Speaker
Description
Parameters are fundamental components of biological models and play a critical role in determining model behavior. While some parameters can be estimated directly from experimental or observational data, many remain difficult to measure or infer. This work investigates the role of parameters in a malaria transmission model when the available data consist only of the number of malaria tests performed, the number of positive test results, and the population size. We examine parameter identifiability by analyzing the relationship between model parameters and observable quantities, both in the presence and absence of measurement noise. To assess the influence of parameters on model outputs, we compute Sobol sensitivity indices, which quantify how variations in parameter values contribute to changes in the model predictions. Finally, we apply data assimilation techniques to perform forward prediction and to estimate parameters that cannot be determined from experimental data alone.
