Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.
Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America.
PLoS Negl Trop Dis. 2020 Sep 14;14(9):e0008620. doi: 10.1371/journal.pntd.0008620. eCollection 2020 Sep.
Few human infectious diseases have been driven as close to eradication as dracunculiasis, caused by the Guinea worm parasite (Dracunculus medinensis). The number of human cases of Guinea worm decreased from an estimated 3.5 million in 1986 to mere hundreds by the 2010s. In Chad, domestic dogs were diagnosed with Guinea worm for the first time in 2012, and the numbers of infected dogs have increased annually. The presence of the parasite in a non-human host now challenges efforts to eradicate D. medinensis, making it critical to understand the factors that correlate with infection in dogs. In this study, we evaluated anthropogenic and environmental factors most predictive of detection of D. medinensis infection in domestic dog populations in Chad. Using boosted regression tree models to identify covariates of importance for predicting D. medinensis infection at the village and spatial hotspot levels, while controlling for surveillance intensity, we found that the presence of infection in a village was predicted by a combination of demographic (e.g. fishing village identity, dog population size), geographic (e.g. local variation in elevation), and climatic (e.g. precipitation and temperature) factors, which differed between northern and southern villages. In contrast, the presence of a village in a spatial infection hotspot, was primarily predicted by geography and climate. Our findings suggest that factors intrinsic to individual villages are highly predictive of the detection of Guinea worm parasite presence, whereas village membership in a spatial infection hotspot is largely determined by location and climate. This study provides new insight into the landscape-scale epidemiology of a debilitating parasite and can be used to more effectively target ongoing research and possibly eradication and control efforts.
很少有人类传染病像麦地那龙线虫病(由麦地那龙线虫寄生虫引起)那样被接近消灭。1986 年,人类麦地那龙线虫病例估计有 350 万例,到 2010 年代已降至数百例。2012 年,乍得首次在当地的家犬中诊断出麦地那龙线虫病,并且感染犬的数量逐年增加。这种寄生虫在非人类宿主中的存在现在对消灭麦地那龙线虫的努力构成了挑战,因此了解与犬类感染相关的因素至关重要。在这项研究中,我们评估了与乍得国内犬种群中麦地那龙线虫感染最相关的人为和环境因素。我们使用增强回归树模型来确定对预测村庄和空间热点水平的麦地那龙线虫感染的重要协变量,同时控制监测强度,我们发现村庄中感染的存在由人口统计学(例如渔村身份,犬只数量)、地理(例如海拔的局部变化)和气候(例如降水和温度)因素的组合预测,北部和南部村庄之间存在差异。相比之下,村庄是否处于空间感染热点主要由地理和气候决定。我们的研究结果表明,村庄自身的因素高度预测了麦地那龙线虫寄生虫存在的检测,而村庄在空间感染热点中的成员资格在很大程度上取决于位置和气候。本研究为一种使人衰弱的寄生虫的景观尺度流行病学提供了新的见解,并可用于更有效地针对正在进行的研究,甚至可能针对根除和控制工作。