ArticleActa parasitologica2022
Spatio-temporal Prediction of the Malaria Transmission Risk in Minab District (Hormozgan Province, Southern Iran).
Article in Acta parasitologica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Suitable ecological niches of invasive malaria vector under present and projected climatic conditions in South of Iran.PLoS neglected tropical diseases · 2026Article
- Assessing receptivity to malaria using case surveillance and forest data in a near-elimination setting in northeast Thailand.Malaria journal · 2024Article
- Absence of Asymptomatic Malaria Reservoirs in an Area with a Previous History of Local Malaria Transmission: A Successful Experience in Line with the Malaria Elimination Program in Iran.Journal of arthropod-borne diseases · 2023Article
- Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases.Tropical medicine and infectious disease · 2023Review
- Molecular Method Is Essential to Identify Asymptomatic Malaria Reservoirs: A Successful Experience in the Malaria Elimination Program in Iran.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
introductionMalaria is the most important parasitic disease in tropical and subtropical regions, with more than 240 million cases reported annually. In Iran, indigenous cases occur in its south-eastern region. The aim of this study is to assess the environmental risk of malaria transmission in an endemic area of southern Iran.
methodsThe study was carried out in Minab district (Hormozgan province, southern Iran), with the aim to assess the environmental risk of malaria, based on a spatio-temporal study, using Growing Degree Days (GDD)-based predictions, larval habitat ecology, MaxEnt spatial predictions and malaria transmission data.
resultsThe Gradient Model Risk index showed the highest malaria transmission risk period to be during January-April and October-December. The ecological conditions of water bodies of larval habitats of the four vector species (Anopheles culicifacies, A. dthali, A. fluviatilis and A. stephensi) were assessed, with A. stephensi being the most prevalent and the most widely distributed species.
conclusionThese findings, together with the MaxEnt Anopheles predictive distribution models, allowed identifying villages in danger of malaria transmission in Minab district. This spatio-temporal prediction of malaria transmission risk should be incorporated in the design of malaria control initiatives towards a local malaria early warning system. Moreover, the proposed transmission risk model can be extrapolated, at local scale, to other malaria endemic areas of tropical and subtropical regions.
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Registered trials
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