J Vector Borne Dis 51, September 2014, pp. 153–164

Research Articles

On the risk of severe dengue during secondary infection: A systematic
review coupled with mathematical modeling
Kenji Mizumoto1, 2, Keisuke Ejima2, Taro Yamamoto3 & Hiroshi Nishiura2
1Graduate School of Arts and Sciences; 2Graduate School of Medicine, The University of Tokyo, Tokyo; 3Department of International Health,

Nagasaki University Institute of Tropical Medicine and GCOE, Sakamoto, Nagasaki, Japan

ABSTRACT
Background & objectives: The present study aimed to systematically quantify the well known risk of severe
dengue during secondary infection in literature and to understand how epidemiological mechanisms of enhancement
during the secondary infection influence the empirically estimated risk of severe dengue by means of mathematicalmodeling.
Methods: Two conditional risks of severe dengue, i.e. symptomatic illness and dengue hemorrhagic fever (DHF)or dengue shock syndrome (DSS), given secondary infection were explored based on systematically searchedprospective studies. A two-strain epidemiological model was employed to simulate the transmission dynamics ofdengue and to identify the relevant data gaps in empirical observations.
Results: Using the variance-based weighting, the pooled relative risk (RR) of symptomatic illness during secondary
infection was estimated at 9.4 [95% confidence interval (CI): 6.1–14.4], and similarly, RR of DHF/DSS wasestimated to be 23.7 (95% CI: 15.3–36.9). A variation in the RR of DHF/DSS was observed among prospective
studies. Using the mathematical modeling technique, we identified the duration of cross-protective immunity as
an important modulator of the time-dependent behaviour of the RR of severe dengue. Different epidemiologicalmechanisms of enhancement during secondary infection yielded different RR of severe dengue.
Interpretation & conclusion: Optimal design of prospective cohort study for dengue should be considered,accounting for the time-dependence in the RR during the course of dengue epidemic. It is critical to statisticallyinfer the duration of cross-protective immunity and clarify how the enhancement influences the epidemiologicaldynamics during secondary infection.
Key words

Dengue; dengue hemorrhagic fever; enhancement; epidemiology; Flaviviridae; interference; mathematical model;
susceptibility

INTRODUCTION
Dengue fever (DF), dengue hemorrhagic fever (DHF)
and dengue shock syndrome (DSS) are caused by dengue
virus (DENV) that belongs to the family of Flaviviridae,
consisting of four closely related serotypes1. The virus istransmitted by mosquito vector Aedes spp including Aedes
aegypti and Ae. albopictus (Diptera: Culicidae), contributing to high disease burden in tropical and sub-tropicalareas where the vectors are abundant2. Over the decades,
the geographic area with ongoing DENV transmission has
been magnified3. Although the pathogenesis of “severe
dengue”, namely, the symptomatic infection, DF, DHF
and DSS (especially the last two) has yet to be fully clarified, a number of apparent risk factors for DHF and
DSS including secondary infection with heterologous
strain4–5 and different virulence and susceptibility6–8 has
been reported in literature.

The elevated risk of severe dengue during secondary
infection (as compared to primary infection) has beenoriginally identified from epidemiological observations9.
During the secondary infection, the so-called antibody
dependent enhancement (ADE) is known to be observed
and is considered responsible for the pathophysiologicaldevelopment of DHF or the vascular permeability syndrome4. While a number of prospective studies have been
conducted to observe the elevated risk of severe dengue
during secondary (and tertiary) infection, and also a review study has taken place elsewhere10, we have yet tounderstand the link between ADE (or other biologicalmechanisms of severe dengue) and the data generatingprocess of epidemiologically observed severe dengue.
However, epidemiological mechanisms of severe dengue
during secondary infection have yet to be clarified: it has
not been fully understood how the enhancement influences the epidemiological dynamics, e.g. is severe den-

