#Ebola virus #outbreak in North Kivu and Ituri provinces, #DRC, and the potential for further #transmission through commercial #air #travel (J Travel Med., abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

J Travel Med. 2019 Aug 15. pii: taz063. doi: 10.1093/jtm/taz063. [Epub ahead of print]

Ebola virus outbreak in North Kivu and Ituri provinces, Democratic Republic of Congo, and the potential for further transmission through commercial air travel.

Tuite AR1,2, Watts AG2,3, Khan K2,3,4, Bogoch II4,5.

Author information: 1 Dalla Lana School of Public Health, University of Toronto, Toronto, Canada. 2 BlueDot, Toronto, Canada. 3 Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada. 4 Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada. 5 Divisions of General Internal Medicine and Infectious Diseases, Toronto General Hospital, University Health Network, Toronto, Canada.

 

Abstract

BACKGROUND:

The 2018-2019 Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces, Democratic Republic of Congo (DRC), continues to spread. The recent discovery of cases in Uganda and in Goma, a major city in the eastern DRC, raises concern for potential EVD transmission in distant locales via commercial air travel.

METHODS:

We examined air travel patterns from the affected region with itinerary-level data from the International Air Transport Association for the year 2018 between July through October, inclusive. We focused on three scenarios; 1) travel from Beni airport, 2) travel from Beni, Goma and Bunia airports, and 3) travel from Beni, Goma, and Bunia, and Kigali airports. We evaluate country-level Infectious Disease Vulnerability Index (IDVI) scores for traveler destinations.

RESULTS:

There were 2,255 commercial air passengers departing from Beni Airport during the specified time frame, all with domestic destinations, and 55% of which were to Goma. 29,777 passengers traveled from Beni, Bunia, and Goma airports during this time frame, with most travel (94.6%) departing from Goma airport. 72.4% of passengers’ final destination from these three airports were within the DRC, primarily to Kinshasa. There were 166,281 outbound passengers from Beni, Bunia, Goma, and Kigali airports with the majority (82.1%) of passengers departing from Kigali. The most frequent destinations from these airports were Nairobi, Kinshasa, and Entebbe. Eight of the 10 destinations with greatest passenger volumes are to countries with IDVI scores less than 0.4.

CONCLUSION:

There is little commercial airline connectivity from the current EVD-affected area, however larger cities in DRC and throughout East Africa should be aware of the low potential for EVD importation through this route. Most countries at greatest risk for EVD importation have limited capacity to manage these cases.

© International Society of Travel Medicine 2019. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

KEYWORDS: Democratic Republic of Congo; Ebola; air travel; epidemic; outbreak; travel

PMID: 31414699 DOI: 10.1093/jtm/taz063

Keywords: Ebola; DRC.

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#Pandemic #bonds: designed to #fail in #Ebola (Nature, summary)

[Source: Nature, full page: (LINK). Summary, edited.]

Pandemic bonds: designed to fail in Ebola

Olga Jonas

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The final toll of the Ebola outbreak in West Africa in 2014–16 was more than 11,000 lives, plus an estimated US$53 billion from economic disruption and collapse of health systems. In the outbreak’s wake, the global health community scrambled to deliver initiatives for increased health security. One flagship programme was the World Bank’s Pandemic Emergency Financing Facility (PEF). Under the scheme, investors who buy pandemic bonds receive generous ‘coupons’, which annually pay about 13% interest. This compensates investors for the risk that the bonds will make ‘insurance’ payouts to fight pandemics under certain conditions. Otherwise, cash returns to the investors when the bonds mature in July 2020.

(…)

Nature 572, 285 (2019) / doi: 10.1038/d41586-019-02415-9

Keywords: International cooperation; Pandemic preparedness; Ebola; DRC.

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Real-time #predictions of the 2018-2019 #Ebola virus disease #outbreak in the #DRC using #Hawkes point process #models (Epidemics, abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Epidemics. 2019 Jul 23:100354. doi: 10.1016/j.epidem.2019.100354. [Epub ahead of print]

Real-time predictions of the 2018-2019 Ebola virus disease outbreak in the Democratic Republic of the Congo using Hawkes point process models.

