Ethical Considerations

This paper is based on data collected during surveillance and response activities for Ebola in Guinea, Liberia, and Sierra Leone. The work is based on routine national surveillance for Ebola, for which informed consent is not needed. The surveillance was carried out under WHO Integrated Disease Surveillance and Response guidelines in Africa (the recommended disease notification system). Data were reported to WHO under the obligations of the International Health Regulations. All work was carried out under this legal framework. All information on individual patients has been anonymized for analysis and presentation.

Data

Information from Ebola cases was collected using a standardised case investigation form [12] and the Epi Info Viral Hemorrhagic Fever Application (https://epiinfovhf.codeplex.com/). The cleaning of the data has been described previously [11,12].

The case investigation form records information on exposures that may have led to the infection of the case. Cases were asked if in the month prior to symptom onset they had had contact with a potential Ebola case and/or had attended a funeral. Cases could report details of up to five exposure events with potential source contacts (up to three exposures with potential source contacts outside of a funeral context, and up to two exposures at funerals of potential source contacts); from Autumn 2014, an abbreviated form was introduced with up to two non-funeral and one funeral exposure (see S2–S6 Texts). Cases reported the name of the person they were exposed to and provided details about their relationship to that person (e.g., family, friend, neighbour) and the nature of the exposure (e.g., touched bodily fluids or shared belongings). All names were anonymized using the SHA-1 hashing algorithm [15]. Here, we use “potential source contact”, “source contact”, or “contact” to describe the person who was named as a potential Ebola source, and “exposure” to describe the potential transmission event that took place between the case and the potential source contact (see Box 1 for terminology). The potential source contact may be alive or dead at the time of the exposure.

Ebola cases are classified as confirmed, probable, or suspected. Here we analysed data from all CP cases unless otherwise stated. In practice, different countries implemented slightly different case definitions; hence, we also performed sensitivity analyses considering confirmed, probable, and suspected cases combined (see section 3 in S1 Text).

Analysis of Predictors of Naming an Exposure

We assessed whether cases who reported at least one non-funeral or funeral exposure were different from cases who did not report that type of exposure. This allowed us to assess whether cases who report exposures are representative of the line-list. Multivariable logistic regressions were performed using predictors identified as significant (p < 0.05) in univariable logistic regressions (see section 1.11 in S1 Text for a list of predictors included in univariable analyses). The most parsimonious yet adequate multivariable model was then identified (using the Akaike information criterion) through backwards stepwise model selection (see final results in Tables b and c in S1 Text).

Matching Named Potential Source Contacts to the Case Line-List

To identify potential transmission pairs and thus elucidate transmission networks, we examined whether named potential source contacts were themselves Ebola cases. We searched for all named contacts among the names of cases in the line-list to find possible matches. Multiple checks were undertaken to verify the consistency of all matched case-contact pairs according to a set of rules (see section 1.4 and Figure a in S1 Text), in particular comparing dates of symptom onset and exposure.

Importantly, the exposure data analysed here are retrospective and are completely distinct from prospective contact tracing data, where cases are asked who they had contact with after they became ill; we do not analyse prospective contact tracing data here, as such data were not available to us, but rather focus on identified potential source contacts.

Assessing Changes in Infection Control in Health Care Facilities over Time

We hypothesized that the observed decrease in the proportion of HCWs (the positions reported include medical staff and other health-care-facility-based workers) among cases over time (see Figure f in S1 Text) was due to decreased unprotected exposure to patients. To address this issue, we compared the estimated slopes from two linear regression models of (1) the proportion of HCWs among cases over time and (2) the proportion of hospitalised patients over time (see section 1.5 in S1 Text). Note that we define “hospitalisation” as admission to a number of different health care facilities (see Box 2).

Fitting Distributions to Intervals from Clinical Event to Exposure

Understanding when onward exposure occurred in the clinical course of infection in a primary case could inform the focus of interventions, for instance for early detection of cases or for improving the safety of funerals. We analysed the timing of non-funeral exposure events relative to the following clinical events of the named contacts: onset of symptoms, hospitalisation, and death. Here, we describe the analysis of the time from symptom onset to onward exposure; the analyses for hospitalisation and death were similar. We fitted (by maximum likelihood) a distribution to the observed delays between symptom onset and exposure. In brief (see section 1.8 in S1 Text for more detail), this likelihood accounted for (1) multiple exposures (when multiple exposures were reported, only one was likely to have led to infection, so exposures were weighted accordingly), (2) inaccurate recall of contacts, (3) inaccurate recall of dates of exposures, (4) errors in data entry. In order to address issues 2 to 4, we fitted a mixture of two offset lognormal distributions: the more peaked we regarded as the signal, and the broader we regarded as the noise (see section 1.8 in S1 Text). We then interpreted the signal distribution as our best estimate of actual infection times. Missing delays were imputed by random draws from the observed data.

