Mizumoto et al. BMC Medicine (2015) 13:228
DOI 10.1186/s12916-015-0468-3

RESEARCH ARTICLE

Open Access

Real-time characterization of risks of deathassociated with the Middle East respiratory
syndrome (MERS) in the Republic of Korea, 2015
Kenji Mizumoto1, Akira Endo2, Gerardo Chowell3,4, Yuichiro Miyamatsu2,5, Masaya Saitoh2,5,6 and Hiroshi Nishiura2,5*

Abstract
Background: An outbreak of the Middle East respiratory syndrome (MERS), comprising 185 cases linked tohealthcare facilities, occurred in the Republic of Korea from May to July 2015. Owing to the nosocomial nature of
the outbreak, it is particularly important to gain a better understanding of the epidemiological determinants
characterizing the risk of MERS death in order to predict the heterogeneous risk of death in medical settings.
Methods: We have devised a novel statistical model that identifies the risk of MERS death during the outbreak inreal time. While accounting for the time delay from illness onset to death, risk factors for death were identified
using a linear predictor tied to a logit model. We employ this approach to (1) quantify the risks of death and (2)characterize the temporal evolution of the case fatality ratio (CFR) as case ascertainment greatly improved
during the course of the outbreak.
Results: Senior persons aged 60 years or over were found to be 9.3 times (95 % confidence interval (CI), 5.3–16.9)
more likely to die compared to younger MERS cases. Patients under treatment were at a 7.8-fold (95 % CI, 4.0–16.7)
significantly higher risk of death compared to other MERS cases. The CFR among patients aged 60 years or older under
treatment was estimated at 48.2 % (95 % CI, 35.2–61.3) as of July 31, 2015, while the CFR among other cases
was estimated to lie below 15 %. From June 6, 2015, onwards, the CFR declined 0.3-fold (95 % CI, 0.1–1.1)
compared to the earlier epidemic period, which may perhaps reflect enhanced case ascertainment following
major contact tracing efforts.
Conclusions: The risk of MERS death was significantly associated with older age as well as treatment forunderlying diseases after explicitly adjusting for the delay between illness onset and death. Because MERS
outbreaks are greatly amplified in the healthcare setting, enhanced infection control practices in medical
facilities should strive to shield risk groups from MERS exposure.
Keywords: Case fatality ratio, Fatal outcome, Middle East respiratory syndrome, Risk factors, Statistical model,
South Korea

Background
When a novel infectious disease emerges, it is vital toassess the potential global public health impact, i.e. thepotential of a pathogen to cause a devastating pandemic[1]. The public health impact is mainly characterized by
the transmission potential and virulence, the latter of* Correspondence: nishiurah@m.u-tokyo.ac.jp
2
Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 1130033, Japan
5
CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi,
Saitama 332-0012, Japan
Full list of author information is available at the end of the article

which is usually measured by the case fatality ratio(CFR), namely the proportion of deaths among cases [2].
Due to the importance of generating accurate estimatesof the CFR in real time as an outbreak progresses andpublic health interventions are implemented, a numberof epidemiological methods have been proposed, including those that account for the time delay from illness
onset to death [3–5] and those that correct for ascertainment bias through the combination of several different
observations by employing a Bayesian evidence synthesis
method [6]. Other methods have developed a slightly© 2015 Mizumoto et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Mizumoto et al. BMC Medicine (2015) 13:228

