Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability

• Published in: Infectious diseases of poverty Vol. 8; no. 1; pp. 98 - 9
• Main Authors: Zheng, Xueli, Zhong, Daibin, He, Yulan, Zhou, Guofa
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.12.2019; BioMed Central; BMC
• Subjects: Accuracy; Aedes - physiology; Aedes albopictus; Analysis; Animal Distribution; Animals; Asian tiger mosquito; China; Climate; Climate change; Data analysis; Dengue fever; Diseases; Distribution; Environment; Environmental conditions; Environmental quality; Epidemics; Machine Learning; Models, Biological; Mosquito Control; Mosquito Vectors - physiology; Mosquitoes; Nonnative species; Northern margin; Pathogenic microorganisms; Precipitation; Seasonal variations; Seasons; South China Sea; Surveillance; Tropical diseases; Vaccines; China; South China Sea; Environment; Aedes albopictus; Climate; China; Distribution; Northern margin
• ISSN: 2049-9957; 2095-5162; 2049-9957
• DOI: 10.1186/s40249-019-0612-y

Aedes albopictus is a highly invasive mosquito species and a major vector of numerous viral pathogens. Many recent dengue fever outbreaks in China have been caused solely by the vector. Mapping of the potential distribution ranges of Ae. albopictus is crucial for epidemic preparedness and the monitoring of vector populations for disease control. Climate is a key factor influencing the distribution of the species. Despite field studies indicating seasonal population variations, very little modeling work has been done to analyze how environmental conditions influence the seasonality of Ae. albopictus. The aim of the present study was to develop a model based on available observations, climatic and environmental data, and machine learning methods for the prediction of the potential seasonal ranges of Ae. albopictus in China. We collected comprehensive up-to-date surveillance data in China, particularly records from the northern distribution margin of Ae. albopictus. All records were assigned long-term (1970-2000) climatic data averages based on the WorldClim 2.0 data set. Machine learning regression tree models were developed using a 10-fold cross-validation method to predict the potential seasonal (or monthly) distribution ranges of Ae. albopictus in China at high resolution based on environmental conditions. The models were assessed based on sensitivity, specificity, and accuracy, using area under curve (AUC). WorldClim 2.0 and climatic and environmental data were used to produce environmental conduciveness (probability) prediction surfaces. Predicted probabilities were generated based on the averages of the 10 models. During 1998-2017, Ae. albopictus was observed at 200 out of the 242 localities surveyed. In addition, at least 15 new Ae. albopictus occurrence sites lay outside the potential ranges that have been predicted using models previously. The average accuracy was 98.4% (97.1-99.5%), and the average AUC was 99.1% (95.6-99.9%). The predicted Ae. albopictus distribution in winter (December-February) was limited to a small subtropical-tropical area of China, and Ae. albopictus was predicted to occur in northern China only during the short summer season (usually June-September). The predicted distribution areas in summer could reach northeastern China bordering Russia and the eastern part of the Qinghai-Tibet Plateau in southwestern China. Ae. albopictus could remain active in expansive areas from central to southern China in October and November. Climate and environmental conditions are key factors influencing the seasonal distribution of Ae. albopictus in China. The areas predicted to potentially host Ae. albopictus seasonally in the present study could reach northeastern China and the eastern slope of the Qinghai-Tibet Plateau. Our results present new evidence and suggest the expansion of systematic vector population monitoring activities and regular re-assessment of epidemic risk potential.