The analyses described above showed that the non-funeral exposure events were concentrated shortly after the onset of symptoms and around the day of death (see Results). We performed an additional analysis to further explore whether one of these two clinical events was more influential for the timing of onward non-funeral exposure events. We compared the ability to predict (1) the date of death of the source contact based on date of symptom onset of the source contact and the date of exposure reported by the case and (2) the date of symptom onset of the source contact based on date of death of the source contact and date of onward exposure (see Figure o and section 1.9 in S1 Text). These two models assume that the timing of onward exposure is mainly determined by the date of symptom onset (model 1) or by the date of death (model 2).

Analysis of the Network of Reported Exposures

The transmission network can be described by a network of nodes (cases) linked by directed edges (exposures leading to transmission). In order to characterise the heterogeneities in transmission, we analysed the properties of this network. We looked at separate networks for funeral and non-funeral exposures (see section 1.10 in S1 Text). For each, we analysed the out-degree distribution, i.e., the distribution of the number of cases naming the same matched contact, which can be seen as a proxy for the distribution of the number of secondary cases per index case. Several parametric distributions were fitted to each of the two observed out-degree distributions using maximum likelihood estimation (see section 1.10 in S1 Text for a list of the distributions explored). The Akaike information criterion corrected for finite sample sizes (AICc) [17] was then used to select the best distribution. For the best-fitting distribution, 95% confidence intervals for the distribution parameters were obtained using the likelihood ratio test, and the confidence interval for the corresponding distribution was obtained by numerical sampling.

We derived the variance and the coefficient of variation for the offspring distribution (the distribution of the number of secondary cases infected by each case), based on the assumption that the network of exposures shown in Figure p in S1 Text is a sample of the full transmission network (see section 1.10 in S1 Text).

Analysis of Predictors of Being Named and Being Named Multiple Times as a Potential Source Contact

To obtain further insight into potential drivers of transmission, we analysed the data to see if there were predictors of being a potential source contact. We performed four analyses: (1) a logistic regression, which identified predictors of being named as a non-funeral contact, one or more times; (2) a logistic regression restricted to cases who died, which identified predictors of being named as a funeral contact, one or more times; (3) a negative binomial regression, which identified predictors of being named multiple times as a non-funeral contact, conditional on being named at least once; and (4) a negative binomial regression restricted to cases who died, which identified predictors of being named multiple times as a funeral contact, conditional on being named at least once. Predictors included in the univariable regressions were as defined in section 1.11 of S1 Text, and the method for identifying the final parsimonious multivariable model is described above. We performed these analyses on confirmed, probable, and suspected contacts who had been named by CP cases. This allowed us to understand the role of suspected contacts in onward transmission compared to CP contacts.

Correlates of Transmission Intensity

We explored the relationship between district-level (second administrative division) transmission intensity and (1) the proportion of cases reporting funeral attendance amongst those reporting any exposure and (2) the proportion of cases ever hospitalised and the proportion of cases hospitalised ≤4 days (4 days was the median delay from symptom onset to hospitalisation; see Figure m in S1 Text for sensitivity analysis to that threshold). The transmission intensity was quantified by the reproduction number, R, which estimates the average number of secondary cases per index case [11,18] (see section 1.6 in S1 Text). We estimated the reproduction number () and the proportion (), for every district (d) in all three countries over monthly intervals (m). ( was estimated with the R package “EpiEstim” [19] using district-level incidence, presented in Fig 1 at the country level; see section 1.6 in S1 Text.) The relationship between () and the proportions was explored using linear regressions. As these quantities were estimated from a sample of data with a level of uncertainty, we used a custom linear regression method that accounts for measurement error [20,21]. The method to compute the correlation coefficient was also adapted to account for measurement error (see section 1.7 in S1 Text).

All data cleaning and analyses were performed using R software [22].

