Decreases in dengue transmission may act to increase the incidence of dengue hemorrhagic fever

Projection of dengue fever transmissibility under climate change in South and Southeast Asian countries

Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.

Effects of boosting and waning in highly exposed populations on dengue epidemic dynamics

Sequential infection with multiple dengue virus (DENV) serotypes is thought to induce enduring protection against dengue disease. However, long-term antibody waning has been observed after repeated DENV infection. Here, we provide evidence that highly immune Nicaraguan children and adults (n = 4478) experience boosting and waning of antibodies during and after major Zika and dengue epidemics. We develop a susceptible-infected-recovered-susceptible (SIRS-type) model that tracks immunity by titer rather than number of infections to show that boosts in highly immune individuals can contribute to herd immunity, delaying their susceptibility to transmissible infection. In contrast, our model of lifelong immunity in highly immune individuals, as previously assumed, results in complete disease eradication after introduction. Periodic epidemics under this scenario can only be sustained with a constant influx of infected individuals into the population or a high basic reproductive number. We also find that Zika virus infection can boost DENV immunity and produce delays and then surges in dengue epidemics, as observed with real epidemiological data. This work provides insight into factors shaping periodicity in dengue incidence and may inform vaccine efforts to maintain population immunity.

Temporary Cross-Immunity as a Plausible Driver of Asynchronous Cycles of Dengue Serotypes

Many infectious diseases exist as multiple variants, with interactions between variants potentially driving epidemiological dynamics. These diseases include dengue, which infects hundreds of millions of people every year and exhibits complex multi-serotype dynamics. Antibodies produced in response to primary infection by one of the four dengue serotypes can produce a period of temporary cross-immunity (TCI) to infection by other serotypes. After this period, the remaining antibodies can facilitate the entry of heterologous serotypes into target cells, thus enhancing severity of secondary infection by a heterologous serotype. This represents antibody-dependent enhancement (ADE). In this study, we analyze an epidemiological model to provide novel insights into the importance of TCI and ADE in producing cyclic outbreaks of dengue serotypes. Our analyses reveal that without TCI, such cyclic outbreaks are synchronous across serotypes and only occur when ADE produces high transmission rates. In contrast, the presence of TCI allows asynchronous cycles of serotypes by inducing a time lag between recovery from primary infection by one serotype and secondary infection by another, with such cycles able to occur without ADE. Our results suggest that TCI is a fundamental driver of asynchronous cycles of dengue serotypes and possibly other multi-variant diseases.

Hygienic behaviors during the COVID-19 pandemic may decrease immunoglobulin G levels: Implications for Kawasaki disease

Background

Due to the coronavirus disease 2019 (COVID-19) pandemic, hygienic behaviors became a new norm since January 2020. The hygiene hypothesis predicts that an excessively hygienic environment may adversely affect human health.

Objective

We quantified the effect of COVID-19 on immunological parameters linked to the hygiene hypothesis.

Methods

We examined age-specific levels of total nonspecific immunoglobulin G (IgG) and IgE in individuals who visited Fukuoka Tokushukai Hospital between 2010 and 2021. Pre-COVID (2010–2019) and COVID (2020–2021) periods were compared.

Results

IgG levels steadily decreased throughout Pre-COVID period. IgG levels fell abruptly from the pre-COVID period to the COVID period in all age groups (P = 0.0271, < 0.3 years; P = 0.0096, 0.3–5 years; P = 0.0074, ≥ 5 years). The declines in IgG in < 0.3 years and that in ≥ 5 years accelerated during the COVID period. IgE levels were seasonal, but did not change noticeably from the pre-COVID to COVID period. IgG levels recorded for patients with Kawasaki disease (KD) (mean 709 mg/dL) were significantly lower than for matched control subjects (826 mg/dL) (P<0.0001).

Discussion

Hygienic behaviors during the COVID-19 outbreak decreased the chance of infection, which may explain the decreases in IgG levels in children and adults. Neonatal IgG declined, possibly because of the decrease in maternal IgG.

Conclusion

Hygienic behaviors decreased the IgG levels in all age groups, from neonates to adults. This downturn in IgG may lead to vulnerability to infections as well as to KD.

Assessing the role of multiple mechanisms increasing the age of dengue cases in Thailand

The age of reported dengue hemorrhagic fever (DHF) cases, the severe form of dengue infections, has been increasing in Thailand for four decades. Factors underlying this shift remain poorly understood, challenging public health planning. Here, we found aging of the population and its effect on the hazard of transmission to be the dominant contributors, with temporal changes in surveillance practices playing a lesser role. With ongoing population aging, we expect a continuing shift of DHF toward older individuals, heightening the chance of clinical complications with comorbidities. With most other highly endemic countries facing similar shifts in age structure, the pattern is expected to appear elsewhere. Awareness is needed to improve diagnosis and treatment.

The mean age of dengue hemorrhagic fever (DHF) cases increased considerably in Thailand from 8.1 to 24.3 y between 1981 and 2017 (mean annual increase of 0.45 y). Alternative proposed explanations for this trend, such as changes in surveillance practices, reduced mosquito–human contact, and shifts in population demographics, have different implications for global dengue epidemiology. To evaluate the contribution of each of these hypothesized mechanisms to the observed data, we developed 20 nested epidemiological models of dengue virus infection, allowing for variation over time in population demographics, infection hazards, and reporting rates. We also quantified the effect of removing or retaining each source of variation in simulations of the age trajectory. Shifts in the age structure of susceptibility explained 58% of the observed change in age. Adding heterogeneous reporting by age and reductions in per-serotype infection hazard to models with shifts in susceptibility explained an additional 42%. Reductions in infection hazards were mostly driven by changes in the number of infectious individuals at any time (another consequence of shifting age demographics) rather than changes in the transmissibility of individual infections. We conclude that the demographic transition drives the overwhelming majority of the observed change as it changes both the age structure of susceptibility and the number of infectious individuals. With the projected Thai population age structure, our results suggest a continuing increase in age of DHF cases, shifting the burden toward individuals with more comorbidity. These insights into dengue epidemiology may be relevant to many regions of the globe currently undergoing comparable changes in population demographics.

Knowledge gaps in the epidemiology of severe dengue impede vaccine evaluation

The most severe consequences of dengue virus infection include shock, haemorrhage, and major organ failure; however, the frequency of these manifestations varies, and the relative contribution of pre-existing anti-dengue virus antibodies, virus characteristics, and host factors (including age and comorbidities) are not well understood. Reliable characterisation of the epidemiology of severe dengue first depends on the use of consistent definitions of disease severity. As vaccine trials have shown, severe dengue is a crucial interventional endpoint, yet the infrequency of its occurrence necessitates the inclusion of thousands of study participants to appropriately compare its frequency among participants who have and have not been vaccinated. Hospital admission is frequently used as a proxy for severe dengue; however, lack of specificity and variability in clinical practices limit the reliability of this approach. Although previous infection with a dengue virus is the best characterised risk factor for developing severe dengue, the influence of the timing between dengue virus infections and the sequence of dengue virus infections on disease severity is only beginning to be elucidated. To improve our understanding of the diverse factors that shape the clinical spectrum of disease resulting from dengue virus infection, prospective, community-based and clinic-based immunological, virological, genetic, and clinical studies across a range of ages and geographical regions are needed.

