Mapping global variation in dengue transmission intensity and

implications for control policy planning

Authors: Lorenzo Cattarino1*, Isabel Rodriguez-Barraquer2, Natsuko Imai1, Derek A. T.

Cummings3 and Neil M. Ferguson1

Affiliations:

1MRC Centre for Global Infectious Disease Analysis. School of Public Health, Imperial College

London, Norfolk Place, London W2 1PG, UK.

2 Department of Medicine, University of California San Francisco, San Francisco, CA 94110,

USA.

3Department of Biology and Emerging Pathogens Institute, University of Florida, Post Office

Box 100009, Gainesville, FL 32610, USA.

*Corresponding author. Email: l.cattarino@imperial.ac.uk

Overline: DENGUE

One Sentence Summary: A downloadable high-resolution map of global dengue transmission

intensity helps predict the potential impact of control strategies.

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Abstract: Intervention planning for dengue requires reliable estimates of dengue transmission

intensity. However, current maps of dengue risk provide estimates of disease burden or the

boundaries of endemicity rather than transmission intensity. We therefore developed a global

high-resolution map of dengue transmission intensity by fitting environmentally-driven

geospatial models to geolocated force of infection estimates derived from cross-sectional

serological surveys and routine case surveillance data. We assessed the impact of interventions

on dengue transmission and disease using Wolbachia-infected mosquitoes and the Sanofi-Pasteur

vaccine as specific examples. We predicted high transmission intensity in all continents

straddling the tropics, with hot spots in South America (Colombia, Venezuela and Brazil), Africa

(western and central African countries), and South East Asia (Thailand, Indonesia and the

Philippines). We estimated that 105 (95% confidence interval [CI] 95-114) million dengue

infections occur each year with 51 (95% CI 32-66) million febrile disease cases. Our analysis

suggests that transmission-blocking interventions such as Wolbachia, even at intermediate

efficacy levels (50% transmission reduction), might reduce global annual disease incidence by

up to 90%. The Sanofi-Pasteur vaccine, targeting only seropositive recipients, might reduce

global annual disease incidence by 20-30%, with the greatest impact in high transmission

settings. The transmission intensity map presented here, and made available for download, can

help further assessment of the impact of dengue control interventions and prioritization of global

public health efforts.

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Introduction

Dengue is an acute viral infection transmitted between humans by Aedes mosquitos. The

virus is responsible for a substantial burden of disease across the tropics and sub-tropics (1).

Secondary infections with one of the four dengue serotypes (DENV 1-4) are on average more

severe than primary infections, an observation thought to be explained by heterotypic antibody-

dependent enhancement (2). Dengue prevention options have historically been limited and

largely restricted to insecticide-based mosquito population suppression (3), but new

opportunities are now offered by the first licensed dengue vaccine (4, 5) and vector control

measures such as Wolbachia (6). However, these new interventions have imperfect efficacy,

meaning their impact will depend on local dengue transmission intensity (7, 8) as routinely

quantified by the force of infection (per capita rate at which susceptible individuals become

infected with a pathogen) or the reproduction number (average number of secondary cases

resulting from the introduction of a single infectious individual into a susceptible population).

Given that dengue transmission intensity is highly spatiotemporally variable and this variation is

poorly characterized for much of the world, optimally targeting of interventions against dengue

represents a major public health challenge.

Modern geostatistical methods provide powerful tools for characterizing the relationships

between geolocated outcome variables of interest and possible environmental, social, or

demographic drivers. Their application to vector-borne diseases has grown in popularity, but for

dengue has hitherto been restricted to modelling the local probability of reported cases (or

occurrence) of dengue rather than local transmission intensity (9). Probability of occurrence is

well-suited to delineating the boundaries of endemicity of dengue, but not for distinguishing

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between different degrees of transmission intensity within endemic regions. However, predictive

modelling of dengue transmission intensity is particularly challenging as individuals develop

lifelong homotypic immunity upon infection with any of the serotypes which limits the

maximum number of dengue infections an individual can have to four. This causes incidence

(and disease burden) to saturate at high transmission intensities, limiting our ability to infer the

latter from the former (10, 11).

