Integrated risk mapping and targeted snail control to support schistosomiasis elimination in Brazil and Cote d'Ivoire under future climate change

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Schistosomiasis is a debilitating, poverty-linked disease caused by a parasitic worm with a complex life cycle: its transmission requires infection of an obligate intermediate freshwater snail host that is parasitized by larvae from eggs released by infected people. In turn, infected snails shed other parasitic larval stages that eventually reinfect people exposed to parasite-contaminated waters whilst doing their daily chores. The inflammatory processes caused by schistosome eggs cause abdominal pain, diarrhea, blood in stool or urine, that lead to chronic anemia, cognitive impairment in children, growth stunting, infertility, a higher risk of contracting HIV in women and, at high levels of infection, to death from liver failure or bladder cancer. With 800 million people at risk and more than 200 million infected, the vast majority of which are school age children, schistosomiasis is one of the most important, but also most neglected tropical diseases.As both the freshwater snails and the parasite larvae are ectotherms, they have limited ability to thermoregulate . Therefore, their survival, reproduction and dispersal are influenced even by small changes in temperature and temperature variability. Projected climate change is emerging as one of the most powerful drivers of schistosomiasis transmission by altering the geographical distribution of the parasite and its snail hosts and the associated risk of human infections. Understanding climatic, environmental and socio-economic drivers of schistosomiasis and developing novel ecological solutions to fight the disease, improve health, increase food security and alleviate poverty is a scientific and public health priority and will benefit the health of millions of people where schistosomiasis is endemic.We have three main goals: FIRST, we intend to investigate the effect of increasing temperatures, temperature variability and shift in precipitation patterns due to climate change on the dynamics of snail-born schistosomiasis, a debilitating parasitic disease of poverty affecting more than 200 million people worldwide. We will focus on Brazil and Cote d'Ivoire for these compelling reasons: Brazil suffers the highest schistosomiasis burden in the Americas, with an estimated 2-6 million people infected by the S. mansoni worm. Cote d'Ivoire has a high disease prevalence with an estimated 4 million people infected by S. hameatobium and S. mansoni. Projected climate change - along with growing human population, deforestation, expansion of agriculture and of marginal urban settings and the development of dams and irrigation canals known to be associated with increased schistosomiasis risk caused by habitat expansion for, and extirpation of natural predators of, the snail intermediate host - make these two countries particularly vulnerable to this parasitic disease. We will use a hybrid approach integrating species distribution models accounting for the most relevant socio-economic and environmental drivers of schistosomiasis with temperature-driven, process-based mathematical models of the parasite and its intermediate snail hosts to produce maps of present and future risk for schistosomiasis under climate change. SECOND, we intend to conduct initial feasibility and nutritional studies and market analyses of aquaculture of freshwater prawns native of Brazil and of Cote d'Ivoire with the goal of using them as novel biological control agents of schistosome's snail host while, at the same time, promoting the development of small-scale aquaculture businesses. THIRD, to track future changes in the distribution and abundance of the snail hosts, we will develop machine learning algorithms that enable computer vision as an "environmental diagnosis" tool for the quick and accurate identification of potential schistosome-host snails and parasites from field-acquired cellphone images, trained on thousands of images that have been identified to species by DNA barcoding and PCR-RFLP.