Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries

Age-specific case data reveal varying dengue transmission intensity in US states and territories

Dengue viruses (DENV) are endemic in the US territories of Puerto Rico, American Samoa, and the US Virgin Islands, with focal outbreaks also reported in the states of Florida and Hawaii. However, little is known about the intensity of dengue virus transmission over time and how dengue viruses have shaped the level of immunity in these populations, despite the importance of understanding how and why levels of immunity against dengue may change over time. These changes need to be considered when responding to future outbreaks and enacting dengue management strategies, such as guiding vaccine deployment. We used catalytic models fitted to case surveillance data stratified by age from the ArboNET national arboviral surveillance system to reconstruct the history of recent dengue virus transmission in Puerto Rico, American Samoa, US Virgin Islands, Florida, Hawaii, and Guam. We estimated average annual transmission intensity (i.e., force of infection) of DENV between 2010 and 2019 and the level of seroprevalence by age group in each population. We compared models and found that assuming all reported cases are secondary infections generally fit the surveillance data better than assuming all cases are primary infections. Using the secondary case model, we found that force of infection was highly heterogeneous between jurisdictions and over time within jurisdictions, ranging from 0.00008 (95% CrI: 0.00002-0.0004) in Florida to 0.08 (95% CrI: 0.044-0.14) in American Samoa during the 2010-2019 period. For early 2020, we estimated that seropositivity in 10 year-olds ranged from 0.09% (0.02%-0.54%) in Florida to 56.3% (43.7%-69.3%) in American Samoa. In the absence of serological data, age-specific case notification data collected through routine surveillance combined with mathematical modeling are powerful tools to monitor arbovirus circulation, estimate the level of population immunity, and design dengue management strategies.

Increasing transmission of dengue virus across ecologically diverse regions of Ecuador and associated risk factors

The distribution and intensity of viral diseases transmitted by Aedes aegypti mosquitoes, including dengue, have rapidly increased over the last century. Here, we study dengue virus (DENV) transmission across the ecologically and demographically distinct regions or Ecuador. We analyzed province-level age-stratified dengue incidence data from 2000-2019 using catalytic models to estimate the force of infection of DENV over eight decades. We found that provinces established endemic DENV transmission at different time periods. Coastal provinces with the largest and most connected cities had the earliest and highest increase in DENV transmission, starting around 1980 and continuing to the present. In contrast, remote and rural areas with reduced access, like the northern coast and the Amazon regions, experienced a rise in DENV transmission and endemicity only in the last 10 to 20 years. The newly introduced chikungunya and Zika viruses have age-specific distributions of hospital-seeking cases consistent with recent emergence across all provinces. To evaluate factors associated with geographic differences in DENV transmission potential, we modeled DENV vector risk using 11,693 Aedes aegypti presence points to the resolution of 1 hectare. In total, 56% of the population of Ecuador, including in provinces identified as having increasing DENV transmission in our models, live in areas with high risk of Aedes aegypti, with population size, trash collection, elevation, and access to water as important determinants. Our investigation serves as a case study of the changes driving the expansion of DENV and other arboviruses globally and suggest that control efforts should be expanded to semi-urban and rural areas and to historically isolated regions to counteract increasing dengue outbreaks.

Previous Dengue Infection among Children in Puerto Rico and Implications for Dengue Vaccine Implementation

Limited dengue virus (DENV) seroprevalence estimates are available for Puerto Rico, which are needed to inform the potential use and cost-effectiveness of DENV vaccines. The Communities Organized to Prevent Arboviruses (COPA) is a cohort study initiated in 2018 in Ponce, Puerto Rico, to assess arboviral disease risk and provide a platform to evaluate interventions. We recruited participants from households in 38 study clusters, who were interviewed and provided a serum specimen. Specimens from 713 children aged 1 to 16 years during the first year of COPA were tested for the four DENV serotypes and ZIKV using a focus reduction neutralization assay. We assessed the seroprevalence of DENV and ZIKV by age and developed a catalytic model from seroprevalence and dengue surveillance data to estimate the force of infection for DENV during 2003-2018. Overall, 37% (n = 267) were seropositive for DENV; seroprevalence was 9% (11/128) among children aged 1 to 8 years and 44% (256/585) among children aged 9 to 16 years, exceeding the threshold over which DENV vaccination is deemed cost-effective. A total of 33% were seropositive for ZIKV, including 15% among children aged 0 to 8 years and 37% among children aged 9 to 16 years. The highest force of infection occurred in 2007, 2010, and 2012-2013, with low levels of transmission from 2016 to 2018. A higher proportion of children had evidence of multitypic DENV infection than expected, suggesting high heterogeneity in DENV risk in this setting.