different CFR concept, e.g. the so-called “infection fatality risk”, the ratio of excess mortality to the serologically determined infected fraction of the population [7].
The CFR is an important epidemiological quantity toestimate in real time during outbreaks, and the recent
outbreak of the Middle East respiratory syndrome(MERS) in the Republic of Korea in 2015 has notbeen an exception [8]. Besides the MERS outbreak in
the Republic of Korea, earlier studies of MERS in the
Middle East have consistently indicated that the CFR
for MERS has been about 40 % [9] and could be as
low as 20 % for estimates that only include secondary
cases [10].
Despite the availability of useful methods to estimate
the CFR for MERS, it is crucial to identify risk factors
associated with MERS death, as the risk of death mayvary significantly with age, occupation, and underlying
comorbidities [11]. During the epidemic of severe acuterespiratory syndrome (SARS), a real-time analysis foundthat confirmed or probable cases aged 60 years or older
were at greater risk of death than younger cases [12].
Moreover, infections with SARS-associated coronavirus
with comorbidities were found to be 1.7 times more
likely to die than those without comorbidities [13]. Due
to similarities in clinical characteristics between MERS
and SARS patients as both coronaviruses are closely
related [9], we can expect that the risk of MERS death
can also be similarly characterized by a set of underlying
epidemiological features of the cases. While the methods
for identifying the risk of death in real time during the
SARS epidemic have relied on survival analysis techniques, including non-parametric Kaplan-Meier-like
methods [12, 14], there is a need to develop a simple yet
tractable method which is inspired on the adjustment ofcensoring for CFR [4, 5] with particular application tosmall outbreak sizes such as the MERS outbreak in
the Republic of Korea. Such a modelling framework
should adjust for case ascertainment, which is widely
recognized as a key issue that arises during emerging
disease epidemics, including the H1N1 pandemic in
2009 [2, 7, 8]. During the MERS outbreak in the Republicof Korea, an extensive contact tracing effort wasmade by public health authorities soon after the firstfew cases were reported, with all cases being confirmed by laboratory testing irrespective of clinicalsigns and symptoms. As in any outbreak, it is likelythat the ascertainment rate was substantially improved as the outbreak progressed.
The present study aims to statistically identify risk
factors associated with MERS death in the Republicof Korea using a simple but novel statistical modelling approach. We also use our approach to assess
the time dependent variation in the ascertainment ofcases.

Page 2 of 7

Methods
Epidemiological data

The present study employs published secondary data of
the confirmed MERS cases arising from the outbreak in
the Republic of Korea [15–17]. As of July 31, 2015, a
total of 185 cases have been diagnosed (excluding one
case diagnosed and recovered in China) including 36
deceased cases. During the course of this outbreak, adetailed line list of cases has been made publicly available[15]. Data on individual cases include the (1) dates of
illness onset, (2) age-group, (3) gender, and (4) background health status either as outpatient or inpatientof a specific healthcare facility. Since the dates of illness onset among those recently reported were not yet
available, we relied on the dates of confirmatory diagnosis as an alternative. We believe it is reasonable toapproximate the date of illness onset by the date ofconfirmed diagnosis in this setting, because from the
midst of the outbreak, all suspected contacts havebeen regularly monitored and have been subjected tolaboratory testing regardless of clinical signs andsymptoms. We limited ourselves to handle the abovementioned covariates (2) to (4) only, not each specificindividual comorbidity, because it is not clear if thepresence of all common underlying comorbidities wereroutinely evaluated and consistently documented in
the case list; the identification of most closely associated specific comorbidities may be the subject of future clinical studies.
Statistical model

Herein, we develop an estimation model composed of a
mixture of a survival model and logistic regression
model. Let S(τ) and p be the survival probability at time
τ since illness onset and the CFR, respectively. The relationship of the two is described as
p ¼ 1−S ð∞Þ;ðiÞ

where S(τ) is given byZτS ðτ; pÞ ¼ 1−p

f ðxÞdx;ðiiÞ0where f(τ) is the conditional probability density functionof the time from illness onset to death given fatal outcome [4, 18]. In the majority of the following analyses,f(τ) was assumed as known and based on the data fromfirst 10 reported cases who eventually resulted in deathin South Korea, using the moment-based estimates ofthe mean (13.2 days) and standard deviation (7.1 days).
Nevertheless, we also examined the heterogeneous riskof death by jointly quantifying heterogeneous risk factors

Mizumoto et al. BMC Medicine (2015) 13:228

Page 3 of 7

and parameters of f(τ), verifying that the estimates of
the risk of death in the joint estimation do not
greatly differ from those we obtained using the assumed known f(τ).
Since we aim to identify risk factors (or explanatory
variables) of p, the notation p is set to be changeable byindividual i, i.e. pi. Such individual variation is modelledusing the logit model:


pi
ln
1−pi


¼ a0 þ

NX

ak xk;i ;