Kelly JD1, Park J2, Harrigan RJ3, Hoff NA4, Lee SD2, Wannier R5, Selo B6, Mossoko M6, Njoloko B6, Okitolonda-Wemakoy E7, Mbala-Kingebeni P8, Rutherford GW5, Smith TB3, Ahuka-Mundeke S8, Muyembe-Tamfum JJ8, Rimoin AW4, Schoenberg FP2.

Author information: 1 Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA; F.I. Proctor Foundation, University of California, San Francisco, CA USA. Electronic address: Dan.Kelly@ucsf.edu. 2 Department of Statistics, University of California, Los Angeles, CA, USA. 3 Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA. 4 Department of Epidemiology, University of California, Los Angeles, CA, USA. 5 Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. 6 Ministry of Health, Kinshasa, Congo. 7 School of Public Health, University of Kinshasa, Kinshasa, Congo. 8 Institut National de Recherche Biomedicale, Kinshasa, Congo.

 

Abstract

As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts.

Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

KEYWORDS: Compartmental models; Democratic Republic of Congo; Ebola virus disease; Hawkes point process; Mathematical modeling

PMID: 31395373 DOI: 10.1016/j.epidem.2019.100354

Keywords: Ebola; DRC; Mathematical models.

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#Ebola in eastern #DRC (Lancet Infect Dis., summary)

[Source: The Lancet Infectious Diseases, full page: (LINK). Abstract, edited.]

Ebola in eastern DRC

Pierre E Rollin

Published: August 08, 2019 / DOI: https://doi.org/10.1016/S1473-3099(19)30422-0

 

Summary

When the current Ebola outbreak was declared on Aug 1, 2018, in North Kivu, Democratic Republic of the Congo (DRC), confidence was high that the hundreds of experienced responders and the new therapeutics and vaccines would be able to quickly stop this tenth outbreak in DRC. As of July 28, 2019, 2671 probable and confirmed cases and 1790 probable and confirmed deaths have been reported.1
Leadership and coordination shortfalls, increased insecurity, mistrust, and denial from both the community and the responders are now hallmarks of the response. 2

 

Keywords: Ebola; DRC.

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‘The cat that kills people:’ #community #beliefs about #Ebola origins and implications for disease #control in Eastern #DRC (Pathog Glob Health, abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Pathog Glob Health. 2019 Aug 6:1-9. doi: 10.1080/20477724.2019.1650227. [Epub ahead of print]

‘The cat that kills people:’ community beliefs about Ebola origins and implications for disease control in Eastern Democratic Republic of the Congo.

Kasereka MC1, Hawkes MT2,3,4.

Author information: 1a Department of Medicine, Université Catholique de Graben , Butembo , Democratic Republic of Congo. 2b Department of Pediatrics, University of Alberta , Edmonton , Canada. 3c Department of Medical Microbiology and Immunology, University of Alberta , Edmonton , Canada. 4d School of Public Health, University of Alberta , Edmonton , Canada.

 

Abstract

The current Ebola epidemic in Eastern Democratic Republic of Congo (DRC) has surpassed 1 700 deaths. Social resistance, a major barrier to control efforts, invites exploration of community beliefs around Ebola and its origins. We conducted a mixed-methods study, using four focus group discussions (FGDs) involving 20 participants, and a 19-item survey questionnaire, administered to a nonprobability sample of 286 community members throughout the outbreak zone. FGDs and surveys were conducted between 4 and 17 August 2018. FGDs revealed a widespread rumor early in the epidemic of two twins bewitched by their aunt after eating her cat, who developed bleeding symptoms and triggered the epidemic. However, this myth appeared to dissipate as the epidemic progressed and biomedical transmission became generally accepted. In our survey, 6% of respondents endorsed supernatural origins of Ebola. These respondents were more likely to believe that traditional medicine practitioners can cure Ebola. Wild animals were recognized as sources of Ebola by 53% and FGD participants commented that ‘Ebola leaves the forest and hides in the hospital,’ recognizing that zoonotic origins gave way to nosocomial transmission as the epidemic progressed. Taken together, our findings suggest that a dynamic syncretism of mythical and biomedical understanding of Ebola may have shaped transmission patterns. Mythical conceptions and fear of contagion may have fueled the ‘underground’ transmission of Ebola, as patients sought care from traditional healers, who are ill-equipped to deal with a highly contagious biohazard. A deeper understanding of beliefs around Ebola origins may illuminate strategies to engage communities in control efforts.

