ArticleEpidemiology and infection2017
Estimating age-time-dependent malaria force of infection accounting for unobserved heterogeneity.
Article in Epidemiology and infection, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Exploring heterogeneity in mosquito exposure and attraction and its implications for malaria transmission.PLoS computational biology · 2026Article
- From multiplicity of infection to force of infection in sparsely sampled high-transmissioneLife · 2026Article
- An age-structured mathematical model to assess the combined effects of vaccine and non-vaccine interventions on malaria transmission and burden.Infectious Disease Modelling · 2026Article
- An integrative review of the combined use of mathematical and statistical models for estimating malaria transmission parameters.Malaria journal · 2025Review
- Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study.Malaria journal · 2022Article
- Households or Hotspots? Defining Intervention Targets for Malaria Elimination in Ratanakiri Province, Eastern Cambodia.The Journal of infectious diseases · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
Despite well-recognized heterogeneity in malaria transmission, key parameters such as the force of infection (FOI) are generally estimated ignoring the intrinsic variability in individual infection risks. Given the potential impact of heterogeneity on the estimation of the FOI, we estimate this quantity accounting for both observed and unobserved heterogeneity. We used cohort data of children aged 0·5-10 years evaluated for the presence of malaria parasites at three sites in Uganda. Assuming a Susceptible-Infected-Susceptible model, we show how the FOI relates to the point prevalence, enabling the estimation of the FOI by modelling the prevalence using a generalized linear mixed model. We derive bounds for varying parasite clearance distributions. The resulting FOI varies significantly with age and is estimated to be highest among children aged 5-10 years in areas of high and medium malaria transmission and highest in children aged below 1 year in a low transmission setting. Heterogeneity is greater between than within households and it increases with decreasing risk of malaria infection. This suggests that next to the individual's age, heterogeneity in malaria FOI may be attributed to household conditions. When estimating the FOI, accounting for both observed and unobserved heterogeneity in malaria acquisition is important for refining malaria spread models.
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