Abstract
Multiple epidemiological models have been proposed to predict the spread of Ebola in West Africa. These models include consideration of countermeasures meant to slow and, eventually, stop the spread of the disease. Here, we examine one component of Ebola dynamics that is of ongoing concern – the transmission of Ebola from the dead to the living. We do so by applying the toolkit of mathematical epidemiology to analyze the consequences of postdeath transmission. We show that underlying disease parameters cannot be inferred with confidence from earlystage incidence data (that is, they are not “identifiable”) because different parameter combinations can produce virtually the same epidemic trajectory. Despite this identifiability problem, we find robustly that inferences that don't account for postdeath transmission tend to underestimate the basic reproductive number – thus, given the observed rate of epidemic growth, larger amounts of postdeath transmission imply larger reproductive numbers. From a control perspective, we explain how improvements in reducing postdeath transmission of Ebola may reduce the overall epidemic spread and scope substantially. Increased attention to the proportion of postdeath transmission has the potential to aid both in projecting the course of the epidemic and in evaluating a portfolio of control strategies.
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Introduction
A recent, influential modeling paper concluded, based on data available as of September 2014, that the ongoing Ebola epidemic in Guinea, Liberia and Sierra Leone had the potential to exceed 1 million new cases by midJanuary 2015, in the absence of intervention^{1}. Even with intervention and changes in behavior, a followup study by an independent group in October 2014 estimated that 100, 000 additional cases could be expected in Liberia alone by midDecember 2014, unless a coordinated, largescale response is implemented rapidly^{2}. These predictions leveraged the structure of previous epidemiological models^{3,4} that encapsulate the infection cycle of Ebola virus disease (EVD), by tracking the dynamics and interactions of different types of individuals within a population including Susceptible, Exposed, Infectious and Removed types. Exposed individuals are infected but not yet infectious (i.e., also referred to as latently infected). In a SEIR model representation of EVD dynamics, the R class accounts for two types of individuals: those who recovered from the disease and those who have died from the disease (and are therefore “removed”).
However, a complication in modeling EVD arises because EVD may be contracted by direct contact with bodily uids from individuals who are alive and from those who have died from the disease^{5,6}. In the early stages of the present epidemic, contact tracing of 701 individuals confirmed to have been infected with EVD in the ongoing epidemic found that 67 patients reported contacts with individuals who died of EVD, but not with any living EVD cases, while 148 patients reported contacts with both living and dead individuals infected with EVD^{7}, consistent with approximately to of cases caused by postdeath transmission. If funerals and burial rites can act as “superspreader” events^{8,9,10}, the true fraction may lie outside this range: for example, Legrand and colleagues^{4} estimated that 2/3 of the total for the 1995 EVD outbreak in the Democratic Republic of Congo could be attributed to postdeath transmission. The improvement of burial practice has become an issue of ongoing concern, e.g., Ref. 11. Some early^{4} and recent (e.g., Refs. 2, 10, 12, 13) epidemiological models of EVD have incorporated a D class, thereby distinguishing between recovered and dead individuals. Other models treat postdeath transmission implicitly, by increasing the effective transmission rate and/or duration in the I class^{1,14}. Yet, the implications of postdeath transmission for inferences about epidemic spread have not been evaluated systematically. As we show, uncertainty in the relative force of infection before and after death has a number of consequences for estimating and the potential for control of the ongoing Ebola epidemic.
Results
The basic reproductive number, , of EVD includes the effects of postdeath transmission
The basic reproductive number, , denotes the average number of secondary cases caused by a single infected individual in an otherwise susceptible population. The criterion for epidemic spread in standard epidemiological models is that so that the initial infection gives rise, on average, to more than one infected case.