ðiiiÞ

k¼1

parameter δ, a constant factor multiplied to the original logit model, i.e.
!
NXδ exp a0 þ
ak xk;i
pi ¼

k¼1
N
X

1 þ exp a0 þ

!;

ðvÞ

ak xk;ik¼1where δ was dealt with as
1;for t < t 0;
δ¼
ε;for t 0 ≤ tðviÞwhere αi and βi represent the observed dates of illness
onset and death of an individual i, respectively, withcoefficient vector a = (a0, a1, ⋯, aN).

where t0 represents the first day on which the ascertainment rate increased while ε scales the extent of ascertainment (where ε is expected to be less than 1); t0 was
objectively sought by comparing the Akaike Information
Criterion (AIC) among models with different t0.
When the joint estimation was conducted, we estimated not only coefficients of the linear predictor in thelikelihood L but also parameters for f(τ), i.e. mean andstandard deviation of the gamma distribution. Parameters were estimated using the maximum likelihood
method (i.e. by minimizing negative logarithm of (iv)).
The 95 % confidence intervals (CI) were derived fromthe profile likelihood. Different models were comparedusing the AIC.

Estimation settings

Ethical considerations

In our analysis of the Korean MERS data in real time,
univariate analyses using the abovementioned logistic
regression were conducted to detect any variable associated with the risk of death. Explanatory dichotomous variables include age-group (below or above
60 years old), gender, and patients under treatment.
Patients under treatment include both outpatientsand inpatients, while non-patient cases represent allother cases including healthcare workers, visitors, andso on. The univariate test was achieved by estimating
the CFR at the most recent time tm for each subgroup using the solution of pi obtained from (iii) andcalculating the difference of CFR between the estimates. After identifying variables that were significantly associated with MERS death from univariate
analyses, a multivariate version of the model (iii) was
run to adjust confounding factors and identify epidemiological factors that were significantly associatedwith death.
Subsequently, we used exactly the same model as(iii) to test if there was any time-dependent change
in the risk of death, perhaps due to increased ascertainment involving diagnoses of a substantial numberof mild and asymptomatic cases. The time dependence in the CFR was modelled by introducing a

The present study reanalyzed the publicly available
secondary data from the Korean Government and WHO
which collected the notification data with ethical approval and written consent from patients, and adheringto the International Health Regulations. The secondary
data were de-identified by these organizations in advance of our access. As such, the datasets employed inour study have been deemed exempted from the ethicalapproval.

where a0 is the intercept, ak the coefficient of variable k,xk,i the k-th variable of individual i of the linear predictor, and N the total number of independent variables.
Let A and B represent the groups of cases who have survived and died by the most recent calendar time tm, thelikelihood function to parameterize the linear predictorin (iii) isLða; α; β; t m Þ ¼

Yi∈A

S ðt m −αi ; pi ÞY pi f βi −αi ;

ðivÞ

i∈B

Availability of supporting data

The present study fully rests on published data, andessential components of the data consisting of dates of
illness onset and death are downloadable from the
WHO website [15].

Results
Risk factors associated with MERS death

Table 1 shows the results of univariate analyses. Cases
among senior persons aged 60 years or over (P <0.01)
and patients under treatment (P <0.01) were significantly
associated with MERS death: senior persons appeared tobe 9.3-fold (95 % CI, 5.3–16.9) more likely to die compared to younger cases and patients under treatment
were 7.8-fold (95 % CI, 4.0–16.7) more likely to be fatalMizumoto et al. BMC Medicine (2015) 13:228

Page 4 of 7

Table 1 Univariate association with the risk of death from
Middle East respiratory syndrome (MERS) (n = 185)

Estimated time from illness onset to death

Variables

Univariate odds ratio(95 % confidence intervals)

P valueAge (≥60 years old)

9.3 (5.3–16.9)<0.01

Sex (female to male)