Lack of a fine scale geographical characterization of dengue transmission intensity has

hindered assessment of the impact of candidate control strategies such as Wolbachia and

vaccination. To date, analyses of intervention impact have been restricted to single geographic

settings or have provided impact estimates for a range of transmission intensities without

mapping those onto specific geographies (5, 8). Here we quantify dengue transmission intensity

globally at high spatial resolution and project the likely impact of control measures on dengue

transmission and disease burden, drawing on examples from a recent vector control strategy,

namely the release of Wolbachia-infected mosquitoes and application of the first licensed dengue

vaccine.

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Results

Global predictions of dengue force of infection

Under the hypothesis that climate drives vector carrying capacity and competence, we

fitted a random forest model to a dataset of first administrative unit-level geolocated estimates of

the average force of infection (FOI) of dengue (Fig. 1), derived from age-stratified

seroprevalence or case notification data (table S1-S2), using a set of environmental explanatory

variables (fig. S1-S8, table S3) (11-14). The resulting FOI map predicted areas of high

transmission in all continents straddling the tropics: South America (Colombia, Venezuela, and

North-Eastern Brazil), Africa (western and central African countries), South East Asia (Thailand,

Indonesia, and the Philippines), and Australasia (Papua New Guinea) (Fig. 2A). Our model had

high in-sample predictive performance as indicated by the strong correlation between data and

predictions (coefficient of determination, R2 = 0.99) (table S4-S5, fig. S9-S10). Out-of-sample

predictive performance for a complete random validation set was good (R2 = 0.75). As expected,

out-of-sample accuracy decreased (R2 = 0.69) when making predictions for locations distant

(approximately 500km) from data points included in the training set. Our predictions were robust

to the number of explanatory variables used, as demonstrated by the maps generated with 16 and

25 predictors, which showed similar patterns (fig. S11-S14).

Mean nocturnal and diurnal temperature and their annual seasonal variations were among

the most highly explanatory variables (fig. S15-S16). Biannual seasonal variations in diurnal

temperature and annual and biannual seasonal variations in precipitation, middle infrared

reflectance (mean, annual and biannual seasonal variations), birth rate, and population density

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also contributed to the model fit. After accounting for the effect of other variables, elevation had

a minor effect.

Model fit was poorest for the highest transmission intensity locations, likely due to the

limited numbers of data points for these settings. Uncertainty around our mean estimates was

spatially heterogenous, with south-east Brazil and east Africa among the areas with the highest

variability in predictions (Fig. 2B), reflecting the sparseness of data in these regions. Uncertainty

was also high in northern Thailand due to relatively high variation in estimated FOI in the

underlying data points used for model fitting in that area.

Predictions of global dengue burden

The global FOI map produced by our model also allowed us to estimate mean annual

dengue infection and disease incidence. We estimate there are an average of 105 (95% CI 95-

114) million dengue infections globally per year, 51 (95% CI 32-66) million febrile disease

cases, and 4 (95% CI 2-5) million symptomatic infections which might require hospitalization

(Table 1). Most burden of disease (58%) was concentrated in South and South-East Asia (India,

Bangladesh, Indonesia, the Philippines, Thailand, and Vietnam), with half of the burden in the

region occurring in India (data file S1). Sub-Saharan Africa carried almost 26% of the global

dengue burden, with hot spots in Central and Eastern Africa (Nigeria, the Democratic Republic

of the Congo, and Ethiopia). Latin America had 16% of the global burden, mostly occurring in

Brazil, Mexico, Colombia, and Venezuela.

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Global estimates of the reproduction number of dengue

Under the simplifying assumption that dengue dynamics have equilibrated in each region,

we translated our FOI predictions into estimates of the average basic reproduction number (R0)

of dengue (fig. S17-S18). Given the lack of definitive data on infectiousness differences between

serotypes, we estimated R0 under two alternative assumptions about the relative infectiousness of

sequential number of dengue infections (with any serotype): that primary to quaternary infections

have the same infectiousness, or that only primary and secondary infections contribute to

transmission (table S6). The former assumption gives optimistic (lower) estimates of R0, and the

latter, pessimistic (higher) estimates. Geographic heterogeneity in R0 predictions largely mirrored

the variation seen in FOI, albeit modified by country-to-country variation in demography.

Projected impact of interventions

We used the resulting R0 maps to predict the potential impact of interventions on dengue

transmission and disease burden. We first considered an intervention which reduces transmission

by a fixed proportion (modelled as a multiplicative reduction of R0). Interventions such as the

release of Wolbachia-infected mosquitoes have been predicted to act on transmission in this

manner (7).

The predicted long-term ‘best-case’ impact of such a transmission-reducing control

measure varied with the infectiousness scenario assumed, but even with the scenario producing

the highest R0 estimates (namely that only primary and secondary infections contribute to

transmission), we predicted that an intervention capable of reducing R0 by 80% in theory could

come close to effectively eliminating dengue globally (Fig. 3, fig. S19-S21) – though note the

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caveats to this result highlighted in the Discussion. For the other infectiousness scenario, a 60%

reduction in R0 was predicted to achieve a similar impact (Fig. 3). Even an intervention with

intermediate efficacy (50% reduction) was predicted to reduce global case incidence by at least

70% (95% CI 68-77) in the more pessimistic infectiousness scenario. To put these results into

context, successful release of Aedes aegypti mosquitos carrying the wMel strain of Wolbachia

has been predicted based on laboratory studies to achieve a 70% reduction in R0 (7).

For lower levels of intervention effectiveness, impacts were predicted to be highly

spatially heterogeneous, with the least impact seen in the highest transmission intensity areas

(Fig. 4). This highlights one of the potential caveats to these predictions of potential intervention

impact: local hotspots of dengue transmission intensity not able to be resolved at the spatial

resolution of our analysis may see persistence of transmission even at higher intervention

efficacies.

We also explored the impact of introducing childhood vaccination with the Sanofi

Pasteur dengue vaccine (CYD-TDV), a recombinant chimeric live-attenuated vaccine that has

been licensed for use in 20 countries. The vaccine, which has a complex efficacy profile (8, 15),

acts on reducing the risk of symptomatic disease rather than transmission. Based on the results of

a published transmission dynamic model previously used to analyze the Sanofi vaccine trial data

(8, 16), we examined the impact of a single round of screen-and-vaccinate (that is, only

vaccinating seropositive individuals, as currently recommended by WHO (17)) per birth cohort

targeting 9-year-olds. We assumed 80% policy coverage and that the diagnostic test for

seropositivity has 90% sensitivity and 95% specificity – meaning 72% of eligible seropositive

individuals and 4% of seronegative individuals who falsely tested positive were assumed to

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receive the vaccine. We predicted this policy would lead to reductions of 11% to 31% in

symptomatic dengue cases (Fig. 5) and 15% to 39% in hospitalized dengue (fig. S22-24) over the

first 30 years, depending on the infectiousness scenario assumed. Targeting 16-year-olds was

predicted to give a slightly larger effect, as individuals from this age group are more likely to be

seropositive in low to moderate transmission intensity settings and thus more likely to receive the

vaccine. Tuning the age of vaccination to be optimal for the intensity of transmission seen at the

local level 1 administrative unit is expected to have a slightly greater effect (Fig. 5, fig. S24).

Given the Sanofi vaccine can increase the risk of hospitalized dengue in seronegative recipients

and that no test for seropositivity is 100% specific, minimizing individual harm requires

vaccinating at an age where seroprevalence is expected to be high. Figures S25-S26 quantify the

potential magnitude of this issue by showing maps of the proportion of 9 and 16 year-olds

expected to be seronegative (and thus at risk of a false positive screening test). For each

infectiousness scenario, geographic variation in the predicted impact of vaccination was lower

than seen for a pure transmission-reducing intervention (Fig. 6 vs Fig. 4). Unlike previous

predictions for the blanket use of the vaccine without serological testing (16), our results (Fig. 6)

suggest that vaccine use in conjunction with a serological test would have a slightly larger

relative impact on dengue incidence in low transmission settings than in higher transmission

settings.

It is also notable that the impact of vaccination was substantially larger in the higher-R0

infectiousness scenario (where only primary and secondary infections contribute to

transmission). This is because vaccination had a much larger impact on transmission in this

scenario, as all breakthrough infections in seropositive recipients were assumed not to be

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infectious (given the assumed effect of the vaccine is to make secondary natural infections

‘tertiary-like’).