In a conventional SEIR model, EVD transmission between infected and susceptible individuals occurs at an average rate β_{I} over a period of infectiousness T_{I}. A fraction f of infected individuals die and the remainder, 1 − f, recover and are assumed to be permanently immune to subsequent infection. A SEIRD model includes an additional transmission route: dead individuals can transmit EVD to susceptible individuals at a rate β_{D} over a period of infectiousness T_{D}, after which they are permanently removed from the system via burial or loss of infectiousness (see Figure 1). The methods provides the mathematical details of the model. The basic reproductive number in the SEIRD model is:
The first term denotes the average number of secondary infections due to contact with an infected individual beforedeath. The second term denotes the average number of secondary infectious due to contact with an infected individual afterdeath. The number of cases arising from contact with dead individuals is modulated by the fraction, f, of infected individuals that die due to EVD. In contrast, the basic reproductive number in the SEIR model is:
It might seem that the basic reproductive number of a SEIRD model should tautologically exceed that of a SEIR model. In fact, this will depend on how parameters are estimated. If the SEIR model is fit from data, then β_{SEIR} and T_{SEIR} will reflect transmission from both living and dead infectious individuals. Thus, we ask: What is the change in the estimated value of given alternative model frameworks meant to explain the same infected case data?
Identifiability problems in estimating the basic reproductive number,
The SEIRD model, like the SEIR, SIR and other epidemiological models, predicts that there should be an exponential increase in the number of infected cases, i.e., I(t) ~ e^{λt}, after an initial transient phase and before interventions, largescale behavioral changes or populationlevel depletion of susceptibles have taken effect^{16}. The exponential growth rate, λ, is a function of epidemiological parameters, including the transmission rate and ^{17,18}. For EVD, prior information is available to constrain the mean duration of the latent phase on the order of 8–12 days^{7,19}, the mean infectious period before death or recovery on the order of 5–9 days^{1,7} and the fraction of diseaseinduced mortality of approximately 70%^{3,7}. However, even with these prior constraints, theory does not predict a onetoone relationship between – the feature we want to infer – and λ – the feature that we can measure. This lack of a onetoone relationship gives rise to a socalled identifiability problem in estimating epidemiological parameters, including , from earlystage epidemic data alone. The Supplementary Information presents a rationale for why identifiability problems arise more generally when fitting epidemiological models.
To examine the identifiability problem as it pertains to EVD, we fit both the SEIR and SEIRD models to an exponentially growing epidemic with rate for which the number of cases is increasing with a characteristic time of days. Further, we assume that T_{E} = 11 days, T_{I} = 6 days and f = 0.7. We utilize standard epidemiological methods to infer for the SEIR model and, in turn, β_{SEIR}, given observations of (see Eq. 10). We find a point estimate of β_{SEIR} of 0.33 and a corresponding for the SEIR model of 1.95. Uncertainty in the duration of periods, risk of mortality and noise in epidemic case count data would lead to corresponding uncertainty in the value of .
In contrast, there are three unknown parameters in the SEIRD model: β_{I}, β_{D} and T_{D}. The time to burial is, in part, culturally determined, with prior estimates of 2 days applied to Ebola outbreaks in Uganda and the Democratic Republic of Congo^{4} having been carried forward to current models (e.g. Ref. 10). Yet, given the size of the outbreak, additional delays between death and burial are likely. Even with a fixed value of T_{D}, the transmission rates β_{I} and β_{D} remain unknown. Hence, trying to fit a SEIRD model to epidemic case data poses an identifiability problem. That is to say: there are potentially many combinations of transmission parameters, β_{I} and β_{D} that can yield the same observed epidemic growth rate. Here, we consider three scenarios, where T_{D} = 2, 4 and 6 days. For each scenario, we must solve for the combination of β_{I} and β_{D} that yield the epidemic growth rate λ = 1/21. The mathematical details are in the Supplementary Information.
For a given value of T_{D}, we evaluate a continuum of models in which the proportion of attributable to postdeath transmission varies from 0 to 1. We define this fraction as . We find a negative relationship between the estimated pre and postdeath transmission rate (compare Figure 2upper left and middleleft panels). This negative relationship is a consequence of trying to fit the same observed case data while modifying the relative importance of pre and postdeath transmission. Importantly, the pointestimate of increases with increasing force of transmission postdeath (Figure 2lower left). Increasing postdeath transmission implies that the average infectious period also increases. As a consequence, there are fewer epidemic generations that nonetheless led to the same rise in cases. This means that the average number of secondary infections per infected individual must be higher. This is a generic feature of epidemiological models. Here, the predicted growth rate for epidemics with these distinct epidemiological parameters are equivalent − λ = 1/21 (Figure 2right) – despite the differences in underlying rates. We provide further support for this result by investigating dynamics in which the period of infectiousness of individuals who recover from EVD exceeds that of the infectious period for individuals who die from EVD (see the Supplementary Information). Even in this scenario, increases in the relative fraction of postdeath transmission are associated with increases in the inferred value of given the same epidemic case data.