Estimating Dengue Transmission Intensity in China Using Catalytic Models Based on Serological Data

In recent decades, the global incidence of dengue has risen sharply, with more than 75% of infected people showing mild or no symptoms. Since the year 2000, dengue in China has spread quickly. At this stage, there is an urgent need to fully understand its transmission intensity and spread in China. Serological data provide reliable evidence for symptomatic and recessive infections. Through a literature search, we included 23 studies that collected age-specific serological dengue data released from 1980 to 2021 in China. Fitting four catalytic models to these data, we distinguished the transmission mechanisms by deviation information criterion and estimated force of infection and basic reproduction number (R₀), important parameters for quantifying transmission intensity. We found that transmission intensity varies over age in most of the study populations, and attenuation of antibody protection is identified in some study populations; the R₀ of dengue in China is between 1.04-2.33. Due to the scarceness of the data, the temporal trend cannot be identified, but data shows that transmission intensity weakened from coastal to inland areas and from southern to northern areas in China if assuming it remained temporally steady during the study period. The results should be useful for the effective control of dengue in China.

Periodic synchronisation of dengue epidemics in Thailand over the last 5 decades driven by temperature and immunity

The spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, less work has been done to understand how climate variability and change affects dengue in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatiotemporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last 30 years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronisation events. We show that although patterns in synchrony of dengue are similar to those observed in temperature, the relationship between the two is most consistent during synchronous periods, while during asynchronous periods, temperature plays a less prominent role. With simulations from temperature-driven models, we explore how dynamics of immunity interact with temperature to produce the observed patterns in synchrony. The simulations produced patterns in synchrony that were similar to observations, supporting an important role of immunity. We demonstrate that multiannual oscillations produced by immunity can lead to asynchronous dynamics and that synchrony in temperature can then synchronise these dengue dynamics. At higher mean temperatures, immune dynamics can be more predominant, and dengue dynamics more insensitive to multiannual fluctuations in temperature, suggesting that with rising mean temperatures, dengue dynamics may become increasingly asynchronous. These findings can help underpin predictions of disease patterns as global temperatures rise.

This study shows that spatially large-scale shifts in temperature can synchronize dengue dynamics across Thailand; however, as average temperatures rise, dengue dynamics may increasingly be dictated by dynamics of immunity, which may in turn mean fewer synchronous outbreaks in the future.

Estimating the annual dengue force of infection from the age of reporting primary infections across urban centres in endemic countries

BACKGROUND

Stratifying dengue risk within endemic countries is crucial for allocating limited control interventions. Current methods of monitoring dengue transmission intensity rely on potentially inaccurate incidence estimates. We investigated whether incidence or alternate metrics obtained from standard, or laboratory, surveillance operations represent accurate surrogate indicators of the burden of dengue and can be used to monitor the force of infection (FOI) across urban centres.

METHODS

Among those who reported and resided in 13 cities across the Philippines, we collected epidemiological data from all dengue case reports between 2014 and 2017 (N 80,043) and additional laboratory data from a cross-section of sampled case reports (N 11,906) between 2014 and 2018. At the city level, we estimated the aggregated annual FOI from age-accumulated IgG among the non-dengue reporting population using catalytic modelling. We compared city-aggregated FOI estimates to aggregated incidence and the mean age of clinically and laboratory diagnosed dengue cases using Pearson's Correlation coefficient and generated predicted FOI estimates using regression modelling.

RESULTS

We observed spatial heterogeneity in the dengue average annual FOI across sampled cities, ranging from 0.054 [0.036-0.081] to 0.249 [0.223-0.279]. Compared to FOI estimates, the mean age of primary dengue infections had the strongest association (ρ -0.848, p value<0.001) followed by the mean age of those reporting with warning signs (ρ -0.642, p value 0.018). Using regression modelling, we estimated the predicted annual dengue FOI across urban centres from the age of those reporting with primary infections and revealed prominent spatio-temporal heterogeneity in transmission intensity.