1.3 (0.8–2.1)

0.57

Patients under treatment

7.8 (4.0–16.7)<0.01

Univariate odds ratio was derived from the logit model with time-delay from
illness onset to death, while P values were derived from Fisher’s exact test

than others. Gender was not found to be a significant
predictor of death (P = 0.57).
Table 2 summarizes the results derived from ourmultivariate model analyses and includes the smallest
AIC value. Cases among senior persons and patients
under treatment were included in the final model.
Figure 1a indicates that the estimates of coefficientsremained stable over time, whereas Figure 1b illustrates
the estimated CFR by subgroups. As of July 31, 2015, the
CFR among patients aged 60 years or older under treatment was estimated at 48.2 % (95 % CI, 35.2–61.3). The
CFRs in other subgroups were low and estimated as follows: 1.8 % in the lowest risk group (age <60 years without any treatment), 13.6 % among those aged 60 years orolder without any treatment, and 11.1 % among those
aged younger than 60 years old under treatment.
Time dependent risk of MERS death

Using the time-dependent logit model, we quantified theimpact of improved case ascertainment. Importantly, thedate at which the risk of death most likely declined wasestimated to be June 6, 2015, as determined by the minimum AIC value (401.9) with models (v) and (vi). ε wasestimated to be 0.3 (95 % CI, 0.1–1.1), indicating thatthe case fatality ratio sharply declined by 70 % from
June 6, 2015, onwards. Although the estimated relativechange in the case fatality ratio was not significantly below
1, it should be noted that the AIC value of the model with
time-dependent decline in the CFR was smaller than thealternative model without time-dependent decline in therisk of death (AIC = 405.3).
Table 2 Predicted risk factors of death from Middle East
respiratory syndrome (MERS) death using a real-time
multivariate model
Variables

Coefficient a
Adjusted odds ratio(95 % confidence interval) (95 % confidence interval)Intercept−3.9 (−4.7 to −3.1)

0.02 (0.01–0.04)

Age (≥60 years old) 2.0 (1.4–2.6)

7.6 (4.2–14.0)

Patients under
treatment

5.9 (2.9–12.8)

1.8 (1.1–2.6)

Negative log likelihood = 199.7; Dependent nominal variable = deathaCoefficient of the linear predictor

Jointly quantifying the time from illness onset to death,the mean and standard deviation of the delay were estimated at 14.6 (95 % CI, 11.5–19.5) days and 10.4 (95 %CI, 7.6–16.2) days, respectively. The fixed mean andstandard deviation using first 10 deceased cases were
not significantly deviated from these estimates. Accordingly, the estimated CFR by different types of individuals
were also not significantly different from those describedabove (Fig. 2). As of July 31, 2015, the CFR among
patients aged 60 years or older under treatment wasestimated at 48.4 % (95 % CI, 35.4–61.6). The CFRs
in other subgroups were 1.8 % in the lowest risk group(age <60 years without any treatment), 13.7 % among
those aged 60 years or older without any treatment, and11.2 % among those aged younger than 60 years oldunder treatment. The AIC value of the most up-to-datejoint model was 406.7, i.e. a value greater than that obtained using the fixed mean and the standard deviation.
It should be noted that the joint estimation model
resulted in successful convergence only on and after
July 12, 2015, i.e. 10 days since illness onset of thelatest case on July 2, 2015.