Impacts of El Niño Southern Oscillation on the dengue transmission dynamics in the Metropolitan Region of Recife, Brazil

Background:

This research addresses two questions: (1) how El Niño Southern Oscillation (ENSO) affects climate variability and how it influences dengue transmission in the Metropolitan Region of Recife (MRR), and (2) whether the epidemic in MRR municipalities has any connection and synchronicity.

Methods:

Wavelet analysis and cross-correlation were applied to characterize seasonality, multiyear cycles, and relative delays between the series. This study was developed into two distinct periods. Initially, we performed periodic dengue incidence and intercity epidemic synchronism analyses from 2001 to 2017. We then defined the period from 2001 to 2016 to analyze the periodicity of climatic variables and their coherence with dengue incidence.

Results:

Our results showed systematic cycles of 3-4 years with a recent shortening trend of 2-3 years. Climatic variability, such as positive anomalous temperatures and reduced rainfall due to changes in sea surface temperature (SST), is partially linked to the changing epidemiology of the disease, as this condition provides suitable environments for the Aedes aegypti lifecycle.

Conclusion:

ENSO may have influenced the dengue temporal patterns in the MRR, transiently reducing its main way of multiyear variability (3-4 years) to 2-3 years. Furthermore, when the epidemic coincided with El Niño years, it spread regionally and was highly synchronized.

Antigenic evolution of dengue viruses over 20 years

Infection with one of dengue viruses 1 to 4 (DENV1-4) induces protective antibodies against homotypic infection. However, a notable feature of dengue viruses is the ability to use preexisting heterotypic antibodies to infect Fcγ receptor–bearing immune cells, leading to higher viral load and immunopathological events that augment disease. We tracked the antigenic dynamics of each DENV serotype by using 1944 sequenced isolates from Bangkok, Thailand, between 1994 and 2014 (348 strains), in comparison with regional and global DENV antigenic diversity (64 strains). Over the course of 20 years, the Thailand DENV serotypes gradually evolved away from one another. However, for brief periods, the serotypes increased in similarity, with corresponding changes in epidemic magnitude. Antigenic evolution within a genotype involved a trade-off between two types of antigenic change (within-serotype and between-serotype), whereas genotype replacement resulted in antigenic change away from all serotypes. These findings provide insights into theorized dynamics in antigenic evolution.

Dengue and Zika virus infections in children elicit cross-reactive protective and enhancing antibodies that persist long term

[Figure: see text].

Boosting can explain patterns of fluctuations of ratios of inapparent to symptomatic dengue virus infections

Dengue is the most prevalent arboviral disease worldwide, and the four dengue virus (DENV) serotypes circulate endemically in many tropical and subtropical regions. Numerous studies have shown that the majority of DENV infections are inapparent, and that the ratio of inapparent to symptomatic infections (I/S) fluctuates substantially year-to-year. For example, in the ongoing Pediatric Dengue Cohort Study (PDCS) in Nicaragua, which was established in 2004, the I/S ratio has varied from 16.5:1 in 2006-2007 to 1.2:1 in 2009-2010. However, the mechanisms explaining these large fluctuations are not well understood. We hypothesized that in dengue-endemic areas, frequent boosting (i.e., exposures to DENV that do not lead to extensive viremia and result in a less than fourfold rise in antibody titers) of the immune response can be protective against symptomatic disease, and this can explain fluctuating I/S ratios. We formulate mechanistic epidemiologic models to examine the epidemiologic effects of protective homologous and heterologous boosting of the antibody response in preventing subsequent symptomatic DENV infection. We show that models that include frequent boosts that protect against symptomatic disease can recover the fluctuations in the I/S ratio that we observe, whereas a classic model without boosting cannot. Furthermore, we show that a boosting model can recover the inverse relationship between the number of symptomatic cases and the I/S ratio observed in the PDCS. These results highlight the importance of robust dengue control efforts, as intermediate dengue control may have the potential to decrease the protective effects of boosting.

Evolution and epidemiologic dynamics of dengue virus in Nicaragua during the emergence of chikungunya and Zika viruses

Arthropod-borne viruses (arboviruses) comprise a significant and ongoing threat to human health, infecting hundreds of millions annually. Three such arboviruses include circumtropical dengue, Zika, and chikungunya viruses, exhibiting continuous emergence primarily via Aedes mosquito vectors. Nicaragua has experienced endemic dengue virus (DENV) transmission involving multiple serotypes since 1985, with chikungunya virus (CHIKV) reported in 2014-2015, followed by Zika virus (ZIKV) first reported in 2016. In order to identify patterns of genetic variation and selection pressures shaping the evolution of co-circulating DENV serotypes in light of the arrival of CHIKV and ZIKV, we employed whole-genome sequencing on an Illumina MiSeq platform of random-amplified total RNA libraries to characterize 42 DENV low-passage isolates, derived from viremic patients in Nicaragua between 2013 and 2016. Our approach also revealed clinically undetected co-infections with CHIKV. Of the three DENV serotypes (1, 2, and 3) co-circulating during our study, we uncovered distinct patterns of evolution using comparative phylogenetic inference. DENV-1 genetic variation was structured into two distinct co-circulating lineages with no evidence of positive selection in the origins of either lineage, suggesting they are equally fit. In contrast, the evolutionary history of DENV-2 was marked by positive selection, and a unique, divergent lineage correlated with high epidemic potential emerged in 2015 to drive an outbreak in 2016. DENV-3 genetic variation remained unstructured into lineages throughout the period of study. Thus, this study reveals insights into evolutionary and epidemiologic trends exhibited during the circulation of multiple arboviruses in Nicaragua.

Considering Genomic and Immunological Correlates of Protection for a Dengue Intervention

Over three billion are at risk of dengue infection with more than 100 million a year presenting with symptoms that can lead to deadly haemorrhagic disease. There are however no treatments available and the only licensed vaccine shows limited efficacy and is able to enhance the disease in some cases. These failures have mainly been due to the complex pathology and lack of understanding of the correlates of protection for dengue virus (DENV) infection. With increasing data suggesting both a protective and detrimental effect for antibodies and CD8 T-cells whilst having complex environmental dynamics. This review discusses the roles of genomic and immunological aspects of DENV infection, providing both a historical interpretation and fresh discussion on how this information can be used for the next generation of dengue interventions.

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.

Dengue genetic divergence generates within-serotype antigenic variation, but serotypes dominate evolutionary dynamics

Dengue virus (DENV) exists as four genetically distinct serotypes, each of which is historically assumed to be antigenically uniform. Recent analyses suggest that antigenic heterogeneity may exist within each serotype, but its source, extent and impact remain unclear. Here, we construct a sequence-based model to directly map antigenic change to underlying genetic divergence. We identify 49 specific substitutions and four colinear substitution clusters that robustly predict dengue antigenic relationships. We report moderate antigenic diversity within each serotype, resulting in genotype-specific patterns of heterotypic cross-neutralization. We also quantify the impact of antigenic variation on real-world DENV population dynamics, and find that serotype-level antigenic fitness is a dominant driver of dengue clade turnover. These results provide a more nuanced understanding of the relationship between dengue genetic and antigenic evolution, and quantify the effect of antigenic fitness on dengue evolutionary dynamics.

