PLOS NEGLECTED TROPICAL DISEASES

RESEARCH ARTICLE

Epidemiology of dengue fever in Guatemala

Leticia del Carmen Castillo Signor1, Thomas Edwards2, Luis E. Escobar3,
Yolanda Mencos1, Agnes Matope4, Mariana Castaneda-Guzman ID3, Emily R. Adams2, Luis
E. Cuevas ID2,4*
1 Ministerio de Salud Publica y Asistencia Social de Guatemala, Guatemala City, Guatemala, 2 Centre for
Drugs and Diagnostics Research, Liverpool School of Tropical Medicine, Liverpool, United Kingdom,
3 Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, United States of America,
4 Tropical Clinical Trials Unit. Liverpool School of Tropical Medicine, Liverpool, United Kingdom

* luis.cuevas@lstmed.ac.uk
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Abstract
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Dengue fever occurs worldwide and about 1% of cases progress to severe haemorrhage
and shock. Dengue is endemic in Guatemala and its surveillance system could document
long term trends. We analysed 17 years of country-wide dengue surveillance data in Guate-
mala to describe epidemiological trends from 2000 to 2016.Data from the national dengue
OPEN ACCESS surveillance database were analysed to describe dengue serotype frequency, seasonality,
Citation: Castillo Signor LdC, Edwards T, Escobar and outbreaks. We used Poisson regression models to compare the number of cases each
LE, Mencos Y, Matope A, Castaneda-Guzman M, et
year with subsequent years and to estimate incidence ratios within serotype adjusted by
al. (2020) Epidemiology of dengue fever in
Guatemala. PLoS Negl Trop Dis 14(8): e0008535. age and gender. 91,554 samples were tested. Dengue was confirmed by RT-qPCR, culture
https://doi.org/10.1371/journal.pntd.0008535 or NS1-ELISA in 7097 (7.8%) cases and was IgM ELISA-positive in 19,290 (21.1%) cases.
Editor: Benjamin Althouse, Institute for Disease DENV1, DENV2, DENV3, and DENV4 were detected in 2218 (39.5%), 2580 (45.9%), 591
Modeling, UNITED STATES (10.5%), and 230 (4.1%) cases. DENV1 and DENV2 were the predominant serotypes, but
Received: December 11, 2019 all serotypes caused epidemics. The largest outbreak occurred in 2010 with 1080 DENV2
cases reported. The incidence was higher among adults during epidemic years, with signifi-
Accepted: June 29, 2020
cant increases in 2005, 2007, and 2013 DENV1 outbreaks, the 2010 DENV2 and 2003
Published: August 19, 2020
DENV3 outbreaks. Adults had a lower incidence immediately after epidemics, which is likely
Copyright: © 2020 Castillo Signor et al. This is an linked to increased immunity.
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited. Author summary
Data Availability Statement: An unrestricted Dengue is the most common mosquito-borne virus, and a major cause of fever, with an
anonymised database will be available from the
estimated 390 million infections annually. Guatemala, in Central America, has had ongo-
Liverpool School of Tropical Medicine database
repository. If you would like to access the ing dengue transmission since the 1990s. Its national surveillance system monitors out-
database, please contact the library repository by breaks and seasonal trends of infections to inform public health responses. We have
emailing Dr Julia Martin, Head of Information analysed 17 years of surveillance data collected from 2000 to 2016, to describe seasonal
Services (Julia.Martin@lstmed.ac.uk). trends, outbreak years, and the fluctuating prevalence of the four dengue serotypes. Labo-
Funding: This publication was supported by grants ratory data from 91,554 individual serum samples were included, of which 7.8% were pos-
received by LdelCCS from Guatemala’s Secretaria itive for dengue. All four dengue serotypes circulate in the country, with dengue 1 and 2
Nacional de Ciencia y Tecnologia, Senacyt and being the predominant serotypes. This is important, as it increases the likelihood of den-
from the UK MRC Zika Rapid Response Initiative
gue infections being followed by a new infection with a different serotype, which can lead
(Award: ZK/16-025, PI LEC). The funders had no

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PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

role in study design, data collection and analysis,

decision to publish, or preparation of the to severe dengue. We also report that adults in Guatemala have a lower likelihood of infec-
manuscript. tion the year after an epidemic, which might be linked to an increased immunity in the
Competing interests: The authors have declared population.
that no competing interests exist.

