Influencing public health policy with data-informed mathematical models of infectious diseases: recent developments and new challenges

Journal article

Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models’ usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model’s parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

Authors: Alahmadi, A; Belet, S; Andrew; Cromer, D; Flegg, JA

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Epidemics
• Volume: 32
• Article number: 100393
• Publication date: 2020-05-17
• Acceptance date: 2020-04-25
• DOI: 10.1016/j.epidem.2020.100393
• EISSN: 1878-0067
• ISSN: 1755-4365
• Pmid: 32674025
• Language: English
• Keywords: computational methodology; parameter identifiability; Bayesian analysis; prior knowledge; FFR; policy and communication; data challenges