2016/11/24 by I. Andrianakis, Ioannis Andrianakis, Ian Vernon +13 · 1 citation
Computer Science · #Machine Learning and Algorithms
paper · pdf · doi:10.1111/rssc.12198
crossref issued 2016/11/24 · crossref published 2016/11/24 · crossref published-online 2016/11/24 · openalex publication_date 2016/11/24 · crossref created 2016/11/24 · crossref published-print 2017/08/01 · crossref deposited 2024/06/20 · openalex created_date 2025/10/10 · crossref indexed 2026/07/27 · openalex updated_date 2026/08/04
Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed.