Time-varying, serotype-specific force of infection of dengue virus

Quantifying viral pandemic potential from experimental transmission studies

In the past two decades, two pandemic respiratory viruses (H1N1 and SARS-CoV-2) have emerged via spillover from animal reservoirs. In an effort to avert future pandemics, surveillance studies aimed at identifying zoonotic viruses at high risk of spilling over into humans act to monitor the 'viral chatter' at the animal-human interface. These studies are hampered, however, by the diversity of zoonotic viruses and the limited tools available to assess pandemic risk. Methods currently in use include the characterization of candidate viruses using in vitro laboratory assays and experimental transmission studies in animal models. However, transmission experiments yield relatively low-resolution outputs that are not immediately translatable to projections of viral dynamics at the level of a host population. To address this gap, we present an analytical framework to extend the use of measurements from experimental transmission studies to generate more quantitative risk assessments. Specifically, we develop modeling approaches for estimating transmission parameters and gauging population-level emergence risk using within-host viral titer data from index and contact animals. To illustrate the use of these approaches, we apply them to two recently published influenza A virus (IAV) ferret transmission experiments: one using influenza A/California/07/2009 (H1N1pdm09) and one using influenza A/Hong Kong/1/1968 (H3N2). We find that, when controlling for viral titers, the H3N2 virus tends to be less transmissible than the H1N1 virus. Because of this difference in infectiousness and more robust replication of H1N1 in ferrets, we further find that the H1N1 virus has a higher projected reproduction number than the H3N2 virus and therefore more likely to cause an epidemic following introduction. Incorporating estimates of the generation interval for each virus, we find that the H1N1 virus has a higher projected epidemic growth rate than the H3N2 virus. The methods we present to assess relative pandemic risk across viral isolates can be used to improve quantitative risk assessment of other emerging viruses of pandemic concern.

Spatiotemporal analysis of foot and mouth disease outbreaks in cattle and small ruminants in Türkiye between 2010 and 2019

Determining the dynamics associated with foot-and-mouth disease (FMD) outbreaks is important for being able to develop effective strategic plans against the disease. In this direction, spatiotemporal analysis of FMD virus (FMDV) epidemic data that occurred in Türkiye between 2010 and 2019 was carried out. Spatiotemporal analysis was performed by the space-time scan statistic using data from a total of 7,796 FMD outbreaks. Standard deviational ellipse analysis (SDE) was performed to analyse the directional trend of FMD. Five, six, and three significant and high-risk clusters were identified by the space-time cluster analysis for serotypes A, O, and Asia-1, respectively. The SDE analysis indicated that direction of FMD transmission was northeast to southwest. A significant decrease in the number of outbreaks and cases were observed between 2014 and 2019 compared to 2010-2013 (p = 0.010). Most of the serotype A, serotype O, and serotype Asia-1 associated FMD outbreaks were observed during the dry season (April to September). Among FMD cases, cattle and small ruminants accounted for 80.75% (180,932 cases) and 19.25% (43,116 cases), respectively. Among the serotypes detected in the cases, the most frequently detected serotype was serotype O (50.84%), followed by serotypes A (35.67%) and Asia-1 (13.49%). The results obtained in this study may contribute to when and where control programs could be implemented more efficiently for the prevention and control of FMD. Developing risk-defined regional control plans by taking into account the current livestock production including uncontrolled animal movements in border regions, rural livestock, livestock trade between provinces are recommended.

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.

Dengue in Cambodia 2002-2020: Cases, Characteristics and Capture by National Surveillance

Objective

Data from 19 years of national dengue surveillance in Cambodia (2002-2020) were analyzed to describe trends in dengue case characteristics and incidence.

Methods

Generalized additive models were fitted to dengue case incidence and characteristics (mean age, case phenotype, fatality) over time. Dengue incidence in a pediatric cohort study (2018-2020) was compared to national data during the same period to evaluate disease under-estimation by national surveillance.

Findings

During 2002-2020, there were 353,270 cases of dengue (average age-adjusted incidence 1.75 cases/1,000 persons/year) recorded in Cambodia, with an estimated 2.1-fold increase in case incidence between 2002 and 2020 (slope = 0.0058, SE = 0.0021, p = 0.006). Mean age of infected individuals increased from 5.8 years in 2002 to 9.1 years in 2020 (slope = 0.18, SE = 0.088, p <0.001); case fatality rates decreased from 1.77% in 2002 to 0.10% in 2020 (slope = -0.16, SE = 0.0050, p <0.001). When compared to cohort data, national data under-estimated clinically apparent dengue case incidence by 5.0-fold (95% CI 0.2 - 26.5), and overall dengue case incidence (both apparent and inapparent cases) by 33.6-fold (range: 18.7- 53.6).

Conclusion

Dengue incidence in Cambodia is increasing and disease is shifting to older pediatric populations. National surveillance continues to under-estimate case numbers. Future interventions should account for disease under-estimation and shifting demographics for scaling and to target appropriate age groups.