Discussion

This study characterized geographic variation in dengue transmission intensity by using

environmental and demographic routine predictors to fit a machine-learning model to FOI

estimates derived from serological surveys and routine surveillance data. Our finding that

temperature and precipitation, as well as their annual and multiannual variation, are key

predictors of dengue transmission variations is in agreement with previous evidence on the

environmental determinants of both Aedes aegypti ecology and dengue transmission (18-20). We

also found that human population density contributed to the model predictive capacity,

explaining why dengue transmission seems to be higher in urban environments.

By mapping annual average FOI, we were able to generate ‘bottom-up’ estimates of

dengue disease burden. Our estimates of the burden of symptomatic disease are relatively

consistent with recent studies (1), but lower than a notable earlier study (9). However, our

estimates of the number of infections are over three-fold lower than the estimates from the latter

study (9). This is because our approach explicitly takes into account that dengue is an

immunizing disease, meaning incidence is constrained by a biologically realistic cap of a

maximum of four lifetime infections per individual. In agreement with past work (1, 9), we

found that sub-Saharan Africa accounts for a disproportionally high proportion (27%) of the

global disease burden due to the high birth rate of this region. However, dengue surveillance is

traditionally weak in this region and few serological surveys have been undertaken, meaning our

current estimates are based on very limited data. More data would therefore be valuable in

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allowing current uncertainty in our transmission intensity and disease burden estimates to be

reduced.

Only a minority of clinically apparent dengue cases seek healthcare in many settings (21,

22). However, since the estimates of force of infection we use here are derived from the age-

specific trends in serology and case notification data, not absolute case incidence, they are robust

to under-ascertainment of incidence so long as this does not vary substantially with age.

A unique benefit of our approach to understanding global variation in dengue

transmission and disease burden is that it allows the potential public health impact of

interventions to be assessed. Like malaria, the vector-borne nature of dengue leads to much

larger geographic variation in R0 than is typical of most directly-transmitted infections (10).

Hence results from a randomized control study conducted in one setting cannot be directly and

easily extrapolated to other settings. A mechanistic, model-based understanding of the impact of

an intervention on transmission is required, together with reliable estimates of transmission

intensity across the range of endemic settings where that intervention might be considered. It is

the latter gap this current study fills. We take existing models and associated effect size estimates

for generic transmission-reduction interventions and project the potential impact of these – and

two existing interventions (the Sanofi-Pasteur vaccine and the release of Wolbachia-infected

mosquitoes) (7, 8, 16) – across the entire region of dengue endemicity.

We find that there is geographic variation in the predicted impact of both transmission-

reduction interventions and vaccination, driven by the spatial variation in local transmission

intensity and by the mechanism of impact of these interventions. Both classes of interventions

show larger effects in lower transmission intensity settings – though it should be noted that in the

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case of the vaccine, this is a result of modelling a policy of pre-vaccination testing of potential

recipients for seropositivity with a very high-specificity diagnostic test (required when used in

settings where a higher proportion of potential recipients are expected to be seronegative).

Previous predictions for the blanket use of the vaccine without serological testing (16) showed

the opposite trend, namely larger impact in high transmission settings. It should also be noted

that the predictions of vaccine impact presented here are of overall population impact;

vaccination may still enhance the risk of hospitalized dengue in the small proportion of

seronegative individuals who receive the vaccine due to falsely testing positive as a result of

imperfect test specificity (15, 17).

Interventions that reduce R0 by a fixed proportion (as Wolbachia has been predicted to

do (7)) can achieve disease elimination in low to moderate transmission settings (by reducing R0

to below the threshold of 1 for self-sustaining transmission), but have a smaller impact in the

highest transmission settings. It should be noted that even in very high transmission intensity

settings where an intervention such as Wolbachia may fail to reduce R0 to below 1, high baseline

herd immunity means that introduction of such an intervention is predicted to stop dengue

transmission for a decade or more until that herd-immunity declines (due to new births into the

population) sufficiently to allow low-level transmission to resume (6).

Caution is needed in evaluating our estimates of the impact of transmission reduction

interventions as predictive of what release of Wolbachia-infected mosquitoes might achieve.

Field-based evaluation of Wolbachia is ongoing (6, 23) and in the absence of results from

randomized control trials, we have relied on effect size estimates derived from laboratory

studies. In addition, we have assumed that the effect size will be uniform across settings, that

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estimates assume that the efficacy of Wolbachia on reducing dengue transmission shown in

laboratory experiments will be maintained at the population level in the field and will have a

uniform effect across settings. Neither of these assumptions have yet been tested. Once data from

intervention studies becomes available, more precise estimates of the potential global public

health impact of Wolbachia will be able to be derived.

