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马亚罗病毒在南美洲的分布情况。

Mayaro virus distribution in South America.

作者信息

Lorenz Camila, Freitas Ribeiro Ana, Chiaravalloti-Neto Francisco

机构信息

Department of Epidemiology, School of Public Health - Universidade de São Paulo, Sao Paulo, Brazil.

Epidemiology Service, Institute of Infectiology Emílio Ribas, Sao Paulo, Brazil; Health Administration Graduate Department, Universidade Nove de Julho, Sao Paulo, Brazil.

出版信息

Acta Trop. 2019 Oct;198:105093. doi: 10.1016/j.actatropica.2019.105093. Epub 2019 Jul 17.

Abstract

Mayaro virus (MAYV) is a pathogen endemic to South America and some Caribbean islands, with reports of occasional outbreaks. However, its current distribution and high-risk areas are little known. We conducted a modelling study to determine the areas with highest prevalence of MAYV occurrence in South America, based on confirmed cases and serological detection over the last 20 years and socio-environmental variables. We performed our analysis using Maxent software, a machine learning algorithm used for species distribution modeling. Our results showed that the occurrence of MAYV is mainly associated with the biome type, population density, annual rainfall, annual vapor rate, and elevation. Among biome types, the one most related to the occurrence of MAYV is Cerrado, probably related to the lifecycle of the Haemagogus vector and human population concentrations. According to our modelling, there is high yet undetectable MAYV concentration in the central region of Brazil and west-coastal region of the continent. A change in virus dispersion patterns was observed. The virus was previously predominantly in forests but now occupied rural areas and was becoming increasingly urbanized, which is increases the risk of outbreaks. Our results will serve to identify priority areas in the development of preventive actions and structuring of epidemiological surveillance.

摘要

马亚罗病毒(MAYV)是一种南美洲和一些加勒比岛屿特有的病原体,有偶尔爆发的报道。然而,其当前的分布和高风险地区鲜为人知。我们进行了一项建模研究,根据过去20年的确诊病例、血清学检测以及社会环境变量,来确定南美洲马亚罗病毒发生率最高的地区。我们使用Maxent软件进行分析,这是一种用于物种分布建模的机器学习算法。我们的结果表明,马亚罗病毒的发生主要与生物群落类型、人口密度、年降雨量、年蒸发率和海拔有关。在生物群落类型中,与马亚罗病毒发生最相关的是塞拉多,这可能与嗜血蚊属媒介的生命周期和人口聚集有关。根据我们的建模,在巴西中部地区和该大陆的西海岸地区存在高浓度但尚未被检测到的马亚罗病毒。观察到病毒传播模式发生了变化。该病毒以前主要存在于森林中,但现在占据了农村地区并日益城市化,这增加了爆发的风险。我们的结果将有助于确定预防行动发展和流行病学监测架构中的优先领域。

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