Abstract
Tungiasis is a neglected parasitic disease that significantly affects communities, especially in developing countries. This study developed a Bayesian severity of the jigger infestation model and its spatial counterpart. Putative determinants leading to different levels of infestation and the most affected areas were to be identified through the model. We collected data through a cross-sectional study with a multi-stage sampling design. A structured questionnaire was administered in each household to capture variables used for modelling jigger infestations. The severity of jigger infestation categorized for each individual was modelled against all the other predictor variables. It was also integrated with spatial data to determine the spatial distribution pattern of jigger infestation. A Bayesian multinomial logistic regression model was used to assess the association between various predictors and different infestation levels. Specifically, an ordered Bayesian Severity Hierarchical (OBSH) categorical model was obtained. This model was categorical based on the Counties (1-Nyeri, 2-Murang'a and 3-Kiambu). Results from this model showed that for a one-unit decrease in the poverty index at level 1 (individuals categorized as poor) there was about a 69% increase in the severity of jigger infestation. A one-unit increase in the percentage of clay in the soil increased the odds ratio of the severity of jigger infestation by a factor of 11.21 while a high percentage of nitrogen in the soil lowered the severity of infestation.  Severity of jigger infestation reduced from the baseline, Nyeri County to Kiambu County. It also increased with increasing altitude due to a decrease in nitrogen levels.
Highlights
Tunga Penetrans, is the etiological agent of Tungiasis, a seriously debilitating disease that is prevalent in Central America, South America, India, and tropical Africa as established by Darvin et al, 2018
This study developed a Bayesian severity of the jigger infestation model and its spatial counterpart
Basing summaries on frequencies calculated from N = 100, 000 simulations followed by an equal number of iterations, N = 200, 000 samples were obtained with acceptable MC errors of less than 5% of the posterior standard deviations
Summary
Tunga Penetrans, is the etiological agent of Tungiasis, a seriously debilitating disease that is prevalent in Central America, South America, India, and tropical Africa as established by Darvin et al, 2018. Whereas earlier studies conducted in the area were more on the social detail (Mwangi et al, 2015; Nyangacha et al, 2019; Nsanzimana et al, 2019; Keiyoro et al, 2016), a mathematical perspective was required to infuse some modelling facet This would enable finding a better solution to the menace by first establishing the key factors that affect the transmission of Tunga Penetrans. This research, incorporated a hierarchical Bayesian cumulative logit model to establish the extensiveness of jigger infestation with or without the spatial structure. Through incorporating the spatial structure of the data in a hierarchical Bayesian cumulative logit model, maps together with posterior distribution estimates from the outputs obtained led to informed and objective decision making about targeted areas for disease control through uncertainty assessments. Though documented studies on the comparison on the severity of jigger infestation in these 3 Counties is not found, this study found such severity of infestation to be higher in Nyeri County than Kiambu County while increasing with increase in altitude
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