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Title: | From the 100 Day Mission to 100 lines of software development: how to improve early outbreak analytics | Authors: | Cuartero, Carmen Tamayo CARNEGIE, Anna Cucunuba, Zulma M Cori, Anne Hollis, Sara M Van Gaalen, Rolina D Baidjoe, Amrish Y Spina, Alexander F Lees, John A Cauchemez, Simon Santos, Mauricio Umaña, Juan D Chen, Chaoran Gruson, Hugo Gupte, Pratik Tsui, Joseph Shah, Anita A Millan, Geraldine Gomez Quevedo, David Santiago Batra, Neale TORNERI, Andrea Kucharski, Adam J |
Issue Date: | 2025 | Publisher: | Source: | The Lancet. Digital health, 7 (2) , p. e161 -e166 | 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. | Document URI: | http://hdl.handle.net/1942/45236 | e-ISSN: | 2589-7500 | DOI: | 10.1016/S2589-7500(24)00218-8 | ISI #: | 001420881800001 | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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