1. From the 100 Day Mission to 100 lines of software development: how to improve early outbreak analytics.
- Author
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Tamayo Cuartero C, Carnegie AC, Cucunuba ZM, Cori A, Hollis SM, Van Gaalen RD, Baidjoe AY, Spina AF, Lees JA, Cauchemez S, Santos M, Umaña JD, Chen C, Gruson H, Gupte P, Tsui J, Shah AA, Millan GG, Quevedo DS, Batra N, Torneri A, and Kucharski AJ
- Subjects
- Humans, Public Health, Software Design, Software, SARS-CoV-2, Pandemics, COVID-19 epidemiology, COVID-19 prevention & control, Disease Outbreaks prevention & control
- Abstract
Since the COVID-19 pandemic, considerable advances have been made to improve epidemic preparedness by accelerating diagnostics, therapeutics, and vaccine development. However, we argue that it is crucial to make equivalent efforts in the field of outbreak analytics to help ensure reliable, evidence-based decision making. To explore the challenges and key priorities in the field of outbreak analytics, the Epiverse-TRACE initiative brought together a multidisciplinary group of experts, including field epidemiologists, data scientists, academics, and software engineers from public health institutions across multiple countries. During a 3-day workshop, 40 participants discussed what the first 100 lines of code written during an outbreak should look like. The main findings from this workshop are summarised in this Viewpoint. We provide an overview of the current outbreak analytic landscape by highlighting current key challenges that should be addressed to improve the response to future public health crises. Furthermore, we propose actionable solutions to these challenges that are achievable in the short term, and longer-term strategic recommendations. This Viewpoint constitutes a call to action for experts involved in epidemic response to develop modern and robust data analytic approaches at the heart of epidemic preparedness and response., Competing Interests: Declaration of interests This Viewpoint was written by the attendees of a workshop held in December, 2022, hosted by Epiverse-TRACE at the Wellcome Trust (London, UK). Epiverse-TRACE is funded by Data.org, which is supported by The Rockefeller Foundation and Mastercard Center for Inclusive Growth. Data.org is also supported by the Wellcome Trust and the International Development Research Centre (IDRC). The authors of this study have not been paid to write this article by a pharmaceutical company or other agency. CTC received funding from Data.org. AC received funding from the UK National Institute of Health Research, UK Medical Research Council, Pfizer, and the Academy of Medical Sciences. ZMC received funding from the Ministry of Science and Technology of Colombia via the AGORA Research Grant and the IDRC via the TRACE-LAC Research Grant; a research grant for enhancing tools for response, analytics, and control of epidemics in Latin America and the Caribbean (Project ID 109848); a mathematical modelling and development of epidemiological analysis, estimates, and projections for priority public health diseases in the Americas region grant; and support for attending meetings and travel from WHO, Pan American Health Organization, and The Lancet Commission. AYB received funding from Medecins Sans Frontières. CC is supported by ETH Zurich and the Centers for Disease Control and Prevention. JAL received funding from Pfizer. JT received funding from the Yeotown Scholarship, awarded by New College, Oxford. DSQ received funding from the Pontificia Universidad Javeriana. AFS received funding from Epiverse-TRACE to attend meetings and travel. AJK received funding from the Wellcome Trust and Data.org. All other authors declare no competing interests., (Copyright © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2025
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