Inapparent infections shape the transmission heterogeneity of dengue

Transmission heterogeneity, whereby a disproportionate fraction of pathogen transmission events result from a small number of individuals or geographic locations, is an inherent property of many, if not most, infectious disease systems. For vector-borne diseases, transmission heterogeneity is inferred from the distribution of the number of vectors per host, which could lead to significant bias in situations where vector abundance and transmission risk at the household do not correlate, as is the case with dengue virus (DENV). We used data from a contact tracing study to quantify the distribution of DENV acute infections within human activity spaces (AS), the collection of residential locations an individual routinely visits, and quantified measures of virus transmission heterogeneity from two consecutive dengue outbreaks (DENV-4 and DENV-2) that occurred in the city of Iquitos, Peru. Negative-binomial distributions and Pareto fractions showed evidence of strong overdispersion in the number of DENV infections by AS and identified super-spreading units (SSUs): i.e. AS where most infections occurred. Approximately 8% of AS were identified as SSUs, contributing to more than 50% of DENV infections. SSU occurrence was associated more with DENV-2 infection than with DENV-4, a predominance of inapparent infections (74% of all infections), households with high Aedes aegypti mosquito abundance, and high host susceptibility to the circulating DENV serotype. Marked heterogeneity in dengue case distribution, and the role of inapparent infections in defining it, highlight major challenges faced by reactive interventions if those transmission units contributing the most to transmission are not identified, prioritized, and effectively treated.

Etiologies of Acute Undifferentiated Febrile Illnesses in and near Iquitos from 1993 to 1999 in the Amazon River Basin of Peru

The objective of this study was to determine the etiology of febrile illnesses among patients from October 1, 1993 through September 30, 1999, in the urban community of Iquitos in the Amazon River Basin of Peru. Epidemiological and clinical data as well as blood samples were obtained from consenting patients at hospitals, health clinics and private residences. Samples were tested for arboviruses in cell cultures and for IgM and IgG antibodies by ELISA. Blood smears were examined for malaria, and sera were tested for antibodies to Leptospira spp. by ELISA and microscopic agglutination. Among 6,607 febrile patients studied, dengue viruses caused 14.6% of the cases, and Venezuelan equine encephalitis virus caused 2.5%, Oropouche virus 1.0%, Mayaro virus 0.4%, and other arboviruses caused 0.2% of the cases. Also, 22.9% of 4,844 patients tested were positive for malaria, and of 400 samples tested, 9% had evidence of acute leptospirosis. Although the study was not designed to assess the importance of these pathogens as a cause of human morbidity in the total population, these results indicate that arboviruses, leptospirosis, and malaria were the cause of approximately 50% of the febrile cases. Although the arboviruses that were diagnosed can produce asymptomatic infections, our findings increased the overall understanding of the relative health burden of these infections, as well as baseline knowledge needed for designing and implementing further studies to better assess the health impact and threat of these pathogens in the Amazon Basin of Peru.

Effects of COVID-19 Non-Pharmacological Interventions on Dengue Infection: A Systematic Review and Meta-Analysis

Non-pharmacological interventions (NPIs) implemented during the coronavirus disease 2019 (COVID-19) pandemic have demonstrated significant positive effects on other communicable diseases. Nevertheless, the response for dengue fever has been mixed. To illustrate the real implications of NPIs on dengue transmission and to determine the effective measures for preventing and controlling dengue, we performed a systematic review and meta-analysis of the available global data to summarize the effects comprehensively. We searched Embase, PubMed, and Web of Science in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines from December 31, 2019, to March 30, 2022, for studies of NPI efficacy on dengue infection. We obtained the annual reported dengue cases from highly dengue-endemic countries in 2015–2021 from the European Centre for Disease Prevention and Control to determine the actual change in dengue cases in 2020 and 2021, respectively. A random-effects estimate of the pooled odds was generated with the Mantel-Haenszel method. Between-study heterogeneity was assessed using the inconsistency index (I²) and subgroup analysis according to country (dengue-endemic or non-endemic) was conducted. This review was registered with PROSPERO (CRD42021291487). A total of 17 articles covering 32 countries or regions were included in the review. Meta-analysis estimated a pooled relative risk of 0.39 (95% CI: 0.28–0.55), and subgroup revealed 0.06 (95% CI: 0.02-0.25) and 0.55 (95% CI: 0.44-0.68) in dengue non-endemic areas and dengue-endemic countries, respectively, in 2020. The majority of highly dengue-endemic countries in Asia and Americas reported 0–100% reductions in dengue cases in 2020 compared to previous years, while some countries (4/20) reported a dramatic increase, resulting in an overall increase of 11%. In contrast, there was an obvious reduction in dengue cases in 2021 in almost all countries (18/20) studied, with an overall 40% reduction rate. The overall effectiveness of NPIs on dengue varied with region and time due to multiple factors, but most countries reported significant reductions. Travel-related interventions demonstrated great effectiveness for reducing imported cases of dengue fever. Internal movement restrictions of constantly varying intensity and range are more likely to mitigate the entire level of dengue transmission by reducing the spread of dengue fever between regions within a country, which is useful for developing a more comprehensive and sustainable strategy for preventing and controlling dengue fever in the future.

Cross immunity protection and antibody-dependent enhancement in a distributed delay dynamic model

Dengue fever is endemic in tropical and subtropical countries, and certain important features of the spread of dengue fever continue to pose challenges for mathematical modelling. Here we propose a system of integro-differential equations (IDE) to study the disease transmission dynamics that involve multi-serotypes and cross immunity. Our main objective is to incorporate and analyze the effect of a general time delay term describing acquired cross immunity protection and the effect of antibody-dependent enhancement (ADE), both characteristics of Dengue fever. We perform qualitative analysis of the model and obtain results to show the stability of the epidemiologically important steady solutions that are completely determined by the basic reproduction number and the invasion reproduction number. We establish the global dynamics by constructing a suitable Lyapunov functional. We also conduct some numerical experiments to illustrate bifurcation structures, indicating the occurrence of periodic oscillations for a specific range of values of a key parameter representing ADE.