Spatial-Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in São Paulo, Brazil

Dengue fever, a disease caused by a vector-borne flavivirus, is endemic to tropical countries, but its occurrence has been reported worldwide. This study aimed to understand important factors contributing to the spatial and temporal patterns of dengue occurrence in São Paulo, the largest municipality of Brazil. The temporal assessment of dengue occurrence covered the 2011-2016 time period and was based on climatological data, such as the El Niño indices and time series statistical tools such as the continuous wavelet transformation. The spatial assessment used Landsat 8 data for years 2014-2016 to estimate land surface temperature and normalized indices for vegetation, urban areas, and leaf water. Results from a cross correlation for the temporal analysis found a relationship between the sea surface temperature anomalies index and the number of reported dengue cases in São Paulo (r = 0.5) with a lag of +29 (weeks) between the climatic event and the response on the dengue incidence. This relationship, initially nonlinear, became linear after correcting for the lag period. For the spatial assessment, the linear stepwise regression model detected a low relationship between dengue incidence and minimum surface temperature (r = 0.357) and no relationship with other environmental parameters. The poor relationship might be due to confounding effects of socioeconomic factors as these seem to influence the spatial dynamics of dengue incidence. More testing is needed to validate these methods in other locations. Nevertheless, we presented possible tools to be used for the improvement of dengue control programs.

Force of Infection and True Infection Rate of Dengue in Singapore: Implications for Dengue Control and Management

National data on dengue notifications do not capture all dengue infections and do not reflect the true intensity of disease transmission. To assess the true dengue infection rate and disease control efforts in Singapore, we conducted age-stratified serosurveys among residents after a 2013 outbreak that was the largest dengue outbreak on record. The age-weighted prevalence of dengue immunoglobulin G among residents was 49.8% (95% confidence interval: 48.4, 51.1) in 2013 and 48.6% (95% confidence interval: 47.0, 50.0) in 2017; prevalence increased with age. Combining these data with those from previous serosurveys, the year-on-year estimates of the dengue force of infection from 1930 to 2017 revealed a significant decrease from the late 1960s to the mid-1990s, after which the force of infection remained stable at approximately 10 per 1,000 persons per year. The reproduction number (R₀) had also declined since the 1960s. The reduction in dengue transmission may be attributed to the sustained national vector program and partly to a change in the age structure of the population. The improved estimated ratio of notified cases to true infections, from 1:14 in 2005–2009 to 1:6 in 2014–2017, signifies that the national notification system, which relies on diagnosed cases, has improved over time. The data also suggest that the magnitudes of dengue epidemics cannot be fairly compared across calendar years and that the current disease control program remains applicable.

Cross-serotype interactions and disease outcome prediction of dengue infections in Vietnam

Dengue pathogenesis is extremely complex. Dengue infections are thought to induce life-long immunity from homologous challenges as well as a multi-factorial heterologous risk enhancement. Here, we use the data collected from a prospective cohort study of dengue infections in schoolchildren in Vietnam to disentangle how serotype interactions modulate clinical disease risk in the year following serum collection. We use multinomial logistic regression to correlate the yearly neutralizing antibody measurements obtained with each infecting serotype in all dengue clinical cases collected over the course of 6 years (2004–2009). This allowed us to extrapolate a fully discretised matrix of serotype interactions, revealing clear signals of increased risk of clinical illness in individuals primed with a previous dengue infection. The sequences of infections which produced a higher risk of dengue fever upon secondary infection are: DEN1 followed by DEN2; DEN1 followed by DEN4; DEN2 followed by DEN3; and DEN4 followed by DEN3. We also used this longitudinal data to train a machine learning algorithm on antibody titre differences between consecutive years to unveil asymptomatic dengue infections and estimate asymptomatic infection to clinical case ratios over time, allowing for a better characterisation of the population’s past exposure to different serotypes.

Prediction of annual dengue incidence by hydro-climatic extremes for southern Taiwan

Dengue is one of the most rapidly spreading mosquito-borne viral diseases in the world. An increase in the incidence of dengue is commonly thought to be a consequence of variability of weather conditions. Taiwan, which straddles the Tropic of Cancer, is an excellent place to study the relationship between weather conditions and dengue fever cases since the island forms an isolated geographic environment. Therefore, clarifying the association between extreme weather conditions and annual dengue incidence is one of important issues for epidemic early warning. In this paper, we develop a Poisson regression model with extreme weather parameters for prediction of annual dengue incidence. A leave-one-out method is used to evaluate the performance of predicting dengue incidence. Our results indicate that dengue transmission has a positive relationship with the minimum temperature predictors during the early summer while a negative relationship with all the maximum 24-h rainfall predictors during the early epidemic phase of dengue outbreaks. Our findings provide a better understanding of the relationships between extreme weather and annual trends in dengue cases in Taiwan and it could have important implications for dengue forecasts in surrounding areas with similar meteorological conditions.

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.

Potential effects of climate change on dengue transmission dynamics in Korea

Dengue fever is a major international public health concern, with more than 55% of the world population at risk of infection. Recent climate changes related to global warming have increased the potential risk of domestic outbreaks of dengue in Korea. In this study, we develop a two-strain dengue model associated with climate-dependent parameters based on Representative Concentration Pathway (RCP) scenarios provided by the Korea Meteorological Administration. We assess the potential risks of dengue outbreaks by means of the vector capacity and intensity under various RCP scenarios. A sensitivity analysis of the temperature-dependent parameters is performed to explore the effects of climate change on dengue transmission dynamics. Our results demonstrate that a higher temperature significantly enhances the potential threat of domestic dengue outbreaks in Korea. Furthermore, we investigate the effects of countermeasures on the cumulative incidence of humans and vectors. The current main control measures (comprising only travel restrictions) for infected humans in Korea are not as effective as combined control measures (travel restrictions and vector control), dramatically reducing the possibilities of dengue outbreaks.

Transmission-clearance trade-offs indicate that dengue virulence evolution depends on epidemiological context

An extensive body of theory addresses the topic of pathogen virulence evolution, yet few studies have empirically demonstrated the presence of fitness trade-offs that would select for intermediate virulence. Here we show the presence of transmission-clearance trade-offs in dengue virus using viremia measurements. By fitting a within-host model to these data, we further find that the interaction between dengue and the host immune response can account for the observed trade-offs. Finally, we consider dengue virulence evolution when selection acts on the virus’s production rate. By combining within-host model simulations with empirical findings on how host viral load affects human-to-mosquito transmission success, we show that the virus’s transmission potential is maximized at production rates associated with intermediate virulence and that the optimal production rate critically depends on dengue’s epidemiological context. These results indicate that long-term changes in dengue’s global distribution impact the invasion and spread of virulent dengue virus genotypes.

Theory predicts that pathogens will evolve towards intermediate virulence, yet the necessary trade-offs invoked by this theory have rarely been demonstrated empirically. Here, the authors show that dengue virus dynamics exhibit a trade-off between transmission and clearance rates.

