Sensitivity and Specificity of Suspected Case Definition Used during West Africa Ebola Epidemic.

Rapid early detection and control of Ebola virus disease (EVD) is contingent on accurate case definitions. Using an epidemic surveillance dataset from Guinea, we analyzed an EVD case definition developed by the World Health Organization (WHO) and used in Guinea. We used the surveillance dataset (March-October 2014; n = 2,847 persons) to identify patients who satisfied or did not satisfy case definition criteria. Laboratory confirmation determined cases from noncases, and we calculated sensitivity, specificity and predictive values. The sensitivity of the defintion was 68.9%, and the specificity of the definition was 49.6%. The presence of epidemiologic risk factors (i.e., recent contact with a known or suspected EVD case-patient) had the highest sensitivity (74.7%), and unexplained deaths had the highest specificity (92.8%). Results for case definition analyses were statistically significant (p<0.05 by χ2 test). Multiple components of the EVD case definition used in Guinea contributed to improved overall sensitivity and specificity.

Ebola virus disease in West Africa--the first 9 months of the epidemic and forward projections.

BACKGROUND: On March 23, 2014, the World Health Organization (WHO) was notified of an outbreak of Ebola virus disease (EVD) in Guinea. On August 8, the WHO declared the epidemic to be a "public health emergency of international concern." METHODS: By September 14, 2014, a total of 4507 probable and confirmed cases, including 2296 deaths from EVD (Zaire species) had been reported from five countries in West Africa--Guinea, Liberia, Nigeria, Senegal, and Sierra Leone. We analyzed a detailed subset of data on 3343 confirmed and 667 probable Ebola cases collected in Guinea, Liberia, Nigeria, and Sierra Leone as of September 14. RESULTS: The majority of patients are 15 to 44 years of age (49.9% male), and we estimate that the case fatality rate is 70.8% (95% confidence interval [CI], 69 to 73) among persons with known clinical outcome of infection. The course of infection, including signs and symptoms, incubation period (11.4 days), and serial interval (15.3 days), is similar to that reported in previous outbreaks of EVD. On the basis of the initial periods of exponential growth, the estimated basic reproduction numbers (R0 ) are 1.71 (95% CI, 1.44 to 2.01) for Guinea, 1.83 (95% CI, 1.72 to 1.94) for Liberia, and 2.02 (95% CI, 1.79 to 2.26) for Sierra Leone. The estimated current reproduction numbers (R) are 1.81 (95% CI, 1.60 to 2.03) for Guinea, 1.51 (95% CI, 1.41 to 1.60) for Liberia, and 1.38 (95% CI, 1.27 to 1.51) for Sierra Leone; the corresponding doubling times are 15.7 days (95% CI, 12.9 to 20.3) for Guinea, 23.6 days (95% CI, 20.2 to 28.2) for Liberia, and 30.2 days (95% CI, 23.6 to 42.3) for Sierra Leone. Assuming no change in the control measures for this epidemic, by November 2, 2014, the cumulative reported numbers of confirmed and probable cases are predicted to be 5740 in Guinea, 9890 in Liberia, and 5000 in Sierra Leone, exceeding 20,000 in total. CONCLUSIONS: These data indicate that without drastic improvements in control measures, the numbers of cases of and deaths from EVD are expected to continue increasing from hundreds to thousands per week in the coming months.

Epidemiology of Epidemic Ebola Virus Disease in Conakry and Surrounding Prefectures, Guinea, 2014-2015.

In 2014, Ebola virus disease (EVD) in West Africa was first reported during March in 3 southeastern prefectures in Guinea; from there, the disease rapidly spread across West Africa. We describe the epidemiology of EVD cases reported in Guinea's capital, Conakry, and 4 surrounding prefectures (Coyah, Dubreka, Forecariah, and Kindia), encompassing a full year of the epidemic. A total of 1,355 EVD cases, representing ≈40% of cases reported in Guinea, originated from these areas. Overall, Forecariah had the highest cumulative incidence (4× higher than that in Conakry). Case-fatality percentage ranged from 40% in Conakry to 60% in Kindia. Cumulative incidence was slightly higher among male than female residents, although incidences by prefecture and commune differed by sex. Over the course of the year, Conakry and neighboring prefectures became the EVD epicenter in Guinea.

Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study.

BACKGROUND: The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved. METHODS AND FINDINGS: Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications). CONCLUSIONS: Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population.

Contact Tracing Activities during the Ebola Virus Disease Epidemic in Kindia and Faranah, Guinea, 2014.

The largest recorded Ebola virus disease epidemic began in March 2014; as of July 2015, it continued in 3 principally affected countries: Guinea, Liberia, and Sierra Leone. Control efforts include contact tracing to expedite identification of the virus in suspect case-patients. We examined contact tracing activities during September 20-December 31, 2014, in 2 prefectures of Guinea using national and local data about case-patients and their contacts. Results show less than one third of case-patients (28.3% and 31.1%) were registered as contacts before case identification; approximately two thirds (61.1% and 67.7%) had no registered contacts. Time to isolation of suspected case-patients was not immediate (median 5 and 3 days for Kindia and Faranah, respectively), and secondary attack rates varied by relationships of persons who had contact with the source case-patient and the type of case-patient to which a contact was exposed. More complete contact tracing efforts are needed to augment control of this epidemic.

Ebola Virus Disease in Health Care Workers--Guinea, 2014.

An outbreak of Ebola virus disease (Ebola) began in Guinea in December 2013 and has continued through September 2015. Health care workers (HCWs) in West Africa are at high risk for Ebola infection owing to lack of appropriate triage procedures, insufficient equipment, and inadequate infection control practices. To characterize recent epidemiology of Ebola infections among HCWs in Guinea, national Viral Hemorrhagic Fever (VHF) surveillance data were analyzed for HCW cases reported during January 1­December 31, 2014. During 2014, a total of 162 (7.9%) of 2,210 laboratory-confirmed or probable Ebola cases among Guinean adults aged ≥15 years occurred among HCWs, resulting in an incidence of Ebola infection among HCWs 42.2 times higher than among non-HCWs. The disproportionate burden of Ebola infection among HCWs taxes an already stressed health infrastructure, underscoring the need for increased understanding of transmission among HCWs and improved infection prevention and control measures to prevent Ebola infection among HCWs.

Developing One Health surveillance systems.

The health of humans, domestic and wild animals, plants, and the environment are inter-dependent. Global anthropogenic change is a key driver of disease emergence and spread and leads to biodiversity loss and ecosystem function degradation, which are themselves drivers of disease emergence. Pathogen spill-over events and subsequent disease outbreaks, including pandemics, in humans, animals and plants may arise when factors driving disease emergence and spread converge. One Health is an integrated approach that aims to sustainably balance and optimize human, animal and ecosystem health. Conventional disease surveillance has been siloed by sectors, with separate systems addressing the health of humans, domestic animals, cultivated plants, wildlife and the environment. One Health surveillance should include integrated surveillance for known and unknown pathogens, but combined with this more traditional disease-based surveillance, it also must include surveillance of drivers of disease emergence to improve prevention and mitigation of spill-over events. Here, we outline such an approach, including the characteristics and components required to overcome barriers and to optimize an integrated One Health surveillance system.