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gue caused by: (i) simply increasing the conditional probability of severe clinical manifestations (given infection);(ii) increasing susceptibility of host who was previously
exposed to other serotype(s); or (iii) delaying recovery
from infection during secondary infection and extending
infectious period?11 To appropriately describe and interpret the epidemiology of severe dengue, epidemiologicalstudies should be designed based on a firm understanding of the transmission dynamics.
Whereas, the causal relationship between DHF/DSS
and secondary infection has been well demonstrated inliterature, it is fruitful to understand how the secondary
infection leads to an increase in severe dengue in an epidemiological manner. The purposes of the present study
are two-folds. First, we aimed to quantitatively analyze
the known “risks” of severe dengue during secondary infection by systematically reviewing prospective observational studies at a population level, thereby characterizing the elevated risks of severe dengue in literature.
Second, we exploited a simple mathematical modelwith two interacting serotypes (and enhancement),
simulating epidemics caused by two serotypes andmeasuring differential impact of enhancement on the relative risk estimates of severe dengue during secondary infection.
MATERIAL & METHODS
The present study consists of two major steps, (i) asystematic review and meta-analysis of published literature on severe dengue; and (ii) mathematical modelingand scenario analysis that intend to clarify the data gaps
and help consider appropriate study design in the future.
As for the former, this systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement12.
Search strategy
Prospective studies containing data on enhancement
were retrieved from the Medline (PubMed) and Web of
Science electronic databases on 1 March 2013. We used
the following free text search terms in ‘All fields’: ‘dengue’, ‘enhancement’ OR ‘secondary infection’, ‘cohort’OR ‘prospective study’ and their combinations.
The search was limited to studies published after 1984,
i.e. subsequent to the first long-term dengue prospective
cohort study in Thailand5. Additional relevant studies
identified by authors were manually retrieved from otherdatabases.

Study selection
To select studies, all titles, identified by the searchstrategy, were screened by two authors independently(K.M. and H.N.). Subsequently, abstracts of only relevant
titles were further screened for review of eligibility, andarticles were selected for closer examination of full-text
if it turns out to be a prospective (cohort) study of dengue. For clarity, retrospective and clinical studies (including prospective ones with recruitment only at medical
facilities) were excluded. Eligible articles must explicitly
define secondary infection (e.g. having been confirmed
as immune to a single serotype before an epidemic of
the other serotype) as well as primary infection (e.g.
having been confirmed as fully susceptible before an
epidemic). Multiple reports of the same dataset (i.e.
the same geographic area at the same time) were jointly
assessed.
Ascertainment of primary infection and secondary
infection
During the systematic review, we focused on two risksof severe dengue during secondary infection, including
the conditional risk of symptomatic illness and the conditional risk of DHF (inclusive of DSS), both given DENV
infection. Relative risk (RR) was calculated as the proportion of severe dengue during secondary infection divided by the same proportion during primary infection.
DENV infection was identified by virologic testing or
serologic evidence of infection. Primary and secondary
infections have been defined as infections among thosewith previous sero-negative and sero-positive states prior
to the infection event, respectively. For simplicity, we
discarded the serotype information and the sequence of
infections with different serotypes.
Data extraction
We extracted the information regarding primary/secondary infection, asymptomatic/symptomatic infection
and DHF/DSS. If reported, we also analyzed additional
results from the serological survey (including the timingof the survey relative to the epidemic time), absenteeism
survey, and attendance to fever clinic. All the datasets
were summarized in a standardized form.
Meta-analysis
RRs of the above mentioned two outcomes were extracted from each eligible study, and subsequently, thepooled estimate was obtained. We used the inverse of thevariance of RR as the weight of each study when calculating the pooled estimate. As an alternative method, weMizumoto et al: Severe dengue during secondary infection

also computed the pooled effect size by employing therandom effects model. Statistical heterogeneity was assessed by Q statistic as well as I2 statistic (representing
the extent of the degree of variation). All statistical data
were analyzed using a statistical software JMP version
9.0.0 (SAS Institute Inc., Cary, NC, USA).
Modeling method
As a second part of the present study, we employed adeterministic epidemiological model that describes thetime-dependent transmission dynamics of dengue withtwo distinct serotypes. Specifically, we tackled two questions: (i) how the timing of observation influences theestimate of effect size (i.e. RR of severe dengue duringsecondary infection) during the course of epidemics, and(ii) how different epidemiological scenarios with different mechanisms of enhancement influence the RR estimate. As for the former, we computed the RR of severe155

