About: 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

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About: 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 Goto Sponge NotDistinct Permalink

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	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
Creator	Worden, Lee Okitolonda-Wemakoy, Emile Selo, Bernice Mossoko, Mathias Musene, Kamy Daniel, J Hoff, Nicole Id, Kelly Id, Rae Jacques, Jean Lietman, Thomas Porco Id, Travis Rimoin, Anne Rutherford, George Tamfum, Muyembe
Source	PMC
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.
has issue date	2019-08-05(xsd:dateTime)
bibo:doi	10.1371/journal.pntd.0007512
bibo:pmid	31381606
has license	cc-by
sha1sum (hex)	4fbfedadab204c5165514312dd7cba7a907aaaba
schema:url	https://doi.org/10.1371/journal.pntd.0007512
resource representing a document's title	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
has PubMed Central identifier	PMC6695208
has PubMed identifier	31381606
schema:publication	PLoS Negl Trop Dis
resource representing a document's body	covid:4fbfedadab204c5165514312dd7cba7a907aaaba#body_text
is schema:about of	named entity 'DEVELOPMENT' named entity 'Ituri' named entity 'reach' named entity 'active' named entity 'Health' named entity 'PRIMARY HEALTH CARE' named entity 'BEGINNING' named entity 'REACH' named entity '875' named entity 'PROJECT' named entity 'CASE' named entity 'COUNT' named entity 'DATA' named entity 'CONFLICT' named entity 'TIME SERIES' named entity 'ARMED' named entity '2019' named entity 'WORKERS' named entity '2018' named entity 'HAVE' named entity 'EBOLA VIRUS DISEASE' named entity 'NORTH' named entity 'USED' named entity 'LONG-TERM' named entity 'HEALTH' named entity 'REPORTED' named entity 'AVAILABLE' named entity 'OUTBREAK' named entity 'ACTIVE' named entity 'LARGELY' named entity 'CURRENT' named entity 'HEALTH' named entity 'OCCURRED' named entity 'REGIONS' named entity 'CONTACTS' named entity 'FEBRUARY' named entity 'COURSE' named entity 'SHIFTED' named entity 'REPUBLIC OF CONGO' named entity 'PRIOR' named entity 'THEIR' named entity 'OCTOBER' named entity 'DIFFICULT' named entity 'CASES' named entity 'SHORT-TERM' named entity 'February 25' named entity 'outbreak' named entity 'contacts' named entity 'Congo' named entity 'North Kivu' named entity 'health workers' named entity 'PDF' named entity '95%' named entity 'data set' named entity 'September 15' named entity 'particle filtering' named entity 'Ebola outbreak' named entity 'random time' named entity 'cross-validation' named entity 'vaccination' named entity 'Mathematical models' named entity 'stochastic simulation' named entity 'epidemic curve' named entity 'EBOV' named entity 'data set' named entity 'October 13' named entity 'negative binomial'

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