Distinct kinetics of antibodies to 111 Plasmodium falciparum proteins identifies markers of recent malaria exposure

Strengthening malaria surveillance is a key intervention needed to reduce the global disease burden. Reliable serological markers of recent malaria exposure could improve current surveillance methods by allowing for accurate estimates of infection incidence from limited data. We studied the IgG antibody response to 111 Plasmodium falciparum proteins in 65 adult travellers followed longitudinally after a natural malaria infection in complete absence of re-exposure. We identified a combination of five serological markers that detect exposure within the previous three months with >80% sensitivity and specificity. Using mathematical modelling, we examined the antibody kinetics and determined that responses informative of recent exposure display several distinct characteristics: rapid initial boosting and decay, less inter-individual variation in response kinetics, and minimal persistence over time. Such serological exposure markers could be incorporated into routine malaria surveillance to guide efforts for malaria control and elimination.

Serological markers of recent Plasmodium falciparum infection could be useful to estimate incidence. Here, the authors identify a combination of five serological markers to detect exposure to infection within the previous three months with >80% sensitivity and specificity.

Dengue Endemicity, Force of Infection, and Variation in Transmission Intensity in 13 Endemic Countries

Dengue endemicity varies but comparative, multicountry data are extremely limited. An improved understanding is needed to prioritize prevention, including vaccination, which is currently recommended only under specific epidemiological conditions. We used serological study data from 46 geographical sites in 13 countries to estimate dengue force of infection (FOI, the proportion of children seroconverting per year) under assumptions of either age-constant or age-varying FOI, and the age at which 50% and 80% of children had been infected. After exclusions, 13 661 subjects were included. Estimated constant FOI varied widely, from 1.7% (Singapore) to 24.1% (the Philippines). In the site-level analysis 44 sites (96%) reached 50% seroconversion and 35 sites (75%) reached 80% seroconversion by age 18 years, with significant heterogeneity. These findings confirm that children living in dengue-endemic countries receive intense early dengue exposure, increasing risk of secondary infection, and imply serosurveys at fine spatial resolutions are needed to inform vaccination campaigns.

Fine-scale estimation of effective reproduction numbers for dengue surveillance

The effective reproduction number Rt is an epidemiological quantity that provides an instantaneous measure of transmission potential of an infectious disease. While dengue is an increasingly important vector-borne disease, few have used Rt as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of Rt for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010-2020, we estimated Rt by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of Rt from each proposed method and determined exogenous temporal and spatial drivers for Rt in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved satisfactory model performance (R2EpiEstim = 0.95, R2EpiFilter = 0.97), but disparities in performance were large at finer spatial scales when case counts are low (MASE EpiEstim = 1.23, MASEEpiFilter = 0.59). Impervious surfaces and vegetation with structure dominated by human management (without tree canopy) were positively associated with increased transmission intensity. Vegetation with structure dominated by human management (with tree canopy), on the other hand, was associated with lower dengue transmission intensity. We showed that dengue outbreaks were preceded by sustained periods of high transmissibility, demonstrating the potential of Rt as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of Rt at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response.

Pandemic-associated mobility restrictions could cause increases in dengue virus transmission

BACKGROUND

The COVID-19 pandemic has induced unprecedented reductions in human mobility and social contacts throughout the world. Because dengue virus (DENV) transmission is strongly driven by human mobility, behavioral changes associated with the pandemic have been hypothesized to impact dengue incidence. By discouraging human contact, COVID-19 control measures have also disrupted dengue vector control interventions, the most effective of which require entry into homes. We sought to investigate how and why dengue incidence could differ under a lockdown scenario with a proportion of the population sheltered at home.

METHODOLOGY & PRINCIPAL FINDINGS

We used an agent-based model with a realistic treatment of human mobility and vector control. We found that a lockdown in which 70% of the population sheltered at home and which occurred in a season when a new serotype invaded could lead to a small average increase in cumulative DENV infections of up to 10%, depending on the time of year lockdown occurred. Lockdown had a more pronounced effect on the spatial distribution of DENV infections, with higher incidence under lockdown in regions with higher mosquito abundance. Transmission was also more focused in homes following lockdown. The proportion of people infected in their own home rose from 54% under normal conditions to 66% under lockdown, and the household secondary attack rate rose from 0.109 to 0.128, a 17% increase. When we considered that lockdown measures could disrupt regular, city-wide vector control campaigns, the increase in incidence was more pronounced than with lockdown alone, especially if lockdown occurred at the optimal time for vector control.

CONCLUSIONS & SIGNIFICANCE

Our results indicate that an unintended outcome of lockdown measures may be to adversely alter the epidemiology of dengue. This observation has important implications for an improved understanding of dengue epidemiology and effective application of dengue vector control. When coordinating public health responses during a syndemic, it is important to monitor multiple infections and understand that an intervention against one disease may exacerbate another.

