新兴和研究不足的病原体通常缺乏最常用的分析工具所需的信息,如阴性对照或基线数据;因此,需要新的分析策略来分析疾病出现的传播模式和驱动因素。1999年巴西首次报道人畜共患痘苗病毒(VACV)感染,是一种新兴的人畜共患正痘病毒,主要感染奶牛和与受感染奶牛密切接触的农民。对新兴病原体的前瞻性研究可以提供关键数据,为公共卫生规划和应对疫情提供信息。通过使用87次记录的疫情的位置和公开的生物气候数据,我们展示了一种这样的方法。使用生态位模型(ENM)算法,我们确定了VACV爆发的环境条件,并确定两个受影响国家可能容易传播的其他地点。Further,我们展示了病毒的适用性如何应对不同水平的各种环境因素,并强调了决定其传播的最重要因素。
进行了文献综述,并确定了巴西87次分子确认的VACV暴发的地理空间坐标。使用MaxENT软件通过结合19个生物空间层的主成分分析结果生成ENM,和25个随机选择的子集的原始名单87爆发。
最终的ENM预测了巴西爆发的所有地区,哥伦比亚5个爆发地区之一,并根据生物气候因素确定了巴西境内适合传播的新地区。Further,确定传播适宜性的最重要因素是最潮湿地区的降水,年降水量,最冷季度的平均温度和平均昼夜范围。
这里的分析提供了一种方法,通过该方法可以研究新出现的传染病的模式并确定可能适合其传播的区域。尽管缺乏高质量的关键数据。控制传染病的政策和方法往往采用反动模式,只有在对人类健康产生重大影响后才能解决疾病。本工作中使用的方法可以识别可能出现疾病的区域,可用于定向干预。
Emerging and understudied pathogens often lack information that most commonly used analytical tools require, such as negative controls or baseline data; thus, new analytical strategies are needed to analyze transmission patterns and drivers of disease emergence. Zoonotic infections with
Vaccinia virus (VACV) were first reported in Brazil in 1999, VACV is an emerging zoonotic Orthopoxvirus, which primarily infects dairy cattle and farmers in close contact with infected cows. Prospective studies of emerging pathogens could provide critical data that would inform public health planning and response to outbreaks. By using the location of 87-recorded outbreaks and publicly available bioclimatic data, we demonstrate one such approach. Using an ecological niche model (ENM) algorithm, we identify the environmental conditions under which VACV outbreaks have occurred, and determine additional locations in two affected countries that may be susceptible to transmission. Further, we show how suitability for the virus responds to different levels of various environmental factors and highlight the most important factors in determining its transmission.
A literature review was performed and the geospatial coordinates of 87 molecularly confirmed VACV outbreaks in Brazil were identified. An ENM was generated using MaxENT software by combining principal component analysis results of 19 bioclim spatial layers, and 25 randomly selected subsets of the original list of 87 outbreaks.
The final ENM predicted all areas where Brazilian outbreaks occurred, one out of five of the Colombian outbreak regions and identified new regions within Brazil that are suitable for transmission based on bioclimatic factors. Further, the most important factors in determining transmission suitability are precipitation of the wettest quarter, annual precipitation, mean temperature of the coldest quarter and mean diurnal range.
The analyses here provide a means by which to study patterns of an emerging infectious disease and identify regions that are potentially suitable for its transmission, in spite of the paucity of high-quality critical data. Policy and methods for the control of infectious diseases often use a reactionary model, addressing diseases only after significant impact on human health has ensued. The methodology used in the present work allows the identification of areas where disease is likely to appear, which could be used for directed intervention.