Assessing the impact of aggregating disease stage data in model predictions of human African trypanosomiasis transmission and control activities in Bandundu province (DRC).

• Main Authors: María Soledad Castaño, Martial L Ndeffo-Mbah, Kat S Rock, Cody Palmer, Edward Knock, Erick Mwamba Miaka, Joseph M Ndung'u, Steve Torr, Paul Verlé, Simon E F Spencer, Alison Galvani, Caitlin Bever, Matt J Keeling, Nakul Chitnis
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2020-01-01
• Series: PLoS Neglected Tropical Diseases
• Online Access: https://doi.org/10.1371/journal.pntd.0007976

Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, how including or not disease stage data, or using more updated data sets affect model predictions of future control strategies.