Luke Bawo

Dynamics and control of Ebola virus transmission in Montserrado, Liberia: a mathematical modelling analysis

BACKGROUND A substantial scale-up in public health response is needed to control the unprecedented Ebola virus disease (EVD) epidemic in west Africa. Current international commitments seek to expand intervention capacity in three areas: new EVD treatment centres, case ascertainment through contact tracing, and household protective kit allocation. We aimed to assess how these interventions could be applied individually and in combination to avert future EVD cases and deaths. METHODS We developed a transmission model of Ebola virus that we fitted to reported EVD cases and deaths in Montserrado County, Liberia. We used this model to assess the effectiveness of expanding EVD treatment centres, increasing case ascertainment, and allocating protective kits for controlling the outbreak in Montserrado. We varied the efficacy of protective kits from 10% to 50%. We compared intervention initiation on Oct 15, 2014, Oct 31, 2014, and Nov 15, 2014. The status quo intervention was defined in terms of case ascertainment and capacity of EVD treatment centres on Sept 23, 2014, and all behaviour and contact patterns relevant to transmission as they were occurring at that time. The primary outcome measure was the expected number of cases averted by Dec 15, 2014. FINDINGS We estimated the basic reproductive number for EVD in Montserrado to be 2·49 (95% CI 2·38-2·60). We expect that allocating 4800 additional beds at EVD treatment centres and increasing case ascertainment five-fold in November, 2014, can avert 77 312 (95% CI 68 400-85 870) cases of EVD relative to the status quo by Dec 15, 2014. Complementing these measures with protective kit allocation raises the expectation as high as 97 940 (90 096-105 606) EVD cases. If deployed by Oct 15, 2014, equivalent interventions would have been expected to avert 137 432 (129 736-145 874) cases of EVD. If delayed to Nov 15, 2014, we expect the interventions will at best avert 53 957 (46 963-60 490) EVD cases. INTERPRETATION The number of beds at EVD treatment centres needed to effectively control EVD in Montserrado substantially exceeds the 1700 pledged by the USA to west Africa. Accelerated case ascertainment is needed to maximise effectiveness of expanding the capacity of EVD treatment centres. Distributing protective kits can further augment prevention of EVD, but it is not an adequate stand-alone measure for controlling the outbreak. Our findings highlight the rapidly closing window of opportunity for controlling the outbreak and averting a catastrophic toll of EVD cases and deaths. FUNDING US National Institutes of Health.

Evolution of Ebola Virus Disease from Exotic Infection to Global Health Priority, Liberia, Mid-2014

As the disease spread, the scale of the epidemic required a multi-faceted public health response.

Ebola and Its Control in Liberia, 2014–2015

Several factors explain the successful response to the outbreak in this country.

Use of Capture-Recapture to Estimate Underreporting of Ebola Virus Disease, Montserrado County, Liberia.

