scholarly journals Real-time projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018

2018 ◽  
Author(s):  
J. Daniel Kelly ◽  
Lee Worden ◽  
Rae Wannier ◽  
Nicole A. Hoff ◽  
Patrick Mukadi ◽  
...  
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Vaccination Coverage ◽  
Ebola Virus ◽  
Democratic Republic ◽  
Democratic Republic Of Congo ◽  
Credible Interval ◽  
Transmission Model ◽  
Republic Of Congo ◽  
Outbreak Size

AbstractBackgroundAs of May 27, 2018, 54 cases of Ebola virus disease (EVD) were reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the current outbreak size and duration with and without vaccine use.MethodsWe modeled Ebola virus transmission using a stochastic branching process model with a negative binomial distribution, using both estimates of reproduction number R declining from supercritical to subcritical derived from past Ebola outbreaks, as well as a particle filtering method to generate a probabilistic projection of the future course of the outbreak conditioned on its reported trajectory to date; modeled using 0%, 44%, and 62% estimates of vaccination coverage. Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize a regression model predicting the outbreak size from the number of observed cases from April 4 to May 27.ResultsWith the stochastic transmission model, we projected a median outbreak size of 78 EVD cases (95% credible interval: 52, 125.4), 86 cases (95% credible interval: 53, 174.3), and 91 cases (95% credible interval: 52, 843.5), using 62%, 44%, and 0% estimates of vaccination coverage. With the regression model, we estimated a median size of 85.0 cases (95% prediction interval: 53.5, 216.6).ConclusionsThis outbreak has the potential to be the largest outbreak in DRC since 2007. Vaccines are projected to limit outbreak size and duration but are only part of prevention, control, and care strategies.

scholarly journals Rapid drop in the reproduction number during the Ebola outbreak in the Democratic Republic of Congo

2015 ◽  
Author(s):  
Christian L Althaus
Keyword(s):  
Rural Areas ◽  
Ebola Virus ◽  
Democratic Republic ◽  
Reproduction Number ◽  
Democratic Republic Of Congo ◽  
Virus Disease ◽  
Outbreak Detection ◽  
Transmission Model ◽  
Republic Of Congo ◽  
Net Reproduction

In 2014, the Democratic Republic of Congo (DRC) experienced an outbreak of Ebola virus disease (EVD) with 69 reported cases. I fitted an EVD transmission model to data of this outbreak and estimated the basic reproduction number R0 = 5.2 (95% confidence interval [CI]: 4.0-6.7). The model suggests that the net reproduction number Rt fell below unity 28 days (95% CI: 25-34 days) after the onset of symptoms in the index case. This illustrates that early outbreak detection and rapid implementation of control interventions are crucial for preventing wider spread of EVD in rural areas.

scholarly journals Rapid drop in the reproduction number during the Ebola outbreak in the Democratic Republic of Congo

2015 ◽  
Author(s):  
Christian L Althaus
Keyword(s):  
Rural Areas ◽  
Ebola Virus ◽  
Democratic Republic ◽  
Reproduction Number ◽  
Democratic Republic Of Congo ◽  
Virus Disease ◽  
Outbreak Detection ◽  
Transmission Model ◽  
Republic Of Congo ◽  
Net Reproduction

In 2014, the Democratic Republic of Congo (DRC) experienced an outbreak of Ebola virus disease (EVD) with 69 reported cases. I fitted an EVD transmission model to data of this outbreak and estimated the basic reproduction number R0 = 5.2 (95% confidence interval [CI]: 4.0-6.7). The model suggests that the net reproduction number Rt fell below unity 28 days (95% CI: 25-34 days) after the onset of symptoms in the index case. This illustrates that early outbreak detection and rapid implementation of control interventions are crucial for preventing wider spread of EVD in rural areas.

scholarly journals Rapid drop in the reproduction number during the Ebola outbreak in the Democratic Republic of Congo

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1418 ◽  
Author(s):  
Christian L. Althaus
Keyword(s):  
Ebola Virus ◽  
Democratic Republic ◽  
Reproduction Number ◽  
Democratic Republic Of Congo ◽  
Virus Disease ◽  
Transmission Model ◽  
Published Data ◽  
Republic Of Congo ◽  
Net Reproduction ◽  
Rapid Drop

The Democratic Republic of Congo (DRC) experienced a confined rural outbreak of Ebola virus disease (EVD) with 69 reported cases from July to October 2014. Understanding the transmission dynamics during the outbreak can provide important information for anticipating and controlling future EVD epidemics. I fitted an EVD transmission model to previously published data of this outbreak and estimated the basic reproduction numberR0= 5.2 (95% CI [4.0–6.7]). The model suggests that the net reproduction numberRtfell below unity 28 days (95% CI [25–34] days) after the onset of symptoms in the index case. This study adds to previous epidemiological descriptions of the 2014 EVD outbreak in DRC, and is consistent with the notion that a rapid implementation of control interventions helped reduce further spread.

Assessing the management of healthcare waste for disease prevention and environment protection at selected hospitals in Kinshasa, Democratic Republic of Congo

2021 ◽  
pp. 0734242X2110481
Author(s):  
Gabriel Kalombe Kyomba ◽  
Joêl Nkiama Numbi Konde ◽  
Diafuka Saila-Ngita ◽  
Thomas Kuanda Solo ◽  
Guillaume Mbela Kiyombo
Keyword(s):  
Waste Management ◽  
Ebola Virus ◽  
Democratic Republic ◽  
Democratic Republic Of Congo ◽  
Bottom Ash ◽  
Control Measures ◽  
Structured Interviews ◽  
Republic Of Congo ◽  
Healthcare Waste ◽  
Health Care Waste

Incineration is the most used healthcare waste (HCW) disposal method. Disease outbreaks due to Ebola virus and SARS-CoV2 require attention to HCW management to avoid pathogens spread and spillover. This study describes HCW management prior to incineration and hospital incinerators performance by analysing bottom ashes from hospitals in Kinshasa, Democratic Republic of Congo. We used semi-structured interviews to capture information on pre-incineration waste management and analysed the chemical composition of 27 samples of incinerator bottom ashes using the energy dispersive X-ray fluorescence. Neither sorting nor waste management measures were applied at hospitals surveyed. Incinerator operators were poorly equipped and their knowledge was limited. The bottom ash concentrations of cadmium, chromium, nickel and lead ranged between 0.61–10.44, 40.15–737.01, 9.11–97.55 and 16.37–240.03 mg kg−1, respectively. Compared to Chinese incinerator performance, the concentrations of some elements were found to be lower than those from China. This discrepancy may be explained by the difference in the composition of HCW. The authors conclude that health care waste in Kinshasa hospitals is poorly managed, higher concentrations of heavy metals are found in incinerator bottom ashes and the incinerators quality is poor. They recommend the strict application of infection prevention control measures, the training of incinerator operators and the use of high-performance incinerators.

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