AI Article Synopsis
- The study uses the Geographically and Temporally Weighted Regression (GTWR) model to analyze how weather and imported cases affect dengue fever outbreaks in border regions, specifically in Yunnan.
- Researchers performed a detailed analysis of dengue's distribution from 2013 to 2019, using county data to understand the varying impacts of different factors on the disease's spread.
- Their findings indicated that the GTWR model was more effective than traditional methods (like OLS) in revealing significant differences in how local dengue cases relate to weather conditions and imported cases across various counties.
Article Abstract
Objective: This study employs the Geographically and Temporally Weighted Regression (GTWR) model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks, emphasizing the spatial-temporal variability of these factors in border regions.
Methods: We conducted a descriptive analysis of dengue fever's temporal-spatial distribution in Yunnan border areas. Utilizing annual data from 2013 to 2019, with each county in the Yunnan border serving as a spatial unit, we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.
Results: The GTWR model, proving more effective than Ordinary Least Squares (OLS) analysis, identified significant spatial and temporal heterogeneity in factors influencing dengue fever's spread along the Yunnan border. Notably, the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence, meteorological variables, and imported cases across different counties.
Conclusion: In the Yunnan border areas, local dengue incidence is affected by temperature, humidity, precipitation, wind speed, and imported cases, with these factors' influence exhibiting notable spatial and temporal variation.
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| http://dx.doi.org/10.3967/bes2024.056 | DOI Listing |
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