Heterogeneity of Dengue Illness in Community-Based Prospective Study, Iquitos, Peru

Measuring heterogeneity of dengue illness is necessary to define suitable endpoints in dengue vaccine and therapeutic trials and will help clarify behavioral responses to illness. To quantify heterogeneity in dengue illness, including milder cases, we developed the Dengue Illness Perceptions Response (IPR) survey, which captured detailed symptom data, including intensity, duration, and character, and change in routine activities caused by illness. During 2016–2019, we collected IPR data daily during the acute phase of illness for 79 persons with a positive reverse transcription PCR result for dengue virus RNA. Most participants had mild ambulatory disease. However, we measured substantial heterogeneity in illness experience, symptom duration, and maximum reported intensity of individual symptoms. Symptom intensity was a more valuable predicter of major activity change during dengue illness than symptom presence or absence alone. These data suggest that the IPR measures clinically useful heterogeneity in dengue illness experience and its relation to altered human behavior.

Temperature, traveling, slums, and housing drive dengue transmission in a non-endemic metropolis

Dengue is steadily increasing worldwide and expanding into higher latitudes. Current non-endemic areas are prone to become endemic soon. To improve understanding of dengue transmission in these settings, we assessed the spatiotemporal dynamics of the hitherto largest outbreak in the non-endemic metropolis of Buenos Aires, Argentina, based on detailed information on the 5,104 georeferenced cases registered during summer-autumn of 2016. The highly seasonal dengue transmission in Buenos Aires was modulated by temperature and triggered by imported cases coming from regions with ongoing outbreaks. However, local transmission was made possible and consolidated heterogeneously in the city due to housing and socioeconomic characteristics of the population, with 32.8% of autochthonous cases occurring in slums, which held only 6.4% of the city population. A hierarchical spatiotemporal model accounting for imperfect detection of cases showed that, outside slums, less-affluent neighborhoods of houses (vs. apartments) favored transmission. Global and local spatiotemporal point-pattern analyses demonstrated that most transmission occurred at or close to home. Additionally, based on these results, a point-pattern analysis was assessed for early identification of transmission foci during the outbreak while accounting for population spatial distribution. Altogether, our results reveal how social, physical, and biological processes shape dengue transmission in Buenos Aires and, likely, other non-endemic cities, and suggest multiple opportunities for control interventions.

Dengue fever is mainly transmitted by a mosquito species that is highly urbanized, and lays eggs and develops mostly in artificial water containers. Dengue transmission is sustained year-round in most tropical regions of the world, but in many subtropical/temperate regions it occurs only in the warmest months. To improve understanding of dengue transmission in these regions, we analyzed one of the largest outbreaks in Buenos Aires city, a subtropical metropolis. Based on information on 5,104 georeferenced cases during summer-autumn 2016, we found that most transmission occurred in or near home, that slums had the highest risk of transmission, and that, outside slums, less-affluent neighborhoods of houses (vs. apartments) favored transmission. We showed that the cumulative effects of temperature over the previous few weeks set the temporal limits for transmission to occur, and that the outbreak was sparked by infected people arriving from regions with ongoing outbreaks. Additionally, we implemented a statistical method to identify transmission foci in real-time that improves targeting control interventions. Our results deepen the understanding of dengue transmission as a result of social, physical, and biological processes, and pose multiple opportunities for improving control of dengue and other mosquito-borne viruses such as Zika, chikungunya, and yellow fever.

Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand

Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions.

Natural arbovirus infection rate and detectability of indoor female Aedes aegypti from Mérida, Yucatán, Mexico

Arbovirus infection in Aedes aegypti has historically been quantified from a sample of the adult population by pooling collected mosquitoes to increase detectability. However, there is a significant knowledge gap about the magnitude of natural arbovirus infection within areas of active transmission, as well as the sensitivity of detection of such an approach. We used indoor Ae. aegypti sequential sampling with Prokopack aspirators to collect all mosquitoes inside 200 houses with suspected active ABV transmission from the city of Mérida, Mexico, and tested all collected specimens by RT-PCR to quantify: a) the absolute arbovirus infection rate in individually tested Ae. aegypti females; b) the sensitivity of using Prokopack aspirators in detecting ABV-infected mosquitoes; and c) the sensitivity of entomological inoculation rate (EIR) and vectorial capacity (VC), two measures ABV transmission potential, to different estimates of indoor Ae. aegypti abundance. The total number of Ae. aegypti (total catch, the sum of all Ae. aegypti across all collection intervals) as well as the number on the first 10-min of collection (sample, equivalent to a routine adult aspiration session) were calculated. We individually tested by RT-PCR 2,161 Aedes aegypti females and found that 7.7% of them were positive to any ABV. Most infections were CHIKV (77.7%), followed by DENV (11.4%) and ZIKV (9.0%). The distribution of infected Aedes aegypti was overdispersed; 33% houses contributed 81% of the infected mosquitoes. A significant association between ABV infection and Ae. aegypti total catch indoors was found (binomial GLMM, Odds Ratio > 1). A 10-min indoor Prokopack collection led to a low sensitivity of detecting ABV infection (16.3% for detecting infected mosquitoes and 23.4% for detecting infected houses). When averaged across all infested houses, mean EIR ranged between 0.04 and 0.06 infective bites per person per day, and mean VC was 0.6 infectious vectors generated from a population feeding on a single infected host per house/day. Both measures were significantly and positively associated with Ae. aegypti total catch indoors. Our findings provide evidence that the accurate estimation and quantification of arbovirus infection rate and transmission risk is a function of the sampling effort, the local abundance of Aedes aegypti and the intensity of arbovirus circulation.

