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Chagas disease is a parasitic infection endemic to America, caused by the protozoan Trypanosoma cruzi and mainly transmitted to humans by contact with insect species of the Triatominae subfamily (Hemiptera). The disease is known to affect disproportionally rural impoverished human communities where it is known to cause premature death and is considered a social and economic burden. The Mexican government has made important progress into the detection, surveillance, treatment, and prevention of the disease in the last decades, however, Chagas disease has also been reported in areas where it had not been previously reported, and there are still barriers for access to treatment. In the state of San Luis Potosi, the disease is more studied in the east, nevertheless, it has been estimated that the reported cases of the entire state have been underestimated. New approaches to detect Chagas risk areas could help prioritize locations for Chagas disease education and prevention programs, detect cases of the disease in a timely manner, and provide access to the necessary treatments. The objective of this study was to identify risk areas for the transmission of Chagas disease in San Luis Potosí using species distribution modelling to estimate vectors and reservoirs' distributions. To do this, firstly, important vectors and one reservoir species of T. cruzi were identified by reviewing their reported infection rates in literature and the number of times reported in Mexico. Next, species distribution models were calculated for the chosen vector and reservoir species present in the state. The models were done using the Maxent algorithm. Lastly, the resulting distribution models were combined into a risk map by thresholding the model outputs to produce binary predictions and then performing an overlap spatial analysis. Vector species were found to have suitable areas in 36.08% of the state's territory while areas suitable for both vectors and reservoir were 7.4% of the state's total area. While this figure may look small at first glance, the analysis suggests that 30% of the rural population and 52% of the urban population of the state are living in an area suitable for vectors and reservoir and therefore at risk. Species distribution modelling can be a powerful tool for identifying human populations at risk of contracting Chagas disease. In the future, including different species of reservoirs into the analysis could help to discover new risk areas in the state.

Chagas disease is a parasitic infection endemic to America, caused by the protozoan Trypanosoma cruzi and mainly transmitted to humans by contact with insect species of the Triatominae subfamily (Hemiptera). The disease is known to affect disproportionally rural impoverished human communities where it is known to cause premature death and is considered a social and economic burden. The Mexican government has made important progress into the detection, surveillance, treatment, and prevention of the disease in the last decades, however, Chagas disease has also been reported in areas where it had not been previously reported, and there are still barriers for access to treatment. In the state of San Luis Potosi, the disease is more studied in the east, nevertheless, it has been estimated that the reported cases of the entire state have been underestimated. New approaches to detect Chagas risk areas could help prioritize locations for Chagas disease education and prevention programs, detect cases of the disease in a timely manner, and provide access to the necessary treatments. The objective of this study was to identify risk areas for the transmission of Chagas disease in San Luis Potosí using species distribution modelling to estimate vectors and reservoirs' distributions. To do this, firstly, important vectors and one reservoir species of T. cruzi were identified by reviewing their reported infection rates in literature and the number of times reported in Mexico. Next, species distribution models were calculated for the chosen vector and reservoir species present in the state. The models were done using the Maxent algorithm. Lastly, the resulting distribution models were combined into a risk map by thresholding the model outputs to produce binary predictions and then performing an overlap spatial analysis. Vector species were found to have suitable areas in 36.08% of the state's territory while areas suitable for both vectors and reservoir were 7.4% of the state's total area. While this figure may look small at ...