The COVID-19 pandemic has demonstrated how a lack of accurate, real-time outbreak data and an inconsistent science-based response framework have led to global struggles in responding to the pandemic in a timely and effective manner. Should we fail to fundamentally transform the international pandemic surveillance and response system – as called for by the World Health Organization (WHO) - we will not be ready for the next pandemic. To achieve this transformation, scientists and experts globally must co-develop a global pandemic preparedness and response scheme that is science-based, digitally enabled and works across the continuum of pandemic phases, namely preparedness, surveillance, response, and recovery. Built collaboratively and transparently with international organizations, academia, private sector, civil society, and citizens, the scheme should operate independently, with its data hosted in a neutral digital infrastructure. The alerts and insights generated can thus become a trusted source of information for public health decision making.
As an independent and neutral research collaborative, the International Digital Health and AI Research Collaborative (I-DAIR) has convened a global group of multi-disciplinary scientific experts (32 persons from 17 countries) to develop a research and development (R&D) agenda for the proposed scheme to avoid narrow geopolitical or economic interests from impeding multilateral collaboration. The R&D agenda highlighted four priority R&D areas needed to build out the end-to-end scheme over a span of 5-10 years. These areas include:
i) Discovering unusual and diverse data sources using big data analytics and AI as well as building population cohorts for equitable data curation;
ii) Building models that could be Findable, Accessible, Interoperable and Reusable (FAIR) through standardization and validation and which would allow for citizen inputs through participatory approaches;
iii) Designing visualizations that are targeted for different stakeholders (such as policymakers, and citizens) as well as developing effective communication interfaces between researchers, governments and citizens;
iv) Unpacking cross-cutting issues such as the governance of data and digital technologies, including AI, for pandemic preparedness and response as well as human and infrastructure capacity development efforts.
This Research Topic sets out to understand the current state of the art and existing gaps within these R&D areas.
Through this research topic, we invite contributors to submit original research articles pertaining to the aforementioned R&D areas for future pandemic preparedness and response. Systematic reviews and perspective and policy-focused pieces summarizing recent advances and highlighting research gaps are also welcomed. We particularly aim to feature multi- and transdisciplinary work as well as work arising from multi-sectoral partnerships.
The COVID-19 pandemic has demonstrated how a lack of accurate, real-time outbreak data and an inconsistent science-based response framework have led to global struggles in responding to the pandemic in a timely and effective manner. Should we fail to fundamentally transform the international pandemic surveillance and response system – as called for by the World Health Organization (WHO) - we will not be ready for the next pandemic. To achieve this transformation, scientists and experts globally must co-develop a global pandemic preparedness and response scheme that is science-based, digitally enabled and works across the continuum of pandemic phases, namely preparedness, surveillance, response, and recovery. Built collaboratively and transparently with international organizations, academia, private sector, civil society, and citizens, the scheme should operate independently, with its data hosted in a neutral digital infrastructure. The alerts and insights generated can thus become a trusted source of information for public health decision making.
As an independent and neutral research collaborative, the International Digital Health and AI Research Collaborative (I-DAIR) has convened a global group of multi-disciplinary scientific experts (32 persons from 17 countries) to develop a research and development (R&D) agenda for the proposed scheme to avoid narrow geopolitical or economic interests from impeding multilateral collaboration. The R&D agenda highlighted four priority R&D areas needed to build out the end-to-end scheme over a span of 5-10 years. These areas include:
i) Discovering unusual and diverse data sources using big data analytics and AI as well as building population cohorts for equitable data curation;
ii) Building models that could be Findable, Accessible, Interoperable and Reusable (FAIR) through standardization and validation and which would allow for citizen inputs through participatory approaches;
iii) Designing visualizations that are targeted for different stakeholders (such as policymakers, and citizens) as well as developing effective communication interfaces between researchers, governments and citizens;
iv) Unpacking cross-cutting issues such as the governance of data and digital technologies, including AI, for pandemic preparedness and response as well as human and infrastructure capacity development efforts.
This Research Topic sets out to understand the current state of the art and existing gaps within these R&D areas.
Through this research topic, we invite contributors to submit original research articles pertaining to the aforementioned R&D areas for future pandemic preparedness and response. Systematic reviews and perspective and policy-focused pieces summarizing recent advances and highlighting research gaps are also welcomed. We particularly aim to feature multi- and transdisciplinary work as well as work arising from multi-sectoral partnerships.