Characteristics of Exposures

Of the 19,618 CP Ebola cases in the line-list, 6,403 (33%) cases reported one or more exposures (Table 1), giving a total of 9,711 reported exposures. Temporal trends in case incidence and reported exposures by country are shown in Fig 1. The proportion of cases reporting exposures varied by country (Table 1); in general, we found that exposure data were less complete for Guinea. Cases reporting exposures were broadly representative of all cases in the line-list (see Tables b and c and Figure c in S1 Text for a comparison of characteristics between cases who reported exposures and those who did not).

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Table 1. Number of confirmed or probable (CP) cases, exposures, and matched CP-CP contacts and details of the type of exposure and the reported relationship between the case and potential source contact.

https://doi.org/10.1371/journal.pmed.1002170.t001

Exposure at funerals is a known risk factor for Ebola infection [23–25], and 25% of cases who reported any exposure in the current outbreak reported exposures at funerals. Most cases (89%) reporting a funeral exposure also reported one or more non-funeral exposures.

Cases were asked to provide details on the nature of their exposures and their relationships with the contacts, which are shown in Table 1. Overall, 87% of exposures occurred between family members (of those where the relationship was reported). Up until the introduction of the new case investigation form (see S2–S6 Texts), non-funeral exposures were recorded as one or more types (e.g., an exposure could simultaneously involve shared belongings and exposure to bodily fluids). Of those non-funeral exposures for which the type of exposure was reported, over 90% were reported to involve contact with bodily fluids and/or direct physical contact, and 38% were reported as occurring in a household (defined as having slept, eaten, or spent time in the same household or room as the contact). These patterns did not change significantly over time (see Figure e in S1 Text). For funeral exposures, cases were asked whether they had touched the corpse. Of those giving a response, 65% reported having touched the corpse, with this proportion being greatest for Guinea (71%) and least for Liberia (61%). This proportion declined significantly after October 2014 (p < 0.001; see Figure h in S1 Text), most notably in Sierra Leone.

Matching Named Potential Source Contacts to the Case Line-List

We were able to identify 14% of potential source contacts as cases in the line-list. Our ability to match contacts did not vary substantially over time (see Figure i in S1 Text) or geographically (see Figure c in S1 Text).

Characteristics of the Transmission Network

The analysis of matched exposures provides information on the transmission network underpinning the Ebola epidemic (Fig 2). There was evidence of modest assortativity in exposure patterns by sex (see Table i in S1 Text) and country-specific patterns by age (see Table j in S1 Text). The most important statistic characterising the transmission network is the out-degree distribution, the number of times each person was named as a contact by other Ebola cases. The observed out-degree distribution was best fitted by a logarithmic probability distribution for both funeral and non-funeral contacts (Fig 2A; see sections 1.10 and 2.8 in S1 Text). Since the network is not known in its entirety, but only through a sample of cases and their matched contacts, additional assumptions were needed to infer the true offspring distribution (the distribution of the number of secondary cases infected by each case) from the observed out-degree distribution. Under the assumption that the matched exposures are representative of the underlying transmission network, we find high to extreme variability in the offspring distribution (Fig 2B; see section 1.10 in S1 Text). The estimated coefficient of variation for the offspring distribution ranges from 1.6 to 5.6 depending on assumptions (see section 1.10 in S1 Text). This implies that 5% of cases accounted for at least 30% of all new infections and that 20% of cases accounted for at least 73% of new infections (Fig 2B; see Figure q in S1 Text), a phenomenon termed super-spreading [26]. Super-spreading was found to affect both non-funeral and funeral transmissions equally (see Tables f and g in S1 Text).

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Fig 2. Contact network.

(A) The out-degree distribution of the network of exposures shows the probability that a named contact is named as an exposure by a certain number of cases. The squares represent the observed data. The black line shows the maximum likelihood logarithmic distribution, with 95% confidence interval in grey. (B) The cumulative density function of the derived offspring distribution. The two black lines (edges) show two scenarios (see Figure q in S1 Text for details). (C) A sample of the network of all cases (see Figure p in S1 Text for full network) and the contacts they have named as having been exposed to. Individuals (cases and contacts) are shown as nodes, and exposures as directed arrows from contacts to cases. Arrows are red for funeral exposures, black for non-funeral exposures, and blue for multiple exposures involving both non-funeral and funeral exposures. Square nodes are males, round nodes females, and triangles unknown. Red nodes are cases who have died, blue nodes are cases who have survived, and grey nodes are cases with no recorded outcome.

https://doi.org/10.1371/journal.pmed.1002170.g002

Transmission Risk Relative to Onset of Symptoms and Death

One key potential determinant of the risk of onward transmission is the stage of progression of clinical illness. The risk of transmission was found to increase over time since symptom onset (Fig 3A), peaking 2 days after onset, with some exposures estimated to have occurred more than 2 weeks after onset. Our model estimates a small probability of transmission before symptom onset; however, we do not regard this as strong evidence for pre-symptomatic transmission: all reported dates are prone to recall bias, but it is likely that the date of symptom onset is more uncertain than dates of hospitalisation and death, as it is subjective, so individuals may interpret symptom onset differently.

