Synthesizing environmental, epidemiological and vector and parasite genetic data to assist decision making for disease elimination

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Abstract

We present a framework for identifying when conditions are favourable for transmission of vector-borne diseases between communities by incorporating predicted disease prevalence mapping with landscape analysis of sociological, environmental and host/parasite genetic data. We explored the relationship between environmental features and gene flow of a filarial parasite of humans, Onchocerca volvulus, and its vector, blackflies in the genus Simulium. We generated a baseline microfilarial prevalence map from point estimates from 47 locations in the ecological transition separating the savannah and forest in Ghana, where transmission of O. volvulus persists despite onchocerciasis control efforts. We generated movement suitability maps based on environmental correlates with mitochondrial population structure of 164 parasites from 15 communities and 93 vectors from only four sampling sites, and compared these to the baseline prevalence map. Parasite genetic distance between sampling locations was significantly associated with elevation (r =.793, p =.005) and soil moisture (r =.507, p =.002), while vector genetic distance was associated with soil moisture (r =.788, p =.0417) and precipitation (r =.835, p =.0417). The correlation between baseline prevalence and parasite resistance surface maps was stronger than that between prevalence and vector resistance surface maps. The centre of the study area had high prevalence and suitability for parasite and vector gene flow, potentially contributing to persistent transmission and suggesting the importance of re-evaluating transmission zone boundaries. With suitably dense sampling, this framework can help delineate transmission zones for onchocerciasis and would be translatable to other vector-borne diseases.

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Shrestha, H., McCulloch, K., Chisholm, R. H., Armoo, S. K., Veriegh, F., Sirwani, N., … Hedtke, S. M. (2024). Synthesizing environmental, epidemiological and vector and parasite genetic data to assist decision making for disease elimination. Molecular Ecology, 33(11). https://doi.org/10.1111/mec.17357

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