ArticleInternational journal of biometeorology2025
Modelling the effects of climate and human factor on Lassa fever distribution in Ondo State Nigeria.
Article in International journal of biometeorology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Temporal dynamics, climate associations, and surveillance performance of Lassa fever in Nigeria, 2020-2025: a national longitudinal analysis.Osong public health and research perspectives · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lassa fever is an acute viral haemorrhagic disease caused by the Lassa virus. Transmission to humans primarily occurs through direct contact with Mastomys rats or via the ingestion of food or usage of household items contaminated with the urine or faeces of infected rats. The MaxEnt algorithm was used to estimate the distribution of Lassa fever based on data on the incidence of the disease, ecogeographic features, and human factors. Principal component analysis (PCA) was used to mitigate multicollinearity among the environmental variables. The model's accuracy was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). The prevalence of Lassa fever is anticipated to be substantially affected by human factors (population density, roads, built-settlement, poverty), climatic variables (Prec11, Tmean01, Bio7, Bio12), and altitude. The model distribution map revealed that Owo, Ose, Akure North, Akure South, Akoko South-West, Akoko South-East, Akoko North-East, Ifedore, Idanre, Ondo, and Akoko North-West are very suitable regions. Our suitability map identifies hotspots, aiding public health officials in resource distribution to mitigate the current Lassa fever epidemic in Ondo State, Nigeria.
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.