Challenges in dengue research: A computational perspective

The dengue virus is now the most widespread arbovirus affecting human populations, causing significant economic and social impact in South America and South-East Asia. Increasing urbanization and globalization, coupled with insufficient resources for control, misguided policies or lack of political will, and expansion of its mosquito vectors are some of the reasons why interventions have so far failed to curb this major public health problem. Computational approaches have elucidated on dengue's population dynamics with the aim to provide not only a better understanding of the evolution and epidemiology of the virus but also robust intervention strategies. It is clear, however, that these have been insufficient to address key aspects of dengue's biology, many of which will play a crucial role for the success of future control programmes, including vaccination. Within a multiscale perspective on this biological system, with the aim of linking evolutionary, ecological and epidemiological thinking, as well as to expand on classic modelling assumptions, we here propose, discuss and exemplify a few major computational avenues-real-time computational analysis of genetic data, phylodynamic modelling frameworks, within-host model frameworks and GPU-accelerated computing. We argue that these emerging approaches should offer valuable research opportunities over the coming years, as previously applied and demonstrated in the context of other pathogens.

Analysis of the optimal vaccination age for dengue in Brazil with a tetravalent dengue vaccine

In this paper we study a mathematical model to analyse the optimal vaccination age against Dengue in Brazil. Data from Brazil are used to estimate the basic reproduction numbers for each of the four Dengue serotypes and then the optimal vaccination age is calculated using a method due to Hethcote [1]. The vaccine has different efficacies against each serotype. Vaccination that is too early is ineffective as individuals are protected by maternal antibodies but leaving vaccination until later may allow the disease to spread. First of all the optimal vaccination ages are calculated where there is just one serotype in circulation and then when there are multiple serotypes. The calculations are done using data both assuming constant vaccine efficacy and age-dependent vaccine efficacy against a given serotype. The multiple serotype calculations are repeated assuming that the first infection is a risky infection and that it is not (to model Dengue Antibody Enhancement). The calculations are then repeated when any third or fourth Dengue infections are asymptomatic, so that two Dengue infections with different serotypes provide effective permanent immunity. The calculations are also repeated when the age-dependent risk function (fitted to Brazilian data) is hospitalisation from Dengue and when it is mortality due to Dengue. We find a wide variety of optimal vaccination ages depending on both the serotypes in circulation and the assumptions of the model.

Immune status alters the probability of apparent illness due to dengue virus infection: Evidence from a pooled analysis across multiple cohort and cluster studies

Dengue is an important vector-borne pathogen found across much of the world. Many factors complicate our understanding of the relationship between infection with one of the four dengue virus serotypes, and the observed incidence of disease. One of the factors is a large proportion of infections appear to result in no or few symptoms, while others result in severe infections. Estimates of the proportion of infections that result in no symptoms (inapparent) vary widely from 8% to 100%, depending on study and setting. To investigate the sources of variation of these estimates, we used a flexible framework to combine data from multiple cohort studies and cluster studies (follow-up around index cases). Building on previous observations that the immune status of individuals affects their probability of apparent disease, we estimated the probability of apparent disease among individuals with different exposure histories. In cohort studies mostly assessing infection in children, we estimated the proportion of infections that are apparent as 0.18 (95% Credible Interval, CI: 0.16, 0.20) for primary infections, 0.13 (95% CI: 0.05, 0.17) for individuals infected in the year following a first infection (cross-immune period), and 0.41 (95% CI: 0.36, 0.45) for those experiencing secondary infections after this first year. Estimates of the proportion of infections that are apparent from cluster studies were slightly higher than those from cohort studies for both primary and secondary infections, 0.22 (95% CI: 0.15, 0.29) and 0.57 (95% CI: 0.49, 0.68) respectively. We attempted to estimate the apparent proportion by serotype, but current published data were too limited to distinguish the presence or absence of serotype-specific differences. These estimates are critical for understanding dengue epidemiology. Most dengue data come from passive surveillance systems which not only miss most infections because they are asymptomatic and often underreported, but will also vary in sensitivity over time due to the interaction between previous incidence and the symptomatic proportion, as shown here. Nonetheless the underlying incidence of infection is critical to understanding susceptibility of the population and estimating the true burden of disease, key factors for effectively targeting interventions. The estimates shown here help clarify the link between past infection, observed disease, and current transmission intensity.

Cost-Effectiveness of Dengue Vaccination Programs in Brazil

The first approved dengue vaccine, CYD-TDV, a chimeric, live-attenuated, tetravalent dengue virus vaccine, was recently licensed in 13 countries, including Brazil. In light of recent vaccine approval, we modeled the cost-effectiveness of potential vaccination policies mathematically based on data from recent vaccine efficacy trials that indicated that vaccine efficacy was lower in seronegative individuals than in seropositive individuals. In our analysis, we investigated several vaccination programs, including routine vaccination, with various vaccine coverage levels and those with and without large catch-up campaigns. As it is unclear whether the vaccine protects against infection or just against disease, our model incorporated both direct and indirect effects of vaccination. We found that in the presence of vaccine-induced indirect protection, the cost-effectiveness of dengue vaccination decreased with increasing vaccine coverage levels because the marginal returns of herd immunity decreases with vaccine coverage. All routine dengue vaccination programs that we considered were cost-effective, reducing dengue incidence significantly. Specifically, a routine dengue vaccination of 9-year-olds would be cost-effective when the cost of vaccination per individual is less than $262. Furthermore, the combination of routine vaccination and large catch-up campaigns resulted in a greater reduction of dengue burden (by up to 93%) than routine vaccination alone, making it a cost-effective intervention as long as the cost per course of vaccination is $255 or less. Our results show that dengue vaccination would be cost-effective in Brazil even with a relatively low vaccine efficacy in seronegative individuals.

Cost-effectiveness of dengue vaccination in Yucatán, Mexico using a dynamic dengue transmission model

Background

The incidence of dengue fever (DF) is steadily increasing in Mexico, burdening health systems with consequent morbidities and mortalities. On December 9^th, 2015, Mexico became the first country for which the dengue vaccine was approved for use. In anticipation of a vaccine rollout, analysis of the cost-effectiveness of the dengue vaccination program that quantifies the dynamics of disease transmission is essential.

Methods

We developed a dynamic transmission model of dengue in Yucatán, Mexico and its proposed vaccination program to incorporate herd immunity into our analysis of cost-effectiveness analysis. Our model also incorporates important characteristics of dengue epidemiology, such as clinical cross-immunity and susceptibility enhancement upon secondary infection. Using our model, we evaluated the cost-effectiveness and economic impact of an imperfect dengue vaccine in Yucatán, Mexico.

Conclusions

Our study indicates that a dengue vaccination program would prevent 90% of cases of symptomatic DF incidence as well as 90% of dengue hemorrhagic fever (DHF) incidence and dengue-related deaths annually. We conclude that a dengue vaccine program in Yucatán, Mexico would be very cost-effective as long as the vaccination cost per individual is less than $140 and $214 from health care and societal perspectives, respectively. Furthermore, at an exemplary vaccination cost of $250 USD per individual on average, dengue vaccination is likely to be cost-effective 43% and 88% of the time from health care and societal perspectives, respectively.

