{"id":50368,"date":"2021-06-03T07:41:36","date_gmt":"2021-06-03T06:41:36","guid":{"rendered":"https:\/\/blogs.bmj.com\/bmj\/?p=50368"},"modified":"2021-06-10T15:20:08","modified_gmt":"2021-06-10T14:20:08","slug":"we-must-seize-the-moment-for-a-global-pandemic-surveillance-and-response-scheme","status":"publish","type":"post","link":"https:\/\/blogs.bmj.com\/bmj\/2021\/06\/03\/we-must-seize-the-moment-for-a-global-pandemic-surveillance-and-response-scheme\/","title":{"rendered":"We must seize the moment for a global pandemic surveillance and response scheme"},"content":{"rendered":"

No more pandemics\u2014this is the ambitious goal set by the Independent Panel for Pandemic Preparedness and Response, whose report reviews the international community\u2019s response to covid-19 and identifies lessons learned. [1] Covid-19 has shown that our lack of globally accurate, real-time data on outbreaks is a matter of life and death. A leading group of scientists and experts, from every part of the world, must come together urgently to create a new, neutral, and trusted digital system that can revolutionize how data is gathered and used globally.<\/span><\/p>\n

We have done this before with weather, and more recently with predicting tsunamis through a global system of sensors. The resulting information rich environment enables all of us to adapt to the forecast. To prevent the next disease outbreak becoming a global crisis, we need similarly routinised data flows to generate epidemic intelligence and alerts.<\/span><\/p>\n

Digital technologies, particularly artificial intelligence (AI), are now mature enough to sift the signal from the noise in the mass of mobility data, social media posts, and clinical reports. [2-4] AI can weigh data across human, animal, and plant health and spot patterns. It can help derive intelligence from unusual data sources such as sewage samples or empower citizen scientists to report unusual events using mobile phones. [5,6] Democratization of molecular biology, such as serology or next-generation sequencing of the genomes of pathogens, has opened up new avenues for monitoring risks. [7]<\/span><\/p>\n

But bringing this together globally won\u2019t be easy for three reasons.<\/span><\/p>\n

First, our existing health data systems are fragmented and plagued with gaps. Their architectures are based on a narrow foundation of a few disciplines. You cannot simply wire them together.\u00a0<\/span><\/p>\n

Frameworks to pool information across international organizations dealing with One Health\u2014animals, humans, plants, and their shared environment\u2014have been created, but lie dormant without adequate workflows. Despite laudable initiatives such as the World Health Organization (WHO)\u2019s Global Outbreak Alert and Response Network (GOARN), the Global Health Security Agenda, or the Coalition for Epidemic Preparedness Innovations (CEPI), a winning coalition has never come together to support a truly global and coordinated response.\u00a0<\/span><\/p>\n

Mistrust in global institutions is rife. Nations do not share information in a timely and complete manner despite legally binding obligations under WHO\u2019s International Health Regulations. [8] Current reporting obligations require an outbreak to be reported through a labyrinthine bureaucratic chain before it reaches a global agency, days or even weeks after it was reported by a local clinician.\u00a0<\/span><\/p>\n

The second challenge is privacy and digital trust. Many countries lack data protection laws or fail to enforce them. Few people have basic data literacy and struggle with partial information or misinformation about what happens with their data. Technological solutions could help, including new techniques for protecting data and preserving privacy such as distributed learning, but it is equally important to engage the community in the development of these solutions in order to gain acceptance. The infrastructure for hosting outbreak data, should also be neutral and not beholden to any government or corporate interest. As far as possible, raw data should stay local and contribute to the global picture, without having to leave national jurisdictions. Winning the trust of the public will take time and concerted effort.<\/span><\/p>\n

A third challenge is politics. An agenda set up by a narrow group of countries or disciplines will not result in a critical mass of international support or win the trust of the general public<\/span>. <\/b>A global, science-based, digitally enabled, and end-to-end pandemic surveillance and response scheme needs to be built out collaboratively. Its data architecture and information sources need to be examined from a multi-disciplinary perspective across epidemiology and public health, molecular biology, social and behavioural sciences, complex systems, networks and computer sciences, environmental sciences, gender, ethics and governance as well as health economics and policy. The new generation of researchers from the Global South now emerging should play a role in their design so that its deployment is feasible in all income settings.\u00a0<\/span><\/p>\n

Mutual suspicions and reluctance to collaborate, need to be overcome among government, academia, the private sector and civil society.<\/span> An innovative governance structure tiered to accommodate contextual specificity for data access and data use in different countries would be essential. It should rest on principles such as trustworthiness, transparency, fairness, equity, independence, and neutrality. Investments in data infrastructure, and capacity development of the health workforce,<\/span> will be needed.<\/span><\/p>\n

We should begin by convening a globally representative group of scientists to examine how this could work in practice and to suggest a research and development agenda, ensuring that, as the Independent Panel warned, their report does not gather dust on a shelf, but sparks immediate and transformative global action. We have the tools to prevent the next pandemic before it starts, and cannot afford delay in putting them to work.\u00a0\u00a0<\/span><\/p>\n

Amandeep Gill<\/strong>, project director & CEO, International Digital Health and Artificial Intelligence Research Collaborative (I-DAIR), Geneva<\/span> Switzerland.<\/span><\/em><\/p>\n

