*For correspondence:
Isabel.Rodriguez@ucsf.edu
Competing interests: The
authors declare that no
competing interests exist.
Funding: See page 12
Received: 24 January 2019
Accepted: 21 May 2019
Published: 23 May 2019
Reviewing editor: Jos WM van
der Meer, Radboud University
Medical Centre, Netherlands
Copyright Rodriguez-
Barraquer et al. This article is
distributed under the terms of
the Creative Commons
Attribution License, which
permits unrestricted use and
redistribution provided that the
original author and source are
credited.
Opportunities for improved surveillance
and control of dengue from age-specific
case data
Isabel Rodriguez-Barraquer1*, Henrik Salje2,3,4,5,6, Derek A Cummings6,7
1Department of Medicine, University of California, San Francisco, San Francisco,
United States; 2Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur,
Paris, France; 3CNRS, URA3012, Paris, France; 4Center of Bioinformatics,
Biostatistics and Integrative Biology, Institut Pasteur, Paris, France; 5Department of
Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins
University, Baltimore, United States; 6Department of Biology, University of Florida,
Gainesville, United States; 7Emerging Pathogens Institute, University of Florida,
Gainesville, United States
Abstract One of the challenges faced by global disease surveillance efforts is the lack of
comparability across systems. Reporting commonly focuses on overall incidence, despite
differences in surveillance quality between and within countries. For most immunizing infections,
the age distribution of incident cases provides a more robust picture of trends in transmission. We
present a framework to estimate transmission intensity for dengue virus from age-specific
incidence data, and apply it to 359 administrative units in Thailand, Colombia, Brazil and Mexico.
Our estimates correlate well with those derived from seroprevalence data (the gold standard),
capture the expected spatial heterogeneity in risk, and correlate with known environmental drivers
of transmission. We show how this approach could be used to guide the implementation of control
strategies such as vaccination. Since age-specific counts are routinely collected by masany
surveillance systems, they represent a unique opportunity to further our understanding of disease
burden and risk for many diseases.
DOI: https://doi.org/10.7554/eLife.45474.001
Introduction
A fundamental challenge of disease surveillance systems is how to transform data that is routinely
collected into useful, actionable evidence that can inform control interventions. Disease surveillance
systems typically focus on analyzing aggregate counts of cases over defined time periods to stratify
the risk of populations (WHO, 2012). However, the use of raw case counts, or incidences, as a mea-
sure of disease risk can frequently be misleading because the quality of surveillance often differs sig-
nificantly both across countries and within regions of a country making comparisons inappropriate.
Areas with more complete reporting of disease may inaccurately appear to have more disease sim-
ply because reported cases scale linearly with completeness of reporting. Surveillance systems may
change over time (e.g. improving in completeness over time) making comparisons difficult or even
impossible. These problems are particularly troubling when examining disease trends or ranking
regions according to their disease risk. Recent efforts have tried to improve quantification of disease
burden by pooling numerous sources of data. For example, disease mapping methods that combine
disease presence/absence data, environmental covariates and available incidence data (from cohort
or cross-sectional studies) have been used to predict spatial limits and global case counts for dis-
eases including malaria, dengue and several neglected tropical diseases. (Hay et al., 2009;
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 1 of 28
RESEARCH ARTICLE
Bhatt et al., 2013; Pigott et al., 2014; Hay et al., 2013) However, while these methods are success-
ful in identifying boundaries of endemic areas, the robustness of these approaches to quantify trans-
mission within endemic areas has not been validated.
For most immunizing infections (infections that generate long-lasting sterilizing immunity), sero-
logical surveys are regarded as the gold standard to measure the susceptible fraction and infer the
extent of transmission, as they provide a direct measure of the proportion of the population that has
been infected. This is particularly useful for diseases such as dengue, influenza or Zika, where the
asymptomatic to symptomatic ratios are large or unknown. Methods to estimate transmission
parameters from age-stratified serological data have been available for many years(Muench, 1959)
and have been used to analyze trends in transmission for multiple diseases including
measles (Grenfell and Anderson, 1985), hepatitis A(Schenzle et al., 1979),
dengue (Ferguson et al., 1999; Imai et al., 2015; Rodriguez-Barraquer et al., 2011; Rodrı´guez-
Barraquer et al., 2014), pertussis (Kretzschmar et al., 2010), influenza (Miller et al., 2010),
malaria (Bretscher et al., 2013) and chikungunya (Salje et al., 2016). While well conducted age-
stratified serological surveys can provide an unbiased measure of the susceptibility profile of a popu-
lation and transmission parameters, they are generally not part of routine surveillance activities and
are therefore only available for a limited number of locations and time points. For example, an
extensive review of the dengue literature published recently found only three population-based
serosurveys conducted in Brazil over the past 10 years, despite being the country that currently
reports the largest number of cases worldwide (Fritzell et al., 2018). Similarly, only one and three
studies were identified for the Philippines and Thailand, respectively, even though the burden of
dengue is very high (Fritzell et al., 2018). Thus, the picture provided by serology is very incomplete
and limited when trying to characterize transmission across time and space both within and between
countries.
While aggregated case counts can be misleading when quantifying disease risk, the age distribu-
tion of incident cases contains a lot of information on the age-specific susceptibility of the popula-
tion. Importantly, the age distributionof cases is also largely robust to under-reporting, facilitating
the comparison between locations or over time. By combining age-specific incidence data and
mechanistic models of how population immunity is acquired over time, it is possible to estimate key
transmission parameters and obtain a much more accurate picture of the local and global burdens
of disease. Since age-specific counts are routinely collected by surveillance systems as part of stan-
