Ecology of Andes & Choclo Viruses

由西方多种汉坦病毒引起的汉坦病毒心肺综合征 半球被认为是通过吸入受感染的啮齿动物排泄物而感染的。在南部和中部 智利，安第斯病毒感染长尾寡头啮齿动物，引起一种罕见的 严重的人类感染，死亡率高。在巴拿马西部，乔克罗病毒感染啮齿动物 富氏少枝杆菌引起一种常见但通常轻微或不明显的人类感染，其 死亡率。在这两个地区，传播风险似乎受到许多不断增加的因素的影响 有机会在受感染的啮齿动物和易受感染的人类之间进行密切接触。我们将专注于我们的 对其中三个因素进行调查，目的是获得对未来设计至关重要的见解 减少汉坦病毒传播的环境干预措施。 首先，最近在智利和巴拿马进行的调查表明，感染风险与选定的植被有关。 受感染的媒介啮齿动物喜欢的栖息地。在智利，暴露风险可以通过遥感进行预测 卫星图像分析覆盖了全国三分之二的地区。在巴拿马，一项微观分析表明 牙周病感染的啮齿动物与农业活动之间的复杂关系。我们建议将重点放在 利用标记-再捕获法和无线电遥测法研究啮齿动物与栖息地随时间的空间关系 以标记啮齿动物的活动，特别是在齿周环境中。其次，我们将创建一个 动态模型，扩展了我们目前对风险的静态分析。新的模型将图论应用于领域。 收集啮齿动物栖息地偏好和景观内移动的数据，以预测可能的 受感染的啮齿动物的活动。我们将使用来自不同站点的数据来测试该模型 创建模型。通过定量测定啮齿动物体内的病毒载量，将提高模型的时间精度。 冬季(低传播率)和春季(高传播期)的血液、组织和尿液。最后， 巴拿马高危人群的系列血清调查将量化对人类感染的影响 与啮齿动物密度和活动的时空变化有关。

Hantavirus cardiopulmonary syndrome resulting from any of a number of hantaviruses in the western hemisphere is thought to be contracted through inhalation of infected rodent excreta. In southern and central Chile, the Andes virus infects the sigmodontine rodent Oligoryzomys longicaudatus, and causes a rare but severe human infection with high mortality. In western Panama, the Choclo virus infects the rodent Oligoryzomys fulvescens and causes a common but usually mild or inapparent human infection with a low mortality rate. In both regions the risk of transmission appears to be influenced by many factors increasing the opportunity for close contact between infected rodents and susceptible humans. We will focus our investigations on three of these factors, with the goal of gaining insights critical for the design of future environmental interventions to decrease hantavirus transmission. First, recent investigations in Chile and Panama have associated risk of infection with selected vegetation habitats favored by the infected vector rodents. In Chile exposure risk can be predicted by remote sensing with satellite imagery analysis over 2/3 of the country. In Panama a micro scale analysis has suggested complex relationships between peridomestic infected rodents and agricultural activities. We propose to focus on spatial relationships between rodents and habitats over time, using mark-recapture trapping and radiotelemetry to mark rodent movements, particularly in peridomestic environments. Second we will create a dynamic model that extends our current static analysis of risk. The new model will apply graph theory to field collected data of rodent habitat preferences and movements within the landscape to predict the likely movement of infected rodents. We will test the model using data from sites different from those used to create the model. The temporal precision of the model will be improved by quantitating viral load in rodent blood, tissue and urine in the winter (low transmission) and spring (high transmission) seasons. Finally, serial serosurveys of at-risk human populations in Panama will quantify the impact on human infection associated with spatial-temporal variations in rodent density and movement.