CONCLUSIONS

We show the mean age of those reporting with their first dengue infection or those reporting with warning signs of dengue represent superior indicators of the dengue FOI compared to crude incidence across urban centres. Our work provides a framework for national dengue surveillance to routinely monitor transmission and target control interventions to populations most in need.

Lying in wait: the resurgence of dengue virus after the Zika epidemic in Brazil

After the Zika virus (ZIKV) epidemic in the Americas in 2016, both Zika and dengue incidence declined to record lows in many countries in 2017–2018, but in 2019 dengue resurged in Brazil, causing ~2.1 million cases. In this study we use epidemiological, climatological and genomic data to investigate dengue dynamics in recent years in Brazil. First, we estimate dengue virus force of infection (FOI) and model mosquito-borne transmission suitability since the early 2000s. Our estimates reveal that DENV transmission was low in 2017–2018, despite conditions being suitable for viral spread. Our study also shows a marked decline in dengue susceptibility between 2002 and 2019, which could explain the synchronous decline of dengue in the country, partially as a result of protective immunity from prior ZIKV and/or DENV infections. Furthermore, we performed phylogeographic analyses using 69 newly sequenced genomes of dengue virus serotype 1 and 2 from Brazil, and found that the outbreaks in 2018–2019 were caused by local DENV lineages that persisted for 5–10 years, circulating cryptically before and after the Zika epidemic. We hypothesize that DENV lineages may circulate at low transmission levels for many years, until local conditions are suitable for higher transmission, when they cause major outbreaks.

Zika and dengue incidence in the Americas declined in 2017–2018, but dengue resurged in 2019 in Brazil. This study uses epidemiological, climatological and genomic data to show that the decline of dengue may be explained by protective immunity from pre-exposure to ZIKV and/or DENV in prior years.

Evaluation of the Diagnostic Accuracy of a New Biosensors-Based Rapid Diagnostic Test for the Point-Of-Care Diagnosis of Previous and Recent Dengue Infections in Malaysia

Dengue is a major threat to public health globally. While point-of-care diagnosis of acute/recent dengue is available to reduce its mortality, a lack of rapid and accurate testing for the detection of previous dengue remains a hurdle in expanding dengue seroepidemiological surveys to inform its prevention, especially vaccination, to reduce dengue morbidity. This study evaluated ViroTrack Dengue Serostate, a biosensors-based semi-quantitative anti-dengue IgG (immunoglobulin G) immuno-magnetic agglutination assay for the diagnosis of previous and recent dengue in a single test. Blood samples were obtained from 484 healthy participants recruited randomly from two communities in Petaling district, Selangor, Malaysia. The reference tests were Panbio Dengue IgG indirect and capture enzyme-linked immunosorbent assays, in-house hemagglutination inhibition assay, and focus reduction neutralization test. Dengue Serostate had a sensitivity and specificity of 91.1% (95%CI 87.8-93.8) and 91.1% (95%CI 83.8-95.8) for the diagnosis of previous dengue, and 90.2% (95%CI 76.9-97.3) and 93.2% (95%CI 90.5-95.4) for the diagnosis of recent dengue, respectively. Its positive predictive value of 97.5% (95%CI 95.3-98.8) would prevent most dengue-naïve individuals from being vaccinated. ViroTrack Dengue Serostate's good point-of-care diagnostic accuracy can ease the conduct of dengue serosurveys to inform dengue vaccination strategy and facilitate pre-vaccination screening to ensure safety.

Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling

Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms involved in transmission. Most critical are the bottleneck in transmission imposed by mosquito lifespan relative to the virus' extrinsic incubation period, and the dynamics of human immunity. We developed a differential equation model of dengue transmission and embedded it in a Bayesian hierarchical framework that allowed us to estimate latent time series of mosquito demographic rates from mosquito trap counts and dengue case reports from the city of Vitória, Brazil. We used the fitted model to explore how the timing of a pulse of adult mosquito control influences its effect on the human disease burden in the following year. We found that control was generally more effective when implemented in periods of relatively low mosquito mortality (when mosquito abundance was also generally low). In particular, control implemented in early September (week 34 of the year) produced the largest reduction in predicted human case reports over the following year. This highlights the potential long-term utility of broad, off-peak-season mosquito control in addition to existing, locally targeted within-season efforts. Further, uncertainty in the effectiveness of control interventions was driven largely by posterior variation in the average mosquito mortality rate (closely tied to total mosquito abundance) with lower mosquito mortality generating systems more vulnerable to control. Broadly, these correlations suggest that mosquito control is most effective in situations in which transmission is already limited by mosquito abundance.