dard practice, they represent a missed opportunity to further our understanding of epidemic pat-
terns for many diseases.
Here, we use dengue virus as an example to illustrate how age-specific incidence data can be
used to quantify disease transmission and inform control interventions. Dengue is a relevant example
because, despite being the most widely spread mosquito-transmitted virus, large gaps remain in our
understanding of its global and local epidemiology (Fritzell et al., 2018). We present a model to
estimate the transmission intensity of dengue from age-specific incidence data, and apply it to sur-
veillance data from administrative units in four countries that suffer from endemic dengue transmis-
sion (Thailand, Colombia, Brazil and Mexico). We validate our estimates using serological data and
show that they correlate well with known environmental drivers of dengue transmission at subna-
tional level. Finally, we show how this approach could be used to guide the implementation of den-
gue control strategies such as vaccination.
Results
We estimated the average forces of infection (FOI) over the last 20 years for 148 administrative level
one units where age specific case data was available (Table 1, Figure 1B): 72/76 provinces of Thai-
land, 28/32 departments of Colombia, 21/27 states of Brazil, and 27/31 states of Mexico. These
administrative units comprise 90%, 99%, 90% and 91% of the population at risk of these countries
respectively. We also estimated forces of infection for 211 municipalities (administrative level two
units) of Colombia where at least 200 cases were reported over the period covered by the data.
The average FOI (the rate at which susceptible individuals that will become infected in a year by
any dengue serotype) was 0.096 in Thailand (95%CI 0.092-0.100), 0.132 (95%CI 0.128-0.136) in
Colombia, 0.124 (95%CI 0.116-0.128) in Brazil, and 0.052 (95%CI 0.048-0.056) in Mexico. This implies
that on average 9.6% of the susceptible population in Thailand gets infected every year by any of
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 2 of 28
Research article Epidemiology and Global Health
the circulating serotypes (1 eFOI ). Similarly, 12% of the susceptible population of Colombia and
Brazil, and 5% of the susceptible population of Mexico get infected yearly.
However, as expected, transmission intensity varied greatly within countries, ranging between
0.04 and 0.15 (coefficient of variation (CV) = 0.27) across provinces of Thailand; between 0.02 and
0.20 across departments of Colombia (CV = 0.37), between 0.02 and 0.24 across states of Brazil
(CV = 0.56); and between 0.01 and 0.092 across states of Mexico (CV = 0.45). Transmission was
highest in the North East of Brazil (average FOI of 0.152) and in the Caribbean region of Colombia
(average FOI of 0.156). There was also substantial heterogeneity within departments of Colombia.
The mean CV for 15 departments where we had estimates for more than 5 municipalities was 0.4.
For the 16 locations where we had access to both age-stratified serological data (the gold stan-
dard) and case data, we found good correlation between the estimates of the FOI derived from
both data sources (R2 = 0.73, 95% CI 0.51–0.87, Figure 1C). In contrast, we found no correlation
between recent incidence of dengue in these locations (the average yearly incidence over the most
recent 5 years of data) and the estimates of FOI derived from serological data (R2 = 0.002, Fig-
ure 1—figure supplement 2).
Since estimates of transmission intensity derived from seroprevalence data are only available for a
small number of locations, to further validate our method we also explored the association between
our estimates of the FOI for 211 Colombian municipalities (administrative level 2) with known envi-
ronmental drivers of dengue transmission including temperature, elevation, precipitation a published
metric of Aedes aegypti abundance and population density (Figure 2, Figure 2—figure supplement
1). Models were weighted by the number of cases used to estimate the FOI. On average, the FOI
increased by 0.006 (95% CI 0.004–0.007, R2 = 0.19) for each additional ˚C in temperature and, simi-
larly, it decreased by 0.005 (95% CI 0.003, 0.006, R2 = 0.21) for each 100 m increase in elevation
(Table 2). While population density was not associated with FOI estimates in unadjusted analyses, a
twofold increase in density was associated with a 0.007 (95% 0.004–0.009) increase in FOI in the
best fitting adjusted model. This model included elevation, population density and precipitation,
and explained 35% (95%CI 23–50%) of the variance.
In contrast, the recent incidence of dengue in these municipalities was not correlated with tem-
perature, elevation or Aedes aegypti abundance (R2 0.01, 0.01, and 0.00 respectively, Table 3). We
did find a negative association between population density and incidence, indicating a 6% (95%CI
2–10%) decrease in log incidence for each 2-fold increase in population density.
Application: Guiding dengue vaccination policy
The only available dengue vaccine (Dengvaxia) has been licensed for use in children 9 years or older
in 20 countries including Thailand, Brazil and Mexico. The WHO currently recommends confirmation
(by virology or serology) of prior dengue infection at the individual level before vaccinating individu-
als, and therefore there is interest in identifying populations with high seropositivity to target pre-
vaccination screening (WHO, 2018). In the absence of appropriate rapid serological assays that
would allow implementing this individual screening strategy, an alternative that has been discussed
is rolling out the vaccine in settings with 80% or greater seropositivity among 9 year olds. Using our
Table 1. Data sources used
Country
Years of data
available
Type of report
available
Type of
report used
No. admin units
analyzed* Source of dengue data Source of census data
Thailand 1985–2010 DHF DHF 72 (Bureau of Epidemiology, Department
of Disease Control, 2019)
(National Statistical Office
Thailand, 2017)
Colombia 2007–2012 DHF/DF All cases 28/211 (Instituto Nacional de Salud Colombia,
2016)
(DANE, 2019)
Brazil 1998–2015 DHF/DF† DHF 21 (Ministe´rio da Saude, 2017) (Instituto Brasileiro de
Geografia e Estadistica, 2017)
Mexico 2000–2009 DHF/DF All cases 27 (Direccion General de Epidemiologı´a,
2017)
(Instituto Nacional de
Estadistica y Geografia, 2016)
*Admin level one/Admin level two units with at least 150 cases reported
†Data from Brazil is on hospitalized dengue fever and dengue hemorrhagic fever cases.
DOI: https://doi.org/10.7554/eLife.45474.011
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Research article Epidemiology and Global Health
0.00 0.05 0.10 0.15 0.20 0.25
0.00
0.05
0.10
0.15
0.20
0.25
FOI (seroprevalence data)
F
O
I 
(c
a
s
e
 D
a
ta
)
Thailand (7)
Colombia (5)
Mexico (2)