KEYWORDS: DRC; Ebola; community engagement; epidemic; social resistance; transmission

PMID: 31387518 DOI: 10.1080/20477724.2019.1650227

Keywords: Ebola; DRC; Society.

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Estimating the #impact of #violent #events on #transmission in #Ebola virus disease #outbreak, #DRC, 2018-2019 (Epidemics, abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Epidemics. 2019 Jul 26:100353. doi: 10.1016/j.epidem.2019.100353. [Epub ahead of print]

Estimating the impact of violent events on transmission in Ebola virus disease outbreak, Democratic Republic of the Congo, 2018-2019.

Wannier SR1, Worden L2, Hoff NA3, Amezcua E4, Selo B5, Sinai C6, Mossoko M5, Njoloko B5, Okitolonda-Wemakoy E7, Mbala-Kingebeni P8, Ahuka-Mundeke S8, Muyembe-Tamfum JJ8, Richardson ET9, Rutherford GW2, Jones JH10, Lietman TM11, Rimoin AW3, Porco TC11, Kelly JD12.

Author information: 1 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. 2 Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA. 3 Department of Epidemiology, School of Public Health University of California, Los Angeles, CA, USA. 4 Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA. 5 Ministry of Health, Kinshasa, Democratic Republic of Congo. 6 Department of Geography at University of North Carolina, Chapel Hill, NC, USA. 7 School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of Congo. 8 Insitut National de Recherche Biomedicale, Kinshasa, Democratic Republic of Congo. 9 Global Health and Social Medicine, Harvard Medical School, MA, USA. 10 Department of Earth System Science, Stanford University, Stanford, CA, USA; Woods Institute for the Environment, Stanford University, Stanford, CA, USA. 11 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. 12 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. Electronic address: dan.kelly@ucsf.edu.

 

Abstract

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.

Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

KEYWORDS: Africa; Democratic Republic of Congo; Ebola virus disease; Geospatial; Mathematical modeling; Outbreak

PMID: 31378584 DOI: 10.1016/j.epidem.2019.100353

Keywords: Ebola; Society; DRC.

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#Projections of #epidemic #transmission and #estimation of #vaccination #impact during an ongoing #Ebola virus disease #outbreak in Northeastern #DRC, as of Feb. 25, 2019 (PLoS Negl Trop Dis,. abstract)

[Source: PLoS Neglected Tropical Diseases, full page: (LINK). Abstract, edited.]

OPEN ACCESS /  PEER-REVIEWED / RESEARCH ARTICLE

Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019

Lee Worden, Rae Wannier, Nicole A. Hoff, Kamy Musene, Bernice Selo, Mathias Mossoko, Emile Okitolonda-Wemakoy, Jean Jacques Muyembe Tamfum, George W. Rutherford, Thomas M. Lietman, Anne W. Rimoin, Travis C. Porco, J. Daniel Kelly

Published: August 5, 2019 / DOI: https://doi.org/10.1371/journal.pntd.0007512 / This is an uncorrected proof.

 

Abstract

Background

As of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak.

Methods

For short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott’s rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts.

Results

During validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872–1054) and 955 cases by March 4 (95% prediction interval: 874–1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876–933) and 898 (95% prediction interval: 877–983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013–2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.

Conclusions

Our projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges.

 

Author summary

As of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has been reported, and in which EVD cases and their contacts have been difficult for health workers to reach. We used an ensemble of models to estimate EVD transmission rates and to forecast the short- and long-term course of the outbreak. Our models project that a final size of roughly up to 300 additional cases is most likely, and estimate that transmission rates are higher than would be seen under optimal levels of contact tracing and vaccination. While a catastrophic outbreak is not projected, is it not ruled out, and prevention and vigilance are warranted.

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Citation: Worden L, Wannier R, Hoff NA, Musene K, Selo B, Mossoko M, et al. (2019) Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019. PLoS Negl Trop Dis 13(8): e0007512. https://doi.org/10.1371/journal.pntd.0007512

Editor: Townsend Peterson, The University of Kansas, UNITED STATES

Received: November 28, 2018; Accepted: June 3, 2019; Published: August 5, 2019

Copyright: © 2019 Worden et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: TCP and LW acknowledge (partial) support from U01 GM087728/GM/NIGMS NIH HHS/United States. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Keywords: Ebola; DRC; Vaccines; Mathematical models.

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