Introduction
Dengue is widespread across the tropics with up to 3.97 billion individuals living in areas at
risk of transmission [1], with an estimated 390 million infections occurring each year [2]. Its
extensive geographical range and incidence caused an estimated 1.14 million disability
adjusted life years lost in 2013 alone [3] at an economic cost of at least USD 8.9 billion [4].
The number of cases reported worldwide each year has increased from 15,497 cases in the
1960s, to one million in the 2000s [5] and 96 million cases per year in the last decade [2]. These
increases are due to parallel increases in urbanization, travel and population density, particu-
larly in the Americas and South-East Asia, facilitating dengue’s geographical expansion [6]
outside traditional endemic areas [7] [8] [9].
The four dengue virus (DENV) serotypes (DENV 1 to 4) share 65–70% of their genomic
sequence [10] and overlap spatially and temporally [11], with changing yearly patterns and
fluctuating levels of serotype-specific immunity [12]. Infection with one serotype provides
serotype-specific immunity without significant cross protection for other serotypes and a fol-
low-on infection with a different DENV serotype carries an increased risk of disease severity
through antibody mediated enhancement [13].
Surveillance of the incidence of DENV serotypes and outbreaks is essential to forecast the
risk of disease burden and to enable reactive measures such as vector control and social mobili-
sation [14]. Moreover, regionally emerging arboviruses, particularly Zika and Chikungunya
[15], have complicated dengue diagnosis and surveillance, as symptoms and geographic ranges
are similar and serological diagnostics of flaviviruses cross react [16].
Dengue had a low incidence and was not considered a public health problem in Guatemala
during most of the twentieth century, possibly as a by-product of the national malaria and yel-
low fever eradication campaigns [17]. However dengue viruses were reported since at least
1978, and all four serotypes had been reported by the 1990s [18] with a major outbreak of
DENV3 with over 300,000 cases occurring in 1995 [18]. Systematic descriptions of the inci-
dence of all serotypes in recent decades, however, are missing despite increasing number of
cases reported by health services. We have thus undertaken an analysis of 17-years of dengue
surveillance data in Guatemala, to describe longitudinal trends from 2000 to 2016, and the
emergence and overlap of DENV serotypes in the country.

Methods
Ethics statement
This analysis used secondary sources of data collected for surveillance purposes and ethical
approval was not required. No experimental work was undertaken outside of the routine diag-
nostic tests and all data were anonymised prior to analysis.

National surveillance
Surveillance of arboviral infections in Guatemala is conducted by the National Epidemiology
Centre and the National Public Health Laboratories (Centro Nacional de Epidemiologia and
Laboratorio Nacional de Salud, LNS) of the Ministry of Health. The surveillance network

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PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

comprises 340 primary care centres and 44 hospitals situated in the 29 health areas of the coun-
try. Surveillance is based on the regular reporting of data by the health facilities and is mostly
passive, with some time-limited active surveillance during outbreaks. Health facilities submit
serum samples to the LNS for laboratory confirmation of clinical cases, with samples sent in
ice boxes via road transport. Test requests include patient information such as gender, age,
address, history of travel, hospitalization, date of illness onset, and symptoms. The LNS test all
samples received during endemic months, but only a proportion of samples during epidemic
periods, when the laboratory prioritises severe and hospitalised cases. The laboratory has the
capacity to test for dengue, chikungunya and Zika infections, with the aim of testing samples
for all three viruses, except during outbreaks, when the most likely causative infection is priori-
tised. The LNS reports surveillance data to the National Epidemiology Centre, which in turn
advises the Ministry of Health. Reports of new outbreaks, new arboviruses, and serious cases
are in turn reported to the Pan American Health Organisation, along with monthly reports of
passive surveillance data.
The LNS routinely holds surveillance records of all samples received from 2000 to 2016 for
national and international reporting. We performed descriptive analyses on the data available
to detect spatial and temporal patterns of the cases reported by LNS across the study period.
Information regarding location of the cases and the health centres was available for 2016, and
this information was used to compare the origin of the cases versus the health centre visited to
determine patterns of displacement to receive health services.

Diagnostic algorithms and case definitions

Dengue case definitions have changed with the advent of new diagnostics and updates of inter-
national case definitions. Between 2000 and 2010, testing for DENV was based on culture in
clone C6/36 cells of Aedes albopictus, with confirmation based on immunofluorescent staining
and fluorescence microscopy. In 2009 the LNS introduced the NS1 enzyme-linked immuno-
sorbent assays (ELISA) to detect DENV antigens in serum, and since 2010 testing of acute
cases has utilised the Centers for Disease Control (CDC) DENV 1–4 RT-qPCR assay that sero-
types the virus with a limit of detection of between 5–10 copies [19]. A further assay that simul-
taneously detects DENV, Chikungunya and Zika (Trioplex), became available in 2016 [20].
However, the CDC 1–4 RT-qPCR continues to be used to confirm and serotype all Trioplex-
positive samples, as this test had not been validated at the time of its introduction. Samples in
the surveillance database with positive results to any of these tests are recorded as “confirmed
dengue case”. Since 2000, samples from patients with symptoms over 5 days have also been
tested with IgM antibody capture ELISA (MAC-ELISA) [21], and samples positive solely by
this assay are reported here as “probable cases”.