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Fig 3. Observed and fitted distribution of reported time to non-funeral exposure from symptom onset, hospitalisation, and death of the contact.

Time from symptom onset (A), death (B), and hospitalisation (C) of the contact to time of exposure. The green curves show the overall best fits, and the red curves show the best fits for the “signal” distribution (all obtained by maximum likelihood). The red-shaded areas indicate the 95% confidence intervals of the fitted “signal” distribution. The histogram shows a random set of exposure midpoints (in some instances, only a start or an end date of exposure is recorded; in those instances, the missing date is numerically imputed). Note that the fitting procedure is not performed on the midpoints but fully incorporates the exposure window (see section 1.8 in S1 Text). The inset panels are the observed cumulative distribution functions for the midpoint (black line) and start and end (grey lines) of the exposures.

https://doi.org/10.1371/journal.pmed.1002170.g003

Transmission events from non-funeral exposures were estimated to be strongly peaked on the day of and the day after the death of the contact (Fig 3B). In all, 44% of non-funeral exposures to potential source contacts who died were estimated to occur on or after the date of death of the contact. Furthermore, individuals who died were more likely to be named as non-funeral contacts (univariable odds ratio [OR] = 2.10 [95% CI: 1.68, 2.66]).

We sought to determine whether date of death or date of symptom onset was the stronger predictor of transmission risk. We found that date of death was the stronger predictor, indicating that the timing of death is the strongest determinant of when transmission occurs (see Figure o in S1 Text).

Exposures Occurring in Hospitals and after Hospitalisation

Hospitalisation (see Box 2 for definition of hospitalisation, which in this context is isolation from the community by admission to any health care facility) aims to improve the clinical outcomes of patients with Ebola virus disease, and also to reduce onward exposures. We found that hospitalised cases (who were hospitalised at any stage of their infection) had a reduced risk of being named as a non-funeral source contact (univariable OR = 0.78 [95% CI: 0.66, 0.92]) and as a funeral source contact (OR = 0.43 [95% CI: 0.29, 0.63]) compared to cases never hospitalised. This indicates that hospitalisation reduced but did not eliminate onward exposures. The impact of hospitalisation on limiting onward exposure was stronger in Guinea than in Liberia (significantly so for non-funeral exposures, see Tables m and n in S1 Text). The protective effect of hospitalisation on exposure in Sierra Leone was not statistically significant. There was no significant increase in the effect of hospitalisation over time for any of the three countries (see Tables m and n in S1 Text). Previously, we examined the timing of transmission risk relative to symptom onset and death. Using the same method to characterise the timing of exposure events relative to hospitalisation, we found that a substantial proportion (42% [95% CI: 12%, 54%]) of non-funeral exposures from hospitalised source contacts occurred after their hospitalisation (Fig 3C).

Our definition of hospitalisation covers a broad range of health care facility types (Box 2), each with varying levels of infection control [27]. In particular, as part of the international response to the epidemic, an unprecedented number of Ebola treatment units (ETUs) were built to care for patients. The previous analysis considered all types of hospitalisation together. When we disaggregated hospitalisation by ETU versus other facilities, we found that ETUs were better than other health care facilities at preventing exposure from hospitalised cases and at providing safer management of corpses. In ETUs, the observed proportion of non-funeral exposures from hospitalised cases occurring after hospitalisation was significantly lower than in non-ETU facilities (p < 0.001, see Figure n in S1 Text). Moreover, hospitalisation in an ETU reduced the risk of being named as a funeral contact compared with non-hospitalised cases (univariable OR = 0.30 [95% CI: 0.16, 0.50]) more than hospitalisation in a non-ETU facility did (OR = 0.72 [95% CI: 0.39, 1.23]; also see multivariable analyses in Table l in S1 Text and the next section). Although for non-funeral contacts univariable ORs were not significant, a multivariable analysis indicated similar effects (see Table k in S1 Text).

