EWHALE lab- Inst of Arctic Biology, Department of Biology & Wildlife, University of Alaska, Fairbanks, AK, USA.
Department of Veterinary Medicine, University of Alaska, Fairbanks, AK, USA.
Zoonoses Public Health. 2021 Sep;68(6):677-683. doi: 10.1111/zph.12835. Epub 2021 May 6.
The ecology of rabies in the circumpolar North is still not well understood. We use machine learning, a geographic information system and data explicit in time and space obtained for reported rabies cases and predictors in Canada to develop an ecological niche model for the distribution of reported rabies cases in the American north (Alaska and Canada). The ecological niche model based on reported rabies cases in Canada predicted reported rabies cases in Alaska, suggesting a rather robust inference and even similar drivers on a continental scale. As found in Alaska, proximity to human infrastructure-specifically along the coast-was a strong predictor in the detection of rabies cases in Canada. Also, this finding highlights the need for a more systematic landscape sampling for rabies infection model predictions to better understand and tackle the ecology of this important zoonotic disease on a landscape scale at some distance from human infrastructure in wilderness areas.
在环北极地区,狂犬病的生态学仍未被很好地理解。我们使用机器学习、地理信息系统以及为加拿大报告的狂犬病病例和预测因子获取的明确时间和空间数据,来开发一种用于报告的北美的狂犬病病例(阿拉斯加和加拿大)分布的生态位模型。基于加拿大报告的狂犬病病例的生态位模型预测了阿拉斯加的报告狂犬病病例,这表明在大陆范围内存在相当稳健的推断,甚至存在相似的驱动因素。与在阿拉斯加发现的情况一样,靠近人类基础设施(特别是沿海地区)是加拿大检测狂犬病病例的一个强有力的预测因子。此外,这一发现强调了需要更系统地对景观进行采样,以进行狂犬病感染模型预测,从而在远离人类基础设施的荒野地区的景观尺度上更好地理解和应对这种重要的人畜共患病的生态学问题。