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Data flows during public health emergencies in LMICs: A people-centered mapping of data flows during the 2018 ebola epidemic in Equateur, DRC

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  • Abramowitz, Sharon
  • Stevens, Lys Alcayna
  • Kyomba, Gabriel
  • Mayaka, Serge
  • Grépin, Karen A.

Abstract

In infectious outbreaks, rapid case detection and reporting, coordination, and context-specific strategies are needed for rapid containment. Data sharing between actors, and the speed and content of data flows, is essential for expediting epidemic response. In this study, researchers mapped data flows during the 2018 Ebola Virus Disease (EVD) outbreak in Equateur Province in the Democratic Republic of the Congo using semi-structured interviews, ethnographic research, and focus groups with EVD response actors. During this research, we mapped and tracked data collection, transmission, storage, sharing, and use patterns. Target participants included: key organizational actors in the EVD outbreaks responses, including local (primary health, community-based, hospital), provincial (MoPH, DRC Red Cross), and international (WHO, UN organizations, international first-responders) stakeholders. We found that a community-based surveillance system enabled the rapid detection of a hemorrhagic fever outbreak, resulting in the rapid laboratory confirmation of EVD. With the arrival of international organizations to provide support to the EVD response, routine surveillance systems continued to function robustly. However, the establishment of a vertical EVD response architecture created challenges for the response. Data flows during the Equateur outbreak were hampered by numerous challenges in the domains of early warning, line lists of cases, and contact tracing, which impeded surveillance and data flows. We therefore argue that structuring health information systems for preparedness requires taking a person-centered approach to data production, flow, and analysis.

Suggested Citation

  • Abramowitz, Sharon & Stevens, Lys Alcayna & Kyomba, Gabriel & Mayaka, Serge & Grépin, Karen A., 2023. "Data flows during public health emergencies in LMICs: A people-centered mapping of data flows during the 2018 ebola epidemic in Equateur, DRC," Social Science & Medicine, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:socmed:v:318:y:2023:i:c:s0277953622004221
    DOI: 10.1016/j.socscimed.2022.115116
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    1. Ngo, Diana K.L. & Bauhoff, Sebastian, 2021. "The medium-run and scale-up effects of performance-based financing: An extension of Rwanda’s 2006 trial using secondary data," World Development, Elsevier, vol. 139(C).
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    1. Dalton Breno Costa & Felipe Coelho de Abreu Pinna & Anjni Patel Joiner & Brian Rice & João Vítor Perez de Souza & Júlia Loverde Gabella & Luciano Andrade & João Ricardo Nickenig Vissoci & João Carlos , 2023. "AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal," PLOS Digital Health, Public Library of Science, vol. 2(12), pages 1-12, December.

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