Incorporating human dynamic populations in models of infectious disease transmission: a systematic review

• Published in: BMC infectious diseases Vol. 22; no. 1; pp. 1 - 862
• Main Authors: Magelmose, Signe, Neels, Karel, Hens, Niel
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 18.11.2022; BioMed Central; BMC
• Subjects: Age; Age groups; Aging; Care and treatment; Communicable diseases; Demographic change; Demographics; Demography; Diagnosis; Disease transmission; Epidemiology; Fertility; Households; Infections; Infectious diseases; Literature reviews; Mathematical epidemiology; Methods; Migration; Mortality; Population; Population biology; Population dynamics; Populations; Prevention; Public health; Risk factors; Sensitivity analysis; Sex; Systematic review; Transmission; Trends; Belgium
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-022-07842-0

An increasing number of infectious disease models consider demographic change in the host population, but the demographic methods and assumptions vary considerably. We carry out a systematic review of the methods and assumptions used to incorporate dynamic populations in infectious disease models. We systematically searched PubMed and Web of Science for articles on infectious disease transmission in dynamic host populations. We screened the articles and extracted data in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We identified 46 articles containing 53 infectious disease models with dynamic populations. Population dynamics were modelled explicitly in 71% of the disease transmission models using cohort-component-based models (CCBMs) or individual-based models (IBMs), while 29% used population prospects as an external input. Fertility and mortality were in most cases age- or age-sex-specific, but several models used crude fertility rates (40%). Households were incorporated in 15% of the models, which were IBMs except for one model using external population prospects. Finally, 17% of the infectious disease models included demographic sensitivity analyses. We find that most studies model fertility, mortality and migration explicitly. Moreover, population-level modelling was more common than IBMs. Demographic characteristics beyond age and sex are cumbersome to implement in population-level models and were for that reason only incorporated in IBMs. Several IBMs included households and networks, but the granularity of the underlying demographic processes was often similar to that of CCBMs. We describe the implications of the most common assumptions and discuss possible extensions.