Challenges in fitting earlystage epidemic data of EVD in West Africa due to identifiability problems
The identifiability problem, described in the previous section, suggests why it is more difficult than has been recognized to ascertain the mechanistic details of EVD transmission from earlystage epidemic data alone. Here, we investigate case data from three countries: Guinea, Liberia and Sierra Leone (data from Ref. 15; see Table 1 for more information). We use an exponential growth curve fit to the cumulative case counts as a target to investigate multiple possible scenarios (Figure 3). For this fit, we extend our SEIRD model to include a more realistic distribution period for the E class^{7,19}. The exposed (i.e, latently infected) period is modeled as a gamma distribution with mean of 11 days and 6 classes, so that the standard deviation is 4.5 days (see Supplementary Information for more details). We use the generatingfunction approach of Wallinga and Lipsitch^{18} (see the Supplementary Information) to estimate from λ while accounting for the chosen time distributions within the E, I and D classes. For each country, the resulting model predictions have two key features (see Figure 3). First, multiple scenarios with varying ratios of transmission risk from living and dead individuals all fit the data equally well. Second, estimates that neglect postdeath transmission tend to underestimate . The bottomleft panels of Figure 3 all show an increase in that varies with the fraction of cases caused by postdeath transmission, ρ_{D}. The increase in due to postdeath transmission is of concern. However, there is a tradeoff: larger ρ_{D} means not only a larger , but also a larger potential impact of reducing postdeath transmission.
Reduction in transmission risk after death can have substantial epidemiological benefits
We evaluate the benefits of control in a SEIRD representation of EVD using a gamma distributed E class period. Three scenarios are considered, in which the characteristic epidemic growth times are 1/λ = 14, 21 and 28 days and for which we assume T_{D} = 3 days. Figure 4 summarizes our central findings. We find, as before, that is an increasing function of ρ_{D}, the proportion of transmission that occurs postdeath. We also find that the inferred basic reproductive number increases with increasing epidemic growth rates. These estimates can be used to evaluate the benefit of control strategies that eliminate (even partially) postdeath transmission. In the limit that all postdeath transmission is eliminated, the effective reproductive number would be . In this limit, is reduced by , a substantial amount given estimates of ρ_{D} in the range of 10%–30%^{7}. For example, in the scenarios evaluated, control of postdeath transmission reduces by ≈0.2–1 secondary transmission per infected individual. Thus, controlling postdeath transmission of EVD could be an important component of epidemic control.
Discussion
The relative importance of postdeath transmission is difficult to estimate from epidemic growth rate data alone and has important implications for estimates of key epidemiological quantities and for prediction. This difficulty is due to what is classically termed an “identifiability problem”  relevant to EVD and to other emerging or poorly characterized infectious diseases. Improved estimation of the relative proportion of postdeath transmission would mitigate this problem and improve reliability in estimates of . Despite the challenge in identifying epidemiological parameters, we robustly conclude that neglecting postdeath transmission while fitting to epidemic growth rate tends to lead to underestimates of . The degree of underestimation depends on the fraction of postdeath transmission which, in turn, depends on transmission risk and time spent in the community, in hospitals and in burial ceremonies. Such underestimates are a potential concern for ongoing efforts to develop realistic models of Ebola dynamics and its control.
Here, we have focused on one feature of such control: the use of burial teams and other practices intended to reduce postdeath transmission^{11}. Burial teams are part of a diverse set of responses required to stop the spread of EVD^{10}. These responses include behavioral changes, hospital interventions^{1,20} and (potentially) vaccination. More burial teams were needed, but recruiting was initially difficult due to stigmatization, lack of personal protective equipment and insufficient compensation of workers^{21}. Improvements in burial practices have been documented and are believed to have contributed to decreases in onwards transmission of EVD in the months after the case data analyzed here^{11}. Previous reports have suggested that some individuals in the population experiencing the epidemic may have acquired immunity to Ebola as a result of subclinical infections^{22,23}. If these individuals exist and can be identified, they may be valuable contributors to response efforts if they can be recruited as family healthcare workers^{23} or as part of burial teams. Moving forward, it is essential to consider the logistics of deploying burial teams efficiently and safely while balancing public health benefits and community norms^{11,24,25}. A better understanding of postdeath transmission can help to understand the EVD epidemic in West Africa and plan control efforts, hopefully leading in the longterm to control and elimination of the current outbreak.