Acknowledgements<\/strong>: The author acknowledges the contribution of the following members of the I-DAIR Pandemic Scientific Group Anurag Agrawal<\/b>\u00a0(Council of Scientific and Industrial Research, India);\u00a0Gershim Asiki<\/b>\u00a0(African Population and Health<\/em> Research Center (APHRC), Kenya);\u00a0Hala Audi<\/span><\/b>\u00a0(Trinity Challenge, UK);\u00a0<\/span>Marc Choisy<\/b>\u00a0(University of Oxford, UK, and Oxford University Clinical Research Unit (OUCRU), Vietnam);\u00a0Sara (Meg) Davis<\/b>\u00a0<\/sup>(Graduate Institute of International and Development Studies, Switzerland);\u00a0Ayman El-Mohandes<\/b>\u00a0<\/sup>(City University of New York (CUNY),\u00a0United States of America);\u00a0Antoine Flahault<\/b>\u00a0<\/sup>(University of Geneva, Switzerland);\u00a0<\/span>Emma B Hodcroft<\/b>\u00a0<\/sup><\/b>(NextStrain, United States of America, and University of Bern, Switzerland);<\/span>\u00a0<\/span>Ashish Joshi<\/b>\u00a0(CUNY, United States of America);<\/span>\u00a0<\/span>Olivia Keiser<\/b>\u00a0<\/sup>(University of Geneva, Switzerland);<\/span>\u00a0<\/span>Sylvia Kiwuwa-Muyingo<\/b>\u00a0<\/sup>(APHRC, Kenya)<\/span>,\u00a0Jeffrey V Lazarus<\/b>\u00a0(University of Barcelona, Spain);<\/span>\u00a0<\/span>Sam Makau<\/b>\u00a0<\/sup>(WACI Health, Kenya);<\/span>\u00a0<\/span>Malebona Precious Matsoso<\/b>\u00a0(<\/span>University of the Witwatersrand, South Africa);<\/span>\u00a0<\/span>Rosemary Mburu<\/b>\u00a0<\/sup>(WACI Health, Kenya);<\/span>\u00a0<\/span>Bruce Mellado<\/b>\u00a0<\/sup>(<\/span>University of the Witwatersrand, South Africa)<\/span>;\u00a0<\/span>Vinh-Kim Nguyen<\/b>\u00a0<\/sup>(<\/span>Graduate Institute of International and Development Studies, Switzerland)\u00a0;<\/span>\u00a0<\/span>Patrick Okwen<\/b>\u00a0<\/sup>(eBASE Africa, Cameroon);<\/span>\u00a0<\/span>James Orbinski<\/b>\u00a0<\/sup>(York University, Canada);<\/span>\u00a0<\/span>Tavpritesh Sethi<\/b>\u00a0<\/sup>(Indraprastha Institute of Information Technology-Delhi, India);<\/span>\u00a0<\/span>Serge Stinckwich<\/b>\u00a0<\/sup>(United Nations University, Macau SAR);<\/span>\u00a0<\/span>Le Van Tan<\/b>\u00a0<\/sup>(OUCRU, Vietnam);<\/span>\u00a0<\/span>Yik Ying Teo<\/b>\u00a0<\/sup>(National University of Singapore, Singapore);<\/span>\u00a0<\/span>Guy Thwaites<\/b>\u00a0<\/sup>(<\/span>University of Oxford, UK, and OUCRU, Vietnam);\u00a0Andrea Sylvia Winkler<\/b>\u00a0<\/sup>(University of Oslo, Norway, and Technical University of Munich, Germany) and\u00a0<\/span>\u00a0<\/span>Peiling Yap<\/b>\u00a0<\/sup>(I-DAIR, Switzerland)<\/span><\/em><\/p>\n

Competing interests<\/strong>: <\/span>None declared.<\/span><\/em><\/p>\n

References:<\/b><\/p>\n

    \n
  1. The Independent Panel for Pandemic Preparedness and Response. COVID-19: Make it the last pandemic.\u00a0<\/span><\/li>\n
  2. Budd J, Miller BS, Manning EM, Lampos V, Zhuang M, Edelstein M, et al. Digital technologies in the public-health response to COVID-19. Nat Med. 2020 Aug;26(8):1183\u201392.\u00a0<\/span><\/li>\n
  3. Whitelaw S, Mamas MA, Topol E, Van Spall HGC. Applications of digital technology in COVID-19 pandemic planning and response. The Lancet Digital Health. 2020 Aug;2(8):e435\u201340.\u00a0<\/span><\/li>\n
  4. Rahman MM, Khatun F, Uzzaman A, Sami SI, Bhuiyan MA-A, Kiong TS. A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic. Int J Health Serv. 2021 May 17;002073142110174.\u00a0<\/span><\/li>\n
  5. Jahn K, Dreifuss D, Topolsky I, Kull A, Ganesanandamoorthy P, Fernandez-Cassi X, et al. Detection of SARS-CoV-2 variants in Switzerland by genomic analysis of wastewater samples [Internet]. 2021 Jan [cited 2021 May 18]. Available from: http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2021.01.08.21249379<\/span><\/li>\n
  6. Yano T, Phornwisetsirikun S, Susumpow P, Visrutaratna S, Chanachai K, Phetra P, et al. A Participatory System for Preventing Pandemics of Animal Origins: Pilot Study of the Participatory One Health Disease Detection (PODD) System. JMIR Public Health Surveill. 2018 Mar 21;4(1):e25.\u00a0<\/span><\/li>\n
  7. Gwinn M, MacCannell D, Armstrong GL. Next-Generation Sequencing of Infectious Pathogens. JAMA. 2019 Mar 5;321(9):893.\u00a0<\/span><\/li>\n
  8. Gostin LO, DeBartolo MC, Friedman EA. The International Health Regulations 10 years on: the governing framework for global health security. The Lancet. 2015 Nov;386(10009):2222\u20136.\u00a0<\/span><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    No more pandemics\u2014this is the ambitious goal set by the Independent Panel for Pandemic Preparedness and Response, whose report reviews the international community\u2019s response to covid-19 and identifies lessons learned. […]<\/p>\n

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