The role of older children and adults in wild poliovirus transmission
ABSTRACT As polio eradication inches closer, the absence of poliovirus circulation in most of the world and imperfect vaccination coverage are resulting in immunity gaps and polio outbreaks affecting adults. Furthermore, imperfect, waning intestinal immunity among older children and adults permits reinfection and poliovirus shedding, prompting calls to extend the age range of vaccination campaigns even in the absence of cases in these age groups. The success of such a strategy depends on the contribution to poliovirus transmission by older ages, which has not previously been estimated. We fit a mathematical model of poliovirus transmission to time series data from two large outbreaks that affected adults (Tajikistan 2010, Republic of Congo 2010) using maximum-likelihood estimation based on iterated particle-filtering methods. In Tajikistan, the contribution of unvaccinated older children and adults to transmission was minimal despite a significant number of cases in these age groups [reproduction number, R = 0.46 (95% confidence interval, 0.42-0.52) for >5-y-olds compared to 2.18 (2.06-2.45) for 0- to 5-y-olds]. In contrast, in the Republic of Congo, the contribution of older children and adults was significant [R = 1.85 (1.83-4.00)], perhaps reflecting sanitary and socioeconomic variables favoring efficient virus transmission. In neither setting was there evidence for a significant role of imperfect intestinal immunity in the transmission of poliovirus. Bringing the immunization response to the Tajikistan outbreak forward by 2 wk would have prevented an additional 130 cases (21%), highlighting the importance of early outbreak detection and response.
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ABSTRACT: A priority of the Global Polio Eradication Initiative (GPEI) 2013-2018 strategic plan is to evaluate the potential impact on polio eradication resulting from expanding one or more Supplementary Immunization Activities (SIAs) to children beyond age five-years in polio endemic countries. It has been hypothesized that such expanded age group (EAG) campaigns could accelerate polio eradication by eliminating immunity gaps in older children that may have resulted from past periods of low vaccination coverage. Using an individual-based mathematical model, we quantified the impact of EAG campaigns in terms of probability of elimination, reduction in polio transmission and age stratified immunity levels. The model was specifically calibrated to seroprevalence data from a polio-endemic region: Zaria, Nigeria. We compared the impact of EAG campaigns, which depend only on age, to more targeted interventions which focus on reaching missed populations. We found that EAG campaigns would not significantly improve prospects for polio eradication; the probability of elimination increased by 8% (from 24% at baseline to 32%) when expanding three annual SIAs to 5-14 year old children and by 18% when expanding all six annual SIAs. In contrast, expanding only two of the annual SIAs to target hard-to-reach populations at modest vaccination coverage-representing less than one tenth of additional vaccinations required for the six SIA EAG scenario-increased the probability of elimination by 55%. Implementation of EAG campaigns in polio endemic regions would not improve prospects for eradication. In endemic areas, vaccination campaigns which do not target missed populations will not benefit polio eradication efforts.PLoS ONE 12/2014; 9(12):e113538. DOI:10.1371/journal.pone.0113538 · 3.53 Impact Factor
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ABSTRACT: Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process.Proceedings of the National Academy of Sciences 01/2015; DOI:10.1073/pnas.1410597112 · 9.81 Impact Factor