Brazil (2)
R
2
=0.73 (95%CI 0.53-0.90)
0 10 30 50 0 10 30 50 0 10 30 50 0 10 30 50
In
c
id
e
n
c
e
Age (years)
Thailand (1) Thailand (2) Brazil (1) Brazil (2)
F
O
I 
(c
a
s
e
 D
a
ta
)
F
O
I 
(c
a
s
e
 D
a
ta
)
F
O
I 
(c
a
s
e
 D
a
ta
)
F
O
I 
(c
a
s
e
 D
a
ta
)
F
O
I 
(c
a
s
e
 D
a
ta
)
F
O
I 
(c
a
s
e
 D
a
ta
)
F
O
I 
(c
a
s
e
 D
a
ta
)
0.00
0.05
0.10
0.15
0.20
A.
B. C.
Figure 1. Estimating FOI from age-specific incidence data in Thailand, Colombia, Brazil and Mexico. (A) Examples of the age-specific incidence of
dengue observed in two settings with very high endemic transmission (Thailand 1- Udon Thani, Thailand; Brazil 1 Pernambuco, Brazil) and two settings
with lower and very low transmission (Thailand 2 = Chiang Mai, Thailand; Brazil 2 = Parana, Brazil). (B) Maps of our estimates of the FOI for the four
countries. (C) Correlation between our estimates of the force of infection, with estimates derived from age-stratified serological data (gold standard) for
16 settings where we had both types of data (Thailand: Rayong (Rodrı´guez-Barraquer et al., 2014); Bangkok (Imai et al., 2015); Ratchaburi
(Imai et al., 2015); Lop Buri, Narathiwat, Trang, Ayuttayah (Vongpunsawad et al., 2017). Brazil: Pernambuco (Rodriguez-Barraquer et al., 2011);
Salvador (Wilson et al., 2007). Colombia (unpublished). Mexico: Morelos (Amaya-Larios et al., 2014), Yucatan (Hladish et al., 2016). The locations of
the specific cities are shown in the maps in panel B.
DOI: https://doi.org/10.7554/eLife.45474.002
The following figure supplements are available for figure 1:
Figure supplement 1. Correlation between estimates of the FOI (derived from age-specific case data) and alternative metrics calculated directly from
age-specific incidence data.
DOI: https://doi.org/10.7554/eLife.45474.003
Figure supplement 2. Correlation between estimates of the FOI derived from seroprevalence data (the gold standard) and several metrics derived
from age-specific case data, for 16 spatial units where we had the two sources of data.
DOI: https://doi.org/10.7554/eLife.45474.004
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 4 of 28
Research article Epidemiology and Global Health
estimates of the FOI, we calculated the proportion of the population expected to be seropositive at
0.00
0.05
0.10
0.15
0.20
0.25
F
O
I
0 500 1500
20
50
100
200
500
1000
2000
Elevation (m)
In
c
id
e
n
c
e
 p
p
y
F
O
I
In
c
id
e
n
c
e
 p
p
y
0.00
0.05
0.10
0.15
0.20
0.25
F
O
I
15 20 25 30
20
50
100
200
500
1000
2000
Mean temperature
In
c
id
e
n
c
e
 p
p
y
0.00
0.05
0.10
0.15
0.20
0.25
F
O
I
0.00 0.04 0.08
20
50
100
200
500
1000
2000
Aedes abundance
In
c
id
e
n
c
e
 p
p
y
A. B.
R2=0.19 
(95%CI 0.07-0.32)
C.
R2=0.21
(95%CI 0.10-0.33)
R2=0.24
(95%CI 0.11-0.40)
R2=0.01
(95%CI -0.01-0.04)
R2=0.01
(95%CI -0.01-0.03)
R2=0.00
(95%CI -0.01-0.02)
Figure 2. Correlation between estimates of the FOI and known environmental drivers of dengue transmission. Top panels show correlation between
estimates of the FOI for 211 municipalities (administrative level 2) of Colombia and mean temperature (A), elevation (B) and Aedes abundance (C). Size
of symbols is proportional to the number of cases available to estimate the FOI. Bottom panels shows lack of correlation between environmental
drivers and recent incidence, the most commonly used metric of transmission intensity. R2 values reported were obtained by fitting weighted linear
regression models, with weights proportional to the number of cases used to derive the FOI estimate.
DOI: https://doi.org/10.7554/eLife.45474.005
The following figure supplements are available for figure 2:
Figure supplement 1. Correlation between estimates of the FOI and known environmental drivers of dengue transmission.
DOI: https://doi.org/10.7554/eLife.45474.006
Figure supplement 2. Heterogeneity in FOI within administrative level 1 units of Colombia.
DOI: https://doi.org/10.7554/eLife.45474.007
Table 2. Association between environmental variables and dengue force of infection for 211
municipalities in Colombia
Variable
Unadjusted Adjusted
Estimate 95% CI Estimate 95% CI
Elevation (per 100 m) 0.005 0.003,–0.006 0.007 0.008,–0.006
Mean temperature (per ˚C) 0.006 0.004, 0.007
Mean weekly precipitation 0.0001 0.000, 0.001 0.0005 0.0008, 0.0002
Population density 0.001 0.003, 0.001 0.007 0.004, 0.009
DOI: https://doi.org/10.7554/eLife.45474.012
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Research article Epidemiology and Global Health
age 9 years for each of the subnational units represented in our data. Our results suggest that only a
small minority of locations in Colombia and Brazil have > 80% seropositivity in this age group (Fig-
ure 3, Figure 3—source data 1). The expected proportion seropositive at 9 years of age ranged
between 0.35 and 0.75 in provinces of Thailand; 0.13 and 0.85 in departments of Colombia; 0 and
0.88 in states of Brazil; and 0.07 and 0.56 in states of Mexico. The seroprevalence was estimated to
be high enough in only 2/28 Colombian departments, 4/25 Brazilian states and none of the 72 Thai
Table 3. Association between environmental variables and the log incidence of dengue (over the
last 5 years) for 211 municipalities in Colombia.
Variable
Unadjusted Adjusted*
Estimate 95% CI Estimate 95% CI
Elevation (per 100 m) 0.02 0.03, 0 0.01 0.01, 0.03
Mean temperature (per ˚C) 0.01 0, 0.05
Mean weekly precipitation 0 0, 0 0.002 0.01, 0
Population density 0.04 0.07,–0.01 0.06 0.10, 0.02
* R2 of adjusted model: 0.04
DOI: https://doi.org/10.7554/eLife.45474.013
0.0
0.2
0.4
0.6
0.8
1.0
P
ro
p
o
rt
io
n
 s
e
ro
p
o
s
it
iv
e
 (
9
y
)
A
g
e
 f
o
r 
ro
u
ti
n
e
 v
a
c
c
in
a
ti
o
n
80
30
20
15
10
5
0
A. B. C. D.
Figure 3. Guiding vaccination policy. Estimated dengue seroprevalence at 9 years of for administrative level 1 units of Thailand, Colombia, Brazil and
Mexico Expected seroprevalence of dengue among children aged 9 years, derived from the FOI estimates (see Materials and methods for more
details), for administrative level 1 units of Thailand (A), Colombia (B), Brazil (C) and Mexico (D). For each country, administrative units were ranked by
their FOI. Dashed lines indicate 50% and 80% seroprevalence levels. Therefore, units above the 80% line are those where, according to the WHO-SAGE
recommendation from 2018, it might be reasonable target children aged 9 years old for vaccination. Units below the 50% line are those where
vaccination of this age-group would not be recommended. The axis on the right of the plot indicates the minimum age-group that would need to be
targeted in each location to ensure at least 80% seropositivity.
DOI: https://doi.org/10.7554/eLife.45474.008
The following source data is available for figure 3:
Source data 1. Estimated forces of infection and seroprevalences for 148 spatial units.
DOI: https://doi.org/10.7554/eLife.45474.009