Aedes-borne diseases comprise a serious public health burden in many parts of the world, usually affecting low income areas. The ability to detect virus circulation within a population may be key in responding to the threat of outbreaks, providing a cost-effective approach for triggering vector control. Unfortunately, gaps in the knowledge of natural Aedes-borne virus (ABV) infection in Aedes aegypti have led to uncertainties in the consideration of arbovirus surveillance in mosquitoes. Here, we show that the natural infection rate in a mosquito population may not be a function of where Aedes aegypti are, but rather where key human-mosquito contacts occur. Sampling 200 houses with suspected ABV active transmission led us to quantify high virus infection rates in all Aedes aegypti present in the house and use such information to estimate the sensitivity of indoor aspiration with Prokopack devices and two measures of ABV transmission potential. Our findings provide evidence that the accurate quantification of arbovirus infection rate and transmission risk is a function of the sampling effort, the local abundance of Aedes aegypti and the intensity of arbovirus circulation. Results from this study are relevant to understand the value of virus testing of vector populations, and for the design of entomological endpoints relevant for epidemiological trials quantifying the impact of vector control on ABVs.

Migration rate estimation in an epidemic network

Most of the recent epidemic outbreaks in the world have as a trigger, a strong migratory component as has been evident in the recent Covid-19 pandemic. In this work we address the problem of migration of human populations and its effect on pathogen reinfections in the case of Dengue, using a Markov-chain susceptible-infected-susceptible (SIS) metapopulation model over a network. Our model postulates a general contact rate that represents a local measure of several factors: the population size of infected hosts that arrive at a given location as a function of total population size, the current incidence at neighboring locations, and the connectivity of the network where the disease spreads. This parameter can be interpreted as an indicator of outbreak risk at a given location. This parameter is tied to the fraction of individuals that move across boundaries (migration). To illustrate our model capabilities, we estimate from epidemic Dengue data in Mexico the dynamics of migration at a regional scale incorporating climate variability represented by an index based on precipitation data.

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.

The impact of insecticide treated curtains on dengue virus transmission: A cluster randomized trial in Iquitos, Peru

Dengue is one of the most important vector-borne diseases, resulting in an estimated hundreds of millions of infections annually throughout the tropics. Control of dengue is heavily dependent upon control of its primary mosquito vector, Aedes aegypti. Innovative interventions that are effective at targeting the adult stage of the mosquito are needed to increase the options for effective control. The use of insecticide-treated curtains (ITCs) has previously been shown to significantly reduce the abundance of Ae. aegypti in and around homes, but the impact of ITCs on dengue virus (DENV) transmission has not been rigorously quantified. A parallel arm cluster-randomized controlled trial was conducted in Iquitos, Peru to quantify the impact of ITCs on DENV seroconversion as measured through plaque-reduction neutralization tests. Seroconversion data showed that individuals living in the clusters that received ITCs were at greater risk to seroconverting to DENV, with an average seroconversion rate of 50.6 per 100 person-years (PY) (CI: 29.9–71.9), while those in the control arm had an average seroconversion rate of 37.4 per 100 PY (CI: 15.2–51.7). ITCs lost their insecticidal efficacy within 6 months of deployment, necessitating re-treatment with insecticide. Entomological indicators did not show statistically significant differences between ITC and non-ITC clusters. It’s unclear how the lack of protective efficacy reported here is attributable to simple failure of the intervention to protect against Ae. aegypti bites, or the presence of a faulty intervention during much of the follow-up period. The higher risk of dengue seroconversion that was detected in the ITC clusters may have arisen due to a false sense of security that inadvertently led to less routine protective behaviors on the part of households that received the ITCs. Our study provides important lessons learned for conducting cluster randomized trials for vector control interventions against Aedes-transmitted virus infections.

Dengue is one of the most important mosquito-borne diseases affecting humans, resulting in an estimated hundreds of millions of infections annually throughout the tropics. To control dengue, most public health programs use a variety of methods to kill the primary mosquito vector, Aedes aegypti. Water holding containers that harbor larvae (and other immature stages) are treated or eliminated. During emergencies, large insecticide spray campaigns are deployed to kill infected adult mosquitoes. Innovative interventions that are effective at targeting adult mosquitoes in sustainable ways are needed to increase the options for control of dengue and other Aedes borne virus diseases. The use of insecticide-treated curtains (ITCs) has previously been shown to significantly reduce Ae. aegypti numbers in and around homes, but the impact of ITCs on dengue virus (DENV) transmission has not previously been quantified. Using a rigorous study design in which 10 clusters (~90 houses per cluster) were provided multiple ITCs to place in their homes was compared to 10 clusters of homes without ITCs. Assignment of which clusters received ITCs was randomized. Blood samples were obtained at 9-month intervals from residents living in all the clusters, so that people with serological evidence of a DENV infection could be identified by comparing paired samples. Seroconversion data showed that individuals living in the clusters that received ITCs were at greater risk to DENV seroconverting, with an average seroconversion rate of 50.6 per 100 person-years (PY) (CI: 29.9–71.9). Conversely, those in the control arm had an average seroconversion rate of 37.4 per 100 PY (CI: 15.2–51.7). ITCs lost their insecticidal efficacy within 6 months of deployment, necessitating re-treatment with insecticide. Ae. aegypti populations did not show statistically significant differences between ITC and non-ITC clusters. The reason for higher transmission in the ITC treated clusters could be attributable to failure of the curtains (loss of efficacy) and/or that the curtains were not sufficiently effective at protecting against mosquito bites. The higher risk of DENV seroconversion in ITC clusters may be due to a false sense of security that inadvertently led to less routine protective behaviors on the part of households that received the ITC.