An important additional limitation of our analysis is that we do not consider temporal

variation in dengue incidence but rather focus on characterizing long-term average transmission

intensity and disease burden. Dengue incidence exhibits high inter-annual variability, driven by

climate (for example the El Niño–Southern Oscillation) (20), immune-mediated serotype

dynamics (24), and virus genotype-specific phenotypic variation (25). Of particular importance

in assessing the impact of control measures is that seasonal peak R0 values may be substantially

larger than the annual average values presented here. In highly seasonal settings, our

transmission-reduction intervention impact predictions are approximations, as they

underestimate intervention impact in seasonal troughs but over-estimate it during transmission

intensity peaks.

A further limitation affecting our estimates of R0 and the impact of interventions is that

we have had to assume dengue transmission dynamics are at quasi-equilibrium in the sense of a

balance having been achieved between herd-immunity, infection incidence and new births. In

areas only relatively recently invaded by one or more dengue serotypes (for example Latin

America (26)) this assumption may only hold to a first approximation. However, accurately

characterizing long-term trends in dengue transmission requires incidence or seroprevalence data

spanning decades – data which is available in relatively few settings (27-29).

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In addition, our analysis makes use of force of infection estimates available at a relatively

coarse spatial scale (administrative level 1). While we employed a spatial disaggregation

expectation-maximization algorithm to address this, finer scale raw input data would permit

fuller characterization of local spatial heterogeneity in dengue transmission intensity and might

reveal higher transmission-intensity hotspots where interventions might have a more limited

impact than predicted here.

Last, we have utilized machine-learning based regression to derive predicted maps of

transmission intensity. Although this gives some insight into the relationships between dengue

transmission and environmental predictors such as temperature, rainfall, and vector abundance

and competence, a more mechanistic approach would offer deeper scientific insight (23, 30).

Although these limitations are priorities for ongoing research, the results presented here

represent the most detailed characterization of geographic heterogeneity in dengue transmission

intensity yet undertaken. By using age-stratified data sources and accounting for the immunizing

effect of dengue, our transmission intensity map and burden estimates represent a

methodological advance – most notably in allowing the effect of interventions on disease burden

to be explored. Our analysis has critical relevance for future evidence-based prioritization,

planning and implementation of vaccine- and vector-control based control policies.

Materials and Methods

Study Design

We collated a dataset of geolocated estimates of per-serotype dengue FOI, derived from

the analysis of age-stratified seroprevalence and case notification data. FOI is the per capita rate

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at which susceptible individuals acquire infection and is a key measure of infectious disease

transmission. The resulting 382 georeferenced point estimates are publicly available (31).

Additional methods details are available in the supplementary materials.

Serology-based FOI estimates

We sourced 33 FOI estimates derived from serology studies (13). The surveys tested age-

stratified seroprevalence by IgG ELISAs or plaque reduction neutralization assays (PRNTs).

Average (per single serotype) force of infection estimates were obtained by fitting IgG and

PRNT data to a catalytic model, which assumed all serotypes had the same constant force of

infection over the time span of the ages of the individuals surveyed. As additional seroprevalence

studies became publicly available, more force of infection estimates were generated in the same

manner and included into the dataset. In total, we identified 34 datasets (table S1) from which we

estimated 116 geolocated force of infection estimates.

Case notification-based FOI estimates

We sourced 233 force of infection estimates derived from age-specific routine case

surveillance data (11), at the level-1 administrative unit scale in Thailand, Colombia, Brazil and

Mexico and, separately (but using identical methods), Venezuela and the Philippines (31) (table

S2). For Thailand and Brazil, age-specific reports on suspected cases (according to case

definition) of dengue hemorrhagic fever (DHF) were used. For Colombia and Mexico, data on all

reported suspected dengue cases were used because numbers of DHF were insufficient to

estimate force of infection. For each administrative unit, the average per-serotype force of

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infection over the last 20 years was estimated (11), assuming, in the absence of serotype-specific

data, that all serotypes have the same force of infection.