Hidden heterogeneity and its influence on dengue vaccination impact

The CYD-TDV vaccine was recently developed to combat dengue, a mosquito-borne viral disease that afflicts millions of people each year throughout the tropical and subtropical world. Its rollout has been complicated by recent findings that vaccinees with no prior exposure to dengue virus (DENV) experience an elevated risk of severe disease in response to their first DENV infection subsequent to vaccination. As a result of these findings, guidelines for use of CYD-TDV now require serological screening prior to vaccination to establish that an individual does not fall into this high-risk category. These complications mean that the public health impact of CYD-TDV vaccination is expected to be higher in areas with higher transmission. One important practical difficulty with tailoring vaccination policy to local transmission contexts is that DENV transmission is spatially heterogeneous, even at the scale of neighborhoods or blocks within a city. This raises the question of whether models based on data that average over spatial heterogeneity in transmission could fail to capture important aspects of CYD-TDV impact in spatially heterogeneous populations. We explored this question with a deterministic model of DENV transmission and CYD-TDV vaccination in a population comprised of two communities with differing transmission intensities. Compared to the full model, a version of the model based on the average of the two communities failed to capture benefits of targeting the intervention to the high-transmission community, which resulted in greater impact in both communities than we observed under even coverage. In addition, the model based on the average of the two communities substantially overestimated impact among vaccinated individuals in the low-transmission community. In the event that the specificity of serological screening is not high, this result suggests that models that ignore spatial heterogeneity could overlook the potential for harm to this segment of the population.

Mapping global variation in dengue transmission intensity

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 Southeast Asia (Thailand, Indonesia, and the Philippines). We estimated that 105 [95% confidence interval (CI), 95 to 114] million dengue infections occur each year with 51 (95% CI, 32 to 66) million febrile disease cases. Our analysis suggests that transmission-blocking interventions such as Wolbachia, even at intermediate efficacy (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 to 30%, with the greatest impact in high-transmission settings. The transmission intensity map presented here, and made available for download, may help further assessment of the impact of dengue control interventions and prioritization of global public health efforts.

Predicting re-emergence times of dengue epidemics at low reproductive numbers: DENV1 in Rio de Janeiro, 1986-1990

Predicting arbovirus re-emergence remains challenging in regions with limited off-season transmission and intermittent epidemics. Current mathematical models treat the depletion and replenishment of susceptible (non-immune) hosts as the principal drivers of re-emergence, based on established understanding of highly transmissible childhood diseases with frequent epidemics. We extend an analytical approach to determine the number of ‘skip’ years preceding re-emergence for diseases with continuous seasonal transmission, population growth and under-reporting. Re-emergence times are shown to be highly sensitive to small changes in low R₀ (secondary cases produced from a primary infection in a fully susceptible population). We then fit a stochastic Susceptible–Infected–Recovered (SIR) model to observed case data for the emergence of dengue serotype DENV1 in Rio de Janeiro. This aggregated city-level model substantially over-estimates observed re-emergence times either in terms of skips or outbreak probability under forward simulation. The inability of susceptible depletion and replenishment to explain re-emergence under ‘well-mixed’ conditions at a city-wide scale demonstrates a key limitation of SIR aggregated models, including those applied to other arboviruses. The predictive uncertainty and high skip sensitivity to epidemiological parameters suggest a need to investigate the relevant spatial scales of susceptible depletion and the scaling of microscale transmission dynamics to formulate simpler models that apply at coarse resolutions.