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 6 of 28
Research article Epidemiology and Global Health
provinces or 27 Mexican states. Furthermore, even within the two Colombian departments that met
the 80% seroprevalence threshold, only 9/13 (70%) of the municipalities evaluated reached this level.
This proportion would probably be much lower had we considered all the municipalities in these
departments, as those excluded had, on average, lower FOI (Figure 2—figure supplement 2).
In locations where seroprevalence among 9 year olds was estimated to be less than 80%, we cal-
culated the age-group that could be targeted to ensure a seroprevalence > 80%. Our results sug-
gest that, to comply with the WHO-SAGE recommendations, it would be necessary to target
children 14 years of age or older in over 70% (108/148) of locations. Furthermore, in approximately
50% of the locations evaluated, it would be necessary to target individuals aged 18 years or older,
precluding school-based vaccination strategies.
Alternative metrics
Both the incidence and the mean adjusted incidence performed very poorly in ranking spatial units
compared to the FOI (Figure 1—figure supplement 1). In contrast, the proportion of incidence
under 10 years and the mean age of cases (to a lesser degree) performed quite well at ranking spa-
tial units within countries. However, none of these metrics performed as well as the FOI when com-
paring spatial units from different countries, and they did not correlate as well with the gold
standard (FOI estimates derived from seroprevalence data) (Figure 1—figure supplement 2).
Sensitivity analyses
Since different surveillance systems have different reporting practices, we compared FOI estimates
obtained when using data from severe cases alone (DHF or severe dengue) to estimates obtained
when all dengue cases were considered jointly (Figure 4). This comparison was limited to Colombia,
Brazil and Mexico because we only had DHF data from Thailand.
0.00 0.05 0.10 0.15 0.20 0.25
0.00
0.05
0.10
0.15
0.20
0.25
FOI (all cases)
F
O
I 
(s
e
v
e
re
/D
H
F
 c
a
s
e
s
)
Colombia (r=0.74)
Brazil (r=0.48)
Mexico (r=0.78)
0.00 0.05 0.10 0.15 0.20 0.25
0.00
0.05
0.10
0.15
0.20
0.25
FOI (seroprevalence data)
F
O
I 
(c
a
s
e
 d
a
ta
)
Thailand (7)
Colombia (5)
Mexico (2)
Brazil (2)
DHF/Severe dengue
All cases
A. B.
Figure 4. Sensitivity analysis: Comparing different types of data. (A) Correlation between estimates of the FOI obtained when using data from DHF/
severe dengue infections, vs. data from all cases combined. (B) Correlation between FOI estimates derived from different types of dengue data, with
estimates derived from age-stratified serological data (gold standard).
DOI: https://doi.org/10.7554/eLife.45474.010
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Research article Epidemiology and Global Health
In general, estimates derived from all cases tended to underestimate those obtained from DHF
cases, but the correlation between the estimates was good (Figure 4A) as well as the correlation
with the gold standard (Figure 4B). The correlation was highest for Colombia and Mexico (r = 0.74
and r = 0.78, respectively), but less so for Brazil (r = 0.48). However, 2/3 of the Brazilian locations
that showed discrepant results had the smallest number of DHF cases (n = 217 and 265) suggesting
that sample size may have played a role. Similarly, all the Colombian locations where there was a
large discrepancy had very low counts of DHF cases (n = 41, 55, and 48, respectively).
Discussion
In this paper, we use dengue virus as an example to illustrate how age-specific incidence data can
be used to quantify disease transmission and inform control interventions. We present a method to
estimate the FOI of dengue from age-specific incidence data, and use it to generate country-wide
subnational estimates for Thailand, Colombia, Brazil and Mexico. Our estimates correlate well with
those derived from seroprevalence data (the gold standard), capture the expected spatial heteroge-
neity in risk, and outperform all other metrics traditionally used by surveillance systems, such as the
crude incidence.
Most surveillance systems use crude case counts or incidences to describe temporal and spatial
trends of communicable diseases. Our results underscore the extent to which, for immunizing infec-
tions in endemic circulation, recent incidence may be a poor metric of transmission and may be mis-
leading when ranking spatial units (Figure 1—figure supplement 2). Immunity of the population in
high-transmission settings reduces the number of individuals that are susceptible to infection. As a
result, incidence in places where transmission intensity is lower, but people remain susceptible for a
longer period of time, may be roughly equivalent to that in higher transmission intensity areas. In
contrast, metrics such as the FOI, that quantify the risk among the susceptible population, better
reflect the underlying transmission potential. Since FOI estimates are derived from the age distribu-
tion of incidence, and not from the aggregate counts, they are more robust to differences in surveil-
lance efficiency and can be obtained with relatively small numbers of yearly cases.
Our estimates of the average transmission intensity of dengue in Thailand, Colombia, Brazil and
Mexico are consistent with large variation in dengue transmission between and within countries.
Spatial heterogeneity was substantial not only between, but also within administrative level one
units. While this heterogeneity is probably driven by multiple environmental, socio-economic and
demographic factors, our results suggest that as much as 35% of the variance may be explained by
differences in temperature/elevation and population density alone. Transmission intensity was high-
est in northeastern Brazil, northern Colombia and eastern Thailand. Northeastern Brazil was also the
region that experienced the highest incidence of Zika during the 2015/2016 epidemic, and this is
not surprising given that both viruses are transmitted by the same mosquito vector (A. aegypti)
(Lowe et al., 2018).
While age-stratified serological data remains the gold standard to quantify dengue transmission,
our results illustrate how inferences derived from age-specific surveillance data could be used to
inform control interventions such as vaccination. For example, the high transmission intensities (and
expected seroprevalences) found in Northeastern Brazil suggest that this region might be ideal to
implement pre-vaccination screening strategies. In addition, the large heterogeneity estimated in
Colombia suggests that decisions of where to deploy control interventions, including vaccines,
should be made at least at the municipal/district (administrative level 2) level. Since age-specific
counts are routinely collected by many surveillance systems, they represent a unique opportunity to