dengue during secondary infection by varying the mean
duration of cross-protective immunity and the time-lag
between the invasion of one serotype and that of the otherserotype. For the latter question, we compared the dynamics under three hypothetical scenarios, i.e. the scenario in which: (a) the conditional risk of severe dengue
is elevated given secondary infection, (b) susceptibility
during secondary infection is increased, and (c) the duration of infectious period during secondary infection isextended.
Figure 1 shows the compartment that we used to compute the epidemiological dynamics of dengue. Each stateof infection is represented by single compartment in
Fig. 1 with arrows indicating the direction and rate oftransition. Since, we considered epidemics in a short-timescale, the background demography of human host, likebirth and death, was ignored. First and second letters foreach compartment stand for the state of infection with

Fig. 1: Transmission dynamics of dengue with two serotypes. A compartmental model is employed. First and second letters for each compartment
stand for the state of infection with respect to serotype 1 and serotype 2, respectively. S stands for susceptible, I stands for infectious, R
represents recovered and immune, and W represents protected state due to cross-protective immunity. Thus, for example, RI represents
the host who recovered from infection with serotype 1 while being infectious with serotype 2. The transition rates are written on thearrows. λi, the force of infection with serotype i (for i = 1, 2); 1/γ, the mean duration of infectious period; 1/δ, the mean duration of crossprotective immunity; η, the conditional hazard of severe dengue given dengue virus infection; α1, relative susceptibility during secondary
infection compared to primary infection; 1/α2, relative infectious period during secondary infection compared to primary infection;
κ, relative conditional hazard of severe dengue during secondary infection given dengue virus infection.

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serotype 1 and serotype 2, respectively. Although, there
are four distinct serotypes of dengue virus in reality, we
considered only two theoretical serotypes in modelingexercise, because our outcome (i.e. RR of severe dengue
during secondary infection) for a two-serotype setting
mirrors that of a realistic four-serotype setting. S
stands for susceptible, I stands for infectious, R represents recovered and immune state, and W represents
a protected state due to a short-lasting cross-protective
immunity (i.e. when a host experiences infection with asingle serotype, it is assumed that the infected host is
protected from infection with other serotypes for a shortperiod of time13–14). Thus, for example, RI represents thehost who recovered from infection with serotype 1 while
being infectious with serotype 2. It should be noted that
we ignored the dynamics of mosquito vector in the following model (while incorporating the delay in secondary transmission due to the extrinsic incubation period
into the model by extending the generation time), because
the detailed population dynamics of mosquitoes is out of
the scope of the present study and we focus on the natural
history of dengue in the human host.
Let 1/γ be the mean duration of infectious period and1/δ be the mean duration of cross-protective immunity.
It should be noted that for simplicity, we assumed that allparameters are identical between serotypes (i.e. the dynamics is symmetric). The transmission dynamics is described by the system of ordinary differential equations:... (1)

where, λi represents the force of infection with serotypei (for i = 1 or 2) (or the rate at which susceptible individuals acquire infection with serotype i) calculated as:... (2)where, β scales the rate of transmission. Other two parameters in system (1) during secondary infection, α1 and1/α2 reflect the enhancement in epidemiological dynamics, each interpreted as follows: α1, the relative susceptibility during secondary infection compared to primary
infection, and 1/α2, the relative infectious period duringsecondary infection compared to that during primary infection. Let η be the conditional hazard of severe dengue
given infection. Cumulative numbers of severe dengue
during primary and secondary infections are calculatedas:... (3)where, κ is the conditional relative hazard of severe dengue given secondary infection. Let Y(t) represent the number of hosts who have experienced only primary infection, i.e.
Y (t) = N – SS(t)–RI(t) – IR(t) – RR(t),... (4)