Estimates of the global burden of Japanese encephalitis and the impact of vaccination from 2000-2015

Japanese encephalitis (JE) is a mosquito-borne disease, known for its high mortality and disability rate among symptomatic cases. Many effective vaccines are available for JE, and the use of a recently developed and inexpensive vaccine, SA 14-14-2, has been increasing over the recent years particularly with Gavi support. Estimates of the local burden and the past impact of vaccination are therefore increasingly needed, but difficult due to the limitations of JE surveillance. In this study, we implemented a mathematical modelling method (catalytic model) combined with age-stratifed case data from our systematic review which can overcome some of these limitations. We estimate in 2015 JEV infections caused 100,308 JE cases (95% CI: 61,720-157,522) and 25,125 deaths (95% CI: 14,550-46,031) globally, and that between 2000 and 2015 307,774 JE cases (95% CI: 167,442-509,583) were averted due to vaccination globally. Our results highlight areas that could have the greatest benefit from starting vaccination or from scaling up existing programs and will be of use to support local and international policymakers in making vaccine allocation decisions.

Spatiotemporal variability in dengue transmission intensity in Jakarta, Indonesia

Background

Approximately 70% of the global burden of dengue disease occurs on the Asian continent, where many large urban centres provide optimal environments for sustained endemic transmission and periodic epidemic cycles. Jakarta, the capital of Indonesia, is a densely populated megacity with hyperendemic dengue transmission. Characterization of the spatiotemporal distribution of dengue transmission intensity is of key importance for optimal implementation of novel control and prevention programmes, including vaccination. In this paper we use mathematical models to provide the first detailed description of spatial and temporal variability in dengue transmission intensity in Jakarta.

Methodology/Principal findings

We applied catalytic models in a Bayesian framework to age-stratified dengue case notification data to estimate dengue force of infection and reporting probabilities in 42 subdistricts of Jakarta. The model was fitted to yearly and average annual data covering a 10-year period between 2008 and 2017. We estimated a long-term average annual transmission intensity of 0.130 (95%CrI: 0.129–0.131) per year in Jakarta province, ranging from 0.090 (95%CrI: 0.077–0.103) to 0.164 (95%CrI: 0.153–0.174) across subdistricts. Annual average transmission intensity in Jakarta province during the 10-year period ranged from 0.012 (95%CrI: 0.011–0.013) in 2017 to 0.124 (95%CrI: 0.121–0.128) in 2016.

Conclusions/Significance

While the absolute number of dengue case notifications cannot be relied upon as a measure of endemicity, the age-distribution of reported dengue cases provides valuable insights into the underlying nature of transmission. Our estimates from yearly and average annual case notification data represent the first detailed estimates of dengue transmission intensity in Jakarta’s subdistricts. These will be important to consider when assessing the population-level impact and cost-effectiveness of potential control and prevention programmes in Jakarta province, such as the controlled release of Wolbachia-carrying mosquitoes and vaccination.

Characterization of the spatiotemporal distribution of dengue transmission intensity, a key measure of population infection risk, can inform the optimal use and deployment of control and prevention programmes. Jakarta, the capital of Indonesia, is a large urban centre with hyperendemic dengue transmission. We fitted catalytic models to age-stratified dengue surveillance data reported in Jakarta’s subdistricts from 2008 to 2017. We estimated a long-term average annual transmission intensity of 0.130 (95%CrI: 0.129–0.131) per year in Jakarta province, which varied across subdistricts from 0.090 (95%CrI: 0.077–0.103) per year in Sawah Besar to 0.164 (95%CrI: 0.153–0.174) per year in Pasar Rebo. We observed significant spatiotemporal variation and clustering of transmission intensity in Jakarta. Our estimates obtained from the analysis of yearly and cumulative case-notification data reported between 2008 and 2017 represent the first detailed estimates of average dengue transmission intensity, which will be key to assess the potential impact of future control and prevention programmes in Jakarta province.

Key Findings and Comparisons From Analogous Case-Cluster Studies for Dengue Virus Infection Conducted in Machala, Ecuador, and Kamphaeng Phet, Thailand

