{"title":"[Spatiotemporal distribution of <i>Aedes albopictus</i> and its influencing factors in China from 2000 to 2019].","authors":"Z Jiao, L Qu, D Wang, Y Zhang, S Lü","doi":"10.16250/j.32.1915.2025047","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To investigate the spatial distribution of <i>Aedes albopictus</i> in China at different time periods from 2000 to 2019, so as to provide insights into precise management of <i>Ae. albopictus</i> in China.</p><p><strong>Methods: </strong>Data pertaining to the distribution of <i>Ae. albopictus</i> in China from 2000 to 2019 were collected through literature retrieval with terms of \"<i>Aedes albopictus</i>\", \"monitoring\", \"survey\", \"density\", \"distribution\", and \"outbreak\" in national and international databases. The title and time of the publication, sampling sites, sampling time, mosquito capture methods, and mosquito species and density were extracted, and the longitude and latitude of sampling sites were obtained through Baidu Map. Meteorological element data at meteorological observation stations within China were obtained from the National Climatic Data Center of the United States, and the annual maximum temperature, annual minimum temperature, average temperature in January, average temperature in July, annual temperature range, daily temperature range and relative humidity were calculated and subjected to Kriging interpolation. Monthly cumulative precipitation grid data and monthly average temperature grid data with a resolution of 1 km for China from 2000 to 2019 were obtained from the National Tibetan Plateau Scientific Data Center, and the annual precipitation and annual average temperature were calculated cumulatively. Population density data in China from 2000 to 2019 were obtained from the WorldPop Hub, and the gross domestic product (GDP) in China was obtained from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. The above data were divided into 5-year intervals to calculate data during the periods from 2000 to 2004, from 2005 to 2009, from 2010 to 2014, and from 2015 to 2019. <i>Ae. albopictus</i> distribution data were modeled in China from 2000 to 2019 and during each period with the classification random forest (RF) model, to predict the distribution of <i>Ae. albopictus</i> across the country and analyze the distribution of <i>Ae. albopictus</i> based on the seven major climate zones in China. The performance of RF models was evaluated by accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC), and the importance of each feature in the RF model was evaluated with mean decrease accuracy (MDA).</p><p><strong>Results: </strong>A total of 1 191 Chinese publictions and 391 English publications were retrieved, among which 580 articles provided detailed data on the sampling sites of <i>Ae. albopictus</i> and specific sampling years, meeting the inclusion criteria. A total of 2 234 <i>Ae. albopictus</i> sampling sites were included in China from 2000 to 2019, and RF modeling results showed that the overall <i>Ae. Albopictus</i> distribution area was mainly found in southeastern and southwestern provinces of China from 2000 to 2019, with scattered distribution in coastal areas of northeastern provinces, such as Liaoning Province. The accuracy, precision, recall and AUC of the RF model were 0.915 to 0.947, 0.933 to 0.975, 0.898 to 0.978, and 0.902 to 0.932 for the distribution of <i>Ae. albopictus</i> at different time periods from 2000 to 2019. Among all features in the RF models, population density was the most contributing factor to the distribution of <i>Ae. albopictus</i> in China, followed by GDP, and all meteorological variables contributed relatively less to the predictive power of the RF model. In China's seven major climate zones, <i>Ae. albopictus</i> was almost entirely distributed in the marginal tropical humid region, the north subtropical humid region, and the warm temperate semi-humid region. The combined distribution area of these three zones accounted for 100.0% of the national distribution area from 2000 to 2004, from 2005 to 2009, and from 2010 to 2014, and 99.9% from 2015 to 2019, and the proportion of <i>Ae. albopictus</i> distribution area in the warm temperate semihumid region increased gradually from 20.2% to 30.2%.</p><p><strong>Conclusions: </strong><i>Ae. albopictus</i> is mainly distributed in the southeastern and southwestern provinces of China and is greatly influenced by population and economic factors. The warm temperate semi-humid region in China is gradually becoming a hot spot for the distribution of <i>Ae. albopictus</i>.</p>","PeriodicalId":38874,"journal":{"name":"中国血吸虫病防治杂志","volume":"37 3","pages":"268-275"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国血吸虫病防治杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.16250/j.32.1915.2025047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: To investigate the spatial distribution of Aedes albopictus in China at different time periods from 2000 to 2019, so as to provide insights into precise management of Ae. albopictus in China.