where, N represents the total population size which is assumed to be a constant. Considering that an epidemiological study samples the data from the population at themost recent time T, the relative risk estimate that we obtain is calculated as:... (5)The parameter values that we used for numerical solutions are shown in Table 113,15–17.
We examined the sensitivity of RR in (5) to model
parameters and initial conditions for two different scenarios. In both scenarios, we consider an epidemic in the
population of N = 1,000,000 individuals in which N-1 are
initially fully susceptible and 1 is newly infected withserotype 1 at time t = 0. In the first scenario, it is assumed
that secondary infection induces only the elevated risk ofsevere clinical manifestations at 10 times as comparedwith that during primary infection, and the mean duration of cross-protective immunity is allowed to vary from0 to 360 days. During the course of the epidemic, an infected individual with serotype 2 is introduced to the community at variable timings; before the epidemic peak (atDay 50), nearby the epidemic peak (Day 72) and during
the declining phase of the epidemic (Day 100). We canthen measure how r(t) varies as a function of epidemic
time. In the second scenario, the epidemiological mechanism of enhancement is varied, while the time to introduce a single infected individual with serotype 2 is fixedat Day 72. We can measure r(t) as a function of time forMizumoto et al: Severe dengue during secondary infection

157

Table 1. Parameter values for an epidemiological model of dengue
Parameter’s interpretation

Notation

Baseline value

Reference

Mean duration of cross-protective immunity
Relative susceptibility during secondary infection
compared to primary infection
Relative infectious period during secondary infection
compared to primary infection
Mean duration of infectious period
Basic reproduction number of serotype i
Relative conditional hazard of severe dengue during secondary
infection given infection
Conditional hazard of severe dengue given infection1/δα1

180 (days)>1 (See main text)[13][assumed]

1/α2>1 (See main text)[assumed]

1/γ
Ri
κ

10 (days)
3*>1 (See main text)[15][16–17][assumed]η

1.2 × 10-2

[assumed]

Force of infection with serotype iλi(As calculated in the main text)*The transmission coefficient, β was calculated from the relationship βi = Riγ. We ignored the serotype-specificity (e.g. difference in γ) in
simulations.

three possible types of enhancement, i.e. (a) κ = 10, (b)α1 = 10, or (c) 1/α2 = 10. We also examined the sametypes of enhancement with different level— (a) κ = 5, (b)α1 = 5, or (c) 1/α2 = 5. Subsequently, we investigated
how r(t) is scaled by κ, α1 and 1/α2 at the end of the epidemics of both serotypes. A differential equation solver,
Berkeley Madonna version 8.0.1 (Robert Macey andGeorge Oster, CA, USA) was used to numerically simulate the epidemics.
RESULTS
Reviewed literature
Of the 46 titles that were initially identified, 37 abstracts were assessed for eligibility, of which 22 were excluded. In total, 15 full length articles were assessed for
eligibility (Fig. 2). Of these, eight studies were
determined to be eligible and included in this systematic
review (Table 2)5, 18–25. One of the eight studies was
split into two original research articles in a single volume
of a periodical21–22, and thus, hereafter we combined two
articles into a single study. Of the excluded 22 abstracts,
20 articles were clinical studies (e.g. conductedat healthcare facilities), one article was retrospective
study, and one article was a mathematical modelingstudy. Of the excluded seven full length reports, six
articles were clinical studies enrolling participants at
hospital settings only26–31 and one article was a retrospective study32.
Secondary infection and severe dengue in literature
Of the included eight cohort studies, five studies reported RR of symptomatic illness and DHF/DSS duringsecondary infection compared to primary infection. The
reported estimates are summarized in Fig. 3 with the RR
of symptomatic illness and DHF/DSS ranging from 1.9

Fig. 2: Flow diagram of study selection.

to 14.3 and from 1.4 to 58.1, respectively. There havebeen multiple reports that measured the association between secondary infection and DSS, but DSS during primary infection was too scarce and did not permit precise
calculation of RR. For this reason, we grouped DHF and
DSS into a single category in all of the following results.
Using the variance-based method, the pooled estimate of
RR of symptomatic illness during secondary infection was
9.4 [95% confidence interval (CI): 6.1–14.4], and similarly, RR of DHF/DSS was 23.7 (95% CI: 15.3–36.9).
The pooled estimates using the random effects model were
not significantly different: RR of symptomatic illness and
DHF/DSS were 5.9 (95% CI: 0.99–35.7) and 15.2 (95%
CI: 1.7–137.1), respectively, although the lower boundfor symptomatic illness was slightly below unity. A very
little heterogeneity was identified for the symptomatic

