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菲律宾班氏丝虫病病媒的地理空间分析。

Geospatial analysis of , vector of Bancroftian Filariasis in the Philippines.

作者信息

Recopuerto-Medina Loida M, Atienza Regina Rebecca A, Flaminiano Jorienne Mae F, Manuel Margaret M, Perez Gia Mikaela T, Zeta Corky Philip M, Medina Francis Kristin Nicole R, Dagamac Nikki Heherson A

机构信息

Department of Biological Sciences, College of Science, University of Santo Tomas, 1008 Espana, Manila, Philippines.

College of Nursing, University of Santo Tomas, 1008 Espana, Manila, Philippines.

出版信息

J Parasit Dis. 2025 Jun;49(2):407-418. doi: 10.1007/s12639-024-01766-z. Epub 2024 Dec 6.

Abstract

Bancroftian filariasis, one of the Philippines' neglected tropical diseases, is a parasitic disease caused by and transmitted by , which thrive in the plantations abundant in certain Philippine regions. Eliminating this disease is far from being achieved, thus emphasizing the need for a better control or elimination program by constructing a contemporary predictive model of the mosquito, , and identifying key environmental variables that favor the mosquito species. Modeling of the distribution of lymphatic filariasis was divided into two phases: data collection of disease occurrences and environmental variables from 1985 to 2019 and model calibration and testing utilizing the MaxEnt algorithm. Model sensitivity was validated through the area under the curve (AUC) method. The model had a mean training AUC of 0.995 ± 0.001. The Jackknife test was performed to determine the effect of the assessed variables on the prevalence of the disease and revealed that isothermality has the highest gain when used in isolation. The total frequency of lymphatic filariasis was mapped using the QGIS software to exhibit the suitability of agricultural plantations as breeding grounds for populations.

摘要

班氏丝虫病是菲律宾被忽视的热带病之一,是一种由[具体寄生虫名称1]引起并由[具体传播媒介名称1]传播的寄生虫病,这些寄生虫在菲律宾某些地区大量存在的[具体植物名称1]种植园中繁衍。消除这种疾病远未实现,因此强调需要通过构建当代蚊子[具体蚊子名称1]的预测模型,并确定有利于该蚊种的关键环境变量,来制定更好的控制或消除计划。淋巴丝虫病分布建模分为两个阶段:收集1985年至2019年疾病发生情况和环境变量的数据,以及利用最大熵算法进行模型校准和测试。通过曲线下面积(AUC)方法验证模型敏感性。该模型的平均训练AUC为0.995±0.001。进行刀切法检验以确定评估变量对疾病流行率的影响,结果表明等温性单独使用时增益最高。使用QGIS软件绘制淋巴丝虫病的总发病率图,以展示农业种植园作为[具体蚊子名称1]种群繁殖地的适宜性。

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