About this Research Topic
Climate change impacts global ecosystems at the interface of infectious disease agents and hosts and vectors for animals, humans, and plants. In 2021, the United Nations (UN) held the 26th Climate Change Conference of the Parties (COP26). This pressed nations to urgently address the goals and objectives of the Paris Accord and the UN Framework Convention on Climate Change, within the stated UN Sustainable Development Goals. One likely issue is that evolving weather patterns and increased globalization of trade and travel will expedite climate change impacts. Multidisciplinary engagements need to increase, harnessing and reinforcing technology to identify and track climate change impacts, as a step towards mitigating their effects. The combination of multi-disciplinary and predictive analyses especially using artificial intelligence and machine learning (AI/ML) offers unique opportunities for effective approaches to exploring these problems.
This research topic aims to highlight the connection between climate change and infectious disease, focusing on cooperative engagements and predictive partnerships among scientists working globally. Climate change clearly impacts biosecurity, requiring aspects of infectious disease surveillance, animal-human-plant health, ecology, and environment to be studied in concert. The editors welcome articles related to infectious disease (outbreaks, epidemics, pandemics), seasonal and/or endemic diseases (e.g. vector-borne) and associated AI/ML tools. We are especially interested in work demonstrating successful outputs and returns on investments in predictive analytics, such as design-build-learn-train models, as well as public-private partnerships and how such work may be pivoted to mitigate current and future climate change impacts.
Papers in this research topic will highlight original research and related work in reviews, methods, perspectives and commentaries, covering a range of disciplines, sectors and countries - especially those currently less represented in the scientific literature.
Enhancing capabilities: laboratory diagnostics, epidemiology, and biosecurity; AI/ML; and methods to improve training using remote-distance learning, mobile technology, augmented and virtual reality.
Cooperative research that addresses applications for infectious disease surveillance: diagnostic tools, methods (qPCR, next-generation sequencing, CRISPR), epidemiology (risk maps, models, prediction), and climate change predictions and potential impacts on vectors and hosts using predictive analytical approaches.
Continuing challenges and lessons learned: especially those related to reducing biological threats and maturing capabilities (e.g. national health and laboratory planning); and those that promote scientific transparency, data and material sharing, sustaining funds, and disseminating information.
Potential Authors: partners, performers, and researchers especially those featuring multi-disciplinary work and multi-sectoral partnerships.
• Academia and research institutes, including non-profit and charitable organizations.
• Commercial interests and industry in the private sector.
• National, state, local government institutes and agencies in the public sector
Acknowledgement: We recognize Dr. Falgunee Parekh, and Mr. Kenneth Yeh, who helped develop and prepare this Research Topic. Their contribution as Research Topic Coordinators is greatly appreciated.
This research topic aims to highlight the connection between climate change and infectious disease, focusing on cooperative engagements and predictive partnerships among scientists working globally. Climate change clearly impacts biosecurity, requiring aspects of infectious disease surveillance, animal-human-plant health, ecology, and environment to be studied in concert. The editors welcome articles related to infectious disease (outbreaks, epidemics, pandemics), seasonal and/or endemic diseases (e.g. vector-borne) and associated AI/ML tools. We are especially interested in work demonstrating successful outputs and returns on investments in predictive analytics, such as design-build-learn-train models, as well as public-private partnerships and how such work may be pivoted to mitigate current and future climate change impacts.
Papers in this research topic will highlight original research and related work in reviews, methods, perspectives and commentaries, covering a range of disciplines, sectors and countries - especially those currently less represented in the scientific literature.
Enhancing capabilities: laboratory diagnostics, epidemiology, and biosecurity; AI/ML; and methods to improve training using remote-distance learning, mobile technology, augmented and virtual reality.
Cooperative research that addresses applications for infectious disease surveillance: diagnostic tools, methods (qPCR, next-generation sequencing, CRISPR), epidemiology (risk maps, models, prediction), and climate change predictions and potential impacts on vectors and hosts using predictive analytical approaches.
Continuing challenges and lessons learned: especially those related to reducing biological threats and maturing capabilities (e.g. national health and laboratory planning); and those that promote scientific transparency, data and material sharing, sustaining funds, and disseminating information.
Potential Authors: partners, performers, and researchers especially those featuring multi-disciplinary work and multi-sectoral partnerships.
• Academia and research institutes, including non-profit and charitable organizations.
• Commercial interests and industry in the private sector.
• National, state, local government institutes and agencies in the public sector
Acknowledgement: We recognize Dr. Falgunee Parekh, and Mr. Kenneth Yeh, who helped develop and prepare this Research Topic. Their contribution as Research Topic Coordinators is greatly appreciated.
Keywords: Infectious disease surveillance, cooperative research, biosecurity, climate, AI/ML
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
