Multitask deep learning for the emulation and calibration of an agent-based malaria transmission model.

ABSTRACT: Agent-based models of malaria transmission are useful tools for understanding disease dynamics and planning interventions, but they can be computationally intensive to calibrate. We present a multitask deep learning approach for emulating and calibrating a complex agent-based model of malaria transmission. Our neural network emulator was trained on a large suite of simulations from the EMOD malaria model, an agent-based model of malaria transmission dynamics, capturing relationships between immunological parameters and epidemiological outcomes such as age-stratified incidence and prevalence across eight sub-Saharan African study sites. We then use the trained emulator in conjunction with parameter estimation techniques to calibrate the underlying model to reference data. Taken together, th

SUBMITTER: Mondal A 

PROVIDER: S-EPMC12327647 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature