Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008–2016)

Autoři: Binghua Zhu aff001;  Ligui Wang aff001;  Haiying Wang aff003;  Zhidong Cao aff004;  Lei Zha aff001;  Ze Li aff001;  Zhongyang Ye aff001;  Jinping Zhang aff002;  Hongbin Song aff001;  Yansong Sun aff005
Působiště autorů: Chinese PLA Center for Disease Control and Prevention, Beijing, China aff001;  305 Hospital of PLA, Beijing, China aff002;  Joint Service Institute, National Defense University of PLA, Beijing, China aff003;  The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China aff004;  College of Military Medicine, Academy of Military Sciences, Beijing, China aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article



In 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.


Dengue 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.


We 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.


Our model was accurate and timely, with consideration of interactive effects between meteorological factors.

Klíčová slova:

Dengue fever – Humidity – China – Infectious diseases – Mosquitoes – Statistical data


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2019 Číslo 12
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