Spatial analysis and risk mapping of Fasciola hepatica infection in dairy cattle at the Peruvian central highlands

IF 2 Q3 INFECTIOUS DISEASES

Parasite Epidemiology and Control Pub Date : 2023-11-01 DOI:10.1016/j.parepi.2023.e00329

Daniel Alexis Zárate-Rendón , David Godoy Padilla , Samuel Pizarro Carcausto , Alberto del Águila , Eric Wetzel , Javier Ñaupari Vásquez

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引用次数: 0

Abstract

This study aimed to develop maps for Fasciola hepatica infection occurrence in dairy cattle in the districts of Matahuasi and Baños in the Peruvian central highlands. For this, a model based on the correlation between environmental variables and the prevalence of infection was constructed. Flukefinder® coprological test were performed in samples from dairy cattle from 8 herds, during both the rainy and wet season. Grazing plots were geo-referenced to obtain information on environmental variables. Monthly temperature, monthly rainfall, elevation, slope, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), distance to rivers, urban areas and roads were obtained by using remote sensor images and ArcGIS®. Multilayer perceptron Artificial Neural Networks modeling were applied to construct a predictive model for the occurrence of fasciolosis, based on the relationship between environmental variables and level of infection. Kappa coefficient (k > 0.6) was used to evaluate concordance between observed and forecasted risk by the model. Coprological results demonstrated an average prevalence from 20% to 100%, in Matahuasi, and between 0 and 87.5%, in Baños. A model with a high level of concordance between predicted and observed infection risk (k = 0.77) was obtained, having as major predicting variables: slope, NDWI, NDVI and EVI. Fasciolosis risk was categorized as low (p < 20%), medium (20% < p < 50%) and high (p ≥ 50%) level. Using ArcGIS 10.4.1, risk maps were developed for each risk level of fasciolosis. Maps of fasciolosis occurrence showed that 87.2% of Matahuasi area presented a high risk for bovine fasciolosis during the dry season, and 76.6% in the wet season. In contrast, 21.9% of Baños area had a high risk of infection during the dry season and 12.1% during the wet season. In conclusion, our model showed areas with high risk for fasciolosis occurrence in both districts during both dry and rainy periods. Slope, NDWI, NDVI and EVI were the major predictors for fasciolosis occurrence.

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秘鲁中部高地奶牛肝片形吸虫感染的空间分析和风险制图

本研究旨在绘制秘鲁中部高地马塔瓦西和Baños地区奶牛肝片吸虫感染分布图。为此，构建了基于环境变量与感染流行率相关性的模型。在雨季和雨季，对来自8个牛群的奶牛样本进行了Flukefinder®coprology测试。对放牧地进行地理参考以获取环境变量信息。利用遥感影像和ArcGIS®获取月气温、月降雨量、高程、坡度、归一化植被指数(NDVI)、增强植被指数(EVI)、归一化水指数(NDWI)、到河流、城区和道路的距离。基于环境变量与感染水平的关系，应用多层感知器人工神经网络模型构建片形虫病发生的预测模型。Kappa系数(k >0.6)用于评价模型观测风险与预测风险的一致性。血液学结果显示，Matahuasi的平均患病率为20%至100%，Baños的平均患病率为0%至87.5%。预测感染风险与观测感染风险高度一致(k = 0.77)，主要预测变量为斜率、NDWI、NDVI和EVI。片形吸虫病风险为低(p <20%)，中(20% <p & lt;50%)和高(p≥50%)水平。利用ArcGIS 10.4.1软件，绘制各风险等级片形虫病风险图。片形虫病发病图显示，干季和湿季分别有87.2%和76.6%的牛片形虫病高发区。在干旱季和雨季，分别有21.9%和12.1%的地区存在较高的感染风险。总之，我们的模型显示，在干旱和雨季，这两个地区的片形虫病发生的高风险地区。斜率、NDWI、NDVI和EVI是片形吸虫病发生的主要预测因子。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Parasite Epidemiology and Control Medicine-Infectious Diseases

CiteScore

5.70

自引率

3.10%

发文量

审稿时长

17 weeks

期刊介绍： Parasite Epidemiology and Control is an Open Access journal. There is an increasing amount of research in the parasitology area that analyses the patterns, causes, and effects of health and disease conditions in defined populations. This epidemiology of parasite infectious diseases is predominantly studied in human populations but also spans other major hosts of parasitic infections and as such this journal will have a broad remit. We will focus on the major areas of epidemiological study including disease etiology, disease surveillance, drug resistance and geographical spread and screening, biomonitoring, and comparisons of treatment effects in clinical trials for both human and other animals. We will also look at the epidemiology and control of vector insects. The journal will also cover the use of geographic information systems (Epi-GIS) for epidemiological surveillance which is a rapidly growing area of research in infectious diseases. Molecular epidemiological approaches are also particularly encouraged.