Overall, our analyses demonstrate that hospitalisation reduced infectious exposures to cases, but that the effectiveness of hospital isolation measures varied by country and facility type, with substantial scope for improvements.

Epidemiological Characteristics of Potential Transmitters

To further explore the characteristics of individuals who were named as potential source contacts (and therefore potential transmitters), we also performed systematic multivariable logistic regressions on the risk of being named as a potential source contact (see Tables k and l in S1 Text). Based on the final multivariable model, individuals significantly more likely to be named as a non-funeral contact were those who were more severely affected at presentation (with fever [versus no fever], OR = 1.52 [95% CI: 1.20, 1.95]; with bleeding [versus no bleeding], OR = 1.40 [95% CI: 1.07, 1.81]; unconscious [versus conscious], OR = 1.98 [95% CI: 1.26, 3.03]); those who died (versus survivors, OR = 2.04 [95% CI: 1.50, 2.79]; confirmed cases (versus suspected cases, OR = 2.45 [95% CI: 2.01, 2.99]); and probable cases (versus suspected cases, OR = 1.91, [95% CI: 1.51, 2.41]). In addition, cases who were never hospitalised (versus cases hospitalised in an ETU) were more likely to be named as a non-funeral contact (OR = 1.61 [95% CI: 1.30, 2.00]). Finally, adults (≥16 versus <16 years old, OR = 1.87 [95% CI: 1.52, 2.34]) and those who reported travelling outside of their village before they became ill (versus not travelling, OR = 1.73 [95% CI: 1.35, 2.18]) were also more likely to be named as a non-funeral potential source contact.

Similar predictors were found for individuals being named as funeral contacts: more severely affected cases (fever versus no fever, OR = 1.81 [95% CI: 1.08, 3.18]; respiratory versus no respiratory symptoms, OR = 1.65 [95% CI: 1.09, 2.54]), adults (≥16 versus <16 years old, OR = 2.44 [95% CI: 1.47, 4.36]), cases not hospitalised (versus hospitalised in an ETU, OR = 5.56 [95% CI: 2.94, 11.11]), those who reported travelling before they became ill (versus not travelling, OR = 2.47 [95% CI: 1.50, 3.90]), confirmed cases (versus suspected cases, OR = 1.98 [95% CI: 1.28, 3.11]), probable cases (versus suspected cases, OR = 2.03 [95% CI: 1.21, 3.42]), and those who were reported as Ebola cases after death (versus before death, OR = 1.64 [95% CI: 1.12, 2.40]).

Epidemiological Factors Associated with Being a Multiple Transmitter

To determine whether there were any factors associated with being multiply identified as a potential source contact, we conducted a multivariable negative binomial regression to explore predictors of the number of times cases were named as non-funeral and funeral source contacts, conditional on being named at least once (see Tables p and q in S1 Text). Cases with reported haemorrhagic symptoms were found to be named as non-funeral contacts 1.67 times more often than cases not reporting these symptoms (95% CI: 1.07, 2.64). Cases hospitalised in an ETU were found to be named as non-funeral contacts 0.50 as many times as non-hospitalised cases (95% CI: 0.33, 0.73). Also, HCWs were found to be named as non-funeral contacts 0.48 as many times as non-HCWs (95% CI: 0.23, 0.96). Travel outside one’s village of residence prior to becoming ill was the only significant predictor for a case to be named multiple times as a funeral contact (relative number of times being named compared to cases who did not report travelling was on average 4.41 [95% CI: 2.01, 10.55]).

No Difference in Exposure Risk between the Sexes

Sex did not appear as an important predictor of exposure risk in any of the analyses that we performed.

Exposures Involving Health Care Workers

HCWs have been at particularly high risk of infection, especially during the early part of the epidemic [28]. HCWs reported that 49% of their non-funeral exposures were with other HCWs or patients, versus 0.6% reported by non-HCWs. Consequently, HCWs reported a lower percentage of exposures with family members (44%) compared to non-HCWs (91%; see Figure f in S1 Text). This demonstrates that HCWs were often infected whilst working. Consistent with these data, the matched contacts reported by HCWs were more likely to have evidence of hospitalisation at some point in their illness than the matched contacts of non-HCWs (OR = 2.91 [95% CI: 1.57, 5.81]).