RC, Perkins TA. Optimizing the deployment of ultra-low volume and targeted indoor residual spraying for dengue outbreak response

Recent years have seen rising incidence of dengue and large outbreaks of Zika and chikungunya, which are all caused by viruses transmitted by Aedes aegypti mosquitoes. In most settings, the primary intervention against Aedes-transmitted viruses is vector control, such as indoor, ultra-low volume (ULV) spraying. Targeted indoor residual spraying (TIRS) has the potential to more effectively impact Aedes-borne diseases, but its implementation requires careful planning and evaluation. The optimal time to deploy these interventions and their relative epidemiological effects are, however, not well understood. We used an agent-based model of dengue virus transmission calibrated to data from Iquitos, Peru to assess the epidemiological effects of these interventions under differing strategies for deploying them. Specifically, we compared strategies where spray application was initiated when incidence rose above a threshold based on incidence in recent years to strategies where spraying occurred at the same time(s) each year. In the absence of spraying, the model predicted 361,000 infections [inter-quartile range (IQR): 347,000-383,000] in the period 2000-2010. The ULV strategy with the fewest median infections was spraying twice yearly, in March and October, which led to a median of 172,000 infections [IQR: 158,000-183,000], a 52% reduction from baseline. Compared to spraying once yearly in September, the best threshold-based strategy utilizing ULV had fewer median infections (254,000 vs. 261,000), but required more spraying (351 vs. 274 days). For TIRS, the best strategy was threshold-based, which led to the fewest infections of all strategies tested (9,900; [IQR: 8,720-11,400], a 94% reduction), and required fewer days spraying than the equivalent ULV strategy (280). Although spraying twice each year is likely to avert the most infections, our results indicate that a threshold-based strategy can become an alternative to better balance the translation of spraying effort into impact, particularly if used with a residual insecticide.

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.

The Optimal Age of Vaccination Against Dengue with an Age-Dependent Biting Rate with Application to Brazil

In this paper we introduce a single serotype transmission model, including an age-dependent mosquito biting rate, to find the optimal vaccination age against dengue in Brazil with Dengvaxia. The optimal vaccination age and minimal lifetime expected risk of hospitalisation are found by adapting a method due to Hethcote (Math Biosci 89:29–52). Any number and combination of the four dengue serotypes DENv1–4 is considered. Successful vaccination against a serotype corresponds to a silent infection. The effects of antibody-dependent enhancement (ADE) and permanent cross-immunity after two heterologous infections are studied. ADE is assumed to imply risk-free primary infections, while permanent cross-immunity implies risk-free tertiary and quaternary infections. Data from trials of Dengvaxia indicate vaccine efficacy to be age and serostatus dependent and vaccination of seronegative individuals to induce an increased risk of hospitalisation. Some of the scenarios are therefore reconsidered taking these findings into account. The optimal vaccination age is compared to that achievable under the current age restriction of the vaccine. If vaccination is not considered to induce risk, optimal vaccination ages are very low. The assumption of ADE generally leads to a higher optimal vaccination age in this case. For a single serotype vaccination is not recommended in the case of ADE. Permanent cross-immunity results in a slightly lower optimal vaccination age. If vaccination induces a risk, the optimal vaccination ages are much higher, particularly for permanent cross-immunity. ADE has no effect on the optimal vaccination age when permanent cross-immunity is considered; otherwise, it leads to a slight increase in optimal vaccination age.

An open challenge to advance probabilistic forecasting for dengue epidemics

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Dengue in Santander State, Colombia: fluctuations in the prevalence of virus serotypes are linked to dengue incidence and genetic diversity of the circulating viruses

OBJECTIVE

To investigate the link between fluctuations in the prevalence of dengue virus (DENV) serotypes and the number of dengue cases in the metropolitan area of Bucaramanga, Santander State, Colombia, in the 2007-2010 and 2014-2017 periods.

METHOD

Viruses were isolated from febrile patient samples by direct application to C6/36-HT cells and typed using monoclonal antibodies. We performed autocorrelation and cross-correlation analyses to determine whether fluctuations in the prevalence of DENV serotypes and dengue cases were correlated. Full envelope (E) gene sequences were employed to examine the genetic diversity of serotypes circulating by using a phylogenetic approach.

RESULTS

All four dengue virus serotypes were detected. DENV-1 was the dominant serotype in both periods followed by DENV-3 or DENV-2 depending on the period; DENV-4 was the least prevalent virus in both periods. Cross-correlation analyses suggest a temporal relation between the fluctuations in the prevalence of DENV serotypes, which were almost simultaneous (lag = 0) or related to recent past fluctuations (lag > 1.0) in the number of dengue cases. Data suggest that a sustained predominance of DENV-1, an increase of the DENV-4 prevalence, and a switch from DENV-3 to DENV-2 could be linked to an outbreak. Circulating viruses were grouped into Genotype V, Asia/American III and II for DENV-1, -2, -3 and -4, respectively; intragenotypic diversity was detected.

