National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory of Parasite and Vector Biology, National Health Commission of the People's Republic of China; World Health Organization Collaborating Center for Tropical Diseases, Shanghai, 200025, China.
National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Infect Dis Poverty. 2023 Apr 11;12(1):35. doi: 10.1186/s40249-023-01085-0.
Cryptosporidiosis is a zoonotic intestinal infectious disease caused by Cryptosporidium spp., and its transmission is highly influenced by climate factors. In the present study, the potential spatial distribution of Cryptosporidium in China was predicted based on ecological niche models for cryptosporidiosis epidemic risk warning and prevention and control.
The applicability of existing Cryptosporidium presence points in ENM analysis was investigated based on data from monitoring sites in 2011-2019. Cryptosporidium occurrence data for China and neighboring countries were extracted and used to construct the ENMs, namely Maxent, Bioclim, Domain, and Garp. Models were evaluated based on Receiver Operating Characteristic curve, Kappa, and True Skill Statistic coefficients. The best model was constructed using Cryptosporidium data and climate variables during 1986‒2010, and used to analyze the effects of climate factors on Cryptosporidium distribution. The climate variables for the period 2011‒2100 were projected to the simulation results to predict the ecological adaptability and potential distribution of Cryptosporidium in future in China.
The Maxent model (AUC = 0.95, maximum Kappa = 0.91, maximum TSS = 1.00) fit better than the other three models and was thus considered the best ENM for predicting Cryptosporidium habitat suitability. The major suitable habitats for human-derived Cryptosporidium in China were located in some high-population density areas, especially in the middle and lower reaches of the Yangtze River, the lower reaches of the Yellow River, and the Huai and the Pearl River Basins (cloglog value of habitat suitability > 0.9). Under future climate change, non-suitable habitats for Cryptosporidium will shrink, while highly suitable habitats will expand significantly (χ = 76.641, P < 0.01; χ = 86.836, P < 0.01), and the main changes will likely be concentrated in the northeastern, southwestern, and northwestern regions.
The Maxent model is applicable in prediction of Cryptosporidium habitat suitability and can achieve excellent simulation results. These results suggest a current high risk of transmission and significant pressure for cryptosporidiosis prevention and control in China. Against a future climate change background, Cryptosporidium may gain more suitable habitats within China. Constructing a national surveillance network could facilitate further elucidation of the epidemiological trends and transmission patterns of cryptosporidiosis, and mitigate the associated epidemic and outbreak risks.
隐孢子虫病是一种由隐孢子虫属引起的人畜共患肠道传染病，其传播受气候因素的影响很大。本研究基于隐孢子虫病流行风险预警和防控的生态位模型，预测了中国隐孢子虫的潜在空间分布。
基于 2011-2019 年监测点的数据，调查了现有隐孢子虫存在点在 ENM 分析中的适用性。提取中国及周边国家的隐孢子虫发生数据，构建 Maxent、Bioclim、Domain 和 Garp 等 ENM 模型。根据接收者操作特征曲线、Kappa 和真技能统计系数对模型进行评估。利用 1986-2010 年的隐孢子虫数据和气候变量构建最佳模型，并分析气候因素对隐孢子虫分布的影响。将 2011-2100 年的气候变量投射到模拟结果中，预测未来中国隐孢子虫的生态适应性和潜在分布。
Maxent 模型（AUC=0.95，最大 Kappa=0.91，最大 TSS=1.00）比其他三种模型拟合得更好，因此被认为是预测隐孢子虫生境适宜度的最佳 ENM。中国人类来源的隐孢子虫的主要适宜生境位于一些人口密度较高的地区，特别是长江中下游、黄河下游和淮河流域和珠江流域（生境适宜度 cloglog 值>0.9）。在未来气候变化下，隐孢子虫的非适宜生境将缩小，而高度适宜的生境将显著扩大（χ2=76.641，P<0.01；χ2=86.836，P<0.01），主要变化可能集中在东北、西南和西北地区。
Maxent 模型适用于预测隐孢子虫生境适宜度，并能获得良好的模拟结果。这些结果表明，目前中国隐孢子虫病的传播风险很高，防控压力很大。在未来气候变化背景下，中国可能会有更多适合隐孢子虫的栖息地。构建全国性监测网络有助于进一步阐明隐孢子虫病的流行病学趋势和传播模式，并减轻相关的流行和暴发风险。
Zotero 插件