Discussion
The present study has successfully devised a novel hybrid statistical model to characterize the heterogeneousrisk of death associated with MERS in real time in thecontext of the most recent MERS outbreak in the
Republic of Korea from May to July 2015. Our method,
relying on the logit model, enabled us not only to identify epidemiological determinants of MERS death but
also to examine the time-dependent variation in CFR
due to an increased case ascertainment rate influenced
by extensive contact tracing efforts. We have shown thatcases among senior persons under treatment were atparticularly high risk of MERS death. In addition, we
have found that the CFR most likely declined sinceJune 6, 2015, by about 70 %.
We found marked similarities with SARS as the CFR
for MERS among patients aged 60 years or older under
treatment was estimated as high as 48.2 % (95 % CI,
35.2–61.3), while that for younger healthy individuals
was less than 15 % [12, 13]. Our CFR estimate for seniorpersons was consistent with an overall CFR estimate of
40 % from prior studies [9]. Moreover, it can also explaina decline in the CFR estimate among secondary cases in
the Middle East at 20 % [10, 19]: most secondary transmissions were among healthcare workers and visitors
without underlying comorbidities. Because MERS outbreaks tend to be amplified in healthcare settings comprised by a significant fraction of vulnerable populations(e.g. older populations with underlying comorbidities),
an important caveat derived from our analysis is that itMizumoto et al. BMC Medicine (2015) 13:228

Page 5 of 7

Fig. 1 Estimated case fatality ratios of Middle East respiratory syndrome (MERS) by subgroups in the Republic of Korea, 2015. a Estimated value ofregression coefficient as a function of calendar time. The horizontal straight grey line represents zero value. b Estimated case fatality ratio (CFR) by
subgroup as a function of calendar time. The horizontal dashed grey lines correspond to the published value of the CFR (i.e., 20 % and 40 %)[9, 10]. In both panels, the horizontal axis represents the date at which the estimation was implemented. Upper and lower 95 % confidence
intervals (CI) for each parameter derived from the profile likelihood are indicated by the whiskers

is critical to enhance and maintain infection control
measures in healthcare settings not only to avoid secondary transmissions but also mitigate the mortality
burden. Countermeasures that minimize infections
among patients in healthcare settings, e.g. restricting thenumber of visits to different hospitals per case, should
be taken into consideration.
It should be noted that the present study is not thefirst to utilize statistical methods to estimate the heterogeneous risks of death in real time. Specifically, priorstudies on SARS have employed non-parametric survival
analysis during the course of the epidemic [3, 12] and amethod for estimating relative case fatality was proposed
elsewhere [20]. However, compared to earlier models,the proposed model was shown to be advantageous in
two aspects. First, our approach allowed us to pinpoint a
sharp decline in the CFR over time by using only a single multiplicative parameter. Indeed, the reason behind a
decline in CFR by 70 % since June 6, 2015, is consistent
with an increased case ascertainment due to extensive
contact tracing efforts during the outbreak which has
inherently increased the diagnosis of very mild cases. If
we aim to estimate the risk of death among all infectedindividuals [7], the corresponding estimate is likely lower

than what we presented here. Second, as we employed
the logistic type model, our method is able to handle
very small sample sizes for each subgroup of cases. By
using the logit model, our approach was able to successfully identify risk factors from very small samples(i.e. 185 cases and 36 deaths) for the MERS outbreakin the Republic of Korea.
In addition to characterizing the heterogeneous andtime dependent risks of MERS death, we have also
estimated the time from illness onset to death jointlywith other parameters, confirming that the risk estimatesof each group were consistent with those derived from amodel with fixed mean and standard deviation of thetime from illness onset to death. The goodness-of-fit
assessed by AIC indicated that joint estimation did notcontribute to improve model fit to the data in a comparative sense, but as practiced in the present study, thejoint estimation procedure can help verify the CFR estimates of each risk group using the known distribution oftime from illness onset to death.
Two limitations have to be noted. First, our exercise
involved only a limited number of covariates as potential
determinants of MERS death. The publicly available caselist sometimes included descriptions regarding specificMizumoto et al. BMC Medicine (2015) 13:228

