Socio-ecological drivers of multiple zoonotic hazards in highly urbanized cities

GLOBAL CHANGE BIOLOGY(2022)

引用 19|浏览19
暂无评分
摘要
The ongoing COVID-19 pandemic is a stark reminder of the devastating consequences of pathogen spillover from wildlife to human hosts, particularly in densely populated urban centers. Prevention of future zoonotic disease is contingent on informed surveillance for known and novel threats across diverse human-wildlife interfaces. Cities are a key venue for potential spillover events because of the presence of zoonotic pathogens transmitted by hosts and vectors living in close proximity to dense human settlements. Effectively identifying and managing zoonotic hazards requires understanding the socio-ecological processes driving hazard distribution and pathogen prevalence in dynamic and heterogeneous urban landscapes. Despite increasing awareness of the human health impacts of zoonotic hazards, the integration of an eco-epidemiological perspective into public health management plans remains limited. Here we discuss how landscape patterns, abiotic conditions, and biotic interactions influence zoonotic hazards across highly urbanized cities (HUCs) in temperate climates to promote their efficient and effective management by a multi-sectoral coalition of public health stakeholders. We describe how to interpret both direct and indirect ecological processes, incorporate spatial scale, and evaluate networks of connectivity specific to different zoonotic hazards to promote biologically-informed and targeted decision-making. Using New York City, USA as a case study, we identify major zoonotic threats, apply knowledge of relevant ecological factors, and highlight opportunities and challenges for research and intervention. We aim to broaden the toolbox of urban public health stakeholders by providing ecologically-informed, practical guidance for the evaluation and management of zoonotic hazards.
更多
查看译文
关键词
hazard, landscape, management, mesomammal, mosquito, rodent, spillover, tick, urban, zoonosis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要