ArticlePloS one2019
Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008-2016).
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of published literature on mosquito control action thresholds across the world.PLoS neglected tropical diseases · 2023Pooled it
- A systematic review of dengue outbreak prediction models: Current scenario and future directions.PLoS neglected tropical diseases · 2023Pooled it
- Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis.Scientific reports · 2025Article
- Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing.Scientific data · 2025Article
- Epidemiological Characteristics and the Dynamic Transmission Model of Dengue Fever in Zhanjiang City, Guangdong Province in 2018.Tropical medicine and infectious disease · 2022Article
- Climatic and socio-economic factors supporting the co-circulation of dengue, Zika and chikungunya in three different ecosystems in Colombia.PLoS neglected tropical diseases · 2021Article
- Geoclimatic, demographic and socioeconomic characteristics related to dengue outbreaks in Southeastern Brazil: an annual spatial and spatiotemporal risk model over a 12-year period.Revista do Instituto de Medicina Tropical de Sao Paulo · 2021Article
- Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China.Ecotoxicology and environmental safety · 2020Article
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10 authors.
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Abstract
introductionIn order to improve the prediction accuracy of dengue fever incidence, we constructed a prediction model with interactive effects between meteorological factors, based on weekly dengue fever cases in Guangdong, China from 2008 to 2016.
methodsDengue fever data were derived from statistical data from the China National Notifiable Infectious Disease Reporting Information System. Daily meteorological data were obtained from the China Integrated Meteorological Information Sharing System. The minimum temperature for transmission was identified using data fitting and the Ross-Macdonald model. Correlations and interactive effects were examined using Spearman's rank correlation and multivariate analysis of variance. A probit regression model to describe the incidence of dengue fever from 2008 to 2016 and forecast the 2017 incidence was constructed, based on key meteorological factors, interactive effects, mosquito-vector factors, and other important factors.
resultsWe found the minimum temperature suitable for dengue transmission was ≥18°C, and as 97.91% of cases occurred when the minimum temperature was above 18 °C, the data were used for model training and construction. Epidemics of dengue are related to mean temperature, maximum/minimum and mean atmospheric pressure, and mean relative humidity. Moreover, interactions occur between mean temperature, minimum atmospheric pressure, and mean relative humidity. Our weekly probit regression prediction model is 0.72. Prediction of dengue cases for the first 41 weeks of 2017 exhibited goodness of fit of 0.60.
conclusionOur model was accurate and timely, with consideration of interactive effects between meteorological factors.
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