Jagadesh Soushieta, Combe Marine, Gozlan Rodolphe Elie
Heath Geography and Policy, ETH Zurich, Sonneggstrasse 33, 8092 Zurich, Switzerland.
ISEM, Université de Montpellier, CNRS, IRD, 34090 Montpellier, France.
Trop Med Infect Dis. 2022 Jul 1;7(7):124. doi: 10.3390/tropicalmed7070124.
Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on the following three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses.
We modelled presence-absence data in spatially explicit binomial and zero-inflation binomial logistic regressions with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of the EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models.
For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of the disease reservoirs and hosts of the three viral groups. Common influencing factors of disease emergence were climatic covariates (minimum temperature and rainfall) and human-induced land modifications.
Using topographical, climatic, and previous disease outbreak reports, we can identify and predict future high-risk areas for disease emergence and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales.
人畜共患病占新发传染病（EID）的70%以上。由于其发病率不断上升且对全球健康和经济产生影响，人畜共患病的出现是一项重大的公共卫生挑战。在此，我们采用生物地理学方法来预测未来的热点地区，并确定影响疾病出现的因素。我们重点关注以下三类令人担忧的病毒性疾病：丝状病毒科、冠状病毒科和亨尼帕病毒。
我们在空间明确的二项式和零膨胀二项式逻辑回归中对存在-缺失数据进行建模，包括有无自回归。存在数据从已发表的针对这三类新发传染病的研究中提取。使用各种环境和人口统计学栅格来解释新发传染病的分布。使用真技能统计量和偏差参数来比较不同模型的准确性。
对于每一组病毒，我们能够根据这三类病毒的疾病储存宿主和宿主的空间分布，识别并绘制出疾病出现高风险区域的地图。疾病出现的常见影响因素是气候协变量（最低温度和降雨量）以及人为导致的土地变化。
利用地形、气候和以往疾病爆发报告，我们可以识别并预测未来疾病出现的高风险区域及其具体的潜在人类和环境驱动因素。我们建议，在制定地方和全球尺度上病原体出现和流行病的主动监测系统时，应仔细考虑这种针对新发传染病的预测方法。