Methods: Data pertaining to the distribution of Ae. albopictus in China from 2000 to 2019 were collected through literature retrieval with terms of "Aedes albopictus", "monitoring", "survey", "density", "distribution", and "outbreak" in national and international databases. The title and time of the publication, sampling sites, sampling time, mosquito capture methods, and mosquito species and density were extracted, and the longitude and latitude of sampling sites were obtained through Baidu Map. Meteorological element data at meteorological observation stations within China were obtained from the National Climatic Data Center of the United States, and the annual maximum temperature, annual minimum temperature, average temperature in January, average temperature in July, annual temperature range, daily temperature range and relative humidity were calculated and subjected to Kriging interpolation. Monthly cumulative precipitation grid data and monthly average temperature grid data with a resolution of 1 km for China from 2000 to 2019 were obtained from the National Tibetan Plateau Scientific Data Center, and the annual precipitation and annual average temperature were calculated cumulatively. Population density data in China from 2000 to 2019 were obtained from the WorldPop Hub, and the gross domestic product (GDP) in China was obtained from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. The above data were divided into 5-year intervals to calculate data during the periods from 2000 to 2004, from 2005 to 2009, from 2010 to 2014, and from 2015 to 2019. Ae. albopictus distribution data were modeled in China from 2000 to 2019 and during each period with the classification random forest (RF) model, to predict the distribution of Ae. albopictus across the country and analyze the distribution of Ae. albopictus based on the seven major climate zones in China. The performance of RF models was evaluated by accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC), and the importance of each feature in the RF model was evaluated with mean decrease accuracy (MDA).
Results: A total of 1 191 Chinese publictions and 391 English publications were retrieved, among which 580 articles provided detailed data on the sampling sites of Ae. albopictus and specific sampling years, meeting the inclusion criteria. A total of 2 234 Ae. albopictus sampling sites were included in China from 2000 to 2019, and RF modeling results showed that the overall Ae. Albopictus distribution area was mainly found in southeastern and southwestern provinces of China from 2000 to 2019, with scattered distribution in coastal areas of northeastern provinces, such as Liaoning Province. The accuracy, precision, recall and AUC of the RF model were 0.915 to 0.947, 0.933 to 0.975, 0.898 to 0.978, and 0.902 to 0.932 for the distribution of Ae. albopictus at different time periods from 2000 to 2019. Among all features in the RF models, population density was the most contributing factor to the distribution of Ae. albopictus in China, followed by GDP, and all meteorological variables contributed relatively less to the predictive power of the RF model. In China's seven major climate zones, Ae. albopictus was almost entirely distributed in the marginal tropical humid region, the north subtropical humid region, and the warm temperate semi-humid region. The combined distribution area of these three zones accounted for 100.0% of the national distribution area from 2000 to 2004, from 2005 to 2009, and from 2010 to 2014, and 99.9% from 2015 to 2019, and the proportion of Ae. albopictus distribution area in the warm temperate semihumid region increased gradually from 20.2% to 30.2%.
Conclusions: Ae. albopictus is mainly distributed in the southeastern and southwestern provinces of China and is greatly influenced by population and economic factors. The warm temperate semi-humid region in China is gradually becoming a hot spot for the distribution of Ae. albopictus.
期刊介绍:
Chinese Journal of Schistosomiasis Control (ISSN: 1005-6661, CN: 32-1374/R), founded in 1989, is a technical and scientific journal under the supervision of Jiangsu Provincial Health Commission and organised by Jiangsu Institute of Schistosomiasis Control. It is a scientific and technical journal under the supervision of Jiangsu Provincial Health Commission and sponsored by Jiangsu Institute of Schistosomiasis Prevention and Control. The journal carries out the policy of prevention-oriented, control-oriented, nationwide and grassroots, adheres to the tenet of scientific research service for the prevention and treatment of schistosomiasis and other parasitic diseases, and mainly publishes academic papers reflecting the latest achievements and dynamics of prevention and treatment of schistosomiasis and other parasitic diseases, scientific research and management, etc. The main columns are Guest Contributions, Experts‘ Commentary, Experts’ Perspectives, Experts' Forums, Theses, Prevention and Treatment Research, Experimental Research, The main columns include Guest Contributions, Expert Commentaries, Expert Perspectives, Expert Forums, Treatises, Prevention and Control Studies, Experimental Studies, Clinical Studies, Prevention and Control Experiences, Prevention and Control Management, Reviews, Case Reports, and Information, etc. The journal is a useful reference material for the professional and technical personnel of schistosomiasis and parasitic disease prevention and control research, management workers, and teachers and students of medical schools.
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