To the Editor: Underreporting of cases during a large outbreak of disease is not without precedent (1–5). Health systems in West Africa were ill-prepared for the arrival of Ebola virus disease (Ebola) (6). The Ebola outbreak in Liberia was declared on March 31, 2014, and peaked in September 2014. However, by mid-June, the outbreak had reached Montserrado County, where the capital, Monrovia, is located. In response, the Liberia Ministry of Health and Social Welfare (MOHSW) created a National Ebola Hotline: upon receipt of a call, a MOHSW case investigation team was dispatched to the site of the possible case. Additionally, persons could seek care at an Ebola Treatment Unit (ETU) or be referred to an ETU by another health care facility. During June 1–August 14, 2014, MOHSW, Medecins Sans Frontieres, and the US nongovernment organization Samaritan’s Purse managed 3 ETUs in Montserrado County, including 2 in Monrovia operated by Eternal Love Winning Africa (ELWA). In August 2014, to assess the extent of underreporting in the midst of the Ebola outbreak, we analyzed 2 sources of data collected during June 1–August 14. The first comprised data collected by MOHSW case investigation teams. These data were collected on MOHSW case forms and entered into a database emulating these forms using Epi Info version 7 software (Centers for Disease Control and Prevention, Atlanta, GA, USA). The second data source (designed on Excel 2003; Microsoft, Redmond, WA, USA) comprised data on all patients admitted to the 2 ELWA ETUs (ELWA1 and ELWA2). We used a capture–recapture (CRC) approach. CRC can evaluate the completeness of reporting and thereby be used to correct for underreporting (7). CRC methods use data from overlapping databases to estimate the number of unreported cases and thus more closely derive the true number of Ebola cases. Both databases were populated and managed separately, although the included Ebola cases are assumed to reflect the same patient population in Montserrado County. These 2 databases enabled us to use CRC to estimate the true number of Ebola cases in Montserrado County. To be included in either database, a case must have been classified as suspected, probable, or confirmed Ebola. The case definitions, following the official MOHSW definition for Ebola, were identical in both databases. Eventually, after laboratory confirmation, cases could be reclassified as “not a case” and thus be excluded from the analysis. To estimate the total number of Ebola cases during the study period, we used Chapman’s 2-sample CRC population estimate (7); we calculated the 95% CI as proposed by Wittes et al. (8). We performed a sensitivity analysis measuring impact of error in matching cases during record linkage. A total of 227 Ebola cases were recorded in the MOHSW database and 99 Ebola cases in the Montserrado County ETUs database (Table). Of these, 25 were found in both databases, 202 in the MOHSW database only, and 74 in the Montserrado County ETU database only. We estimated that the cumulative number of Ebola cases for Montserrado County during the study period was 876 (95% CI 608–1,143). A sensitivity analysis performed with ±5 cases showed that, with 5 additional cases in common between databases, the cumulative number of cases would decrease to 734 (95% CI 537–931); with 5 additional discordant cases, the estimate would increase to 1,085 (95% CI 700–1,469). Our analysis shows that the number of cases in Montserrado Country was at least 3-fold higher than that reported during the study period. Our study had several limitations. According to the doctor in charge of data collection up to August 4, some forms (<10) completed at the beginning of June 2014 might have been misplaced. Additionally, some patients who entered the ETU were not recorded in the registry book (˂5). CRC assumes a closed population. In Montserrado County, persons can move freely. In both databases, we included only cases that occurred in or were reported in Montserrado County. CRC assumes that links between the 2 sources based on identifying case information are error free. The sensitivity analysis suggested that even if up to 5 case matches were not detected, our conclusion was relatively robust. CRC assumes homogeneity in the likelihood of being captured and recaptured and that data sources are independent. In our analysis, homogeneity is unlikely. For example, the MOHSW database was more likely to capture cases in persons more likely to seek care; the ETU database was more likely to detect cases in persons referred by health workers. Similar behaviors might have resulted in positive dependency in each data source. Both heterogeneity and positive dependency with data sources leads to underestimation. Despite these limitations, we estimated more Ebola cases than were reported through official channels during the beginning of the outbreak in Montserrado County. Routine studies similar to ours can rapidly provide public health officials managing the outbreak response with estimates of underreporting and enable timely mobilization of appropriate resources. However, we believe that further exploration of this technique to better understand the possible difference of capture preference of each source may help improve the technique and benefit future outbreaks.

Reducing under-reporting of stigmatized health events using the List Experiment: results from a randomized, population-based study of abortion in Liberia

BACKGROUND Direct measurement of sensitive health events is often limited by high levels of under-reporting due to stigma and concerns about privacy. Abortion in particular is notoriously difficult to measure. This study implements a novel method to estimate the cumulative lifetime incidence of induced abortion in Liberia. METHODS In a randomly selected sample of 3219 women ages 15–49 years in June 2013 in Liberia, we implemented the ‘Double List Experiment’. To measure abortion incidence, each woman was read two lists: (A) a list of non-sensitive items and (B) a list of correlated non-sensitive items with abortion added. The sensitive item, abortion, was randomly added to either List A or List B for each respondent. The respondent reported a simple count of the options on each list that she had experienced, without indicating which options. Difference in means calculations between the average counts for each list were then averaged to provide an estimate of the population proportion that has had an abortion. RESULTS The list experiment estimates that 32% [95% confidence interval (CI): 0.29-0.34) of respondents surveyed had ever had an abortion (26% of women in urban areas, and 36% of women in rural areas, P-value for difference < 0.001), with a 95% response rate. CONCLUSIONS The list experiment generated an estimate five times greater than the only previous representative estimate of abortion in Liberia, indicating the potential utility of this method to reduce under-reporting in the measurement of abortion. The method could be widely applied to measure other stigmatized health topics, including sexual behaviours, sexual assault or domestic violence.