Explanatory variables

A range of environmental, socio-economic and demographic factors drives the

distribution and spread of dengue, with rainfall, temperature and humidity being the key

determinants of vector efficacy in virus transmission (9, 32). In addition, FOI is strongly affected

by human demography (13, 28, 33). We selected a set of environmental and socio-economic

variables which facilitate dengue transmission and for which data were available at the global

scale. We considered 8 environmental and demographic variables, which were available at high

resolution (1-10 km), measuring: (1) precipitation, (2) diurnal temperature, (3) nocturnal

temperature, (4) Enhanced Vegetation Index, (5) Middle Infrared Reflectance, (6) altitude, (7)

population density and (8) per capita human birth rate. The environmental variables were

available as mean and harmonic amplitudes derived from a Fast Fourier Transform of the

original time series data. This resulted in a suite of 28 potential explanatory variables. See the

supplementary material for further discussion on choice and source of explanatory variables.

Predicting dengue burden

For each 1/6-degree pixel we used a simple hazard model to estimate total annual number

of dengue infections, febrile dengue cases and cases requiring hospitalization (“hospitalized

cases”) from the prediction of FOI for that pixel. We first calculated the total number of

secondary, tertiary and quaternary infections at endemic equilibrium (14). We then calculated the

number of mild febrile and hospitalized cases using data on the proportions of primary,

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secondary and tertiary infections which are symptomatic derived from a published analysis of the

phase III trials of the Sanofi-Pasteur vaccine (8).

Generating R0 estimates and predicting the impact of control measures

We explored how the impact of interventions varied under two different infectiousness

scenarios: (i) all four infections (with any of the four serotypes) have the same infectiousness and

(ii) only primary and secondary infections contribute to transmission. We converted the 1/6-

degree resolution FOI predictions into R0 estimates using a Susceptible-Infected-Recovered

model of dengue transmission at endemic equilibrium. We modelled transmission-reducing

interventions (such as Wolbachia) by multiplying our R0 estimates by a scaling factor (),

representing the assumed reduction in transmission intensity. We then calculated the FOI

corresponding to the modified reproduction number . The resulting reduced FOI was used to

estimate total number of infections, febrile dengue cases and hospitalized cases in the presence

of the intervention using the same approach used for burden estimation.

To model the impact of the Sanofi-Pasteur vaccine, we assumed 80% coverage and that

potential recipients were screened for seropositivity using a test with 90% sensitivity and 95%

specificity, with only those testing positive being vaccinated. We used the transmission dynamic

model of Ferguson et al. (8) to estimate the proportion of infections, febrile dengue cases and

hospitalized cases averted within 30 years following introduction of such a vaccination program,

for a grid of 20 values of transmission intensity, for ages at vaccination between 9 and 18 years,

and for both of the infectiousness scenarios outlined above. We used linear interpolation to

generate pixel level predictions from this grid of impacts. We also considered a scenario where

vaccination age was optimally chosen (at admin level 1) in each setting to maximize the 17

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reduction in infections, mild febrile cases and hospitalized cases, as commented in the revised

guidance issued by WHO (34). See SM for more details.

Statistical analysis

Full details are given in the SM. In brief, we used random forests to model the statistical

relationship between FOI and the explanatory variables (35). To calibrate the model and better

discriminate between predicted areas of endemicity, we generated pseudo-absence data. We

randomly sampled pseudo-absence points from areas which are known to be free of dengue

based on a past study of national-level dengue endemicity status (36). To generate predictions at

finer spatial scales than the original training data, we employed an Expectation-Maximization

algorithm to spatially disaggregate the original FOI dataset. A spatial block bootstrap approach

was used to select predictor variables based on out-of-sample predictive accuracy and to assess

prediction uncertainty (37). We conducted a sensitivity analysis of the effect of the random forest

parameters on model out-of-sample accuracy (fig. S27-S31). Further outputs from the variable

selection and EM fitting routine are provided in the SM (table S7, fig. S32-34). We assessed

model predictive performance by calculating the coefficient of determination (R2) which

represents the proportion of variance in the data explained by the model. Prediction 95%

confidence intervals were calculated using spatial block bootstrapping at a scale depending on

the distance of the point to be predicted from the nearest data point.

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Supplementary Materials

Materials and Methods

Fig. S1. NOAA RFE2 precipitation.