Results
A total of 91,554 samples were received by the LNS from 2000 to 2016. The number of samples
received varied over the years (Fig 1). Dengue infection was confirmed by RT-qPCR, viral cul-
ture or NS1 ELISA in 7097 (7.8%) samples and a further 19,290 (21.1%) were positive by IgM
ELISA (probable cases). Samples were more likely to be confirmed after the introduction of
RT-qPCR in 2009. The number of probable cases was higher between 2005 and 2007 and
2012–2014, which were years preceded by periods of high dengue incidence (S1 Fig). Most
cases occurred in the second half of the year, with the majority occurring between July and
October and a peak incidence in August (Figs 1 and 2 and S2 Table). Notable exceptions to
this pattern were the outbreaks of DENV2 in 2010, which was detected in May, and DENV4 in
2001, with 94% of cases recorded in June (S3 Fig). The DENV3 outbreak in 2003, DENV1/

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PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

Fig 1. Temporal distribution of dengue in Guatemala. A. Annual number of samples received for dengue screening.
The dashed line indicates the mean number of samples submitted across the study period (~5,300). B. Cumulative
number of monthly cases of dengue serotypes 1, 2, 3, and 4.
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DENV3 outbreak in 2004 and DENV1 outbreak in 2005 also had the highest case detection in
October.
Serotyping by culture or RT-qPCR was available for 5619 (70.2%) of 7097 confirmed cases.
DENV1, DENV2, DENV3, and DENV4 were detected in 2218 (39.5%), 2580 (45.9%), 591
(10.5%) and 230 (4.1%) cases, respectively. DENV1 and DENV2 had the highest frequencies,
often over consecutive years (Figs 2 and 3). Peaks of DENV2 were observed in 2002 (262
cases) and 2003, followed by low numbers between 2006 and 2008 and a resurgence in 2009,
when it caused the largest outbreak of the study period (1080 cases). DENV2 was often
replaced by DENV1, most notably after the 2003 and 2010 epidemics. DENV1 predominated
between 2004 and 2008, with peaks in 2005 and 2011–2015, causing 103 cases in 2011, 392 in
2012 and 542 in 2013 (S2 Fig). DENV3 caused outbreaks in 2003 and 2004 and in 2013 and
2014, when it circulated together with DENV1 and DENV2. DENV4 was the main serotype in
2001 and 2004 but was detected sporadically in other years.
Dengue affected populations of all ages, with an average of 24.4 years and 18% of cases
occurring in infants. The mean age of the cases varied over the years ranging from 19.1 to 26.5
years (Fig 4). However, during outbreaks the proportion of cases occurring among 5–20 and

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PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

Fig 2. Monthly distribution of dengue cases in Guatemala (2000–2016). The mean annual number of cases is shown
by a black line and the shaded areas describe the 95% confidence interval.
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20–60 age groups (2005, 2007 and 2013 DENV1, 2010 DENV2 and 2003 DENV3) was higher
than in the following years, when the incidence was similar across all age groups (Table 1; S1
Table). Overall, 52.9% of all dengue cases occurred in women. Although some years affected
higher proportions of men or women, there was no significant association with epidemics
years or the predominance of a dengue serotype (S4 Fig).
Some Departments (districts) provided health services to neighbouring Departments. For
example, Departments receiving samples from other Departments included Guatemala, Santa
Rosa, Quetzaltenango, and Zacapa. The Department of Guatemala received most imported
patients from Jutiapa, Escuintla, El Progreso, and Santa Rosa. The Department of Santa Rosa
analysed samples from Jutiapa, Guatemala, Chimaltenango, and Jalapa. The Department of
Quetzaltenango analysed samples from the neighbouring Department of San Marcos and the
Department of Zacapa analysed samples from El Progreso and Chiquimula.
Overall, the Department of Guatemala reported the highest number of cases followed by
the Departments in the East of the country (i.e., Izabal, Zacapa, Santa Rosa, and El Progreso)

Fig 3. Annual percentage of confirmed dengue cases by serotype. Cases detected by NS1 ELISA but not serotyped
are designated DENV unknown.
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PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

Fig 4. Median age of dengue cases per year. Error bars indicate interquartile range.
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(Fig 5), with the incidence by 10,000 population being highest in Zacapa. In contrast, the
Departments in the Central Highlands of the country, such as Sololá, Chimaltenango, Retalhu-
leu, Sacatepéquez, and Huehuetenango had low numbers, reporting less than 100 cases.Quet-
zaltenango, Jalapa and Izabal had low incidence, but a high proportion of samples were
dengue-positive (Fig 5).

