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. 2022 May 31;22(1):1073.
doi: 10.1186/s12889-022-13327-1.

Leveraging human resources for outbreak analysis: lessons from an international collaboration to support the sub-Saharan African COVID-19 response

Affiliations

Leveraging human resources for outbreak analysis: lessons from an international collaboration to support the sub-Saharan African COVID-19 response

Sara Botero-Mesa et al. BMC Public Health. .

Abstract

Emerging infectious diseases are a growing threat in sub-Saharan African countries, but the human and technical capacity to quickly respond to outbreaks remains limited. Here, we describe the experience and lessons learned from a joint project with the WHO Regional Office for Africa (WHO AFRO) to support the sub-Saharan African COVID-19 response.In June 2020, WHO AFRO contracted a number of consultants to reinforce the COVID-19 response in member states by providing actionable epidemiological analysis. Given the urgency of the situation and the magnitude of work required, we recruited a worldwide network of field experts, academics and students in the areas of public health, data science and social science to support the effort. Most analyses were performed on a merged line list of COVID-19 cases using a reverse engineering model (line listing built using data extracted from national situation reports shared by countries with the Regional Office for Africa as per the IHR (2005) obligations). The data analysis platform The Renku Project ( https://renkulab.io ) provided secure data storage and permitted collaborative coding.Over a period of 6 months, 63 contributors from 32 nations (including 17 African countries) participated in the project. A total of 45 in-depth country-specific epidemiological reports and data quality reports were prepared for 28 countries. Spatial transmission and mortality risk indices were developed for 23 countries. Text and video-based training modules were developed to integrate and mentor new members. The team also began to develop EpiGraph Hub, a web application that automates the generation of reports similar to those we created, and includes more advanced data analyses features (e.g. mathematical models, geospatial analyses) to deliver real-time, actionable results to decision-makers.Within a short period, we implemented a global collaborative approach to health data management and analyses to advance national responses to health emergencies and outbreaks. The interdisciplinary team, the hands-on training and mentoring, and the participation of local researchers were key to the success of this initiative.

Keywords: COVID-19; Data management; Health emergency; Outbreak; Pandemic; Sub-Saharan Africa.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig. 1
Fig. 1
Residence countries of members of the GRAPH Network. Legend: The multidisciplinary research network consists of 63 collaborators in 32 countries. Source: “File:BlankMap-World.svg.” Wikimedia Commons, the free media repository. 15 Jul 2021, 00:12 UTC. 27 Jul 2021, 12:51 https://commons.wikimedia.org/w/index.php?title=File:BlankMap-World.svg&oldid=575140996
Fig. 2
Fig. 2
visual outline of the network’s workflow. Legend: The analysts’ output was provided in an Rmd-PDF, with the analysis code and high-resolution figures saved in a separate folder. This process was adapted and standardized for each country and was designed to be automated as much as possible (although some adaptations were needed for every new linelist as the formats changed frequently). Reporters (qualitative analysts) contextualized epidemiological data based on country-specific COVID-19 status whilst utilizing a standardized template
Fig. 3
Fig. 3
The EpigraphHub workflow. Legend: The EpiGraphHub application automates the production of epidemiological reports in multiple modalities

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