Methods
SEIRD model of Ebola dynamics
The SEIRD model includes the dynamics of susceptible, exposed and infectious individuals, just as in the SEIR model. It differs in that the R class stands for recovered individuals while the D class stands for dead individuals, who are nonetheless infectious. The dynamics can be written as:
where β_{I} is the transmission rate for contacts with infected individuals and β_{D} is the transmission rate associated with contacts with dead individuals (who are nonetheless infectious). The other parameters are: T_{E} the average exposed period, T_{I} the average infectious period, T_{D} the average period of infectiousness after death and f the fraction of infected individuals who die. In this model N = S + E + I + R. This model neglects birth/immigration of individuals and natural death/emigration. The conventional derivation of the transmission rate is that a susceptible individual interacts with a certain fixed number of individuals per unit time m, of which a fraction I/N are infectious and only a fraction p of which lead to transmission. The transmission rate β_{I} is a product of m and p. Similarly, here we assume that dead individuals are contacted by a certain fixed number of individuals per unit time n, of which a fraction S/N are susceptible and only a fraction q of contacts lead to transmission. The transmission rate β_{D} is a product of n and q. The SEIRD model can be extended further to take into account the possibility that the duration of the exposed infectious and dead periods are nonexponential.
Estimating the basic reproductive number, , for the SEIR and SEIRD models given exponential intraclass period distributions
Infected case data can then be used to estimate unknown epidemiological parameters, including the transmission rate and . In the case of the SEIR model, the predicted exponential growth rate, λ, can be derived from a solution of the linearized dynamics near the value of (S = N, E = 0, I = 0, R = 0). The growth rate λ correspond to the largest eigenvalue of the Jacobian:
It can be shown that the growth of the number of infected cases is an exponential of the form I(t) = I_{0}e^{λt} where
where σ ≡ 1/T_{E} and γ ≡ 1/T_{I}. The bestfit value of β_{SEIR} can be inferred given a measured value and prior estimates for σ and γ. For example,
where , such that
Similarly, given the SEIRD model, the predicted exponential growth rate λ of the number of infected cases corresponds to the largest eigenvalue of the linearized system near (N, 0, 0, 0, 0), of which only the variables E(t), I(t) and D(t) must be tracked. The Jacobian of this subsystem is:
The solutions can be computed exactly.
Case data information
Cumulative case count data from Guinea, Liberia and Sierra Leone was used as the target of model fits in Figure 3. These data sets were downloaded from Caitlin Rivers' publicly available github site^{15}. The time periods over which data is calibrated is shown in Table 1. The start date was selected based on the first day at which the cumulative case count exceeded 50. The final date was set at the end of August, coinciding with reported increases in intervention and widespread dissemination of the severity of the outbreak^{10}. The exponential fits are based on logtransformed cumulative case counts. Additional challenges for inference arise, in part, due to underreporting^{1} and lags between incidence and reporting events^{7}.
References
Meltzer, M. I. et al. Estimating the future number of cases in the Ebola epidemicLiberia and Sierra Leone, 2014–2015. MMWR Surveill Summ 63, 1–14 (2014).
Lewnard, J. A. et al. Dynamics and control of Ebola virus transmission in Montserrado, Liberia: a mathematical modelling analysis. The Lancet Infectious Diseases 14, 1189–1195 (2014).
Chowell, G., Hengartner, N. W., CastilloChavez, C., Fenimore, P. W. & Hyman, J. The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda. Journal of Theoretical Biology 229, 119–126 (2004).
Legrand, J., Grais, R., Boelle, P., Valleron, A. & Flahault, A. Understanding the dynamics of ebola epidemics. Epidemiology and infection 135, 610–621 (2007).