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Table 2. Available epidemiological information other than symptomatic illness and dengue hemorrhagic fever and the timing of serologicalsurvey in eight selected prospective studies of dengue virus infection
Absenteeism*

Attendance tofever clinic**

Relative timing†

Duration‡

CountryFirst author
reference

ThailandSangkawibha et al5

No

Yes

After epidemic

Seemingly < 3 months

ThailandBurke et al18

Yes

Partly yes

During and after epidemic

Seemingly < 3 months

MyanmarThein et al19

No

Yes

After epidemic

NA

Indonesia

Graham et al20

No

Yes

During epidemic

0 monthThailandEndy et al21–22

Yes

Partly yes

During and after epidemic

Seemingly < 3 months

Indonesia

Porter et al23

Yes

No

During and after epidemic< 3 months

Nicaragua

Balmaseda et al24

Yes

Partly yes

After epidemic

Seemingly < 3 months

ThailandEndy et al25

Yes

Partly yes

During and after epidemic

Seemingly < 3 months*Conducted the survey of children’s absenteeism during dengue epidemic; **Conducted the survey of febrile individuals (e.g. enforcing people
to visit outpatients upon onset of fever); †The timing of serological sampling relative to the epidemic; ‡The time lag between the end of epidemic
and serological survey.

illness (Fig. 3a) with Q = 3.8 and I2 = 0%, while a variation in the RR of DHF/DSS was seen (Fig. 3b) with
Q = 13.1 and I2 = 69.4%.
Table 2 shows additional information of our interest
during secondary infection in eight cohort studies. Of theeight studies, routine surveillance of absenteeism was conducted in five studies (62.5%), while the attendance tofever clinic was observed in seven studies (87.5%). As
for the timing of serological survey relative to the epidemic, four studies (37.5%) obtained samples “during andafter epidemic” and three studies (37.5%) did so “after
epidemic” only. The time lag from the epidemic to serological survey was most commonly <3 months in fivestudies (62.5%).
Simulated epidemics with two serotypes
Figure 4 shows the relative risk estimates (of severe
dengue which can be interpreted as symptomatic illness,
DHF/DSS or other form of severe manifestation) by varying the time to introduce serotype 2 during the course of
the serotype 1 epidemic and also by varying the mean
duration of cross-protective immunity. It is remarkablethat the RR varies as a function of time, and it reaches tothe stable state only after the end of epidemics of both
serotype 1 and 2. Figures 4 (a), (c) and (e) indicate thatthe timing to introduce serotype 2 has only limited
impact on the time-dependency of the RR. However,Figs. 4 (b), (d) and (f) clearly demonstrate that the timedependent behaviour of RR, r(T) is sensitive to the duration of cross-protective immunity.
In Fig. 5, we examined r(T) for three possible typesof enhancement, i.e. when (a) the conditional risk of se-vere illness was elevated, (b) susceptibility to secondary
infection was elevated, and (c) the infectious period during secondary infection is extended. Three different aspects are notable. First, as can be expected from Fig. 4,
the time-dependent behaviour of r(T) is sensitive to theduration of cross-protective immunity. Second, although
the enhancement types (a) and (c) lead to similar equilibrium at the end of the epidemic of both serotypes, it isremarkable that the enhancement in susceptibility finds arather different equilibrium value. That is, although theincrease in susceptibility has been a common assumption(or interpretation) for describing the epidemiologicalmechanism of enhancement in various published studies11, 33, the elevated susceptibility does not lead to anincrease in r(T) and even leads to the relative risk below
unity. Third, for all three types of enhancement, the equilibrium value of the relative risk is different from the scaleof the enhancement in the corresponding parameter. For
instance, Figs. 5 (a), (c) and (e) magnified one of parameters by 10 times, but the equilibrium r(T) value takes
approximately 5 for (a) and (c), and 1 for (b), respectively. Figures 5 (b), (d) and (f) multiply 5 to one ofthe parameters, but r(T) is stable approximately at 3.5 for(a) and (c), and 1 for (b), respectively. Figure 6 shows
the relationship between r(T) value at an equilibrium(i.e. at the end of epidemics of both serotypes) and therelative change in one of the parameters that wereassumed to govern the enhancement during secondary
infection. Again, it is remarkable that an increase insusceptibility does not lead to an observation of increasein the relative risk of severe dengue during secondary
infection.