Fig. S2. MODIS diurnal temperature.

Fig. S3. MODIS nocturnal temperature.

Fig. S4. MODIS Enhanced Vegetation Index.

Fig. S5. MODIS Middle Infrared Reflectance.

Fig. S6. WorldClim altitude.

Fig. S7. Landscan 2015 population density.

Fig. S8. United Nations 2015 per capita human birth rate.

Fig. S9. FOI observations against predictions with pseudo-absence data.

Fig. S10. FOI observations against predictions without pseudo-absence data.

Fig. S11. Predicted FOI from the model with 16 top predictors.

Fig. S12. Predicted FOI from the model with 25 predictors.

Fig. S13. Standard deviation of FOI predictions from the model with 16 predictors.

Fig. S14. Standard deviation of FOI predictions from the model with 25 predictors.

Fig. S15. Partial dependence plots for the model with 16 predictors.

Fig. S16. Partial dependence plots for the model with 25 predictors.

Fig. S17. Predicted R0 assuming only primary and secondary infections are infectious.

Fig. S18. Predicted R0 assuming all infections are equally infectious.

Fig. S19. Estimated impact of Wolbachia on global dengue infections.

Fig. S20. Estimated impact of Wolbachia on global febrile dengue cases.

Fig. S21. Estimated impact of Wolbachia on hospitalized dengue cases.

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Fig. S22. Estimated impact of the Sanofi-Pasteur vaccine on global dengue infections.

Fig. S23. Estimated impact of the Sanofi-Pasteur vaccine on global febrile dengue cases.

Fig. S24. Estimated impact of the Sanofi-Pasteur vaccine on global hospitalized dengue cases.

Fig. S25. Estimated proportion (%) of 9 year-olds expected to be seronegative.

Fig. S26. Estimated proportion (%) of 16 year-olds expected to be seronegative.

Fig. S27. Sensitivity analysis of block bootstrapping grid size.

Fig. S28. Saturating function for setting pseudo-absences case weights.

Fig. S29. Sensitivity analysis of number of trees.

Fig. S30. Sensitivity analysis of minimum node size.

Fig. S31. Sensitivity analysis of pseudo absence value.

Fig. S32. Root Mean Square Error during forward selection algorithm.

Fig. S33. Frequency distribution of numbers of selected predictors.

Fig. S34. Expectation Maximization algorithm convergence diagnostics.

Table S1. Summary of seroprevalence datasets identified and associated demographics.

Table S2. Summary of case notification datasets.

Table S3. Potential explanatory variables.

Table S4. Model R2 calculated with pseudo-absences.

Table S5. Model R2 calculated without pseudo-absences.

Table S6. Weights of primary to quaternary infections for different infectiousness scenarios.

Table S7. Rank of explanatory variables selected by the forward selection algorithm.

References (39 - 80)

Data File S1 Primary data (Excel file)

Data File S2 Burden estimates (Excel file)

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Data File S3 Raster FOI map (GeoTIFF file)

Data File S4 Raster R0 map, assumption 1 (GeoTIFF file)

Data File S5 Raster R0 map, assumption 2 (GeoTIFF file)

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Acknowledgments: We thank Dr Luke Mease and Dr Rodney Coldren for kindly sharing the

age-specific seroprevalence data from Kenya. Funding: This study was primarily funded by the

National Institute of General Medical Sciences ‘MIDAS’ program (5U01GM110721 to NMF)

and received additional support from: the UK National Institute of Health Research (PR-OD-

1017-20002 to NMF), Centre (MR/R015600/1) funding from the UK Medical Research Council

(MRC) and the UK Department for International Development (DFID) under the MRC/DFID

Concordat agreement, Institute funding from the Abdul Latif Jameel Institute for Disease and

Emergency Analytics (J-IDEA), US CDC Southeastern Center of Excellence in Vector-borne

Diseases (CDC Cooperative Agreement U01CK000510 to DATC), US National Institute of

General Medical Sciences (5U54GM08849 to DATC) and the Bill and Melinda Gates

Foundation (OPP1092240 to NMF). Author contributions: NMF conceived the analysis; LC

and NMF conducted the formal analysis; NMF acquired the funding; LC conducted the

investigation; LC and NMF developed the methodology; LC prepared a first draft of the

manuscript; LC, IRB, NI, DATC and NMF contributed to manuscript review & editing.