Discussion
Central America has a considerable burden of dengue, with an estimated incidence of 169
cases per 100,000 inhabitants, compared with 33.8 in the Caribbean, and 586.3 cases per
100,000 inhabitants in South America [22]. Dengue is hyperendemic, and seasonal in Guate-
mala with simultaneous circulation of all DENV serotypes [23]. In Guatemala, most infections

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Table 1. Incidence ratio of cases in each age group in 2010, the year with the major DENV 2 epidemic, and in
2011.
Age Group Cases—Comparisons between epidemic year and subsequent year (DENV 2 versus all DENV)
Age Group Epidemic year vs subsequent year Incidence Ratio (95% CI� ), p-value
<1 2010 vs. 2011 2.13 (0.14;31.86), 1.0000
1–5 2010 vs. 2011 2.01 (0.89;4.50), 0.1299
5–20 2010 vs. 2011 1.61 (1.18;2.20), 0.0003 ��
20–60 2010 vs. 2011 2.23 (1.67;2.97), <0.0001 ��
>60 2010 vs. 2011 1.93 (0.57;6.46), 0.8034
�
Confidence Interval
��
Significant, DENV: Dengue virus.

https://doi.org/10.1371/journal.pntd.0008535.t001

were caused by DENV1 and DENV2, followed by DENV3 and DENV4, which mirrors reports
from other Pan American Health Organization regions. The co-circulation of all four DENV
serotypes increases the risk of second infections [24] with a different serotype, which leads to a
higher risk of severe dengue [25]. Whilst previous DENV exposure can lead to the develop-
ment of severe symptoms with the second infection; there is also some evidence of serological
cross protection over the ensuing one to three years [26], which potentially may account for
the reduction in the number of cases after epidemic years.
Epidemics for all dengue serotypes followed the same general pattern, with annual dengue
cases peaking during the summer and autumn, between July and September, with some excep-
tions. This pattern was consistent among all serotypes. Interestingly the later peaks in dengue
case detection between 2003 and 2005 occurred around two El Niño events [27], which have

Fig 5. Geographic distribution of dengue in Guatemala (2000–2016). A. Number of dengue cases by Department (incidence per × 10,000 inhabitants). B. Proportion of
samples tested that are positive for dengue. Darker colours denote higher values. (Population data from Wikipedia: Departments of Guatemala, 2011 population estimate).
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PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

been shown to increase dengue transmission in Central America [28]. The understanding of
the seasonality of dengue is an important epidemiological feature that enables timely control
efforts for maximize prevention effectiveness, and public health planning [29].
Globally, DENV1 has been the most common serotype detected since 2010, particularly in
Africa, Europe and the Western Pacific region [30]. However, DENV2 is the most commonly
identified serotype in the Americas, and these two serotypes combined account for most cases
in Guatemala. The infrequent detection of DENV4 is also mirrored from other settings, with
monotypic DENV4 being responsible for only four percent of outbreaks globally between
2010 and 2016 [30].
The large 2010 DENV2 epidemic in Guatemala coincided with simultaneous DENV2 out-
breaks across other Central and South American countries, with over 1.6 million reported
cases [17]. Neighbouring Honduras, for example, reported 66,814 cases, with 92.5% them due
to DENV2 [17]. The 2010 epidemic was followed by a lower incidence in the 5–20 and 20–60
age groups. The increased incidence in these age groups during epidemic years had been
reported during DENV1 and DENV2 outbreaks in Singapore [31]. The reduced incidence in
older age groups immediately after an epidemic [26] is thought to reflect herd immunity
afforded by previous infection with the same serotype. Although it is widely believed that
infection by a serotype confers lifelong immunity thanks to the development of serotype spe-
cific heterologous neutralising antibodies, recent studies describing homotypic re-infections in
the short and long term have challenged this concept [32],[33]. This suggests mechanisms
facilitating viral evasion of the host immune system, escaping recognition or inhibiting the
production of an antiviral state [34].
A large proportion of the samples tested here were dengue-negative, which was notably
high in 2015 and 2016, coinciding with the large chikungunya [35] and zika [36] epidemics in
the country. These diseases have a similar, unspecific clinical presentation [15]. Other com-
mon causes of fever compatible with dengue in the Latin American region include leptospiro-
sis, typhoid fever, influenza, Rickettsiosis and less common arboviruses such as Oropouche
and Mayaro [37, 38], which are likely to be investigated as potential dengue cases in surveil-
lance laboratories. Testing samples using syndromic panels that include a wider range of path-
ogens is currently too costly and labour intensive for routine implementation. However, co-
infections and parallel epidemics are well documented. For example, dengue and chikungunya
co-circulated in Guatemala, with a report indicating that both viruses were present in 32% of
samples in 2015 [35]. Thus, testing samples with more comprehensive diagnostic panels could
generate broader information on the co-circulation of pathogens and lead to improved multi-
disease surveillance. The development of multiplex diagnostics such as the CDC Trioplex [20]
has simplified testing algorithms and improved detection of acute co-infections. However,
serological tests still struggle to differentiate between DENV and Zika virus infections [39],
which may have inflated the number of probable dengue cases in our analysis.
Passive surveillance is the standard approach used for dengue surveillance worldwide. This
is due, in part, to the cost of implementing active case detection strategies for a disease with no
specific treatment and limited vaccine uptake. Passive surveillance for dengue in Latin Amer-
ica has underestimated the number of cases by a factor between 3.5 and 19 [14], and as a result,
the number of cases presented here is likely to underestimate the real burden of dengue in
Guatemala. This underreporting can lead to the underappreciation of the impact of dengue in
a region, leading to insufficient public health efforts to control transmission [40]. The reliance
on passive surveillance can also result in an over-representation of severe cases that require the
clinic and hospital services, adding bias to epidemiological data, particularly regarding disease
severity and patient outcomes. It has been estimated that ambulatory patients, who make up
the majority of cases in Latin America, incur the biggest economic cost [41], and that these