Dengue viruses (DENV) pose a significant and increasing threat to human health across broad regions of the globe. Currently, prevention, control, and treatment strategies are limited. Promising interventions are on the horizon, including multiple vaccine candidates under development and a renewed and innovative focus on controlling the vector, Aedes aegypti. However, significant gaps persist in our understanding of the similarities and differences in DENV epidemiology across regions of potential implementation and evaluation. In this manuscript, we highlight and compare findings from two analogous cluster-based studies for DENV transmission and pathogenesis conducted in Thailand and Ecuador to identify key features and questions for further pursuit. Despite a remarkably similar incidence of DENV infection among enrolled neighborhood contacts at the two sites, we note a higher occurrence of secondary infection and severe illness in Thailand compared to Ecuador. A higher force of infection in Thailand, defined as the incidence of infection among susceptible individuals, is suggested by the higher number of captured Aedes mosquitoes per household, the increasing proportion of asymptomatic infections with advancing age, and the high proportion of infections identified as secondary-type infections by serology. These observations should be confirmed in long-term, parallel prospective cohort studies conducted across regions, which would advantageously permit characterization of baseline immune status (susceptibility) and contemporaneous assessment of risks and risk factors for dengue illness.

Considerations for the 2030 Sustainable Development Goals for dengue

Dengue circulates endemically in many tropical and subtropical regions. In 2012, the World Health Organization (WHO) set out goals to reduce dengue mortality and morbidity by 50% and 25%, respectively, between 2010 and 2020. These goals will not be met. This is, in part, due to existing interventions being insufficiently effective to prevent spread. Further, complex and variable patterns of disease presentation coupled with imperfect surveillance systems mean that even tracking changes in burden is rarely possible. As part of the Sustainable Development Goals, WHO will propose new dengue-specific goals for 2030. The 2030 goals provide an opportunity for focused action on tackling dengue burden but should be carefully developed to be ambitious but also technically feasible. Here we discuss the potential for clearly defined case fatality rates and the rollout of new and effective intervention technologies to form the foundation of these future goals. Further, we highlight how the complexity of dengue epidemiology limits the feasibility of goals that instead target dengue outbreaks.

Projected costs associated with school-based screening to inform deployment of Dengvaxia: Vietnam as a case study

Background

After new analysis, Sanofi Pasteur now recommends their dengue vaccine (Dengvaxia) should only be given to individuals previously infected with dengue and the World Health Organization's recommendations regarding its use are currently being revised. As a result, the potential costs of performing large-scale individual dengue screening and/or dengue serosurveys have become an important consideration for decision making by policymakers in dengue-endemic areas.

Methods

We used an ingredients-based approach to estimate the financial costs for conducting both a school-based dengue serosurvey and school-based individual dengue screening within a typical province in Vietnam, using an existing commercial indirect immunoglobulin G enzyme-linked immunosorbent assay kit. This costing is hypothetical and based on estimates regarding the resources that would be required to perform such activities.

Results

We estimated that performing a school-based individual screening of 9-year-olds would cost US$9.25 per child tested or US$197,827 in total for a typical province. We also estimated that a school-based serosurvey would cost US$10,074, assuming one class from each of the grades that include 8- to 11-year-olds are sampled at each of the 12 selected schools across the province.

Conclusions

The study indicates that using this vaccine safely on a large-scale will incur noteworthy operational costs. It is crucial that these be considered in future cost-effectiveness analyses informing how and where the vaccine is deployed.

Serological inference of past primary and secondary dengue infection: implications for vaccination

Owing to the finding that Dengvaxia^® (the only licensed dengue vaccine to date) increases the risk of severe illness among seronegative recipients, the World Health Organization has recommended screening individuals for their serostatus prior to vaccination. To decide whether and how to carry out screening, it is necessary to estimate the transmission intensity of dengue and to understand the performance of the screening method. In this study, we inferred the annual force of infection (FOI; a measurement of transmission intensity) of dengue virus in three locations in Vietnam: An Giang (FOI = 0.04 for the below 10 years age group and FOI = 0.20 for the above 10 years age group), Ho Chi Minh City (FOI = 0.12) and Quang Ngai (FOI = 0.05). In addition, we show that using a quantitative approach to immunoglobulin G (IgG) levels (measured by indirect enzyme-linked immunosorbent assays) can help to distinguish individuals with primary exposures (primary seropositive) from those with secondary exposures (secondary seropositive). We found that primary-seropositive individuals—the main targets of the vaccine—tend to have a lower IgG level, and, thus, they have a higher chance of being misclassified as seronegative than secondary-seropositive cases. However, screening performance can be improved by incorporating patient age and transmission intensity into the interpretation of IgG levels.

Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015-2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R₀. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.

Opportunities for improved surveillance and control of dengue from age-specific case data

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.

