 presence. The latter could occur partly because we assume that vaccine efficacy is the same throughout the country, which results in relatively low population immunity estimates in southern districts (Figure
3). This observation is a direct result of the differential number of SIA campaigns executed in northern and southern Nigeria historically: over the last few years, more than twice as many campaigns have been carried out in northern states than in southern states. There is evidence and theory to suggest that vaccine efficacy is not uniform across Nigeria; it may actually be higher in southern Nigeria [
33,
54], possibly due to a lower burden of non-polio enteroviruses, which are known to interfere with OPV efficacy [
55,
56]. OPV-derived population immunity estimates based on geographically sensitive vaccine efficacies could resolve this anomaly and strengthen the measured association between calculated population immunity and WPV transmission.
There are several possible sources of statistical bias in our analysis. Though we treated NP-AFP as a random sample of the population in an area, NP-AFP may be under-representative of subpopulations in a district, especially higher risk sub-populations in areas with worse sanitation and less access to health services. This NP-AFP bias is also likely to vary by location. Though the annual rate of NP-AFP (8/100,000 under fifteen) is higher than the minimum WHO guideline (2/100,000 under fifteen), small sample sizes limit the temporal and spatial resolution of this analysis.
Another limitation of this analysis is the recall bias that arises from the oral history collected from the mothers of reported AFP cases. It is possible that they may over- or under-report the number of OPV doses received by the child. It is also possible that the literacy level of the caregivers may influence the accuracy of reported doses. Our analysis does not make allowances for the impact of insecurity on the observed AFP detection rates in some states, such as Borno, Yobe and Kano; the effects may be more meaningful in data collected since December 2012.
There likely exist important heterogeneities in WPV transmission and associated factors below the district level: SIA activities are often planned and carried out at the subdistrict (ward) level in Nigeria. The mean population immunity may be poorly representative of at-risk populations, which will attenuate estimated relationships in the model. Certain populations, such as nomadic peoples [
57], may be missed by routine AFP surveillance; in this case, there may be additional WPV cases not included in the analysis. Poorly performing wards may persist in districts with low average risk, as each ward has a focal person responsible for vaccinator selection and training. The population diversity within a district, which often include both rural and urban environments, suggests that the ward level is a more representative level for analysis, although we are severely constrained by a lack of historical data and sparse sample sizes at the subdistrict level.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
AUB designed the analytical approach, carried out the statistical analysis, and wrote the first draft of the manuscript. HML advised on the analytical approach and helped draft the manuscript. MAP helped conceive of the study and helped draft the manuscript. FS assisted in interpretation of results and helped draft the manuscript. SB assisted in interpretation of results and helped draft the manuscript. HH helped conceive of the study and contributed to the analytical approach. PAE helped conceive of the study and help draft the manuscript. GCC helped conceive of the study, contributed to the analytical approach, and helped draft the manuscript. All authors read and approved the final manuscript.