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PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

patients are often particularly under-represented by passive surveillance. Whilst most costs to
hospitalised patients are direct healthcare costs, the effect of dengue on non-hospitalised
patients incurs a large socio-economic costs, mainly due to reduced productivity [42], which
drives the overall cost of dengue to PAHO countries over US$ 3 billion [42].
The surveillance of dengue in Guatemala, and other countries in Latin America, would ben-
efit from studies to optimise thresholds of excess reporting, which could trigger outbreak
responses [43], and the initiation of active surveillance. Data from Australia, one of the few
high income countries with dengue transmission which utilises active surveillance, has shown
that rapid notification and case finding enables the minimisation of transmission by triggering
public health responses [44]. However, active surveillance systems are expensive and imple-
mentation would require a robust cost-benefit analysis [45].
A limitation of the geographical analysis presented here is that the selection of sentinel sites
and reporting centres reflects prior knowledge of areas considered to be endemic. Therefore,
our analysis represents a combination of purposely selected centres that historically have
reported more cases, which in turn resemble the health service referral patterns of patients and
health seeking behaviour; together with a higher incidence of dengue in specific geographical
areas. Our data therefore describes the long term trends of dengue, but does not provide reli-
able data of its prevalence across geographical areas. A further limitation of our data is that
diagnostic tests have changed over the years. These changes modify the proportion of tests that
are positive, particularly the implementation of RT-qPCR in 2010, which is more sensitive
than the cell culture-based methods it replaced, and are more efficient to process samples,
increasing testing capacity and allowing screening a larger numbers of cases. Therefore the
proportion of positive results would have varied over the years due to changes in tests perfor-
mance and the varied case definitions used to select the samples for screening.
Dengue is the most prevalent mosquito-transmitted infection worldwide and causes impor-
tant economic and human costs [45]. The analysis of countrywide surveillance data can pro-
vide insights into the present and past epidemiology of the disease at a country level [31, 46],
and is essential for the direction of future control and surveillance efforts. Such data are partic-
ularly important for future dengue vaccine roll out, and the understanding of the potential
effect of new interventions.

Supporting information
S1 Table. Incidence ratio of cases in each age group in epidemic years and subsequent
years, stratified by dengue serotype.
(DOCX)
S2 Table. Thresholds of the endemic cycle of dengue in Guatemala. Month: month of the
year. Cases expected: best fit model resembling the average numbers of monthly cases during
the period 2000–2016. Minimum expected: lower limit of values from the confidence intervals
of the model. Maximum expected: higher limit of values from the confidence intervals of the
model; Maximum expected denotes the threshold to declare an epidemic in a specific month.
Error: standard error. Note that these values allow declaration of an epidemic during any
month of the year at the country level when Maximum expected cases are above the higher
limit.
(DOCX)
S1 Fig. Percentage of dengue positive, probable and negative cases per year.
(TIF)

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PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

S2 Fig. Number of confirmed dengue cases by month and serotype. Cases detected by NS1
ELISA but not serotyped are designated DENV unknown
(TIF)
S3 Fig. Seasonality of dengue cases each year, stratified by serotype.
(TIF)
S4 Fig. Proportion of male cases per serotype per year.
(TIF)

Acknowledgments
The authors are grateful to Evelin Donis from the LNS for her support in accessing the data
and data management and to Guadalupe Guzmán, Pedro Kouri Institute in Havana, Cuba for
her support in interpreting the information.

