Understanding schistosomiasis transmission: A systematic review of mathematical models

We employ PRISMA guidelines to carry out a systematic review of mathematical models of schistosomiasis organizing them into focus and approach based categories. Our analysis reveals a range of substantive challenges inherent in modelling disease dynamics, including epidemiological heterogeneity, the intrinsic complexity of disease systems, uncertainties surrounding data accuracy, and the critical need for interdisciplinary research collaboration. Furthermore, our review identifies that numerous models are formulated using ordinary differential equations, while others incorporate partial differential equations, stochastic differential equations, delay differential equations, and machine learning techniques, reflecting the methodological diversity employed to capture the multifaceted nature of infectious disease transmission. A few of them incorporate population structure as well as environmental factors such as seasonal conditions and water contact patterns. We also find that many models have limitations, and we highlight areas where future research is needed emphasizing a compelling need for better coordination and standard practices in how models are built. Models about vaccination are useful in showing how vaccines can help once they become available and models that look at co-infection show how schistosomiasis interacts with other diseases. Overall, our study shows how helpful mathematical models are in solving real-world problems. Additionally, the challenges posed by schistosomiasis underscore the importance of investing in research that utilizes mathematical modelling. We stress the need for researchers from different fields to work together more closely to fight the spread of schistosomiasis. It is recommended that future models adopt reliable methodologies that integrate multistage modelling, hybrid approaches, and agent-based frameworks. These models should also explicitly account for regional differences to enhance accuracy, relevance, and applicability across diverse contexts.