Francesconi, P. et al. Ebola hemorrhagic fever transmission and risk factors of contacts, uganda. Emerging infectious diseases 9, 1430 (2003).
Centers for Disease Control and Prevention. Ebola (Ebola Virus Disease)  Q&As on Transmission – accessed 1/28/2015. (2014). URL http://www.cdc.gov/vhf/ebola/transmission/qas.html.
WHO Ebola Response Team. Ebola virus disease in West Africa – the first 9 months of the epidemic and forward projections. New England Journal of Medicine 371, 1481–1495 (2014).
LloydSmith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005).
Galvani, A. P. & May, R. M. Epidemiology: dimensions of superspreading. Nature 438, 293–295 (2005).
Pandey, A. et al. Strategies for containing Ebola in West Africa. Science 346, 991–995 (2014).
Nielsen, C. F. et al. Improving burial practices and cemetery management during an ebola virus disease epidemic  Sierra Leone, 2014. MMWR Surveill Summ 64, 1–8 (2015).
Gomes, M. F. et al. Assessing the international spreading risk associated with the 2014 West African Ebola outbreak. PLoS Currents Outbreaks (2014 Sep 2. Edition 1.).
Rivers, C. M., Lofgren, E. T., Marathe, M., Eubank, S. & Lewis, B. L. Modeling the Impact of Interventions on an Epidemic of Ebola in Sierra Leone and Liberia. PLoS Currents Outbreaks (2014 Oct 16. Edition 1.).
Althaus, C. L. Estimating the reproduction number of Ebola virus (EBOV) during the 2014 outbreak in West Africa. PLoS Currents Outbreaks (2014 Sep 2. Edition 1.).
Ebola Case Data. Maintained by Caitlin Rivers. Accessed: 11/4/2014. URL https://github.com/cmrivers/ebola.
Chowell, G. & Nishiura, H. Transmission dynamics and control of Ebola virus disease EVD: a review. BMC Medicine 12, 196 (2014).
Keeling, M. J. & Rohani, P. Modeling Infectious Diseaes in Humans and Animals (Princeton University Press, Princeton, NJ, 2007).
Wallinga, J. & Lipsitch, M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proceedings of the Royal Society B: Biological Sciences 274, 599–604 (2007).
Eichner, M., Dowell, S. F. & Firese, N. Incubation period of Ebola hemorrhagic virus subtype Zaire. Osong Public Health and Research Perspectives 2, 3–7 (2011).
Drake, J. M. et al. Ebola cases and health system demand in liberia. PLoS Biol 13, e1002056 (2015).
Sierra Leone's burial teams for Ebola victims strike over hazard pay – Reuters. Accessed: 11/4/2014. URL http://in.reuters.com/article/2014/10/07/ushealthebolaleoneidINKCN0HW21U20141007.
Leroy, E. et al. Human asymptomatic ebola infection and strong inammatory response. The Lancet 355, 2210–2215 (2000).
Bellan, S. E., Pulliam, J. R., Dushoff, J. & Meyers, L. A. Ebola control: effect of asymptomatic infection and acquired immunity. The Lancet 384, 1499–1500 (2014).
Hewlett, B. S. & Amola, R. P. Cultural contexts of ebola in northern uganda. Emerging infectious diseases 9, 1242 (2003).
Bauch, C. T. & Galvani, A. P. Social factors in epidemiology. Science 342, 47–49 (2013).
Acknowledgements
The authors are indebted to YaoHsuan Chen, John Glasser, Brian Gurbaxani, Andrew Hill and participants in the Modeling Infectious Disease Group at the CDC for critical suggestions and feedback that made this effort possible. The authors thank Hayriye Gulbudak, Luis Jover, Bradford Taylor and Charles Wigington for critiques of the manuscript and for the feedback of three anonymous reviewers. JSW is supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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J.S.W. conceived of the project, developed analytic tools, performed analysis, analyzed data and wrote the manuscript. J.D. developed analytic tools, analyzed data and edited the manuscript.
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Weitz, J., Dushoff, J. Modeling Postdeath Transmission of Ebola: Challenges for Inference and Opportunities for Control. Sci Rep 5, 8751 (2015). https://doi.org/10.1038/srep08751
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DOI: https://doi.org/10.1038/srep08751
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