Comparison of stochastic and deterministic frameworks in dengue modelling

We perform estimations of compartment models for dengue transmission in rural Cambodia with increasing complexity regarding both model structure and the account for stochasticity. On the one hand, we successively account for three embedded sources of stochasticity: observation noise, demographic variability and environmental hazard. On the other hand, complexity in the model structure is increased by introducing vector-borne transmission, explicit asymptomatic infections and interacting virus serotypes. Using two sources of case data from dengue epidemics in Kampong Cham (Cambodia), models are estimated in the bayesian framework, with Markov Chain Monte Carlo and Particle Markov Chain Monte Carlo. We highlight the advantages and drawbacks of the different formulations in a practical setting. Although in this case the deterministic models provide a good approximation of the mean trajectory for a low computational cost, the stochastic frameworks better reflect and account for parameter and simulation uncertainty.

Deliberations of the Strategic Advisory Group of Experts on Immunization on the use of CYD-TDV dengue vaccine

The Strategic Advisory Group of Experts (SAGE) on Immunization advises WHO on global policies for vaccines. In April, 2016, SAGE issued recommendations on the use of the first licenced dengue vaccine, CYD-TDV. In November, 2017, a retrospective analysis of clinical trial data, stratifying participants according to their dengue serostatus before the first vaccine dose, showed that although in high seroprevalence settings the vaccine provides overall population benefit, there was an excess risk of severe dengue in seronegative vaccinees. SAGE's working group on dengue vaccines met to discuss the new data and mainly considered two vaccination strategies: vaccination of populations with dengue seroprevalence rates above 80% or screening of individuals before vaccination, and vaccinating only seropositive individuals. We report on the deliberations that informed the recommendation of the pre-vaccination screening strategy, in April, 2018. Important research and implementation questions remain for CYD-TDV, including the development of a highly sensitive and specific rapid diagnostic test to determine serostatus, simplified immunisation schedules, and assessment of the need for booster doses.

Dynamics and determinants of the force of infection of dengue virus from 1994 to 2015 in Managua, Nicaragua

Dengue virus (DENV) is the most prevalent human vector-borne viral disease. The force of infection (FoI), the rate at which susceptible individuals are infected in a population, is an important metric for infectious disease modeling. Understanding how and why the FoI of DENV changes over time is critical for developing immunization and vector control policies. We used age-stratified seroprevalence data from 12 years of the Pediatric Dengue Cohort Study in Nicaragua to estimate the annual FoI of DENV from 1994 to 2015. Seroprevalence data revealed a change in the rate at which children acquire DENV-specific immunity: in 2004, 50% of children age >4 years were seropositive, but by 2015, 50% seropositivity was reached only by age 11 years. We estimated a spike in the FoI in 1997-1998 and 1998-1999 and a gradual decline thereafter, and children age <4 years experienced a lower FoI. Two hypotheses to explain the change in the FoI were tested: (i) a transition from introduction of specific DENV serotypes to their endemic transmission and (ii) a population demographic transition due to declining birth rates and increasing life expectancy. We used mathematical models to simulate these hypotheses. We show that the initial high FoI can be explained by the introduction of DENV-3 in 1994-1998, and that the overall gradual decline in the FoI can be attributed to demographic shifts. Changes in immunity and demographics strongly impacted DENV transmission in Nicaragua. Population-level measures of transmission intensity are dynamic and thus challenging to use to guide vaccine implementation locally and globally.

Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity

BACKGROUND

Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data.

METHODS

We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data.

RESULTS

We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting.

CONCLUSIONS

Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns.

Assessment of benefits and risks associated with dengue vaccination at the individual and population levels: a dynamic modeling approach

BACKGROUND

A case-cohort study, using a novel assay and data from three dengue vaccine efficacy trials, highlighted differences in vaccination outcomes according to baseline serostatus. Based on these results, we explored, with a model, the benefits and risks associated with vaccination.

RESEARCH DESIGN AND METHODS

Parameters of a previously developed transmission model were estimated with subject-level data from a case-cohort study. The model was used to assess vaccination outcomes for a range of transmission settings over 5-30 years, with or without indirect protection.

MAIN OUTCOME MEASURES

Symptomatic dengue cases, dengue hospitalizations, and severe dengue cases.

