Introduction: the confluence of global environmental changes, characterized by biodiversity loss, shifts in land use, and escalating climatic instability, is anticipated to have profound implications for the prevalence and distribution of infectious diseases. In response to this pressing concern, our project aimed to investigate the intricate interplay between climate change, land use changes, and the development of water management infrastructures on the distribution of snail-borne schistosomiasis. This debilitating parasitic disease, affecting over 200 million people worldwide, is intrinsically linked to environmental factors, making it a key target for our study.

Goals: Focusing on Ivory Coast and Brazil, both vulnerable to schistosomiasis due to climate change, population growth, deforestation, and agricultural expansion, our project consisted of three main objectives:

1. To develop species distribution models and data driven, thermal sensitive, mechanistic models of schistosomiasis dynamics, and produce comprehensive maps of present and future schistosomiasis risk under climate change.
2. To explore the feasibility of using freshwater organisms that have a commercial value (river prawns, fish) as novel biological control agents for schistosome's snail hosts in Cote d’Ivoire with the goal of boosting disease control, improving nutrition and fighting poverty through integrated aquaculture.
3. To develop machine learning algorithms for computer vision, enabling rapid and accurate identification of potential schistosome-host snails and parasites from field-acquired cellphone images.

The highlights of the study

1) We challenged previous thermal sensitive models for schistosomiasis transmission, refining thermal-response models based on extensive empirical data. Our analysis suggests a higher thermal optimum for transmission than previously estimated, aligning with observed prevalence in Africa and showing that climate change might increase schistosomiasis transmission in two thirds of the regions where it is presently endemic (Aslan et al. 2024). In addition, by using machine learning, remote sensing, and 30 years of snail occurrence records, we mapped the historical and current distribution of the freshwater snails of the genus Biomphalaria, which in Brazil are competent hosts for the human parasite. We identified key features influencing the distribution of suitable habitat and determined how Biomphalaria snail habitat has changed with climate and urbanization over the last three decades. Our models show that climate change has driven broad shifts in snail host range, whereas expansion of urban and peri-urban areas has driven localized increases in habitat suitability.

2) We investigated how commercial aquaculture of snail predators may contribute to the control of schistosomiasis and, at the same time, improve socioeconomic and nutritional health in in Cote d’Ivoire (Ozretich et al. 2022). Through a systematic analysis of published literature, we found strong evidence that aquaculture of snail eating species could improve schistosomiasis control and, at the same time, reduce economic poverty and malnourishment.

3) Finally, we explored the effectiveness of using convolutional neural networks (CNNs) for computer assisted classification of environmental stages of schistosomiasis parasites and their invertebrate host snails. We showed that CNN models achieve high accuracy in classifying snails and parasites, comparable to experienced parasitologists.

Benefits to the Society:  This project was a collaboration involving 9 lead investigators, 18 undergraduate students, 7 graduate students, 4 postgraduate students, 2 lab technicians, 2 early career scientists, 3 middle school teachers, and two high school students from USA, Brazil, Cote d’Ivoire, and UK. This project has created a coalition of scientists working on cutting edge research on schistosomiasis and climate change in Western Africa and in Brazil, two regions that are experiencing unprecedented changes to land use and climate. This coalition will provide information and tools that are key for surveillance systems that monitor schistosomiasis transmission risk, including regions that so far have been too cold for transmission to occur, but might become more suitable under scenarios of climate change. The methods developed in this project can also inform the development of similar projections for other Neglected Tropical Diseases with an important environmental component in their transmission cycle, currently affecting more than one billion people in tropical and subtropical regions.

Discussion and next steps. The international research team is currently refining the thermal sensitive models for schistosomiasis to account for the effect of seasonal fluctuations in temperature and extreme droughts under scenarios of climate change. The team is also active in connecting knowledge to actionable solutions; through a collaboration with the Global Schistosomiasis Alliance, the team is developing a prototype of a rapid assessment software and a set of guidelines to include a Health Impact Assessment focused on schistosomiasis and other water associated diseases in the Environmental Impact Assessment (EIA) of proposed water management infrastructures in Africa under scenarios of climate change. In addition, the research team will continue to investigate the integration of fish aquaculture in irrigated agriculture with the aim to support small-scale aquaculture development, enhance rice yield, and curb schistosomiasis transmission, a multiple win for development, health and nutrition. The team is also committed to expand k-12 education on parasite ecology 

Last Modified: 11/07/2024
Modified by: Giulio De Leo