further our understanding of dengue burden and risk at spatial scales at which serological data is
rarely available. While here we focus on presenting average forces of infection over 20 years, the
same methods can also be used to estimate yearly forces of infection (Hoang Quoc et al., 2016;
Cummings et al., 2009).
There are several limitations of using age-specific surveillance data to estimate transmission
parameters of dengue. Of concern are differences in case definitions and reporting practices
between and within countries. While the case-definition of DHF is quite specific, milder forms of the
disease are characterized by a non-specific febrile syndrome. Therefore, data from locations that
report ‘all cases’ rather than more severe forms (DHF) are more likely to be misclassified, and could
be less useful to infer transmission histories. Nevertheless, our results and sensitivity analyses are
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 8 of 28
Research article Epidemiology and Global Health
encouraging and suggest that that even data from all dengue cases can be useful to infer transmis-
sion patterns.
Our model also makes several assumptions that may be questionable. It assumes that the age dis-
tribution of cases represents the age distribution of secondary infections, thus ignoring the potential
contribution of primary, tertiary and quaternary infections. It also assumes that risk of infection,
symptoms, and health seeking behavior are not age dependent, even though there is some evidence
that suggests that this may not be the case(Katzelnick et al., 2018; Guzma´n and Kourı´, 2002;
Thai et al., 2011). It assumes equal circulation of all serotypes, despite the known dominance of spe-
cific serotypes for extended periods of time in the Americas (Katzelnick et al., 2018; Teixeira et al.,
2009). Finally, it estimates transmission intensities over extended periods of time (20 years), averag-
ing over variations in the FOI that may occur at shorter time scales. Despite these simplifications, val-
idation of our estimates using age-stratified serological data from 16 locations is very encouraging,
as is the good correlation with known drivers of dengue transmission. While further validation is
desirable, it is important to note that some discrepancy between our estimates and those derived
from seroprevalence data is expected as the two sources of data do not represent exactly the same
period of time and location. For example, most of the serosurveys available were conducted in spe-
cific urban centers, while case data represents the full administrative unit.
Targeting control interventions against dengue and other communicable diseases requires good
understanding of when and where transmission is occurring. Careful analyses of age-specific inci-
dence data can provide very useful information to characterize transmission across time and space.
While here we focus on dengue, the same approach should be generalizable to other immunizing
infections including chikungunya and Zika. Open access to age-specific incidence data would greatly
enrich and enhance existing efforts to quantify trends in the global burden of disease.
Materials and methods
Data used
We used data on the yearly age-specific reported number of dengue cases for administrative level 1
units of Thailand, Colombia, Brazil and Mexico (Ministe´rio da Saude, 2017; Bureau of Epidemiol-
ogy, Department of Disease Control, 2019; Direccion General de Epidemiologı´a, 2017;
Instituto Nacional de Salud Colombia, 2016) as well as administrative level two units from Colom-
bia. The period of time available varied between countries, but ranged between 5 years in Colombia
and 25 years in Thailand. Where possible (Thailand and Brazil) we used age-specific case reports on
dengue hemorrhagic fever (DHF) for our analyses. However, for both Colombia and Mexico, we had
to use data on all reported dengue cases because the number of DHF cases reported by a substan-
tial number of spatial units (9/32 units in Colombia and 10/31 in mexico) were insufficient to estimate
the FOI. We also used population data from each administrative unit analyzed, available from the
national statistical office of each country. Information on the type (e.g. DHF vs. all dengue), source
and years of data used are provided in Table 1.
Statistical analyses
Estimating the force of infection
We estimated the average force of infection (FOI, l) of dengue, over the last 20 years, for each
administrative unit for which we had available data. The FOI is a metric used to characterize the
transmission intensity in a specific setting and estimates the per capita rate at which susceptible indi-
viduals are infected. Methods to estimate transmission intensity from age-specific incidence data
have been previously used to reconstruct the transmission history of measles and
dengue (Grenfell and Anderson, 1985; Cummings et al., 2009). Briefly, these methods rely on the
fact that, for immunizing infections, accumulation of immunity shapes the age distribution of future
cases. In settings with high endemic transmission, incident cases are expected to be concentrated in
younger age groups, as adults are likely to be already immune (Figure 1A). In contrast, in places
where there is less population immunity, the age distribution of cases is more likely to resemble the
age distribution of the population itself, with cases in in both children and adult populations.
Methods to estimate dengue forces of infection from case data have been applied to settings
where dengue is thought to be close to endemic circulation (Cummings et al., 2009; Imai et al.,
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 9 of 28
Research article Epidemiology and Global Health
2016). These methods generally rely on the cumulative incidence proportion, and therefore assume
that all individuals are infected by dengue at some point in their lifetime. They also often assume
that the distribution of cases (of dengue hemorrhagic fever cases in particular) is representative of
the distribution of secondary cases. Here, we extend these methods to accommodate settings where
transmission hazards are lower or where dengue may have been more recently introduced. We do
this by modeling directly the age-specific incidence of cases, rather than the cumulative incidence
proportion.
The fraction of the population susceptible to all dengue serotypes at age a and t, x(a,t) is given
by,
x a; tð Þ ¼ e
R a
0
4l ttð Þdt
: (1)
where l(t) is the average FOI per serotype at time t and 4l(t) is the total FOI assuming four circulat-
ing serotypes. The proportion of individuals of age a who have been infected with only serotype at
time t, but are still susceptible to all other serotypes is denoted z1(a,t) and is given by:
z1 a; tð Þ ¼ 4 e