Optimal control problems of mosquito-borne disease subject to changes in feeding behavior of Aedes mosquitoes

Dengue viruses (DENV) are transmitted to humans by the bite of Aedes mosquitoes. It is known that dengue virus infection in Aedes aegypti female mosquitoes makes a change in the feeding behavior of the infected mosquitoes. In this study, using the forces of infection, we incorporated the effect of changes in the feeding behavior of mosquitoes into the standard vector-borne SIR-SI model. It has been proved that both a single-strain model and a two-strain model exhibit forward bifurcations. Moreover, optimal implementations of control with specific prevention measures for dengue transmission are analyzed. As a result we found that more implementation of controls on the secondary infection of humans should be considered for the behavioral changes in feeding of the infected mosquitoes.

A New Look at an Old Disease: Recent Insights into the Global Epidemiology of Dengue

PURPOSE OF REVIEW

By all measures, the morbidity and mortality due to dengue are continuing to worsen worldwide. Although both early and recent studies have demonstrated regional differences in how dengue affects local populations, these findings were to varying extents related to disparate surveillance approaches.

RECENT FINDINGS

Recent studies have broadened the recognized spectrum of disease resulting from DENV infection, particularly in adults, and have also demonstrated new mechanisms of DENV spread both within and between populations. New results regarding the frequency and duration of homo- and heterotypic anti-DENV antibodies have provided important insights relevant to vaccine design and implementation.

SUMMARY

These observations and findings as well as difficulties in comparing the epidemiology of dengue within and between regions of the world underscore the need for population-based dengue surveillance worldwide. Enhanced surveillance should be implemented to complement passive surveillance in countries in the tropics to establish baseline data in order to define affected populations and evaluate the impact of dengue vaccines and novel vector control interventions.

Synchrony of Dengue Incidence in Ho Chi Minh City and Bangkok

Background

Ho Chi Minh City and Bangkok are highly dengue endemic. The extent to which disease patterns are attributable to local versus regional dynamics remains unclear. To address this gap we compared key transmission parameters across the locations.

Methods and Principal Findings

We used 2003–2009 age-stratified case data to inform catalytic transmission models. Further, we compared the spatial clustering of serotypes within each city. We found that annual case numbers were highly consistent across the two cities (correlation of 0.77, 95% CI: 0.74–0.79) as was the annual force of infection (correlation of 0.57, 95% CI: 0.46–0.68). Serotypes were less similar with serotype-specific correlations ranging from 0.65 for DENV1 to -0.14 for DENV4. Significant spatial clustering of serotypes was observed in HCMC at distances <500m, similar to previous observations from Bangkok.

Discussions

Dengue dynamics are comparable across these two hubs. Low correlation in serotype distribution suggests that similar built environments, vector populations and climate, rather than viral flow drives these observations.

All four serotypes of dengue have circulated endemically throughout Southeast Asia for decades. However, despite the enormous burden of disease, there remains poor understanding of the similarity in disease patterns across the region. We analyzed data from over 100,000 cases of dengue from two of the largest cities in the region, Bangkok and Ho Chi Minh City between 2001 and 2009. We use basic statistical methods to reconstruct the annual probability of infection in the two cities during this time period using methods that are robust to differences in reporting mechanisms. We find that both the epidemic curves and annual probabilities of infection were highly correlated across the cities, however, serotype-specific correlations were far more variable. Finally, we used geocoded case homes from Ho Chi Minh to demonstrate that cases in the city clustered at spatial scales (<500m) similar to that previously observed in Bangkok. These findings show that dengue dynamics are highly comparable across these two urban hubs; however, the low correlation in serotype distribution suggests that similar built environments and climate, rather than viral flow drives these observations.

The Impact of the Newly Licensed Dengue Vaccine in Endemic Countries

Background

With approximately 3 billion people at risk of acquiring the infection, dengue fever is now considered the most important mosquito-borne viral disease in the world, with 390 million dengue infections occurring every year, of which 96 million manifest symptoms with any level of disease severity. Treatment of uncomplicated dengue cases is only supportive and severe dengue cases require hospital intensive care. A vaccine now licensed in several countries and developed by Sanofi Pasteur (CYD-TDV, named Dengvaxia), was able to protect, in the first 25 months of the two Phase III, 66% of a subset of 9–16 year old participants. However, a significantly lower efficacy (including negative vaccine efficacy) was noted for children younger than 9 years of age.

Methodology/Principal Findings

Analysis of year 3 results of phase III trials of Dengvaxia suggest high rates of protection of vaccinated partial dengue immunes but high rates of hospitalizations during breakthrough dengue infections of persons who were vaccinated when seronegative, with vaccine appearing to induce enhancing antibodies (ADE). An age structured model was developed based on Sanofi’s recommendation to vaccinate persons age 945 years in dengue endemic countries. The model was used to explore the clinical burden of two vaccination strategies: 1) Vaccinate 4 or 20% of individuals, ages 9–45 years, seropositives and seronegatives, and 2) vaccinate 4 or 20% of individuals, ages 9–45 years, who are dengue immune only.

Conclusions/Significance

Our results show that vaccinating dengue monotypic immune individuals prevents dengue hospitalizations, but at the same time dengue infections of vaccine-sensitized persons increases hospitalizations. When the vaccine is given only to partial immune individuals, after immunological screening of the population, disease burden decreases considerably.

Caused by four antigenically related but distinct serotypes a tetravalent vaccine is needed to protect against the huge burden of dengue disease. Dengvaxia is a vaccine candidate now licensed in several countries for individuals 9–45 years of age living in endemic countries with at least 50% (preferably 70%) of seroprevalence. Modelers from Sanofi Pasteur have predicted that this vaccine has the potential to reduce by about 50% the disease burden within 5 years when 20% of an endemic country population is vaccinated, thus achieving a World Health Organization dengue prevention goal. In this paper, mathematical modeling is used to investigate the impact of the newly licensed dengue vaccine using different scenarios. Our results show that to achieve significant reduction in disease burden, the vaccination program is most effective if it includes only individuals that have been already exposed to at least one dengue virus. Immunological screening of the population prior to vaccination is advised and vaccination strategies must be planned based on epidemiological disease dynamics for each specific endemic region.

Dengue Dynamics and Vaccine Cost-Effectiveness Analysis in the Philippines

Dengue is one of the most problematic vector-borne diseases in the Philippines, with an estimated 842,867 cases resulting in medical costs of $345 million U.S. dollars annually. In December 2015, the first dengue vaccine, known as chimeric yellow fever virus-dengue virus tetravalent dengue vaccine, was approved for use in the Philippines and is given to children 9 years of age. To estimate the cost-effectiveness of dengue vaccination in the Philippines, we developed an age-structured model of dengue transmission and vaccination. Using our model, we compared two vaccination scenarios entailing routine vaccination programs both with and without catch-up vaccination. Our results indicate that the higher the cost of vaccination, the less cost-effective the dengue vaccination program. With the current dengue vaccination program that vaccinates children 9 years of age, dengue vaccination is cost-effective for vaccination costs up to $70 from a health-care perspective and up to $75 from a societal perspective. Under a favorable scenario consisting of 1 year of catch-up vaccinations that target children 9-15 years of age, followed by regular vaccination of 9-year-old children, vaccination is cost-effective at costs up to $72 from a health-care perspective and up to $78 from a societal perspective. In general, dengue vaccination is expected to reduce the incidence of both dengue fever and dengue hemorrhagic fever /dengue shock syndrome. Our results demonstrate that even at relatively low vaccine efficacies, age-targeted vaccination may still be cost-effective provided the vaccination cost is sufficiently low.

