Estimating the impact of violent events on transmission in Ebola virus disease outbreak, Democratic Republic of the Congo, 2018–2019
S. Rae Wannier,
Lee Worden,
Nicole A. Hoff,
Eduardo Amezcua,
Bernice Selo,
Cyrus Sinai,
Mathias Mossoko,
Bathe Njoloko,
Emile Okitolonda-Wemakoy,
Placide Mbala-Kingebeni,
Steve Ahuka-Mundeke,
Jean Jacques Muyembe-Tamfum,
Eugene T. Richardson,
George W. Rutherford,
James H Jones,
Thomas M. Lietman,
Anne W. Rimoin,
Travis C. Porco,
J. Daniel Kelly
Affiliations
S. Rae Wannier
Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA; Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
Lee Worden
Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA
Nicole A. Hoff
Department of Epidemiology, School of Public Health University of California, Los Angeles, CA, USA
Eduardo Amezcua
Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
Bernice Selo
Ministry of Health, Kinshasa, Democratic Republic of Congo
Cyrus Sinai
Department of Geography at University of North Carolina, Chapel Hill, NC, USA
Mathias Mossoko
Ministry of Health, Kinshasa, Democratic Republic of Congo
Bathe Njoloko
Ministry of Health, Kinshasa, Democratic Republic of Congo
Emile Okitolonda-Wemakoy
School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of Congo
Placide Mbala-Kingebeni
Insitut National de Recherche Biomedicale, Kinshasa, Democratic Republic of Congo
Steve Ahuka-Mundeke
Insitut National de Recherche Biomedicale, Kinshasa, Democratic Republic of Congo
Jean Jacques Muyembe-Tamfum
Insitut National de Recherche Biomedicale, Kinshasa, Democratic Republic of Congo
Eugene T. Richardson
Global Health and Social Medicine, Harvard Medical School, MA, USA
George W. Rutherford
Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA
James H Jones
Department of Earth System Science, Stanford University, Stanford, CA, USA; Woods Institute for the Environment, Stanford University, Stanford, CA, USA
Thomas M. Lietman
Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA; Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Ophthalmology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
Anne W. Rimoin
Department of Epidemiology, School of Public Health University of California, Los Angeles, CA, USA
Travis C. Porco
Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA; Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Ophthalmology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
J. Daniel Kelly
Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA; Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA; Corresponding author.
Introduction: As of April 2019, the current Ebola virus disease (EVD) outbreak in the Democratic Republic of the Congo (DRC) is occurring in a longstanding conflict zone and has become the second largest EVD outbreak in history. It is suspected that after violent events occur, EVD transmission will increase; however, empirical studies to understand the impact of violence on transmission are lacking. Here, we use spatial and temporal trends of EVD case counts to compare transmission rates between health zones that have versus have not experienced recent violent events during the outbreak. Methods: We collected daily EVD case counts from DRC Ministry of Health. A time-varying indicator of recent violence in each health zone was derived from events documented in the WHO situation reports. We used the Wallinga-Teunis technique to estimate the reproduction number R for each case by day per zone in the 2018–2019 outbreak. We fit an exponentially decaying curve to estimates of R overall and by health zone, for comparison to past outbreaks. Results: As of 16 April 2019, the mean overall R for the entire outbreak was 1.11. We found evidence of an increase in the estimated transmission rates in health zones with recently reported violent events versus those without (p = 0.008). The average R was estimated as between 0.61 and 0.86 in regions not affected by recent violent events, and between 1.01 and 1.07 in zones affected by violent events within the last 21 days, leading to an increase in R between 0.17 and 0.53. Within zones with recent violent events, the mean estimated quenching rate was lower than for all past outbreaks except the 2013–2016 West African outbreak. Conclusion: The difference in the estimated transmission rates between zones affected by recent violent events suggests that violent events are contributing to increased transmission and the ongoing nature of this outbreak. Keywords: Ebola virus disease, Outbreak, Mathematical modeling, Geospatial, Democratic Republic of Congo, Africa