R a
0
3l ttð Þdt
 
1 e

R a
0
l ttð Þdt
 
: (2)
Assuming that the age-specific incidence of cases is representative of the distribution of second-
ary infections, the expected incidence rate among individuals age a at time t is given by
I a; tð Þ ¼ 3l tð Þz1 a; tð Þ (3)
and the expected reported number of cases is
a; tð Þ ¼ I a; tð ÞP a; tð Þf tð Þð Þ; (4)
where P(a,t) is the size of the population aged a at time t, and F(t) represents a time specific
reporting rate/scaling factor.
Likelihood and estimation
Assuming that the observed age-specific case counts C(a,t) follow a Poisson distribution, the likeli-
hood of the data can be expressed as
L Cjl;fð Þ ¼
t
Y
a
Y
L a;tð ÞC a;tð ÞeL a;tð Þð Þ
C a;tð Þ! ; (5)
We fit the model in a Bayesian Markov chain Monte Carlo (MCMC) framework using the RStan
package in R (Stan development Team, 2019) (The R Foundation, 2019). Both the annual hazards
of infection (l) and the reporting rates (F) were estimated on a logit scale using wide priors (Normal
distribution with mean 0 and standard deviation of 1000). We simulated four independent chains,
each of 30000 iterations and discarded the firs 10000 iterations as warm-up. We assessed conver-
gence visually and using Rubin’s R statistic. We obtained 95% credible intervals from the 2.5% and
97.5% percentiles of the posterior distributions. Code to implement the model is available at
https://github.com/isabelrodbar/dengue_foi. (Rodriguez-Barraquer, 2019; copy archived at https://
github.com/elifesciences-publications/dengue_foi).
A limitation of this approach is that, due to the large number (often in the thousands) that are
characteristic of the data for some settings, the estimated confidence intervals produced are
extremely narrow and do not reflect the underlying uncertainty adequately. The observed counts
can also be assumed to follow a negative binomial distribution to account for some overdispersion.
Parameters estimated
Since it is known that the FOI has varied substantially over time in many of the settings considered,
we allowed l(t) to vary as a function of time. To limit the number of parameters estimated, we
assumed constant l(t) for periods of 20 years. Thus, if for a given setting we were estimating hazards
for the period 1935–2015, we assumed piecewise-constant l(t)s for the periods 1935–1954, 1955–
1974, 1975–1994, 1995–2015. Given the objective of this study was to characterize recent
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 10 of 28
Research article Epidemiology and Global Health
transmission in endemic settings, we focused our results on the estimate of the average l(t) for the
most recent 20-year period but present other estimates in the supplementary material (Appendix 1-
Figure 1). Rather than reporting l(t), we focus on reporting the total FOI (4l(t)).
Estimating the proportion expected to be seropositive at a given age
Using our estimates of the average FOI, we estimated the proportion of individuals expected to be
seropositive by age y(x) as:
yðxÞ ¼ 1 xðaÞ
¼ 1 e4la
where x(a) is the proportion of the population susceptible at age a and l is the average FOI per
serotype (assuming four serotypes circulating). Since the vaccine has been registered for use in chil-
dren 9 years of age or older, we report the proportion of individuals expected to be seropositive by
age 9 years for each of the settings.
Estimating the minimum age to achieve a given level of seropositivity
Given that the WHO recommended using this vaccine in places where at least 80% of the target age
group is seropositive, we estimated the minimum age at which this level of seropositivity is expected
for each of the settings. For a given level of transmission l it is possible to estimate the minimum
age (A) at which a given level of seropositivity (s) is expected as:
A sð Þ ¼
log 1 sð Þ
4l
Validation and sensitivity analyses
We validated our estimates of the FOI by comparing them to estimates obtained from age-stratified
serological data (the gold standard) for 16 locations for which both serologic and age-specific case
data was available. Methods to estimate forces of infection from seroprevalence data have been pre-
viously described (Ferguson et al., 1999; Rodriguez-Barraquer et al., 2011).
Since dengue transmission is known to be highly spatially heterogeneous, we also correlated our
administrative level two estimates for Colombia with known environmental drivers of dengue trans-
mission: temperature, elevation, population density and a published composite metric of A. aegypti
abundance (Siraj et al., 2018).
As stated above, a key assumption of this model is that the age distribution of cases represents
the age distribution of secondary infections. Data from Thailand has consistently suggested that the
majority of dengue hemorrhagic fever (DHF) cases arise from secondary infections (Burke et al.,
1988) and therefore we limited our analysis to reports of DHF where possible (Thailand, Brazil).
However, for Colombia and Mexico we used data from severe and non severe cases because the
severe dengue data alone was too sparse. To assess the impact of the data type, we compared esti-
mates obtained from DHF/severe cases alone, to those obtained when all dengue cases were
considered.
Alternative metrics
Since estimating the FOI requires fitting parametric models to the data, we explored whether alter-
native summary metrics, computed directly from the age-specific case data, could be equally useful
in ranking spatial units within and across countries. The metrics considered included some that are
commonly used by surveillance systems such as a) crude incidence and b) standardized mean inci-
dence, but also alternative metrics that are not commonly used such as c) mean age of cases, d)
cumulative proportion of incidence occurring by age 10 years and e) age at peak incidence. For
each spatial unit, we computed these metrics using the most recent 5 years of data available.
Application: guiding dengue vaccination policy
The first dengue vaccine has been licensed for use in children over 9 years of age in 20 countries.