In the absence of data on the total number of HCWs working in each of the three countries over time, we could not estimate the relative risk of infection of HCWs compared to non-HCWs. However, several indicators point to falling risk through time. Since June 2014, the proportion of HCWs among all cases has decreased over time (see Figure f in S1 Text). The rate of decrease in this proportion (estimated monthly relative decrease since June: 21% [95% CI: 18%, 24%]) was faster than that seen in the incidence of hospitalised cases (estimated monthly relative decrease: 12% [95% CI: 10%, 14%]; see also Figures f and g in S1 Text), suggesting an improvement in infection control measures and increased awareness of Ebola among HCWs over time.

Population Correlates of Epidemic Intensity

The analyses presented so far describe individual risks of infection. We next explored, in a hypothesis-driven manner, whether local epidemic transmission intensity was correlated with local population measures of presumed heightened risk of infection.

We first examined whether the proportion of cases reporting funeral exposures in each district by month was correlated with the estimated reproduction number R in that district at that time. Having accounted for uncertainty in the estimates of both quantities [20] (see section 1.7 in S1 Text), we found a positive correlation for Liberia (r² = 0.30, p = 0.013) and Sierra Leone (r² = 0.23, p < 0.001) and no significant correlation for Guinea (r² = 0.001, p = 0.667) (Fig 4; see Figure j and Table d in S1 Text). This analysis suggests that across the three countries an absolute reduction of 10% in the proportion reporting funeral exposures would lead to an absolute reduction of 0.10 in R (95% CI: 0.05, 0.15; p < 0.001). In particular, reducing the proportion reporting funeral exposures to less than 29% (95% CI: 21%, 38%) would be sufficient to reduce R below 1, the threshold value for achieving control of the epidemic. However, this result varies by country, and is an association that does not prove causation, since reductions in funeral exposures could have been accompanied by changes in other control measures and other effects of community mobilisation. In that case, the measure is a surrogate for the total effect of interventions that correlate with reduced funeral exposures.

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Fig 4. Correlation between local transmission intensity and local population measures of presumed heightened risk of infection.

Correlation between local transmission intensity and proportion of cases reporting funeral exposures among those reporting any exposure (left) and proportion of cases hospitalised within ≤4 days of symptom onset among those hospitalised (right). The scatter plots show these monthly proportions against monthly estimated reproduction numbers R (method as previous [11]) for the supplemented incidence (i.e., incidence based on two data sources including the line-list, see [12]). Each point is a district-month. Trend lines are shown with 95% confidence intervals (shaded areas). We use a weighted regression method that takes account of the uncertainties in the data [20]. The area of each circle is proportional to the weight of that point (see section 1.7 in S1 Text). In the bottom row, the black trend line is for the whole dataset. See Figures j and l and Table d in S1 Text for details, including trend line parameters. exp., exposure.

https://doi.org/10.1371/journal.pmed.1002170.g004

Another factor potentially influencing changes in transmission intensity is how promptly cases are hospitalised and isolated. We found no association between the proportion of cases that were hospitalised and the estimated district-level reproduction number R (see Figure k in S1 Text), likely because the line-list predominantly includes cases who have been hospitalised. However, we found a negative association between the estimated district-level R and the proportion of hospitalised cases admitted within ≤4 days after onset of symptoms in Liberia (r² = 0.22, p = 0.039) and Sierra Leone (r² = 0.32, p < 0.001). No significant correlation was found in Guinea (r² = 0.02, p = 0.377) (Fig 4; see Figures l and m in S1 Text). This analysis quantifies the impact that earlier hospitalisation has in reducing transmission. Across all three countries we estimate that increasing the proportion of cases hospitalised within ≤4 days of symptom onset by 10% would reduce R by 0.19 (95% CI: 0.12, 0.25; p < 0.001). In particular, if 64% (95% CI: 59%, 68%) of hospitalised cases are admitted within ≤4 days, R is predicted to be <1. Again, the effect varied by country.

We found no correlation between the two predictors we analysed, i.e., the proportion of cases reporting funeral exposure and the proportion of cases hospitalised within ≤4 d of symptom onset (naïve r² = 0.001, p = 0.711; see section 2.5 in S1 Text). This shows that both factors are independently associated with the level of transmission, at least in Liberia and Sierra Leone, and, together, they explain a substantial proportion (approximately 55%) of the variance in epidemic transmission intensity observed in these two countries.