ArticleBMC infectious diseases2026
A mathematical model examining the role of self-treated individuals in the geographical spread of COVID-19.
Article in BMC infectious diseases, 2026. 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study examines a spatio-temporal mathematical model that explores the role of self-medicating individuals in the spread of COVID-19. The model consists of a system of equations representing four population groups: susceptible, infected, self-treated, and recovered. It conceptualizes disease transmission as a physical diffusion process, where infected and self-medicated individuals move like fluids through a medium of susceptible individuals. To analyze the spatial dynamics, the Fifth-Order Weighted Essentially Non-Oscillatory (WENO) scheme was applied for spatial discretization of the resulting partial differential equations (PDEs), while the Fourth-Order Runge-Kutta method was used for temporal discretization. The model was tested using COVID-19 data from the Embakasi and Kasarani regions of Nairobi, Kenya. Findings highlighted areas with high contact rates and underscored the need for organized public health initiatives to reduce the prevalence of self-medication in the affected communities.
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.