Due to uncertainty regarding the vaccine’s benefits and risks in individuals who haven’t been previ-
ously infected by dengue, the WHO’s scientific advisory group of experts (SAGE) committee recom-
mended in April 2016 that this vaccine only be used in settings with known high endemicity, defined
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 11 of 28
Research article Epidemiology and Global Health
as places where seroprevalence is greater than 70% in the target vaccination age-group
(SAGE, 2016), and should not be used in places where seroprevalence is under 50%.
This recommendation was later revised, and the WHO now recommends that individuals should
be tested for dengue antibodies prior to vaccination, and the vaccine should only be given to indi-
viduals who have been infected by dengue in the past (WHO, 2018). In the absence of appropriate
serological assays that would allow for pre-vaccination screening, an alternative that has been dis-
cussed is deploying the vaccine in settings were seroprevalence is 80% or greater. These recommen-
dations pose challenges to countries wanting to implement the vaccine, as they require detailed
knowledge of the epidemiology of dengue. Specifically, they require knowledge of the population
seroprevalence against dengue at subnational levels, even though such data is not available.
In order to provide information useful to countries considering deploying the vaccine according
to the WHO recommendations, we used our estimates of the FOI to calculate the proportion of the
population expected to be seropositive at age 9 years for of the subnational units represented in
our data. We also estimated the minimum age group expected to have a seroprevalence of 80% or
greater. In collaboration with the MRC center for Outbreak Analysis and Modeling, these estimates
were made available online in June 2017 (https://mrcdata.dide.ic.ac.uk/_dengue/dengue.php) as a
tool to help countries deciding where to target vaccination.
Acknowledgements
We thank the Ministry of Public Health (Thailand), the Instituto Nacional de Salud (INS, Colombia),
the Ministerio da Saude (Brazil) and the Direccion General de Epidemiologı´a. Sistema Nacional de
Vigilancia Epidemiolo´gica (Sinave, Mexico), for making the data necessary for these analyses publicly
available. We also thank Sompong Vongpunsawad for sharing the raw data of the serological studies
conducted in Thailand. We thank Neil Ferguson, Natsuko Imai and Wes Hinsley for valuable input
and for making these estimates publicly available through the Global Dengue Transmission Map
website.
Additional information
Funding
Funder Grant reference number Author
National Institutes of Health R01AI114703-01 Derek A Cummings
European Research Council 804744 Henrik Salje
The funders had no role in study design, data collection and interpretation, or the
decision to submit the work for publication.
Author contributions
Isabel Rodriguez-Barraquer, Conceptualization, Data curation, Formal analysis, Investigation,
Visualization, Methodology, Writing—original draft, Project administration, Writing—review and
editing; Henrik Salje, Formal analysis, Writing—original draft, Writing—review and editing; Derek A
Cummings, Conceptualization, Data curation, Formal analysis, Writing—original draft, Writing—
review and editing
Author ORCIDs
Isabel Rodriguez-Barraquer https://orcid.org/0000-0001-6784-1021
Henrik Salje https://orcid.org/0000-0003-3626-4254
Decision letter and Author response
Decision letter https://doi.org/10.7554/eLife.45474.031
Author response https://doi.org/10.7554/eLife.45474.032
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 12 of 28
Research article Epidemiology and Global Health
Additional files
Supplementary files
. Transparent reporting form
DOI: https://doi.org/10.7554/eLife.45474.014
Data availability
The code to implement the model described in our study is available at https://github.com/isabel-
rodbar/dengue_foi (copy archived at https://github.com/elifesciences-publications/dengue_foi). The
case data used for the analyses is publicly available and can be accessed through the following links
links: Brazil- http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sih/cnv/mruf.def; Thailand - http://www.
boe.moph.go.th/boedb/surdata/index.php; Colombia - http://www.ins.gov.co/lineas-de-accion/Sub-
direccion-Vigilancia/sivigila/Paginas/vigilancia-rutinaria.aspx and https://www.sispro.gov.co/Pages/
Home.aspx; Mexico - http://www.epidemiologia.salud.gob.mx/anuario/html/anuarios.html.
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WHO. 2012. Global Strategy for Dengue Prevention and Control, 2012–2020: World Health Organization.
https://www.who.int/denguecontrol/9789241504034/en/.
WHO. 2018. Dengue vaccine: who position paper, September 2018 – Recommendations. Vaccine. DOI: https://
doi.org/10.1016/j.vaccine.2018.09.063
Wilson S, Booth M, Jones FM, Mwatha JK, Kimani G, Kariuki HC, Vennervald BJ, Ouma JH, Muchiri E, Dunne
DW. 2007. Age-adjusted Plasmodium falciparum antibody levels in school-aged children are a stable marker of
microgeographical variations in exposure to Plasmodium infection. BMC Infectious Diseases 7:67. DOI: https://
doi.org/10.1186/1471-2334-7-67, PMID: 17603885
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Appendix 1
DOI: https://doi.org/10.7554/eLife.45474.015
●
●
●
●●●0.0
0.1
0.2
0.3
0.4 alagoas
●●
●
●
●
●
amapa
●
●
●●●●
bahia
●
●
●●
●
●
ceara
●
●
●
●●●0.0
0.1
0.2
0.3
0.4 espiritosanto
●
●
●●
●
●
goias
●
●
●●
●
●
maranhao
●
●
●●●●
matogrosso
●
●
●
●●
0.0
0.1
0.2
0.3
0.4 matogrossosul
●
●
●●●
●
minasgerais
●
●●
●
●●
para
●
●
●●●
paraiba
●
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4 parana
●
●
●●●
●
pernambuco
●
●
●
●
●
●
piaui
●
●
●●●●
riodejaneiro
●
●
●●●
●
0.0
0.1
0.2
0.3
0.4 riograndedonorte
●
●
●●●
●
1940 1960 1980 2000
rondonia
●
●
●
●
●
●
1940 1960 1980 2000
saopaulo
●●
●●
●
●
1940 1960 1980 2000
sergipe
●
●
●●
●
●
0.0
0.1
0.2
0.3
0.4
1940 1960 1980 2000
tocantins
Brazil
  
