Sydney School of Veterinary Science, The University of Sydney, Camden, NSW, Australia.
Centre for One Health, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, India.
Transbound Emerg Dis. 2022 May;69(3):1131-1143. doi: 10.1111/tbed.14072. Epub 2021 Mar 23.
Understanding human disease, zoonoses and emergence is a global priority. A deep understanding of pathogen ecology and the complex inherent relationships at the agent-environment interface are essential to inform disease control and mitigation and to predict the next zoonotic pandemic. Here, we present the first analysis of social and environmental factors associated with human, zoonotic and emerging pathogen diversity at a global scale, controlling for research effort. Predictor-response associations were captured by generalized additive models. We used national level data to aid in policy development to inform control and mitigation. We show that human population density, land area, temperature and the human development index at the country level are associated with human, emerging and zoonotic pathogen diversity. Multiple models demonstrating society-agent-environment interactions demonstrate that social, environmental and geographical factors predict global pathogen diversity. The analyses demonstrate that weather variables (temperature and rainfall) have the potential to influence pathogen diversity.
了解人类疾病、人畜共患病和新出现的病原体是全球优先事项。深入了解病原体生态学以及在病原体-环境界面内在的复杂关系，对于提供疾病控制和缓解措施的信息以及预测下一次人畜共患病大流行至关重要。在这里，我们首次在全球范围内分析了与人类、人畜共患病和新出现的病原体多样性相关的社会和环境因素，并控制了研究力度。通过广义加性模型捕捉预测因子-响应关联。我们使用国家一级的数据来帮助制定政策，为控制和缓解措施提供信息。我们表明，人口密度、土地面积、温度和国家一级的人类发展指数与人类、新出现的和人畜共患病病原体多样性有关。多个展示社会-病原体-环境相互作用的模型表明，社会、环境和地理因素可以预测全球病原体多样性。分析表明，天气变量（温度和降雨量）有可能影响病原体多样性。