CONCLUSIONS

The present work highlights the need of comprehensive studies on dynamics of DENV in Colombia to understand transmission of dengue and evaluate the effectiveness of a vaccination programme.

Climate drives spatial variation in Zika epidemics in Latin America

Between 2015 and 2017, Zika virus spread rapidly through populations in the Americas with no prior exposure to the disease. Although climate is a known determinant of many Aedes-transmitted diseases, it is currently unclear whether climate was a major driver of the Zika epidemic and how climate might have differentially impacted outbreak intensity across locations within Latin America. Here, we estimated force of infection for Zika over time and across provinces in Latin America using a time-varying susceptible–infectious–recovered model. Climate factors explained less than 5% of the variation in weekly transmission intensity in a spatio-temporal model of force of infection by province over time, suggesting that week to week transmission within provinces may be too stochastic to predict. By contrast, climate and population factors were highly predictive of spatial variation in the presence and intensity of Zika transmission among provinces, with pseudo-R² values between 0.33 and 0.60. Temperature, temperature range, rainfall and population size were the most important predictors of where Zika transmission occurred, while rainfall, relative humidity and a nonlinear effect of temperature were the best predictors of Zika intensity and burden. Surprisingly, force of infection was greatest in locations with temperatures near 24°C, much lower than previous estimates from mechanistic models, potentially suggesting that existing vector control programmes and/or prior exposure to other mosquito-borne diseases may have limited transmission in locations most suitable for Aedes aegypti, the main vector of Zika, dengue and chikungunya viruses in Latin America.

Enteropathogen antibody dynamics and force of infection among children in low-resource settings

Little is known about enteropathogen seroepidemiology among children in low-resource settings. We measured serological IgG responses to eight enteropathogens (Giardia intestinalis, Cryptosporidium parvum, Entamoeba histolytica, Salmonella enterica, enterotoxigenic Escherichia coli, Vibrio cholerae, Campylobacter jejuni, norovirus) in cohorts from Haiti, Kenya, and Tanzania. We studied antibody dynamics and force of infection across pathogens and cohorts. Enteropathogens shared common seroepidemiologic features that enabled between-pathogen comparisons of transmission. Overall, exposure was intense: for most pathogens the window of primary infection was <3 years old; for highest transmission pathogens primary infection occurred within the first year. Longitudinal profiles demonstrated significant IgG boosting and waning above seropositivity cutoffs, underscoring the value of longitudinal designs to estimate force of infection. Seroprevalence and force of infection were rank-preserving across pathogens, illustrating the measures provide similar information about transmission heterogeneity. Our findings suggest antibody response can be used to measure population-level transmission of diverse enteropathogens in serologic surveillance.

Diarrhea, which is caused by bacteria such as Salmonella or by viruses like norovirus, is the fourth leading cause of death among children worldwide, with children in low-resource settings being at highest risk. The pathogens that cause diarrhea spread when stool from infected people comes into contact with new hosts, for example, through inadequate sanitation or by drinking contaminated water. Currently, the best way to track these infections is to collect stool samples from people and test them for the presence of the pathogens. Unfortunately, this is costly and difficult to do on a large scale outside of clinical settings, making it hard to track the spread of diarrhea-causing pathogens.

The body produces antibodies – small proteins that can detect specific pathogens – in response to an infection. These antibodies help ward off future infections by the same pathogen, so if they are present in the blood, this indicates a current or previous infection. Scientists already collect blood samples to track malaria, HIV and vaccine-preventable diseases in low-resource settings. These samples could be tested more broadly to measure the levels of antibodies against diarrhea-causing pathogens.

Now, Arnold et al. have used blood samples collected from children in Haiti, Kenya, and Tanzania to measure antibody responses to 8 diarrhea-causing pathogens. The results showed that many children in these settings had been infected with all 8 pathogens before age three, and that all of the pathogens shared similar age-dependent patterns of antibody response. This finding enabled Arnold et al. to combine antibody measurements with statistical models to estimate each pathogen’s force of infection, that is, the rate at which susceptible individuals in the population become infected. This is a key step for epidemiologists to understand which pathogens cause the most infections in a population.

The experiments show that testing blood samples for antibodies could provide scientists with a new tool to track the transmission of diarrhea-causing pathogens in low-resource settings. This information could help public health officials design and test efforts to prevent diarrhea, for example, by improving water treatment or developing vaccines.

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.

Inter-outbreak stability reflects the size of the susceptible pool and forecasts magnitudes of seasonal epidemics

For dengue fever and other seasonal epidemics we show how the stability of the preceding inter-outbreak period can predict subsequent total outbreak magnitude, and that a feasible stability metric can be computed from incidence data alone. As an observable of a dynamical system, incidence data contains information about the underlying mechanisms: climatic drivers, changing serotype pools, the ecology of the vector populations, and evolving viral strains. We present mathematical arguments to suggest a connection between stability measured in incidence data during the inter-outbreak period and the size of the effective susceptible population. The method is illustrated with an analysis of dengue incidence in San Juan, Puerto Rico, where forecasts can be made as early as three to four months ahead of an outbreak. These results have immediate significance for public health planning, and can be used in combination with existing forecasting methods and more comprehensive dengue models.

Directly measuring the size of the susceptible population is usually unfeasible before dengue outbreaks. Here, the authors show that the stability of low-incidence periods provides a proxy measure, which can be estimated from incidence data, and show its utility for forecasting outbreaks.