The Long-Term Safety, Public Health Impact, and Cost-Effectiveness of Routine Vaccination with a Recombinant, Live-Attenuated Dengue Vaccine (Dengvaxia): A Model Comparison Study

BACKGROUND

Large Phase III trials across Asia and Latin America have recently demonstrated the efficacy of a recombinant, live-attenuated dengue vaccine (Dengvaxia) over the first 25 mo following vaccination. Subsequent data collected in the longer-term follow-up phase, however, have raised concerns about a potential increase in hospitalization risk of subsequent dengue infections, in particular among young, dengue-naïve vaccinees. We here report predictions from eight independent modelling groups on the long-term safety, public health impact, and cost-effectiveness of routine vaccination with Dengvaxia in a range of transmission settings, as characterised by seroprevalence levels among 9-y-olds (SP9). These predictions were conducted for the World Health Organization to inform their recommendations on optimal use of this vaccine.

METHODS AND FINDINGS

The models adopted, with small variations, a parsimonious vaccine mode of action that was able to reproduce quantitative features of the observed trial data. The adopted mode of action assumed that vaccination, similarly to natural infection, induces transient, heterologous protection and, further, establishes a long-lasting immunogenic memory, which determines disease severity of subsequent infections. The default vaccination policy considered was routine vaccination of 9-y-old children in a three-dose schedule at 80% coverage. The outcomes examined were the impact of vaccination on infections, symptomatic dengue, hospitalised dengue, deaths, and cost-effectiveness over a 30-y postvaccination period. Case definitions were chosen in accordance with the Phase III trials. All models predicted that in settings with moderate to high dengue endemicity (SP9 ≥ 50%), the default vaccination policy would reduce the burden of dengue disease for the population by 6%-25% (all simulations: -3%-34%) and in high-transmission settings (SP9 ≥ 70%) by 13%-25% (all simulations: 10%- 34%). These endemicity levels are representative of the participating sites in both Phase III trials. In contrast, in settings with low transmission intensity (SP9 ≤ 30%), the models predicted that vaccination could lead to a substantial increase in hospitalisation because of dengue. Modelling reduced vaccine coverage or the addition of catch-up campaigns showed that the impact of vaccination scaled approximately linearly with the number of people vaccinated. In assessing the optimal age of vaccination, we found that targeting older children could increase the net benefit of vaccination in settings with moderate transmission intensity (SP9 = 50%). Overall, vaccination was predicted to be potentially cost-effective in most endemic settings if priced competitively. The results are based on the assumption that the vaccine acts similarly to natural infection. This assumption is consistent with the available trial results but cannot be directly validated in the absence of additional data. Furthermore, uncertainties remain regarding the level of protection provided against disease versus infection and the rate at which vaccine-induced protection declines.

CONCLUSIONS

Dengvaxia has the potential to reduce the burden of dengue disease in areas of moderate to high dengue endemicity. However, the potential risks of vaccination in areas with limited exposure to dengue as well as the local costs and benefits of routine vaccination are important considerations for the inclusion of Dengvaxia into existing immunisation programmes. These results were important inputs into WHO global policy for use of this licensed dengue vaccine.

Modelling weekly vector control against Dengue in the Guangdong Province of China

We develop a mathematical model to closely mimic the integrated program of impulsive vector control (every Friday afternoon since the initiation of the program) and continuous patient treatment and isolation implemented in the Guangdong Province of China during its 2014 dengue outbreak. We fitted the data of accumulated infections and used the parameterized model to carry out a retrospective analysis to estimate the basic reproduction number 1.7425 (95% CI 1.4443-2.0408), the control reproduction number 0.1709, and the mosquito-killing ratios 0.1978, 0.2987, 0.6158 and 0.5571 on October 3, 10, 17 and 24, respectively. This suggests that integrated intervention is highly effective in controlling the dengue outbreak. We also simulated outbreak outcomes under different variations of the implemented interventions. We showed that skipping one Friday for vector control would not result in raising the control reproduction number to the threshold value 1 but would lead to significant increase in the accumulated infections at the end of the outbreak. The findings indicate that quick and persistent impulsive implementation of vector control result in an effective reduction in the control reproduction number and hence lead to significant decline of new infections.

Dengue serotype immune-interactions and their consequences for vaccine impact predictions

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The firstever dengue vaccine, Dengvaxia^®, has recently been licensed for use in several countries.

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Mathematical models are valuable tools for assessing vaccination impact on dengue burden.

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Model assumptions regarding dengue serotype immune interactions are inconsistent.

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Our results demonstrate how model assumptions critically affect vaccine impact predictions.

Dengue is one of the most important and wide-spread viral infections affecting human populations. The last few decades have seen a dramatic increase in the global burden of dengue, with the virus now being endemic or near-endemic in over 100 countries world-wide. A recombinant tetravalent vaccine candidate (CYD-TDV) has recently completed Phase III clinical efficacy trials in South East Asia and Latin America and has been licensed for use in several countries. The trial results showed moderate-to-high efficacies in protection against clinical symptoms and hospitalisation but with so far unknown effects on transmission and infections per se. Model-based predictions about the vaccine's short- or long-term impact on the burden of dengue are therefore subject to a considerable degree of uncertainty. Furthermore, different immune interactions between dengue's serotypes have frequently been evoked by modelling studies to underlie dengue's oscillatory dynamics in disease incidence and serotype prevalence. Here we show how model assumptions regarding immune interactions in the form of antibody-dependent enhancement, temporary cross-immunity and the number of infections required to develop full immunity can significantly affect the predicted outcome of a dengue vaccination campaign. Our results thus re-emphasise the important gap in our current knowledge concerning the effects of previous exposure on subsequent dengue infections and further suggest that intervention impact studies should be critically evaluated by their underlying assumptions about serotype immune-interactions.

The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach

The epidemiology of dengue fever is characterized by highly seasonal, multi-annual fluctuations, and the irregular circulation of its four serotypes. It is believed that this behaviour arises from the interplay between environmental drivers and serotype interactions. The exact mechanism, however, is uncertain. Constraining mathematical models to patterns characteristic to dengue epidemiology offers a means for detecting such mechanisms. Here, we used a pattern-oriented modelling (POM) strategy to fit and assess a range of dengue models, driven by combinations of temporary cross protective-immunity, cross-enhancement, and seasonal forcing, on their ability to capture the main characteristics of dengue dynamics. We show that all proposed models reproduce the observed dengue patterns across some part of the parameter space. Which model best supports the dengue dynamics is determined by the level of seasonal forcing. Further, when tertiary and quaternary infections are allowed, the inclusion of temporary cross-immunity alone is strongly supported, but the addition of cross-enhancement markedly reduces the parameter range at which dengue dynamics are produced, irrespective of the strength of seasonal forcing. The implication of these structural uncertainties on predicted vulnerability to control is also discussed. With ever expanding spread of dengue, greater understanding of dengue dynamics and control efforts (e.g. a near-future vaccine introduction) has become critically important. This study highlights the capacity of multi-level pattern-matching modelling approaches to offer an analytic tool for deeper insights into dengue epidemiology and control.