Author Contributions
Conceptualization: Leticia del Carmen Castillo Signor, Thomas Edwards, Luis E. Escobar,
Luis E. Cuevas.
Data curation: Leticia del Carmen Castillo Signor, Yolanda Mencos.
Formal analysis: Thomas Edwards, Luis E. Escobar, Agnes Matope, Mariana Castaneda-Guz-
man, Emily R. Adams, Luis E. Cuevas.
Funding acquisition: Leticia del Carmen Castillo Signor, Luis E. Cuevas.
Investigation: Leticia del Carmen Castillo Signor, Luis E. Escobar, Yolanda Mencos, Luis E.
Cuevas.
Methodology: Luis E. Escobar, Mariana Castaneda-Guzman, Emily R. Adams, Luis E. Cuevas.
Project administration: Leticia del Carmen Castillo Signor, Yolanda Mencos.
Resources: Yolanda Mencos.
Supervision: Luis E. Cuevas.
Validation: Leticia del Carmen Castillo Signor, Yolanda Mencos, Luis E. Cuevas.
Visualization: Luis E. Cuevas.
Writing – original draft: Thomas Edwards, Luis E. Cuevas.
Writing – review & editing: Leticia del Carmen Castillo Signor, Luis E. Escobar, Agnes
Matope, Emily R. Adams, Luis E. Cuevas.

References
1. Brady OJ, Gething PW, Bhatt S, Messina JP, Brownstein JS, Hoen AG, et al. Refining the global spatial
limits of dengue virus transmission by evidence-based consensus. PLoS neglect trop dis. 2012; 6(8):
e1760.
2. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and
burden of dengue. Nature. 2013; 496:504. https://doi.org/10.1038/nature12060 PMID: 23563266
3. Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, et al. The global burden
of dengue: an analysis from the Global Burden of Disease Study 2013. Lancet Infect Dis. 2016; 16
(6):712–23. https://doi.org/10.1016/S1473-3099(16)00026-8 PMID: 26874619

PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0008535 August 19, 2020 10 / 12

PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

4. Shepard DS, Undurraga EA, Halasa YA, Stanaway JD. The global economic burden of dengue: a sys-
tematic analysis. Lancet Infect Dis. 2016; 16(8):935–41. https://doi.org/10.1016/S1473-3099(16)
00146-8 PMID: 27091092
5. World Health organization. Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control: New
Edition. Geneva: 2009.
6. Gubler DJ. Dengue, Urbanization and Globalization: The Unholy Trinity of the 21st Century. Trop Med
Health. 2011; 39(4 Suppl):3–11. https://doi.org/10.2149/tmh.2011-S05 PMID: 22500131
7. La Ruche G, Souares Y, Armengaud A, Peloux-Petiot F, Delaunay P, Despres P, et al. First two autoch-
thonous dengue virus infections in metropolitan France, September 2010. Euro Surveill. 2010; 15
(39):19676. PMID: 20929659
8. Gjenero-Margan I, Aleraj B, Krajcar D, Lesnikar V, Klobucar A, Pem-Novosel I, et al. Autochthonous
dengue fever in Croatia, August-September 2010. Euro surveill. 2011; 16(9).
9. Radke EG, Gregory CJ, Kintziger KW, Sauber-Schatz EK, Hunsperger EA, Gallagher GR, et al. Den-
gue outbreak in Key West, Florida, USA, 2009. Emerg infect dis. 2012; 18(1):135–7. https://doi.org/10.
3201/eid1801.110130 PMID: 22257471
10. Sim S, Hibberd ML. Genomic approaches for understanding dengue: insights from the virus, vector,
and host. Genome Biol. 2016; 17(1):38.
11. Nisalak A, Clapham HE, Kalayanarooj S, Klungthong C, Thaisomboonsuk B, Fernandez S, et al. Forty
years of dengue surveillance at a tertiary pediatric hospital in Bangkok, Thailand, 1973–2012. Am J
Trop Med Hyg. 2016; 94(6):1342–7. https://doi.org/10.4269/ajtmh.15-0337 PMID: 27022151
12. Wearing HJ, Rohani P. Ecological and immunological determinants of dengue epidemics. Proc Nat
Acad Sci U S A. 2006; 103(31):11802–7.
13. Goncalvez AP, Engle RE, St. Claire M, Purcell RH, Lai C-J. Monoclonal antibody-mediated enhance-
ment of dengue virus infection in vitro and in vivo and strategies for prevention. Proc Nat Acad Sci U S
A. 2007; 104(22):9422–7.
14. Sarti E, L’Azou M, Mercado M, Kuri P, Siqueira JB Jr., Solis E, et al. A comparative study on active and
passive epidemiological surveillance for dengue in five countries of Latin America. Int J Infect Dis. 2016;
44:44–9. https://doi.org/10.1016/j.ijid.2016.01.015 PMID: 26836763
15. Paixão ES, Teixeira MG, Rodrigues LC. Zika, chikungunya and dengue: the causes and threats of new
and re-emerging arboviral diseases. BMJ Glob Health. 2018; 3(Suppl 1):e000530–e. https://doi.org/10.
1136/bmjgh-2017-000530 PMID: 29435366
16. L’Huillier AG, Hamid-Allie A, Kristjanson E, Papageorgiou L, Hung S, Wong CF, et al. Evaluation of
Euroimmun anti-Zika virus IgM and IgG enzyme-linked immunosorbent assays for Zika virus serologic
testing. J Clin Microbiol. 2017; 55(8):2462–71. https://doi.org/10.1128/JCM.00442-17 PMID: 28566316
17. Brathwaite Dick O, San Martı́n JL, Montoya RH, del Diego J, Zambrano B, Dayan GH. The history of
dengue outbreaks in the Americas. Am J Trop Med Hyg. 2012; 87(4):584–93. https://doi.org/10.4269/
ajtmh.2012.11-0770 PMID: 23042846
18. Wilson ME, Chen L. H. Dengue in the Americas. Dengue Bulletin. 2002; 26:44–61.
19. Santiago GA, Vergne E, Quiles Y, Cosme J, Vazquez J, Medina JF, et al. Analytical and clinical perfor-
mance of the CDC real time RT-PCR assay for detection and typing of dengue virus. PLoS Neglect
Trop Dis. 2013; 7(7):e2311.
20. Santiago GA, Vázquez J, Courtney S, Matı́as KY, Andersen LE, Colón C, et al. Performance of the Trio-
plex real-time RT-PCR assay for detection of Zika, dengue, and chikungunya viruses. Nat Comms.
2018; 9(1):1391.
21. Hunsperger EA, Yoksan S, Buchy P, Nguyen VC, Sekaran SD, Enria DA, et al. Evaluation of commer-
cially available diagnostic tests for the detection of dengue Virus NS1 antigen and anti-dengue virus
IgM antibody. PLoS Neglect Trop Dis. 2014; 8(10):e3171.
22. Pan American Health Organisation. PLISA Health Information Platform for the Americas. 2019.
23. Ramos-Castañeda J, Barreto dos Santos F, Martı́nez-Vega R, Galvão de Araujo JM, Joint G, Sarti E.
Dengue in Latin America: systematic review of molecular epidemiological trends. PLoS Neglect Trop
Dis. 2017; 11(1):e0005224.
24. Mishra B, Turuk J, Sahu SJ, Khajuria A, Kumar S, Dey A, et al. Co-circulation of all four dengue virus
serotypes with concurrent infections in a single dengue season. Int J Infect Dis. 2016; 45:446.
25. Guzman MG, Alvarez M, Halstead SB. Secondary infection as a risk factor for dengue hemorrhagic
fever/dengue shock syndrome: an historical perspective and role of antibody-dependent enhancement
of infection. Arch Virol. 2013; 158(7):1445–59. https://doi.org/10.1007/s00705-013-1645-3 PMID:
23471635

PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0008535 August 19, 2020 11 / 12

