ArticleDiseases (Basel, Switzerland)2026
Modeling of Vector-Borne Disease Across Governorates and Districts in Oman, 2020-2024.
Article in Diseases (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionOman has transitioned from travel-related dengue cases to local outbreaks since 2018, with heterogeneous patterns across governorates and districts. Understanding how climate, population, and vector indicators jointly shape dengue risk at different administrative levels is essential for targeted control.
methodsThis study compiled weekly data (2020-2024) on dengue cases, mosquito surveillance, climate, and population from national sources. Using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS v4, we modelled constructs for Weather, Population, Vector, and Vector-borne Disease (VBD). Measurement quality was assessed using various statistics and with 5000-sample bootstrapping. Multigroup Analysis (MGA) with permutation and Measurement Invariance of Composite Models (MICOM) tested invariance and compared structural paths across governorates (Muscat, North Al Batinah, Ad Dakhiliyah) and districts (Seeb, Sohar, Bahla).
resultsVector abundance mediated climate and population effects on dengue, with marked spatial heterogeneity. At the governorate level, the Vector → VBD path was strongest in Ad Dakhiliyah (β ≈ 0.436) and negligible in Muscat (β ≈ -0.021); indirect effects from Population and Weather to VBD were significantly higher in Ad Dakhiliyah than comparators. At the district level, Bahla showed stronger Vector → VBD and Weather → Vector relationships than Seeb and Sohar, while Seeb exhibited low explanatory power across paths. MICOM indicated partial measurement invariance, suggesting caution in cross-group comparisons.
conclusionsDengue risk in Oman is primarily vector-driven but differs by setting. Inland/rural areas are more sensitive to climate-vector dynamics, requiring enhanced surveillance and climate-informed early warning. Urban centers may need models incorporating mobility and behavior. Findings support localized interventions and the integration of trap positivity and density into district-level prediction and control.
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