ArticlePloS one2018
Dominant malaria vector species in Nigeria: Modelling potential distribution of Anopheles gambiae sensu lato and its siblings with MaxEnt.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Trends in mosquito species distribution modeling: insights for vector surveillance and disease control.Parasites & vectors · 2023Pooled it
- Mapping mosquitoes and their associated pathogens in West Africa.Infectious diseases of poverty · 2026Article
- Projected distributions of 18 mosquito species in the Republic of Korea under climate change scenarios.Parasites & vectors · 2026Article
- Does climate change influence the spread of malaria in Benin? Insights from ecological niche modeling for surveillance efforts.International health · 2026Article
- Epidemiology of Plasmodium spp. Detection Among Acute Febrile Illness Patients in Two Regions of Nigeria.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025Article
- MaxEnt modeling and risk evaluation of chagas disease vectors in the domestic cycle of Hidalgo, Mexico.PLoS neglected tropical diseases · 2025Article
- Economic empowerment and various preventive strategies play a role in reducing asymptomatic malaria towards the end of the rainy season.Open research Africa · 2025Article
- Article
- A study on the effectiveness of (+)-usnic acid as oral toxic sugar bait against adult male and female Anopheles gambiae.Malaria journal · 2024Article
- Temperature dependence of mosquitoes: Comparing mechanistic and machine learning approaches.PLoS neglected tropical diseases · 2024Article
- Range dynamics ofEcology and evolution · 2024Article
- Potential distribution of malaria vectors in Central Vietnam: A MaxEnt modeling approach.Veterinary world · 2024Article
- Article
- Socioeconomic, Demographic, and Environmental Factors May Inform Malaria Intervention Prioritization in Urban Nigeria.International journal of environmental research and public health · 2024Article
- Article
- Spatiotemporal pattern and suitable areas analysis of equine influenza in global scale (2005-2022).Frontiers in veterinary science · 2024Article
- Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data.Scientific reports · 2023Article
- Utilization of insecticide-treated nets and associated factors among childbearing women in Northern Nigeria.Malaria journal · 2023Article
- Modeling the Effect of Climate Change on the Distribution of Main Malaria Vectors in an Endemic Area, Southeastern Iran.Iranian journal of public health · 2023Article
- Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology.Malaria journal · 2023Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria is a major infectious disease that still affects nearly half of the world's population. Information on spatial distribution of malaria vector species is needed to improve malaria control efforts. In this study we used Maximum Entropy Model (MaxEnt) to estimate the potential distribution of Anopheles gambiae sensu lato and its siblings: Anopheles gambiae sensu stricto, and Anopheles arabiensis in Nigeria. Species occurrence data collected during the period 1900-2010 was used together with 19 bioclimatic, landuse and terrain variables. Results show that these species are currently widespread across all ecological zones. Temperature fluctuation from mean diurnal temperature range, extreme temperature and precipitation conditions, high humidity in dry season from precipitation during warm months, and land use and land cover dynamics have the greatest influence on the current seasonal distribution of the Anopheles species. MaxEnt performed statistically significantly better than random with AUC approximately 0.7 for estimation of the Anopheles species environmental suitability, distribution and variable importance. This model result can contribute to surveillance efforts and control strategies for malaria eradication.
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