Page 6 of 7

Fig. 2 Joint estimation of the time from illness onset to death and type-specific case fatality ratio of Middle East respiratory syndrome (MERS) in
the Republic of Korea, 2015. a Mean and standard deviation of the time from illness onset to death. b Estimated case fatality ratio (CFR) by
subgroup as a function of calendar time. In both panels, the horizontal axis represents the date at which the estimation was implemented. Upperand lower 95 % confidence intervals (CI) for each parameter derived from the profile likelihood are indicated by the whiskers

comorbidities, but we were not able to ensure that allcommon comorbidities were systematically and consistently documented in the list. Second, we had to reach acompromise with some data gaps. In particular, whenever the date of illness onset was unavailable, the date ofconfirmatory diagnosis was used as a proxy. However,considering that Korean public health investigators havesuccessfully conducted major contact tracing efforts andtested all suspected cases, the difference between these
two time variables appeared to have been small (e.g.
among cases who developed illness in the latter half ofJune 2015, the median time elapsed from illness onset todiagnosis was only 2 days). The same issue applies to theinterpretation of the date of death. In contrast to thesubstantial differences between the actual and reported
date of death for cases of the Ebola virus disease outbreaks in West Africa, the close monitoring of MERS
cases in South Korea enabled us to analyze the actual
date of death in real time.
Our study was aimed to identify key determinants of
MERS death using a simple framework in real time, andfuture studies could aim to carry out a more thoroughanalysis of the final dataset that could include more
information. While there are certainly further opportunities to improve our methods and conduct further

similar studies in other settings, our statistical approach
has allowed us to successfully identify patients aged
60 years or older under treatment at particularly highrisk of MERS death in real time. Our model also
helped us capture the impact of increased ascertainment in Korea as reflected in our estimate of the CFRfrom the midst of the outbreak. Considering that
MERS tends to be amplified in healthcare settings[21], the need to enhance and maintain strict infection
control measures at hospitals to avoid secondary transmissions, particularly among high risk groups, cannot
be overemphasized.

Conclusion
The risk of MERS death was significantly associated witholder age as well as treatment for underlying diseases
after explicitly adjusting for the delay between illness
onset and death. Because MERS outbreaks are greatly
amplified in the healthcare setting, enhanced infection
control practices in medical facilities should strive toshield risk groups from MERS exposure. The proposedmodel can be used for other outbreaks of emerging infectious diseases including those involving small numberof cases and time-dependent variation in the ascertainment rate.

Mizumoto et al. BMC Medicine (2015) 13:228

Abbreviations
AIC: Akaike Information Criterion; CFR: Case fatality ratio; CI: Confidence
interval; MERS: Middle East respiratory syndrome; SARS: Severe acuterespiratory syndrome.

Page 7 of 78.

9.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
KM, AE, and HN conceptualized the study idea. AE, MS, and HN built themodel. KM, AE, and MS implemented statistical analysis. KM and AE drafted
the earlier version of the manuscript and HN substantially rewrote the text.
MS and YM gave comments on the earlier version of the manuscript andGC and HN subsequently edited the manuscript. All other authors gave
comments on the revised manuscript and approved the final version of
the manuscript.

10.

11.

12.

13.
Funding
HN received funding support from the Japan Society for the Promotion of
Science (JSPS) KAKENHI Grant Numbers 26670308 and 26700028, Japan
Agency for Medical Research and Development, the Tokyo Society of
Medical Sciences, the Japan Science and Technology Agency (JST) CREST
program and RISTEX program for Science of Science, Technology andInnovation Policy. GC acknowledges financial support from the Division ofInternational Epidemiology and Population Studies, The Fogarty International
Center, United States National Institutes of Health, funded in part by the
Office of Pandemics and Emerging Threats at the United States Department
of Health and Human Services, as well as support from grants NSF grant1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious
Diseases programme, United Kingdom Biotechnology and Biological Sciences
Research Council grant BB/M008894/1 and NSF-IIS Grant#1518939. The funders
had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Author details
1
Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1
Komaba, Meguro-ku, Tokyo 1538902, Japan. 2Graduate School of Medicine,
The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 1130033, Japan.
3
School of Public Health, Georgia State University, Atlanta, Georgia, USA.
4
Division of International Epidemiology and Population Studies, Fogarty
International Center, National Institutes of Health, Bethesda, Maryland, USA.
5
CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi,
Saitama 332-0012, Japan. 6The Institute of Statistical Mathematics, Tachikawa,Tokyo, Japan.

14.

15.
16.

17.
18.
19.

20.

21.

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Received: 27 June 2015 Accepted: 28 August 2015

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