Lessons from COVID-19 for rescalable data collection.

Novel data and analyses have had an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in some instances new systems were developed to meet the challenges posed by the magnitude of the pandemic. We describe the routine and novel data that were used to address urgent public health questions during the pandemic, underscore the challenges in sustainability and equity in data generation, and highlight key lessons learnt for designing scalable data collection systems to support decision making during a public health crisis. As countries emerge from the acute phase of the pandemic, COVID-19 surveillance systems are being scaled down. However, SARS-CoV-2 resurgence remains a threat to global health security; therefore, a minimal cost-effective system needs to remain active that can be rapidly scaled up if necessary. We propose that a retrospective evaluation to identify the cost-benefit profile of the various data streams collected during the pandemic should be on the scientific research agenda.

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Declaration of interests AC has received payment from Pfizer for teaching mathematical modelling of infectious diseases. All other authors declare no competing interests. SG was supported by the project SORMAS@DEMIS of the German Ministry of Health. SC acknowledges financial support from the EU's Horizon 2020 research and innovation programme under grants 874735 (VEO) and 101003589 (RECOVER), the Investissement d'Avenir programme, the Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases programme (grant ANR-10-LABX-62-IBEID), Santé Publique France, the INCEPTION project (PIA/ANR-16-CONV-0005), AXA, and Groupama. OJW was supported by a Schmidt Science Fellowship in partnership with the Rhodes Trust. NI is currently employed by the Wellcome Trust. The Wellcome Trust had no role in the preparation of the manuscript or the decision to publish. AC, SB, NI, OJW, MLR-C, and PN acknowledge funding from the Medical Research Centre (MRC) Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK MRC and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the EU. AC was supported by the Academy of Medical Sciences Springboard scheme, funded by the AMS, Wellcome Trust, UK Department for Business, Energy and Industrial Strategy, the British Heart Foundation, and Diabetes UK (reference SBF005\1044). AC acknowledges funding from the National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Modelling and Health Economics, a partnership between the UK Health Security Agency, Imperial College London, and the London School of Hygiene & Tropical Medicine (grant code NIHR200908), and from the International Society for Infectious Diseases (Mapping the Risk of International Infectious Disease Spread II). AC and SB acknowledge funding from Imperial College London through the European Partners Fund. The funding was used to organise a workshop that brought together leading experts from the UK, Germany, and France, and public health experts from the European Centre for Disease Prevention, International Society for Infectious Diseases, and WHO. PA has an unpaid advisory role on the Advisory Council for Epiverse.