RESULTS

The model is consistent with previous results indicating a transitory period at increased risk for dengue-seronegative vaccine recipients (setting-dependent duration) and long-term benefits for dengue-seropositive recipients. At the population level, benefits to seropositive individuals over 10 years outweighed the risk to those seronegative in moderate to high transmission settings (≥50% seropositivity at age 9), especially in high transmission settings (no excess hospitalizations in dengue-seronegative for ≥80% seropositivity at age 9). Results were more favorable when longer time horizons or indirect protection were considered.

CONCLUSIONS

Results indicate a public health benefit associated with dengue vaccination especially in high-transmission settings, even with the initial excess risks to dengue-seronegative patients which diminish over time.

Targeting vaccinations for the licensed dengue vaccine: Considerations for serosurvey design

BACKGROUND

The CYD-TDV vaccine was unusual in that the recommended target population for vaccination was originally defined not only by age, but also by transmission setting as defined by seroprevalence. WHO originally recommended countries consider vaccination against dengue with CYD-TDV vaccine in geographic settings only where prior infection with any dengue serotype, as measured by seroprevalence, was >170% in the target age group. Vaccine was not recommended in settings where seroprevalence was <50%. Test-and-vaccinate strategies suggested following new analysis by Sanofi will still require age-stratified seroprevalence surveys to optimise age-group targeting. Here we address considerations for serosurvey design in the context of vaccination program planning.

METHODS

To explore how the design of seroprevalence surveys affects estimates of transmission intensity, 100 age-specific seroprevalence surveys were simulated using a beta-binomial distribution and a simple catalytic model for different combinations of age-range, survey size, transmission setting, and test sensitivity/specificity. We then used a Metropolis-Hastings Markov Chain Monte-Carlo algorithm to estimate the force of infection from each simulated dataset.

RESULTS

Sampling from a wide age-range led to more accurate estimates than merely increasing sample size in a narrow age-range. This finding was consistent across all transmission settings. The optimum test sensitivity and specificity given an imperfect test differed by setting with high sensitivity being important in high transmission settings and high specificity important in low transmission settings.

CONCLUSIONS

When assessing vaccination suitability by seroprevalence surveys, countries should ensure an appropriate age-range is sampled, considering epidemiological evidence about the local burden of disease.

Temperature modulates dengue virus epidemic growth rates through its effects on reproduction numbers and generation intervals

Epidemic growth rate, r, provides a more complete description of the potential for epidemics than the more commonly studied basic reproduction number, R₀, yet the former has never been described as a function of temperature for dengue virus or other pathogens with temperature-sensitive transmission. The need to understand the drivers of epidemics of these pathogens is acute, with arthropod-borne virus epidemics becoming increasingly problematic. We addressed this need by developing temperature-dependent descriptions of the two components of r—R₀ and the generation interval—to obtain a temperature-dependent description of r. Our results show that the generation interval is highly sensitive to temperature, decreasing twofold between 25 and 35°C and suggesting that dengue virus epidemics may accelerate as temperatures increase, not only because of more infections per generation but also because of faster generations. Under the empirical temperature relationships that we considered, we found that r peaked at a temperature threshold that was robust to uncertainty in model parameters that do not depend on temperature. Although the precise value of this temperature threshold could be refined following future studies of empirical temperature relationships, the framework we present for identifying such temperature thresholds offers a new way to classify regions in which dengue virus epidemic intensity could either increase or decrease under future climate change.

Recurrent, rapidly intensifying epidemics of dengue–the world’s most prevalent mosquito-borne viral disease–pose a challenge to healthcare systems throughout the tropical and subtropical world. An acute disease that tends to respond well to proper treatment, the sometimes intense nature of dengue epidemics has been known to overwhelm healthcare systems and elevate the morbidity and mortality of patients left without adequate medical treatment under peak epidemic conditions. Here, we quantify the temperature dependence of dengue epidemic intensity by quantifying two distinct determinants of epidemic growth rate: the average number of secondary infections arising from each primary infection and the average time between successive infections in humans. Our results show that the time between successive infections in humans decreases steadily with increasing temperatures, whereas the average number of secondary infections peaks at intermediate temperatures. Altogether, this suggests a peak temperature for dengue epidemic intensity. Applying this result to global temperature projections under future climate change scenarios suggests that dengue epidemics in many regions of the world could become more intense under future temperature increases.