The fluctuations of multi-serotype infectious diseases are often highly irregular and hard to predict. Previous theoretical approaches have attempted to disentangle the drivers that may underlie this behaviour in dengue dynamics with variable success. Here, we examine the role of such drivers using a pattern-oriented modelling (POM) approach. In POM, multiple patterns observed at different scales are used to test a model’s proficiency in capturing real-world dynamics. We examined dengue models with combinations of cross-immunity, cross-enhancement, seasonal fluctuations in the transmission rate, and with sensitivity analyses of asymmetric transmission rates between serotypes as well as the possibility for four subsequent heterologous infections. We demonstrate the ability of POM to model dynamical drivers that have gone unnoticed in single pattern or synthetic likelihood approaches. Further, our results present a determining role of seasonality in the selection and operation of these processes in governing dengue dynamics, in particular when full, heterologous immunity is assumed to occur after a secondary infection. We show that this structural model uncertainty can have important practical significance, as demonstrated by the differences in control efforts required to disrupt transmission. These results highlight the importance of localised model selection and calibration using multiple data-matching, as well as taking explicit account of model uncertainty in predicting and planning control efforts for multi-serotype diseases.

Reconstruction of disease transmission rates: Applications to measles, dengue, and influenza

Transmission rates are key in understanding the spread of infectious diseases. Using the framework of compartmental models, we introduce a simple method to reconstruct time series of transmission rates directly from incidence or disease-related mortality data. The reconstruction employs differential equations, which model the time evolution of infective stages and strains. Being sensitive to initial values, the method produces asymptotically correct solutions. The computations are fast, with time complexity being quadratic. We apply the reconstruction to data of measles (England and Wales, 1948-1967), dengue (Thailand, 1982-1999), and influenza (U.S., 1910-1927). The Measles example offers comparison with earlier work. Here we re-investigate reporting corrections, include and exclude demographic information. The dengue example deals with the failure of vector-control measures in reducing dengue hemorrhagic fever (DHF) in Thailand. Two competing mechanisms have been held responsible: strain interaction and demographic transitions. Our reconstruction reveals that both explanations are possible, showing that the increase in DHF cases is consistent with decreasing transmission rates resulting from reduced vector counts. The flu example focuses on the 1918/1919 pandemic, examining the transmission rate evolution for an invading strain. Our analysis indicates that the pandemic strain could have circulated in the population for many months before the pandemic was initiated by an event of highly increased transmission.

Differential and enhanced response to climate forcing in diarrheal disease due to rotavirus across a megacity of the developing world

The role of climate forcing in the population dynamics of infectious diseases has typically been revealed via retrospective analyses of incidence records aggregated across space and, in particular, over whole cities. Here, we focus on the transmission dynamics of rotavirus, the main diarrheal disease in infants and young children, within the megacity of Dhaka, Bangladesh. We identify two zones, the densely urbanized core and the more rural periphery, that respond differentially to flooding. Moreover, disease seasonality differs substantially between these regions, spanning variation comparable to the variation from tropical to temperate regions. By combining process-based models with an extensive disease surveillance record, we show that the response to climate forcing is mainly seasonal in the core, where a more endemic transmission resulting from an asymptomatic reservoir facilitates the response to the monsoons. The force of infection in this monsoon peak can be an order of magnitude larger than the force of infection in the more epidemic periphery, which exhibits little or no postmonsoon outbreak in a pattern typical of nearby rural areas. A typically smaller peak during the monsoon season nevertheless shows sensitivity to interannual variability in flooding. High human density in the core is one explanation for enhanced transmission during troughs and an associated seasonal monsoon response in this diarrheal disease, which unlike cholera, has not been widely viewed as climate-sensitive. Spatial demographic, socioeconomic, and environmental heterogeneity can create reservoirs of infection and enhance the sensitivity of disease systems to climate forcing, especially in the populated cities of the developing world.

Neutralizing antibody titers against dengue virus correlate with protection from symptomatic infection in a longitudinal cohort

The four dengue virus serotypes (DENV1-4) are mosquito-borne flaviviruses that infect ∼ 390 million people annually; up to 100 million infections are symptomatic, and 500,000 cases progress to severe disease. Exposure to a heterologous DENV serotype, the specific infecting DENV strains, and the interval of time between infections, as well as age, ethnicity, genetic polymorphisms, and comorbidities of the host, are all risk factors for severe dengue. In contrast, neutralizing antibodies (NAbs) are thought to provide long-lived protection against symptomatic infection and severe dengue. The objective of dengue vaccines is to provide balanced protection against all DENV serotypes simultaneously. However, the association between homotypic and heterotypic NAb titers and protection against symptomatic infection remains poorly understood. Here, we demonstrate that the titer of preinfection cross-reactive NAbs correlates with reduced likelihood of symptomatic secondary infection in a longitudinal pediatric dengue cohort in Nicaragua. The protective effect of NAb titers on infection outcome remained significant when controlled for age, number of years between infections, and epidemic force, as well as with relaxed or more stringent criteria for defining inapparent DENV infections. Further, individuals with higher NAb titers immediately after primary infection had delayed symptomatic infections compared with those with lower titers. However, overall NAb titers increased modestly in magnitude and remained serotype cross-reactive in the years between infections, possibly due to reexposure. These findings establish that anti-DENV NAb titers correlate with reduced probability of symptomatic DENV infection and provide insights into longitudinal characteristics of antibody-mediated immunity to DENV in an endemic setting.

Modeling the spread and control of dengue with limited public health resources

A deterministic model for the transmission dynamics of dengue fever is formulated to study, with a nonlinear recovery rate, the impact of available resources of the health system on the spread and control of the disease. Model results indicate the existence of multiple endemic equilibria, as well as coexistence of an endemic equilibrium with a periodic solution. Additionally, our model exhibits the phenomenon of backward bifurcation. The results of this study could be helpful for public health authorities in their planning of a proper resource allocation for the control of dengue transmission.

