A Generalizable Data Assembly Algorithm for Modeling Ebola Virus Disease in the Eastern DR Congo

35 Pages Posted: 2 Dec 2018 Last revised: 19 Nov 2019

See all articles by Maimuna S. Majumder

Maimuna S. Majumder

Boston Children's Hospital - Computational Health Informatics Program; Harvard University - Harvard Medical School

Sherri Rose

Harvard University - Department of Health Care Policy

Date Written: November 14, 2019

Abstract

Background. Over the last 19 months, the Democratic Republic of the Congo has reported approximately 3000 cases of Ebola virus disease (EVD). Since August 2018, an experimental vaccine has been deployed to curb transmission.

Methods. A generalizable data assembly algorithm was developed to automatically curate publicly available cumulative reported case count and vaccine deployment data from the Ministère de la Santé. The Incidence Decay and Exponential Adjustment model was then used to estimate basic and observed reproduction numbers associated with the outbreak for three health zones that have been most affected as well as for the country as a whole. Reproduction number estimates at the national level were paired with a sensitivity analysis to assess immunization rates and to project end-of-year case counts under three different transmission scenarios.

Findings. Basic and observed reproduction number estimates at the national level range from 1.13 to 1.99 and from 1.06 to 1.34, respectively. These estimates suggest that approximately 2% to 20% of the affected population across the country has thus far been effectively immunized against EVD and that the outbreak would likely be 4–34 times greater in size today had the vaccine not been deployed. Assuming no changes in transmission dynamics, we estimate that the expected cumulative case count will range between 3479 and 4249 by late December 2019; alternative transmission dynamics scenarios result in lesser and greater case count estimates.

Interpretation. Transmission dynamics associated with the outbreak suggest that though the experimental vaccine has likely helped curb the spread of EVD, the outbreak may continue into 2020. Data assembly algorithms like the one presented here will continue to prove useful as this outbreak and others like it persist.

Funding. Research reported in this work was supported by the National Institutes of Health through an NIH Director’s New Innovator Award DP2-MD012722.

Keywords: Ebola, Ebola Virus Disease, EVD, rVSV-ZEBOV, vaccination, DR Congo, Democratic Republic of the Congo, DRC

Suggested Citation

Majumder, Maimuna and Rose, Sherri, A Generalizable Data Assembly Algorithm for Modeling Ebola Virus Disease in the Eastern DR Congo (November 14, 2019). Available at SSRN: https://ssrn.com/abstract=3291591 or http://dx.doi.org/10.2139/ssrn.3291591

Maimuna Majumder (Contact Author)

Boston Children's Hospital - Computational Health Informatics Program ( email )

United States

Harvard University - Harvard Medical School ( email )

25 Shattuck St
Boston, MA 02115
United States

Sherri Rose

Harvard University - Department of Health Care Policy ( email )

25 Shattuck Street
Boston, MA 02115
United States

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