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

Abstract

BackgroundAedes 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.MethodsWe 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.ResultsDuring 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.ConclusionsClimate 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.

Analysis of the Spatial Distribution of Aedes Albopictus by Indicator Kriging in an Urban Area of Shanghai, China

“…albopictus sampling in Shanghai, China [33]. The indicator Kriging maps revealed different oviposition hot spots in 2018 versus 2019, which may be attributed to the following reasons: rst, the mosquito abundance and seasonal distribution vary from year to year due to changes in temperature, precipitation, and humidity [43,44]; second, the result of MOT2018 may provide a reference for strict vector control in high-density areas in 2019, causing a lowered MPI density in these sites compared to other regions in 2019. The Co-Kriging is recommended to improve the accuracy of spatial interpolation and the prediction of Ae.…”

“…MOTs were placed outdoors on grasslands, kept away from direct sunlight, rain, and wind at ground level by a skilled technician, and maintained unchanged until the end of the study. [41][42][43][44][45][46]. MOTs that were removed, emptied, or interfered for any reasons were excluded from further analysis.…”

Analysis of the Spatial Distribution of Aedes Albopictus by Indicator Kriging in an Urban Area of Shanghai, China

“…Then MPI of 5 was chosen as the threshold of indicator, which was also used as the threshold for the risk of dengue fever in China [32]. The indicator Kriging maps revealed different oviposition hot spots in 2018 versus 2019, which may be attributed to the following reasons: rst, the mosquito abundance and seasonal distribution vary from year to year due to changes in temperature, precipitation, and humidity [44,45]; second, the result of MOT2018 may provide a reference for strict vector control in high-density areas in 2019, causing a lowered MPI density in these sites compared to other regions in 2019. The Co-Kriging is recommended to improve the accuracy of spatial interpolation and the prediction of Ae.…”

Dengue Disease Dynamics are Modulated by the Combined Influence of Precipitation and Landscapes: A Machine Learning-based Approach

“…Most previous studies investigated the effects of either dynamic climate factors (Carvajal, Viacrusis, et al 2018, Zheng, et al 2019, Arcari, Tapper and Pfueller 2007, Tovar-Zamora, et al 2019, Bavia, et al 2020 or static spatial distributions of landscape attributes (Seidahmed, et al 2018, Vanwambeke, Lambin, et al 2007, Vanwambeke, N.Bennett and Kapan 2011, Sarfraz, et al 2012) on the temporal variations or spatial distributions of mosquito occurrence and dengue incidence. However, landscape and climate conditions can change concurrently in time and space, and their spatiotemporal interrelation may play a decisive role in determining the positive or negative effects on mosquitoes and dengue, such as interference during flood events.…”

“…This study aimed to examine the combinatory influence of landscape and climate features on mosquito occurrence and dengue incidence across Metropolitan Manila, the Philippines. We employed some advanced machine learning algorithms due to its growing utilization to explore the influence of landscape features or climate on dengue disease (Carvajal, Viacrusis, et al 2018, Guo, et al 2017, Ong, et al 2017, Chen, et al 2018, Baquero, Santana and Chiaravalloti-Neto 2018 and mosquito occurrence (Mwanga, et al 2019, Jiménez, et al 2019, Früh, et al 2018, Zheng, et al 2019. By selecting important environmental features for RFs, we further examined and described the optimal combination of landscape and climate conditions that influence dengue disease and mosquito occurrence using modelbased (MOB) recursive partitioning.…”