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159

Fig. 3: Relative risk of: (a) symptomatic illness, and (b) dengue hemorrhagic fever or dengue shock syndrome during secondary infection. Notethat logarithmic scale is employed for the horizontal axis, i.e. relative risk =1 corresponds to LN (relative risk = 0). The whiskers extend
out to lower and upper 95% confidence intervals. Pooled estimate using the inverse of the variance of RR is shown as the diamond withboth ends extending to the lower and upper 95% confidence intervals.

DISCUSSION
The present study combined two epidemiologicalapproaches, i.e. systematic review and mathematicalmodeling, to understand known epidemiological risks ofsevere dengue during secondary infection and clarify relevant data gaps and pitfalls in measuring the RR of severe dengue during secondary infection by means of mathematical modeling. In the systematic review andmeta-analysis, we examined two common outcomes ofsevere dengue, namely the RR of symptomatic illness and
DHF/DSS during secondary infection. A variation in theestimated RR of DHF/DSS was observed between studies. In addition, we identified that many serological surveys took place during or shortly after an epidemic season. Using an epidemiological model, we identified theduration of cross-protective immunity as an important
modulator of the time-dependent behaviour of the RR ofsevere dengue. Differently assumed epidemiologicalmechanisms of enhancement during secondary infection

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Fig. 4: Estimated relative risk of severe dengue during secondary infection as a function of time. Sensitivity of relative risk (RR) of severe
dengue (e.g. DHF/DSS) to different lengths of cross-protective immunity following a primary infection (1/δ) and different time lags
between the invasion of 1 serotype and that of the other serotype (tin). Figures (a), (c) and (e) examine the sensitivity of RR to different
mean durations of cross-protective immunity (1/δ ranging from 0 to 360) with the introduction of second serotype at tin = 50 days, tin =72 days and tin = 100 days, respectively, from the start of the epidemic with serotype 1. Figures (b), (d) and (f) examine the sensitivity of
RR to different time lags between the invasion of one serotype and that of the other serotype (tin ranging from 50 to 100) with the fixed
mean durations of cross-protective immunity at 1/δ = 0 day, 180 days and 360 days, respectively. For all scenarios, it is assumed that
secondary infection induces only the elevated risk of severe clinical manifestations at 10 times as compared with during primary
infection).

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161

Fig. 5: Estimated relative risk of severe dengue during secondary infection with varying mechanisms of enhancement. Each Figure multiplies
one of parameters that are considered to govern the enhancement by a factor of 10 [Figs. (a), (c) and (e)]; and 5 [Figs. (b), (d) and (f)].
In each Figure, there are three parameters to reflect the enhancement in epidemiological manners, i.e. the relative hazard of severe
dengue given secondary infection (“Severe infection”), the relative susceptibility to secondary infection (“Susceptibility”) and therelative duration of infectious period given secondary infection (“Infectious period”). The mean duration of cross-protective immunity(1/δ) is also varied, i.e. 1/δ = 0 day (a and b), 180 days (c and d), and 360 days (e and f). The epidemic time (i.e. the time since
introduction of a single index case infected with serotype 1) to introduce a single infected individual with serotype 2 is fixed at Day 72.