Retrospective Analysis of the 2014–2015 Ebola Epidemic in Liberia

The 2014-2015 Ebola epidemic has been the most protracted and devastating in the history of the disease. To prevent future outbreaks on this scale, it is imperative to understand the reasons that led to eventual disease control. Here, we evaluated the shifts of Ebola dynamics at national and local scales during the epidemic in Liberia. We used a transmission model calibrated to epidemiological data between June 9 and December 31, 2014, to estimate the extent of community and hospital transmission. We found that despite varied local epidemic patterns, community transmission was reduced by 40-80% in all the counties analyzed. Our model suggests that the tapering of the epidemic was achieved through reductions in community transmission, rather than accumulation of immune individuals through asymptomatic infection and unreported cases. Although the times at which this transmission reduction occurred in the majority of the Liberian counties started before any large expansion in hospital capacity and the distribution of home protection kits, it remains difficult to associate the presence of interventions with reductions in Ebola incidence.

Characterizing risk of Ebola transmission based on frequency and type of case-contact exposures.

During the initial months of the 2013–2016 Ebola epidemic, rapid geographical dissemination and intense transmission challenged response efforts across West Africa. Contextual behaviours associated with increased risk of exposure included travel to high-transmission settings, caring for sick and preparing the deceased for traditional funerals. Although such behaviours are widespread in West Africa, high-transmission pockets were observed. Superspreading and clustering are typical phenomena in infectious disease outbreaks, as a relatively small number of transmission chains are often responsible for the majority of events. Determining the characteristics of contacts at greatest risk of developing disease and of cases with greatest transmission potential could therefore help curb propagation of infection. Our analysis of contact tracing data from Montserrado County, Liberia, suggested that the probability of transmission was 4.5 times higher for individuals who were reported as having contact with multiple cases. The probability of individuals developing disease was not significantly associated with age or sex of their source case but was higher when they were in the same household as the infectious case. Surveillance efforts for rapidly identifying symptomatic individuals and effectively messaged campaigns encouraging household members to bring the sick to designated treatment centres without administration of home care could mitigate transmission. This article is part of the themed issue ‘The 2013–2016 West African Ebola epidemic: data, decision-making and disease control’.

Estimation of maternal and neonatal mortality at the subnational level in Liberia

To establish representative local‐area baseline estimates of maternal and neonatal mortality using a novel adjusted sisterhood method.

Development, Use, and Impact of a Global Laboratory Database During the 2014 Ebola Outbreak in West Africa

Background The international impact, rapid widespread transmission, and reporting delays during the 2014 Ebola outbreak in West Africa highlighted the need for a global, centralized database to inform outbreak response. The World Health Organization and Emerging and Dangerous Pathogens Laboratory Network addressed this need by supporting the development of a global laboratory database. Methods Specimens were collected in the affected countries from patients and dead bodies meeting the case definitions for Ebola virus disease. Test results were entered in nationally standardized spreadsheets and consolidated onto a central server. Results From March 2014 through August 2016, 256343 specimens tested for Ebola virus disease were captured in the database. Thirty-one specimen types were collected, and a variety of diagnostic tests were performed. Regular analysis of data described the functionality of laboratory and response systems, positivity rates, and the geographic distribution of specimens. Conclusion With data standardization and end user buy-in, the collection and analysis of large amounts of data with multiple stakeholders and collaborators across various user-access levels was made possible and contributed to outbreak response needs. The usefulness and value of a multifunctional global laboratory database is far reaching, with uses including virtual biobanking, disease forecasting, and adaption to other disease outbreaks.

Analysis of patient data from laboratories during the Ebola virus disease outbreak in Liberia, April 2014 to March 2015

An outbreak of Ebola virus disease (EVD) in Liberia began in March 2014 and ended in January 2016. Epidemiological information on the EVD cases was collected and managed nationally; however, collection and management of the data were challenging at the time because surveillance and reporting systems malfunctioned during the outbreak. EVD diagnostic laboratories, however, were able to register basic demographic and clinical information of patients more systematically. Here we present data on 16,370 laboratory samples that were tested between April 4, 2014 and March 29, 2015. A total of 10,536 traceable individuals were identified, of whom 3,897 were confirmed cases (positive for Ebola virus RNA). There were significant differences in sex, age, and place of residence between confirmed and suspected cases that tested negative for Ebola virus RNA. Age (young children and the elderly) and place of residence (rural areas) were the risk factors for death due to the disease. The case fatality rate of confirmed cases decreased from 80% to 63% during the study period. These findings may help support future investigations and lead to a fuller understanding of the outbreak in Liberia.