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巴西南部猫传染性真菌病流行情况及时间动态分析:预测研究。

Profile and temporal dynamics of the feline sporotrichosis epidemic in southern Brazil: A forecasting analysis.

机构信息

Preventive Veterinary Department, Zoonoses Control Center (UFPel), Federal University of Pelotas, Pelotas, Brazil.

Center of Diagnostic and Research of Veterinary Mycology, Universidade Federal de Pelotas, Pelotas/RS, Brazil.

出版信息

Vet Parasitol Reg Stud Reports. 2024 Sep;54:101091. doi: 10.1016/j.vprsr.2024.101091. Epub 2024 Jul 31.

Abstract

A detailed clinical-epidemiological analysis of feline sporotrichosis was conducted, and 288 cases reported between the years 2007 and 2018 were analyzed. The studied cases primarily involved mongrel cats (240/260), males (212/282), and adults (121/200). The main objectives were to identify the risk factors, calculate the monthly incidence rates, and establish a predictive model using the seasonal autoregressive integrated moving average (SARIMA) approach. The statistical analysis revealed significant associations (p < 0.05) between prolonged lesion evolution times and factors such as respiratory signs, prior treatments, and lesion contact. Empirical treatment was identified as a significant risk factor for disease progression. Moreover, the number of cases demonstrated an increasing trend over the study period, with annual peaks noted in disease incidence. The SARIMA model proved to be an effective tool for forecasting the incidence of sporotrichosis, offering robust support for epidemiological surveillance and facilitating targeted public health interventions in endemic regions. The predictive accuracy of the developed model underscored its utility in enhancing disease monitoring and supporting proactive health measures for the effective management of sporotrichosis.

摘要

对猫传染性真菌病(猫孢子丝菌病)进行了详细的临床流行病学分析,分析了 2007 年至 2018 年间报告的 288 例病例。研究病例主要涉及杂种猫(240/260)、雄性(212/282)和成年猫(121/200)。主要目的是确定风险因素,计算每月发病率,并使用季节性自回归综合移动平均(SARIMA)方法建立预测模型。统计分析显示,病变演变时间延长与呼吸症状、先前治疗和病变接触等因素之间存在显著关联(p<0.05)。经验性治疗被确定为疾病进展的一个显著危险因素。此外,病例数量在研究期间呈上升趋势,发病率呈年度高峰。SARIMA 模型被证明是一种预测孢子丝菌病发病率的有效工具,为流行地区的流行病学监测提供了有力支持,并有助于有针对性的公共卫生干预。所开发模型的预测准确性突出了其在增强疾病监测和支持积极健康措施方面的效用,有助于有效管理孢子丝菌病。

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