PLOS NEGLECTED TROPICAL DISEASES Dengue in Guatemala

26. Reich Nicholas G, Shrestha S, King Aaron A, Rohani P, Lessler J, Kalayanarooj S, et al. Interactions
between serotypes of dengue highlight epidemiological impact of cross-immunity. J Royal Soc Inter-
face. 2013; 10(86):20130414.
27. Ferreira MC. Geographical distribution of the association between El Nino South Oscillation and dengue
fever in the Americas: a continental analysis using geographical information system-based techniques.
Geo health. 2014; 9(1):141–51.
28. Anyamba A, Chretien J-P, Britch SC, Soebiyanto RP, Small JL, Jepsen R, et al. Global disease out-
breaks associated with the 2015–2016 El Niño event. Sci Rep. 2019; 9(1):1930–. https://doi.org/10.
1038/s41598-018-38034-z PMID: 30760757
29. Sippy R, Herrera D, Gaus D, Gangnon RE, Patz JA, Osorio JE. Seasonal patterns of dengue fever in
rural Ecuador: 2009–2016. PLoS Neglect Trop Dis. 2019; 13(5):e0007360.
30. Guo C, Zhou Z, Wen Z, Liu Y, Zeng C, Xiao D, et al. Global epidemiology of dengue outbreaks in 1990–
2015: a systematic review and meta-analysis. Front Cell Infect Microbiol. 2017; 7(317).
31. Rajarethinam J, Ang L-W, Ong J, Ycasas J, Hapuarachchi HC, Yap G, et al. Dengue in Singapore from
2004 to 2016: cyclical epidemic patterns dominated by serotypes 1 and 2. Am J Trop Med Hyg. 2018;
99(1):204–10. https://doi.org/10.4269/ajtmh.17-0819 PMID: 29848407
32. Waggoner JJ, Balmaseda A, Gresh L, Sahoo MK, Montoya M, Wang C, et al. Homotypic dengue virus
reinfections in Nicaraguan children. J Infect Dis. 2016; 214(7):986–93. d https://doi.org/10.1093/infdis/
jiw099 PMID: 26984144
33. Forshey BM, Reiner RC, Olkowski S, Morrison AC, Espinoza A, Long KC, et al. Incomplete protection
against dengue virus type 2 re-infection in Peru. PLoS Neglect Trop Dis. 2016; 10(2):e0004398.
34. Uno N, Ross TM. Dengue virus and the host innate immune response. Emerg Microb Infect. 2018; 7
(1):167.
35. Edwards T, Signor LD, Williams C, Donis E, Cuevas LE, Adams ER. Co-infections with chikungunya
and dengue viruses, Guatemala, 2015. Emerg Infect Dis. 2016; 22(11):2003–5. https://doi.org/10.3201/
eid2211.161017 PMID: 27767914
36. Hennessey M, Fischer M, Staples JE. Zika Virus Spreads to New Areas—Region of the Americas, May
2015-January 2016. MMWR Morb Mortal Wkly Rep. 2016; 65(3):55–8. https://doi.org/10.15585/mmwr.
mm6503e1 PMID: 26820163
37. Moreira J, Bressan CS, Brasil P, Siqueira AM. Epidemiology of acute febrile illness in Latin America.
Clin Microbiol Infect. 2018; 24(8):827–35. d https://doi.org/10.1016/j.cmi.2018.05.001 PMID: 29777926
38. Mota MTdO, Ribeiro MR, Vedovello D, Nogueira ML. Mayaro virus: a neglected arbovirus of the Ameri-
cas. Future Virol. 2015; 10(9):1109–22.
39. Chao D-Y, Whitney MT, Davis BS, Medina FA, Munoz JL, Chang G-JJ. Comprehensive Evaluation of
Differential Serodiagnosis between Zika and dengue viral infections. J Clin Microbiol. 2019; 57(3):
e01506–18. https://doi.org/10.1128/JCM.01506-18 PMID: 30541932
40. Gibbons CL, Mangen M-JJ, Plass D, Havelaar AH, Brooke RJ, Kramarz P, et al. Measuring underre-
porting and under-ascertainment in infectious disease datasets: a comparison of methods. BMC public
health. 2014; 14:147. https://doi.org/10.1186/1471-2458-14-147 PMID: 24517715
41. Shepard DS, Coudeville L, Halasa YA, Zambrano B, Dayan GH. Economic impact of dengue illness in
the Americas. Am J Trop Med Hyg. 2011; 84(2):200–7. https://doi.org/10.4269/ajtmh.2011.10-0503
PMID: 21292885
42. Laserna A, Barahona-Correa J, Baquero L, Castañeda-Cardona C, Rosselli D. Economic impact of
dengue fever in Latin America and the Caribbean: a systematic review. Rev Panam Salud Publica.
2018; 42:e111. https://doi.org/10.26633/RPSP.2018.111 PMID: 31093139
43. Runge-Ranzinger S, McCall PJ, Kroeger A, Horstick O. Dengue disease surveillance: an updated sys-
tematic literature review. Trop Med Int Health. 2014; 19(9):1116–60. https://doi.org/10.1111/tmi.12333
PMID: 24889501
44. Viennet E, Ritchie SA, Faddy HM, Williams CR, Harley D. Epidemiology of dengue in a high-income
country: a case study in Queensland, Australia. Parasite Vector. 2014; 7(1):379. https://doi.org/10.
1186/1756-3305-7-379 PMID: 25138897
45. Oliveira LNdS, Itria A, Lima EC. Cost of illness and program of dengue: A systematic review. PloS one.
2019; 14(2):e0211401 https://doi.org/10.1371/journal.pone.0211401 PMID: 30785894
46. Racloz V, Ramsey R, Tong S, Hu W. Surveillance of dengue fever virus: a review of epidemiological
models and early warning systems. PLoS NTDs. 2012; 6(5):e1648.

PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0008535 August 19, 2020 12 / 12