[Spatiotemporal distribution of Aedes albopictus and its influencing factors in China from 2000 to 2019].

Q3 Medicine
Z Jiao, L Qu, D Wang, Y Zhang, S Lü
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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. 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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.

2000 - 2019年中国白纹伊蚊时空分布及影响因素分析
目的:了解2000 - 2019年中国不同时段白纹伊蚊的空间分布情况,为白纹伊蚊的精准防控提供依据。白纹伊蚊在中国方法:收集有关伊蚊分布的资料。以“白纹伊蚊”、“监测”、“调查”、“密度”、“分布”、“疫情”为检索词,在国内外数据库中检索2000 - 2019年中国白纹伊蚊的相关文献。提取出版物的标题和出版时间、采样地点、采样时间、蚊虫捕获方法、蚊虫种类和密度,并通过百度地图获取采样地点的经纬度。利用美国国家气候数据中心提供的中国境内各气象观测站气象要素资料,计算年最高气温、年最低气温、1月平均气温、7月平均气温、年气温差、日气温差和相对湿度,并进行Kriging插值。利用青藏高原国家科学数据中心2000 - 2019年中国逐月累积降水格点数据和1 km分辨率逐月平均气温格点数据,进行年降水量和年平均气温的累积计算。2000 - 2019年中国人口密度数据来源于WorldPop Hub,中国国内生产总值(GDP)来源于中国科学院地理科学与资源研究所。以上数据以5年为间隔,分别计算2000 - 2004年、2005 - 2009年、2010 - 2014年、2015 - 2019年的数据。Ae。采用分类随机森林(RF)模型对2000 - 2019年中国白纹伊蚊分布数据进行建模,预测白纹伊蚊的分布。全国白纹伊蚊分布情况分析。基于中国七大气候带的白纹伊蚊。通过准确度、精密度、召回率和接收者工作特征曲线下面积(AUC)来评价射频模型的性能,并通过平均降低精度(MDA)来评价射频模型中每个特征的重要性。结果:共检索到中文文献1 191篇,英文文献391篇,其中提供伊蚊采样点详细资料的文献580篇。白纹伊蚊和特定采样年份,符合纳入标准。共2 234只Ae。选取2000 - 2019年中国白纹伊蚊采样点,RF建模结果显示,全国白纹伊蚊种群数量总体呈下降趋势。2000 - 2019年白纹伊蚊分布区域主要分布在东南、西南两省,零星分布在辽宁等东北沿海地区。Ae分布的准确度、精密度、召回率和AUC分别为0.915 ~ 0.947、0.933 ~ 0.975、0.898 ~ 0.978和0.902 ~ 0.932。2000年至2019年不同时期的白纹伊蚊。在RF模型的所有特征中,种群密度是对伊蚊分布影响最大的因子。其次是GDP,所有气象变量对RF模型预测能力的贡献相对较小。在中国的七个主要气候带中,Ae。白纹伊蚊几乎全部分布在热带湿润边缘区、北亚热带湿润区和暖温带半湿润区。2000 - 2004年、2005 - 2009年、2010 - 2014年这3个区域的总分布面积占全国分布面积的100.0%,2015 - 2019年占全国分布面积的99.9%。暖温带半湿润地区白纹伊蚊分布面积由20.2%逐渐增加至30.2%。结论:Ae。白纹伊蚊主要分布于中国东南部和西南部省份,受人口和经济因素影响较大。中国暖温带半湿润地区正逐渐成为伊蚊分布的热点地区。蚊。
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来源期刊
中国血吸虫病防治杂志
中国血吸虫病防治杂志 Medicine-Medicine (all)
CiteScore
1.30
自引率
0.00%
发文量
7021
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.    The journal is now included in important domestic databases, such as Chinese Core List (8th edition), China Science Citation Database (Core Edition), China Science and Technology Core Journals (Statistical Source Journals), and is also included in MEDLINE/PubMed, Scopus, EBSCO, Chemical Abstract, Embase, Zoological Record, JSTChina, Ulrichsweb, Western Pacific Region Index Medicus, CABI and other international authoritative databases.
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