背景:钩端螺旋体病在温带地区被认为是一种被忽视的人畜共患疾病,但在南美洲等热带气候国家是一种地方病,南亚,和东南亚。在2005年至2014年期间,马来西亚钩端螺旋体病的发病率从每100,000人中的1.45例增加到25.94例。随着雪兰冶市发病率的增加,马来西亚,和频繁的气候变化动态,对疾病热点地区及其与水文气候因素的关联的研究将进一步加强疾病监测和公共卫生干预。
目的:本研究旨在利用地理信息系统和遥感技术,研究雪兰冶市2011-2019年钩端螺旋体病热点区域的时空分布与水文气候因素之间的关系,建立钩端螺旋体病热点预测模型。
方法:这将是一项关于钩端螺旋体病的生态横断面研究,其中包括地理信息系统和遥感制图,并使用次级数据进行分析。2011年1月至2019年12月在雪兰冶市的钩端螺旋体病病例应从雪兰冶州卫生部获得。将根据经度和纬度确定实验室确认的病例,并提供有关可能感染源的数据。将从测绘部获得地形数据,该数据由分区边界和雪兰冶市的河流分布组成。ArcGISPro软件将用于评估案例的聚类,并使用Getis-OrdGi*工具进行映射。降雨和地表温度的卫星图像将从Giovanni国家航空航天局EarthData网站获取,并进行处理以获得以毫米和摄氏度为单位的平均每月值。同时,每月平均河流水文水平将从排水和灌溉部获得。然后将数据作为主题层输入到ArcGIS软件中进行进一步分析。然后将使用人工智能Phyton软件中的人工神经网络分析来获得钩端螺旋体病热点预测模型。
结果:这项研究于2022年11月获得资助。数据收集,processing,和分析于2022年12月开始,研究结果预计将于2024年底公布。钩端螺旋体病的分布和群集可能与降雨的水文气候因素显着相关。地表温度,和河流水文水平。
结论:本研究将探讨钩端螺旋体病热点地区与雪兰冶市水文气候因素的关联,并随后建立钩端螺旋体病预测模型。构建的预测模型可能用于设计和增强疾病预防的公共卫生计划。
■PRR1-10.2196/43712。
BACKGROUND: Leptospirosis is considered a neglected zoonotic disease in temperate regions but an endemic disease in countries with tropical climates such as South America, Southern Asia, and Southeast Asia. There has been an increase in leptospirosis incidence in Malaysia from 1.45 to 25.94 cases per 100,000 population between 2005 and 2014. With increasing incidence in Selangor, Malaysia, and frequent climate change dynamics, a
study on the disease hotspot areas and their association with the hydroclimatic factors would further enhance disease surveillance and public health interventions.
OBJECTIVE: This
study aims to examine the association between the spatio-temporal distribution of leptospirosis hotspot areas from 2011 to 2019 with the hydroclimatic factors in Selangor using the geographical information system and remote sensing techniques to develop a leptospirosis hotspot predictive model.
METHODS: This will be an ecological cross-sectional
study with geographical information system and remote sensing mapping and analysis concerning leptospirosis using secondary data. Leptospirosis cases in Selangor from January 2011 to December 2019 shall be obtained from the Selangor State Health Department. Laboratory-confirmed cases with data on the possible source of infection would be identified and georeferenced according to their longitude and latitudes. Topographic data consisting of subdistrict boundaries and the distribution of rivers in Selangor will be obtained from the Department of Survey and Mapping. The ArcGIS Pro software will be used to evaluate the clustering of the cases and mapped using the Getis-Ord Gi* tool. The satellite images for rainfall and land surface temperature will be acquired from the Giovanni National Aeronautics and Space Administration EarthData website and processed to obtain the average monthly values in millimeters and degrees Celsius. Meanwhile, the average monthly river hydrometric levels will be obtained from the Department of Drainage and Irrigation. Data are then inputted as thematic layers and in the ArcGIS software for further analysis. The artificial neural network analysis in artificial intelligence Phyton software will then be used to obtain the leptospirosis hotspot predictive model.
RESULTS: This research was funded as of November 2022. Data collection, processing, and analysis commenced in December 2022, and the results of the
study are expected to be published by the end of 2024. The leptospirosis distribution and clusters may be significantly associated with the hydroclimatic factors of rainfall, land surface temperature, and the river hydrometric level.
CONCLUSIONS: This
study will explore the associations of leptospirosis hotspot areas with the hydroclimatic factors in Selangor and subsequently the development of a leptospirosis predictive model. The constructed predictive model could potentially be used to design and enhance public health initiatives for disease prevention.
UNASSIGNED: PRR1-10.2196/43712.