Abstract
Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale.
Highlights
Schistosomiasis is a major parasitic infection that affects about 250 million individuals in many areas of the developing world and that puts at risk about 700 million people in regions where the disease is endemic[1]
In this work we have proposed a big-data-driven modeling framework to study the country-scale dynamics of schistosomiasis transmission in Senegal
We have shown that a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of the regional prevalence patterns, and that the inclusion of human mobility estimates obtained from mobile phone traces significantly improves the explanatory power of the model
Summary
Schistosomiasis is a major parasitic infection that affects about 250 million individuals in many areas of the developing world and that puts at risk about 700 million people in regions where the disease is endemic[1]. Eggs leave the human host through urine (S. haematobium) or feces (S. mansoni or S. japonicum) After reaching freshwater, they hatch into so-called miracidia, a second short-lived larval form of the parasite that can infect snails. While larval transport and snail movement may represent significant propagation pathways for the disease only over short spatial scales (e.g. in the order of hundreds of meters9, 10) or long temporal windows (e.g. because of habitat expansion following water resources development11, 12), human mobility can play a significant role in disease propagation within and from endemic areas[13,14,15]. People can be exposed to water infested with cercariae while visiting endemic regions and import the parasites back to their home communities; if infected, they can contribute to water contamination while traveling outside their home communities Both mechanisms are expected to favor parasite dispersion and may even introduce schistosomes into villages that were previously disease-free. Human mobility is here retained as the most important mechanism for the spatial spread of the disease; human movement patterns are extracted from a large dataset of anonymized CDRs (more than 15 billion records) made available by Sonatel, the largest Senegalese
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