  
  
  
  
  
  
F
o
rc
e
 o
f 
In
fe
c
ti
o
n
Year
Appendix 1—figure 1. Time varying Force of Infection Estimates.
DOI: https://doi.org/10.7554/eLife.45474.016
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 16 of 28
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●●
●
●
●0.00
0.05
0.10
0.15
0.20
0.25 antioquia
●
●
●
●
●
arauca
●
●
●
●●
atlantico
●
●
●
●
●
bolivar
●
●
●
●
●
0.00
0.05
0.10
0.15
0.20
0.25 boyaca
●
●●
●
●
caldas
●
●
●
●
●
caqueta
●
●
●
●
●
casanare
●
●
●
●●0.00
0.05
0.10
0.15
0.20
0.25 cauca
●●
●
●
●
cesar
●●
●
●
●
choco
●
●
●
●
●
cordoba
●
●
●
●●0.00
0.05
0.10
0.15
0.20
0.25 cundinamarca
●
●
●
●●
guaviare
●
●
●
●
●
huila
●
●
●
●●
laguajira
●
●
●
●●
0.00
0.05
0.10
0.15
0.20
0.25 magdalena
●
●
●
●
●
meta
●
●
●●
●
narino
●
●
●
●
●
nortedesantander
●
●
●
●
●
0.00
0.05
0.10
0.15
0.20
0.25 putumayo
●
●
●
●●
quindio
●
●
●
●
●
risaralda
●
●
●
●
●
santander
●
●
●
●
●0.00
0.05
0.10
0.15
0.20
0.25
1950 1970 1990 2010
sucre
●
●
●
●
●
1950 1970 1990 2010
tolima
●
●
●
●
●
1950 1970 1990 2010
valledelcauca
●
●
●
●
●
1950 1970 1990 2010
vichada
Colombia
F
o
rc
e
 o
f 
In
fe
c
ti
o
n
Year
Appendix 1—figure 2. Time varying Force of Infection Estimates, Colombia.
DOI: https://doi.org/10.7554/eLife.45474.017
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 17 of 28
Research article Epidemiology and Global Health
●●
●
●0.00
0.05
0.10
0.15
0.20
0.25 baja california sur
●
●
●
●
campeche
●
●
●
●
chiapas
●
●
●
●
coahuila
●
●
●●0.00
0.05
0.10
0.15
0.20
0.25 colima
●●
●
durango
●
●
●
●
guanajuato
●
●
●●
guerrero
●
●
●0.00
0.05
0.10
0.15
0.20
0.25 hidalgo
●
●
●●
jalisco
●
●●
mexico
●
●
●●
michoacan
●
●
●●0.00
0.05
0.10
0.15
0.20
0.25 morelos
●
●
●●
nayarit
●
●
●
●
nuevo leon
●
●
●
●
oaxaca
●
●
●●0.00
0.05
0.10
0.15
0.20
0.25 puebla
●
●
●
●
queretaro
●
●
●●
quintana roo
●
●
●●
san luis potosi
●
●
●●0.00
0.05
0.10
0.15
0.20
0.25 sinaloa
●
●
●●
sonora
●
●
●●
tabasco
●
●
●●
1940 1960 1980 2000
tamaulipas
●
●
●●0.00
0.05
0.10
0.15
0.20
0.25
1940 1960 1980 2000
veracruz
●
●
●
1940 1960 1980 2000
yucatan
●
●
●●
1940 1960 1980 2000
zacatecas
Mexico
F
o
rc
e
 o
f 
In
fe
c
ti
o
n
Year
Appendix 1—figure 3. Time varying Force of Infection Estimates, Mexico.
DOI: https://doi.org/10.7554/eLife.45474.018
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 18 of 28
Research article Epidemiology and Global Health
●●
●
●
●
0.0
0.1
0.2
0.3
0.4 angthong
Thailand
F
o
rc
e
 o
f 
In
fe
c
ti
o
n
Year
●
●
●
●
●
● buriram
●
●
●
chachoengsao
●
●
●
●
●
chainat
●
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4 chaiyaphum
●
●
●
chanthaburi
●
●
●
chiangmai
●
●
●
●
●
●
chiangrai
●
●
●
0.0
0.1
0.2
0.3
0.4
chonburi
●
●
●
●
chumphon
●
●
●
●
●
●
kalasin
●
●
●
●
●
kamphaengphet
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4
kanchanaburi
●
●
●
●
●
●
khonkaen
●
●
●
krabi
●
●
●
●
krungthep
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4 lampang
●
●
●
●
lamphun
●
●
●
●
●
loei
●
●
●
●●
lopburi
●
●
●
●
0.0
0.1
0.2
0.3
0.4 maehongson
●
●
●
●
●
mahasarakham
●
●
●
●
●
nakhonnayok
●
●
●
●
nakhonpathom
●
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4
1940 1960 1980 2000
nakhonphanom
●
●
●
●
●
●
1940 1960 1980 2000
nakhonratchasima
●
●●
●
1940 1960 1980 2000
nakhonsawan
●
●
●
●
●
1940 1960 1980 2000
nakhonsithammarat
Appendix 1—figure 4. Time varying Force of Infection Estimates, Thailand (1).
DOI: https://doi.org/10.7554/eLife.45474.019
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 19 of 28
Research article Epidemiology and Global Health
●●
●
●●
●
0.0
0.1
0.2
0.3
0.4 nan
Thailand
F
o
rc
e
 o
f 
In
fe
c
ti
o
n
Year
●
●
●
●
narathiwat
●
●
●
●●
●
nongkhai
●
●
●
●
nonthaburi
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4 pathumthani
●
●
●
●
●
pattani
●
●
●
●
phangnga
●
●
●
phatthalung
●
●
●
●
0.0
0.1
0.2
0.3
0.4 phayao
●
●
●
●
●
phetchabun
●
●
●
● phetchaburi
●
●
●
●
phichit
●
●●
●
0.0
0.1
0.2
0.3
0.4 phitsanulok
●
●
●
●
phrae
●
●●
●
●
phranakhonsiayutthaya
●
●
●
phuket
●
●
●
0.0
0.1
0.2
0.3
0.4 prachinburi
●
●●
●
prachuapkhirikhan
●
●
●
●
●
●
ranong
●
●
●●
ratchaburi
●
●
●
0.0
0.1
0.2
0.3
0.4 rayong
●
●
●
●
●
roiet
●
●
●
●
●
●
sakonnakhon
●
●
●
●
samutprakan
●
●
●
●
0.0
0.1
0.2
0.3
0.4
1940 1960 1980 2000
samutsakhon
●
●
●
●
●
1940 1960 1980 2000
samutsongkhram
●
●
●
●
●
1940 1960 1980 2000
saraburi
●
●
●
●
1940 1960 1980 2000
satun
Appendix 1—figure 5. Time varying Force of Infection Estimates, Thailand (2).
DOI: https://doi.org/10.7554/eLife.45474.020
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 20 of 28
Research article Epidemiology and Global Health
●●
●
●
●
●
0.0
0.1
0.2
0.3
0.4 singburi
Thailand
  
  
  
  
  
  
  
  
  
  
  
  
  
F
o
rc
e
 o
f 
In
fe
c
ti
o
n
Year
●
●
●
●
●
sisaket
●
●
●
songkhla
●
●
●
●
●
sukhothai
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4 suphanburi
●
●
●
●
●
suratthani
●
●
●
●
●
●
surin
●
●
●
tak
●
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4 trang
●
●
●
●
trat
●
●
●
●
●●
ubonratchathani
●
●
●
●
●
●
udonthani
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4
1940 1960 1980 2000
uthaithani
●
●
●
●
●
1940 1960 1980 2000
uttaradit
●
●
●
●
●
1940 1960 1980 2000
yala
●
●
●
●
●
1940 1960 1980 2000
yasothon
Appendix 1—figure 6. Time varying Force of Infection Estimates, Thailand (3).
DOI: https://doi.org/10.7554/eLife.45474.021
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 21 of 28
Research article Epidemiology and Global Health
Appendix 2
DOI: https://doi.org/10.7554/eLife.45474.015
Appendix 2—figure 1. Selected model fits (1).
DOI: https://doi.org/10.7554/eLife.45474.023
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 22 of 28
Research article Epidemiology and Global Health
Appendix 2—figure 2. Selected model fits (2).
DOI: https://doi.org/10.7554/eLife.45474.024
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 23 of 28
Research article Epidemiology and Global Health
Appendix 2—figure 3. Selected model fits (3).
DOI: https://doi.org/10.7554/eLife.45474.025
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 24 of 28
Research article Epidemiology and Global Health
Appendix 2—figure 4. Selected model fits (4).
DOI: https://doi.org/10.7554/eLife.45474.026
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 25 of 28
Research article Epidemiology and Global Health
Appendix 2—figure 5. Selected model fits (5).
DOI: https://doi.org/10.7554/eLife.45474.027
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 26 of 28
Research article Epidemiology and Global Health
Appendix 2—figure 6. Selected model fits (6).
DOI: https://doi.org/10.7554/eLife.45474.028
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 27 of 28
Research article Epidemiology and Global Health
Appendix 2—figure 7. Selected model fits (7).
DOI: https://doi.org/10.7554/eLife.45474.029
Rodriguez-Barraquer et al. eLife 2019;8:e45474. DOI: https://doi.org/10.7554/eLife.45474 28 of 28
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