Testing the impact of virus importation rates and future climate change on dengue activity in Malaysia using a mechanistic entomology and disease model

We aimed to reparameterize and validate an existing dengue model, comprising an entomological component (CIMSiM) and a disease component (DENSiM) for application in Malaysia. With the model we aimed to measure the effect of importation rate on dengue incidence, and to determine the potential impact of moderate climate change (a 1 °C temperature increase) on dengue activity. Dengue models (comprising CIMSiM and DENSiM) were reparameterized for a simulated Malaysian village of 10 000 people, and validated against monthly dengue case data from the district of Petaling Jaya in the state of Selangor. Simulations were also performed for 2008-2012 for variable virus importation rates (ranging from 1 to 25 per week) and dengue incidence determined. Dengue incidence in the period 2010-2012 was modelled, twice, with observed daily weather and with a 1 °C increase, the latter to simulate moderate climate change. Strong concordance between simulated and observed monthly dengue cases was observed (up to r = 0·72). There was a linear relationship between importation and incidence. However, a doubling of dengue importation did not equate to a doubling of dengue activity. The largest individual dengue outbreak was observed with the lowest dengue importation rate. Moderate climate change resulted in an overall decrease in dengue activity over a 3-year period, linked to high human seroprevalence early on in the simulation. Our results suggest that moderate reductions in importation with control programmes may not reduce the frequency of large outbreaks. Moderate increases in temperature do not necessarily lead to greater dengue incidence.

Re-assess Vector Indices Threshold as an Early Warning Tool for Predicting Dengue Epidemic in a Dengue Non-endemic Country

Background

Despite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic.

Methodology/Principal Findings

Epidemiological, entomological and meteorological data were analyzed from 2005 to 2012 in Kaohsiung City, Taiwan. The successive waves of dengue outbreaks with different magnitudes were recorded in Kaohsiung City, and involved a dominant serotype during each epidemic. The annual indigenous dengue cases usually started from May to June and reached a peak in October to November. Vector data from 2005–2012 showed that the peak of the adult mosquito population was followed by a peak in the corresponding dengue activity with a lag period of 1–2 months. Therefore, we focused the analysis on the data from May to December and the high risk district, where the inspection of the immature and mature mosquitoes was carried out on a weekly basis and about 97.9% dengue cases occurred. The two-stage model was utilized here to estimate the risk and time-lag effect of annual dengue outbreaks in Taiwan. First, Poisson regression was used to select the optimal subset of variables and time-lags for predicting the number of dengue cases, and the final results of the multivariate analysis were selected based on the smallest AIC value. Next, each vector index models with selected variables were subjected to multiple logistic regression models to examine the accuracy of predicting the occurrence of dengue cases. The results suggested that Model-AI, BI, CI and HI predicted the occurrence of dengue cases with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. The predicting threshold based on individual Model-AI, BI, CI and HI was 0.97, 1.16, 1.79 and 0.997, respectively.

Conclusion/Significance

There was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model.

With the continuously high levels of worldwide dengue transmission, predicting dengue outbreaks in advance of their occurrence or identifying specific locations where outbreak risks are highest is of critical importance. However, only few studies have been conducted in dengue non-endemic countries to evaluate the association of vector index with the occurrence of dengue cases; and the establishment of an early warning signal would significantly enhance the public health intervention. Our study here provided the proof-of-concept results, utilizing a two-stage model to identify the best set of lag effects of meteorological and entomological variables, explaining dengue epidemics based on the data obtained from Taiwan, which is a dengue-non-endemic country. Each of the vector indices when combined with the meteorological factors has better performance compared to the prediction using AI, BI, CI and HI alone, with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. Because of the complex interplays between the size of human hosts and movement, environmental factors and dynamic changes of mosquito population and density, each country should consider its own individual data and situation and apply this two-stage model to find the optimal predictive models for allocating public health resources and prevention strategies.

A Framework for Inferring Unobserved Multistrain Epidemic Subpopulations Using Synchronization Dynamics

A new method is proposed to infer unobserved epidemic subpopulations by exploiting the synchronization properties of multistrain epidemic models. A model for dengue fever is driven by simulated data from secondary infective populations. Primary infective populations in the driven system synchronize to the correct values from the driver system. Most hospital cases of dengue are secondary infections, so this method provides a way to deduce unobserved primary infection levels. We derive center manifold equations that relate the driven system to the driver system and thus motivate the use of synchronization to predict unobserved primary infectives. Synchronization stability between primary and secondary infections is demonstrated through numerical measurements of conditional Lyapunov exponents and through time series simulations.

Predicting the characteristics of the aetiological agent for Kawasaki disease from other paediatric infectious diseases in Japan

Although Kawasaki disease (KD), which was first reported in the 1960s, is assumed to be infectious, its aetiological agent(s) remains unknown. We compared the geographical distribution of the force of infection and the super-annual periodicity of KD and seven other paediatric infectious diseases in Japan. The geographical distribution of the force of infection, which was estimated as the inverse of the mean patient age, was similar in KD and other paediatric viral infections. This similarity was due to the fact that the force of infection was determined largely by the total fertility rate. This finding suggests that KD shares a transmission route, i.e. sibling-to-sibling infection, with other paediatric infections. The super-annual periodicity, which is positively associated with the sum of an infectious disease's incubation period and infectious period, was much longer for KD and exanthema subitum than other paediatric infectious diseases. The virus for exanthema subitum is known to persist across the host's lifespan, which suggests that the aetiological agent for KD may also be capable of persistent infection. Taken together, these findings suggest that the aetiological agent for KD is transmitted through close contact and persists asymptomatically in most hosts.

Capturing the dynamics of pathogens with many strains

Pathogens that consist of multiple antigenic variants are a serious public health concern. These infections, which include dengue virus, influenza and malaria, generate substantial morbidity and mortality. However, there are considerable theoretical challenges involved in modelling such infections. As well as describing the interaction between strains that occurs as a result cross-immunity and evolution, models must balance biological realism with mathematical and computational tractability. Here we review different modelling approaches, and suggest a number of biological problems that are potential candidates for study with these methods. We provide a comprehensive outline of the benefits and disadvantages of available frameworks, and describe what biological information is preserved and lost under different modelling assumptions. We also consider the emergence of new disease strains, and discuss how models of pathogens with multiple strains could be developed further in future. This includes extending the flexibility and biological realism of current approaches, as well as interface with data.

Modeling infectious disease dynamics in the complex landscape of global health

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

The many projected futures of dengue

Dengue is a vector-borne disease that causes a substantial public health burden within its expanding range. Several modelling studies have attempted to predict the future global distribution of dengue. However, the resulting projections are difficult to compare and are sometimes contradictory because the models differ in their approach, in the quality of the disease data that they use and in the choice of variables that drive disease distribution. In this Review, we compare the main approaches that have been used to model the future global distribution of dengue and propose a set of minimum criteria for future projections that, by analogy, are applicable to other vector-borne diseases.

Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand

Models describing dengue epidemics are parametrized on disease incidence data and therefore high-quality data are essential. For Thailand, two different sources of long-term dengue data are available, the hard copy data from 1980 to 2005, where hospital admission cases were notified, and the electronic files, from 2003 to the present, where clinically classified forms of disease, i.e. dengue fever, dengue haemorrhagic fever, and dengue shock syndrome, are notified using separate files. The official dengue notification data, provided by the Bureau of Epidemiology, Ministry of Public Health in Thailand, were cross-checked with dengue data used in recent publications, where an inexact continuous time-series was observed to be consistently used since 2003, affecting considerably the model dynamics and its correct application. In this paper, numerical analysis and simulation techniques giving insights on predictability are performed to show the effects of model parametrization by using different datasets.