Forecasting future trends in canine leishmaniasis through dynamic modeling

. To forecast future incidence, we adapted an existing single-season SEIRD model of human leishmaniasis into a multiseason dual-host system (dogs and sandflies). Model equations were modified, and model parameters were calibrated using observed positivity from the previous collection of data. The resulting simulation model reproduces the historical increase in canine positivity (which grows approximately 4% from 1985-1994 to 2018-2022, meaning it stays pretty stable) and predicts certain stability after an initial increase. However, when considering an increase in the temperature overtime to simulate global warming, the model has the same initial increase but, afterwards, the dog population starts decreasing significantly due to deaths caused by the disease. The model provides a transferable framework for forecasting pathogens transmitted by sandflies, mosquitoes, or other arthropod vectors. This model could be adapted to become a digital twin if there was a constant and sufficient supply of data.