A dengue fever prediction method and device considering extreme weather driving and a medium
By constructing a zero-expansion negative binomial spatiotemporal lag model, and combining case data, extreme weather characteristics, and multi-source data, the problem of insufficient consideration of extreme weather factors in existing technologies has been solved, enabling reliable prediction of dengue fever outbreaks and improving prediction accuracy and credibility.
CN119786073BActive Publication Date: 2025-11-04GUANGZHOU URBAN PLANNING & DESIGN SURVEY RES INST
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Patent Information
- Application Number
- CN202411915180.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-24
- Publication Date
- 2025-11-04
- Estimated Expiration
- 2044-12-24
AI Technical Summary
Technical Problem
Existing dengue fever prediction technologies do not fully consider extreme weather and environmental factors, resulting in weak interpretability of model variables and low reliability of prediction results.
Method used
A zero-inflation negative binomial spatiotemporal lag model was constructed. By combining case data, extreme weather characteristics, spatiotemporal lag data of the epidemic, and multi-source data, an epidemic prediction model was built through data cleaning and multiple screening.
Benefits of technology
It improves the accuracy and reliability of dengue fever outbreak prediction. The model has low complexity and strong interpretability, and can accurately capture the dynamic relationship between extreme weather and dengue fever transmission.
✦ Generated by Eureka AI based on patent content.
Abstract
The application discloses a dengue fever prediction method and device considering extreme weather driving and a medium. The method comprises the following steps: acquiring case data and multi-source data of a target area, wherein the multi-source data comprises meteorological data, environmental data and socio-economic data; constructing extreme weather characteristics according to the meteorological data and a preset extreme weather threshold; obtaining epidemic spatiotemporal lag data according to the spatial autocorrelation relationship and the temporal autocorrelation relationship of the case data; adding the epidemic spatiotemporal lag term in a zero-inflated negative binomial regression model to obtain an initial epidemic prediction model; the epidemic prediction model comprises a quadratic term of the extreme weather characteristics; and performing parameter fitting on the initial epidemic prediction model based on the case data, the extreme weather characteristics, the epidemic spatiotemporal lag data and the multi-source data to obtain a fitted epidemic prediction model, so as to predict future epidemics. The application can effectively improve the credibility of regional dengue fever epidemic prediction under the background of climate change.
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