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Fig. 6: Sensitivity of the relative risk of severe dengue at the end of
epidemic to changes in the parameters governing enhancement.
There are three parameters to reflect the enhancement in
epidemiological manners, i.e. the relative hazard of severe
dengue given secondary infection (“Severe infection”), therelative susceptibility to secondary infection (“Susceptibility”)
and the relative duration of infectious period given secondary
infection (“Infectious period”). Varying one of the parametersfrom 0 to 3 and fixing other parameters, we examined therelative risk at the end of each epidemic. The epidemic time(i.e. the time since introduction of a single index case infectedwith serotype 1) to introduce a single infected individual withserotype 2 is fixed at Day 72. The mean duration of crossprotective immunity is fixed at 180 days.

yielded different estimates of the RR, and in particular,
we observed an unexpectedly small RR when the susceptibility of once-infected host was elevated during secondary infection.
There are two important lessons that should be learnt
from the present study. First, our finding indicates a critical need to consider the optimal design of prospective
study, accounting for the time-dependent aspect of the risk
during the course of epidemics. As we have shown, theduration of cross-protective immunity is likely a key epidemiological driver to regulate appropriate time frame ofempirical observations, and the statistical inference of theduration as well as other natural history is deemed essential to understand the epidemiological dynamics of dengue15, 34–35. Second, it is critical to clarify how the enhancement mechanism during secondary infection
influences the epidemiological dynamics. In particular, it
should be noted that a common assumption of enhancement, i.e. the increased susceptibility, was demonstrated
to be inconsistent with the elevated RR of DHF/DSS during secondary infection. The clarification on the mechanism of enhancement calls for a specialized design ofempirical observations that permit us to measure the mecha-

nism directly, e.g. by using individual-based observational
research design with a possibility to deal with the conditional risk of enhancement given DENV infection (e.g.
household-based study). While various mathematicalmodels have been devised to discuss the dengue control
including vaccination policy36–39, it is vital to address theabove mentioned point to bridge the gap between theempirically observed idea (e.g. elevated RR of DHF/DSS
during secondary infection) and model assumptions.
Three limitations should be noted. First, our systematic review ignored strain-specific characteristics (e.g.
different virulence) and we also combined the data ignoring all other aspects of observations including geographic
location, ethnicity and background health status. The risksof symptomatic infection and severe dengue are known
to depend on age40–41. Moreover, the diagnostic criteria
of DF and DHF could also be different by study42. Nevertheless, these should not be regarded as a serious limitation, because the known risk factors have been alreadyidentified as independent predictors of severe dengue andwould not alter our main results (i.e. accounting for theserisk factors would only contribute to stratifying the risks
by each individual attribute). Second, our mathematicalmodel rested on simple assumptions with two serotypes
only. Homogeneously mixing assumption was employedwith a symmetric structure of natural history, and heterogeneous risks including mosquito dynamics were ignored.
People were assumed to be fully susceptible at time 0,
but usually it is the case that we observe cross reactions
between different viruses in Flaviviridae (e.g. Japanese
encephalitis virus). Third, while we identified the importance to account for the time-dependence and clarify the
epidemiological mechanism of enhancement, the epidemiological determinants of dengue transmission have yet
to be exhaustively identified, and epidemiologists will haveto face many other regulators in empirical observations.
In summary, our study suggested that the duration ofcross-protective immunity should be estimated from empirical datasets and also emphasized that the epidemiological dynamics of enhancement during secondary infection should be appropriately quantified. Clarifications
on these points will help specify optimal study design ofdengue in the future. Despite our simplistic approach, we
believe that the combination of systematic review andmathematical modeling successfully identified an additional crucial element of severe dengue in epidemiological observations43.
ACKNOWLEDGEMENT
This study received funding support from the Com-

Mizumoto et al: Severe dengue during secondary infection

missioned Research Programme of the Ministry of Health,Labour and Welfare, Japan (H26-ShinkoJitsuyoka-General-016) and St Luke’s Life Science Institute Research
Grant for Clinical Epidemiology Research 2014. KE received scholarship support from the Japan Society for
Promotion of Science (JSPS).
Conflict of interests
The authors have declared that no conflict of interest
exists.
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Correspondence to: Dr Hiroshi Nishiura, Graduate School of Medicine, The University of Tokyo, Medical Building No. 3, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-0033, Japan.
E-mail: nishiurah@gmail.com
Received: 22 March 2013

Accepted in revised form: 30 April 2014