Competing interests: Neil Ferguson advises the World Health Organization on aspects of

dengue control and receives expense payments for this activity. Neil Ferguson additionally sits

on an advisory board for Takeda Pharmaceuticals in relation to their dengue vaccine candidate,

but receives no payment of any kind (honorarium, expenses, or research funding) for this

activity. Data and materials availability: All data is available in the main text or the

supplementary materials. Code to reproduce study findings is available at

https://doi.org/10.5281/zenodo.3485123 (38).

36

729

730

731

732

733

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736

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743

744

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747

748

749

750

Table 1. Estimated number of dengue infections, febrile dengue cases and potentially

hospitalized cases by continent quoted to nearest 10,000.

Continent Mean 2.5th

percentile

97.5th

percentile

Infections Africa 26,993,900 22,219,800 31,908,300

Americas 16,679,800 15,564,000 17,702,300

Asia 60,927,500 53,223,900 67,106,800

Oceania 337,300 251,200 414,800

Total 104,938,500 94,509,700 113,618,600

Febrile cases Africa 13,842,500 8,622,100 19,394,500

Americas 7,613,400 4,838,400 9,936,500

Asia 29,255,300 17,660,400 38,649,400

Oceania 163,200 102,800 240,100

Total 50,874,500 31,649,300 65,871,600

Potentially

hospitalized cases

Africa 958,000 615,100 1,311,800

Americas 556,800 361,200 702,300

37

751

752

Asia 2,117,100 1,294,900 2,735,600

Oceania 11,800 7,400 16,900

Total 3,643,700 2,291,500 4,640,600

38

753

33

Figure 1. Geographic location of force of infection data. 382 data points are shown

comprising the national administrative division of all countries, and second national

administrative division (admin 2) of Mexico, Colombia, Venezuela, Brazil, India, and Australia.

Figure 2. Predicted global dengue risk. Means (A) and standard deviations (B) of force of

infection estimates in dengue endemic countries across 200 geographically stratified bootstrap

samples.

Figure 3. Predicted impact of a transmission-reduction intervention. Mean (solid lines) and

95% CI (envelopes) of percentage reduction relative to no-intervention scenario of global

number of dengue mild febrile cases (A), absolute number of mild febrile cases (B) and number

of countries where average R0 is reduced below 1 (C), for different percentage amounts of R0

reduction and serotype infectiousness assumptions. “2S” = primary and secondary infections are

infectious; “4S” = all infections are infectious. The vertical dotted lines indicate 30% and 70%

reductions in transmission intensity.

Figure 4. Geographic variation in the predicted impact of a transmission-reduction

intervention. Maps show the mean percentage reduction in incidence of febrile dengue cases at

level 1 administrative units, across 200 bootstrap samples, for 30% transmission reduction and

“2S” serotype infectiousness assumption (A), 70% transmission reduction and “2S” serotype

infectiousness assumption (B), 30% transmission reduction and “4S” serotype infectiousness 39

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assumption (C) and 70% transmission reduction and “4S” serotype infectiousness assumption

(D).

Figure 5. Predicted impact of the Sanofi dengue vaccine. Effect of child vaccination with the

Sanofi dengue vaccine on the global incidence of febrile dengue cases over the first 30 years

after vaccine introduction, for different vaccine screening ages and serotype infectiousness

assumptions (“2S” = primary and secondary infections are infectious; “4S” = all infections are

infectious). Bar represents mean percentage reduction relative to no-intervention scenario, with

95% CI shown.

Figure 6. Geographic variation in the predicted impact of the Sanofi dengue vaccine. Maps

show the mean percentage reduction in incidence of febrile dengue cases in the first 30 years

after vaccine introduction, for vaccination of 9 year-olds and “2S” serotype infectiousness

assumption (A), vaccination of 16 year-olds and “2S” serotype infectiousness assumption (B),

optimal choice of vaccination age and “2S” serotype infectiousness assumption (C), vaccination

of 9 year-olds and “4S” serotype infectiousness assumption (D), vaccination of 16 year-olds and

“4S” serotype infectiousness assumption (E) and optimal choice of vaccination age and “4S”

serotype infectiousness assumption (F).

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