Mapping the geographical distribution of lymphatic filariasis in Zambia

Abstract

Background: Past case reports have indicated that lymphatic filariasis (LF) occurs in Zambia, but knowledge about its geographical distribution and prevalence pattern, and the underlying potential environmental drivers, has been limited. As a background for planning and implementation of control, a country-wide mapping survey was undertaken between 2003 and 2011. Here the mapping activities are outlined, the findings across the numerous survey sites are presented, and the ecological requirements of the LF distribution are explored.

Methodology/principal findings: Approximately 10,000 adult volunteers from 108 geo-referenced survey sites across Zambia were examined for circulating filarial antigens (CFA) with rapid format ICT cards, and a map indicating the distribution of CFA prevalences in Zambia was prepared. 78% of survey sites had CFA positive cases, with prevalences ranging between 1% and 54%. Most positive survey sites had low prevalence, but six foci with more than 15% prevalence were identified. The observed geographical variation in prevalence pattern was examined in more detail using a species distribution modeling approach to explore environmental requirements for parasite presence, and to predict potential suitable habitats over unsurveyed areas. Of note, areas associated with human modification of the landscape appeared to play an important role for the general presence of LF, whereas temperature (measured as averaged seasonal land surface temperature) seemed to be an important determinant of medium-high prevalence levels.

Conclusions/significance: LF was found to be surprisingly widespread in Zambia, although in most places with low prevalence. The produced maps and the identified environmental correlates of LF infection will provide useful guidance for planning and start-up of geographically targeted and cost-effective LF control in Zambia.

Figure 1. Map of Zambia showing survey sites and prevalences of CFA positivity.

Figure 2. Response curves illustrating the relationship of MaxEnt predicted probability of occurrence to environmental variables.

The values shown on the y-axis is the predicted probability of suitable conditions, as given by the logistic output format, with only the particular predictor variable used to develop the MaxEnt model. (a) The figure shows the relationship between the Human Influence Index and the predicted probability of occurrence of CFA≥5% (model 1), (b) depicts the relationship between day-time land surface temperature in the rainy season (LSTday (rainy)) and the probability of LF as modeled by model 2 (CFA≥15%), (c) shows the relationships between day-time land surface temperature in the hot-dry season (LSTnight (hot-dry) and the probability of LF occurrence as modeled by model 1 and 2, respectively, and (d) shows the relationship between the distance to nearest surface water bodies and the probability of occurrence of LF as modeled by model 1 and model 2, respectively.

Figure 3. Maps of the MaxEnt predicted distributions of CFA prevalence categories.

(A) The heatmap values represent the relative probabilities of presence of LF with at least 5%, CFA prevalence (model 1). (B) The heatmap represent the predicted relative probability of presence of LF with at least 15% CFA prevalence (model 2).

Figure 4. Map resulting from the overlay of the thresholded versions of the maps in Figure 4.

The map depicts areas of predicted presence of ≥15% CFA prevalence (brown), ≥5% CFA prevalence (orange+brown) and areas where no or <5% CFA is predicted to be present (light yellow).