Environmental and Ecological Determinants of Infectious Disease Emergence
Special Issue Information
Dear Colleagues,
The accelerating pace of global change (e.g., land-use change, climate change, urbanization, biodiversity decline, etc.) is reshaping ecological communities and environmental conditions that can alter the dynamics of infectious diseases. These multifaceted perturbations not only influence the distributions of hosts and vectors, but also modulate pathogen survival, replication, and evolution. The resultant shifts in disease ecology pose a significant and escalating threat to public health, wildlife conservation, and livestock production, particularly through the emergence of zoonotic diseases from which the majority of emerging infectious diseases originated.
Understanding the intricate pathways through which environmental and ecological drivers facilitate disease emergence is a pressing scientific and societal challenge. For example, climate change is expanding the geographic range of vectors and altering the transmission seasons of vector-borne diseases. Biodiversity loss, often linked to environmental degradation, can dilute the density of competent reservoir hosts, paradoxically increasing the risk of pathogens’ spillover risk. These processes are further complicated by species invasions, which introduce novel pathogens to naïve populations. The interplay of these global change drivers creates a highly complex system, calling for a paradigm shift from traditional, single-factor epidemiological approaches towards an integrative, holistic ecological framework that explicitly considers the synergies and feedbacks between environmental stressors and pathogen dynamics.
To navigate this complexity, recent technological and methodological advances are revolutionizing our capacity to monitor, model, and predict disease emergence. The proliferation of big data, remote sensing technologies coupled with the rise of artificial intelligence (AI) and machine learning, offers unprecedented opportunities to untangle these ecological determinants.
This Special Issue aims to synthesize cutting-edge research that elucidates the environmental and ecological determinants of transmission and spillover of infectious disease in a rapidly changing world. We invite contributions that explore the mechanisms through which global change drivers influence host–vector–pathogen dynamics and zoonotic spillover risk. By bringing together original research, comprehensive reviews, short communications, and case studies, this Special Issue seeks to advance our fundamental understanding of disease ecology and provide an evidence-based framework for surveillance, risk assessment, and mitigation strategies in a globally connected world. The featured works will align with the broader scope of the journal to directly address challenges including, but not limited to, the following themes:
- Global Change Drivers: Impacts of global change drivers (e.g., land-use change, climate change, urbanization, biodiversity decline, biological invasion, etc.) on the risk patterns of zoonotic diseases.
- Pathogen Ecology and Evolution: Environmental influences on pathogen survival, transmission dynamics, virulence, and the evolution of antimicrobial resistance in wildlife.
- Ecological Interfaces and Zoonotic Spillover: Mechanisms of pathogen transmission at the human–wildlife–livestock interface, including the roles of wildlife trade and habitat encroachment.
- Novel Surveillance and Prediction: Development and application of artificial intelligence, machine learning, and remote sensing for disease mapping, early warning systems, and risk forecasting.
- One Health: Integrated approaches linking environmental health, wildlife conservation, and human well-being in the context of disease management.
We look forward to receiving your contributions.
Prof. Dr. Zheng Huang
Dr. Shenglai Yin
Dr. Yingying Wang
Dr. Qiang Wu
Guest Editors
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Keywords
- wildlife disease ecology
- animal community ecology
- avian influenza virus transmission
- risk of pathogens
- global change
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