ArticleMalaria journal2025
How do recurrent malaria infections occur in clinical cohorts: a mathematical modelling study to support study planning.
Article in Malaria journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundRecurrent events of infectious diseases are common and the subject of analyses in many clinical studies. A proper understanding of disease occurrence over time within a cohort provides a basis for study planning and sample size estimation. This study mathematically describes the recurrence of malaria in a malaria-naïve cohort and highlights the necessary assumptions to inform study planning.
methodsTo represent different disease transmission scenarios, five mathematical models with different levels of complexity were constructed to mimic possible real-life scenarios. Model A represents the simplest model with constant infection risk, Model B includes protection due to treatment and reduced individual susceptibility after each infection, Model C shows preventive effects from a vaccination, Model D explores heterogeneous transmission with varying levels of infection risks, and Model E captures temporal dynamics through seasonal variation in infection risk. The models were implemented as compartmental models using a system of ordinary differential equations.
resultsThe different transmission scenarios strongly affected the pattern of recurrent infections. Models A and B had the same number of cases with infections; however, due to treatment effects and immunity development, the number of recurrent events was lower in Model B. Compared to Model B, Model C showed a substantial reduction in both first and recurring infections. In Model D, the subpopulation with a high transmission risk had a higher proportion of recurrent infections, with nearly 100% of this group experiencing more than one infection. Model E demonstrated how seasonal transmission risk leads to temporal dynamics with strong fluctuations in the occurrence of infections. Based on these models, we provide examples of how final cohort sizes can be estimated for different transmission settings.
conclusionsRecurrent infections in longitudinal studies cannot be estimated directly from disease frequency data. However, this study provides a simple set of equations to calculate the number of expected recurrent events. These models can be easily adapted to represent additional transmission and infection dynamics or to model other recurrent diseases like influenza.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.