Estimating the impact of city-wide Aedes aegypti population control: An observational study in Iquitos, Peru

During the last 50 years, the geographic range of the mosquito Aedes aegypti has increased dramatically, in parallel with a sharp increase in the disease burden from the viruses it transmits, including Zika, chikungunya, and dengue. There is a growing consensus that vector control is essential to prevent Aedes-borne diseases, even as effective vaccines become available. What remains unclear is how effective vector control is across broad operational scales because the data and the analytical tools necessary to isolate the effect of vector-oriented interventions have not been available. We developed a statistical framework to model Ae. aegypti abundance over space and time and applied it to explore the impact of citywide vector control conducted by the Ministry of Health (MoH) in Iquitos, Peru, over a 12-year period. Citywide interventions involved multiple rounds of intradomicile insecticide space spray over large portions of urban Iquitos (up to 40% of all residences) in response to dengue outbreaks. Our model captured significant levels of spatial, temporal, and spatio-temporal variation in Ae. aegypti abundance within and between years and across the city. We estimated the shape of the relationship between the coverage of neighborhood-level vector control and reductions in female Ae. aegypti abundance; i.e., the dose-response curve. The dose-response curve, with its associated uncertainties, can be used to gauge the necessary spraying effort required to achieve a desired effect and is a critical tool currently absent from vector control programs. We found that with complete neighborhood coverage MoH intra-domicile space spray would decrease Ae. aegypti abundance on average by 67% in the treated neighborhood. Our framework can be directly translated to other interventions in other locations with geolocated mosquito abundance data. Results from our analysis can be used to inform future vector-control applications in Ae. aegypti endemic areas globally.

Effects of infection history on dengue virus infection and pathogenicity

The understanding of immunological interactions among the four dengue virus (DENV) serotypes and their epidemiological implications is often hampered by the lack of individual-level infection history. Using a statistical framework that infers full infection history, we analyze a prospective pediatric cohort in Nicaragua to characterize how infection history modulates the risks of DENV infection and subsequent clinical disease. After controlling for age, one prior infection is associated with 54% lower, while two or more are associated with 91% higher, risk of a new infection, compared to DENV-naive children. Children >8 years old have 55% and 120% higher risks of infection and subsequent disease, respectively, than their younger peers. Among children with ≥1 prior infection, intermediate antibody titers increase, whereas high titers lower, the risk of subsequent infection, compared with undetectable titers. Such complex dependency needs to be considered in the design of dengue vaccines and vaccination strategies.

Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China

Vector-borne diseases display wide inter-annual variation in seasonal epidemic size due to their complex dependence on temporally variable environmental conditions and other factors. In 2014, Guangzhou, China experienced its worst dengue epidemic on record, with incidence exceeding the historical average by two orders of magnitude. To disentangle contributions from multiple factors to inter-annual variation in epidemic size, we fitted a semi-mechanistic model to time series data from 2005-2015 and performed a series of factorial simulation experiments in which seasonal epidemics were simulated under all combinations of year-specific patterns of four time-varying factors: imported cases, mosquito density, temperature, and residual variation in local conditions not explicitly represented in the model. Our results indicate that while epidemics in most years were limited by unfavorable conditions with respect to one or more factors, the epidemic in 2014 was made possible by the combination of favorable conditions for all factors considered in our analysis.

An agent-based model of dengue virus transmission shows how uncertainty about breakthrough infections influences vaccination impact projections

Prophylactic vaccination is a powerful tool for reducing the burden of infectious diseases, due to a combination of direct protection of vaccinees and indirect protection of others via herd immunity. Computational models play an important role in devising strategies for vaccination by making projections of its impacts on public health. Such projections are subject to uncertainty about numerous factors, however. For example, many vaccine efficacy trials focus on measuring protection against disease rather than protection against infection, leaving the extent of breakthrough infections (i.e., disease ameliorated but infection unimpeded) among vaccinees unknown. Our goal in this study was to quantify the extent to which uncertainty about breakthrough infections results in uncertainty about vaccination impact, with a focus on vaccines for dengue. To realistically account for the many forms of heterogeneity in dengue virus (DENV) transmission, which could have implications for the dynamics of indirect protection, we used a stochastic, agent-based model for DENV transmission informed by more than a decade of empirical studies in the city of Iquitos, Peru. Following 20 years of routine vaccination of nine-year-old children at 80% coverage, projections of the proportion of disease episodes averted varied by a factor of 1.76 (95% CI: 1.54-2.06) across the range of uncertainty about breakthrough infections. This was equivalent to the range of vaccination impact projected across a range of uncertainty about vaccine efficacy of 0.268 (95% CI: 0.210-0.329). Until uncertainty about breakthrough infections can be addressed empirically, our results demonstrate the importance of accounting for it in models of vaccination impact.

The association between Zika virus infection and microcephaly in Brazil 2015-2017: An observational analysis of over 4 million births

BACKGROUND

In 2015, high rates of microcephaly were reported in Northeast Brazil following the first South American Zika virus (ZIKV) outbreak. Reported microcephaly rates in other Zika-affected areas were significantly lower, suggesting alternate causes or the involvement of arboviral cofactors in exacerbating microcephaly rates.

METHODS AND FINDINGS

We merged data from multiple national reporting databases in Brazil to estimate exposure to 9 known or hypothesized causes of microcephaly for every pregnancy nationwide since the beginning of the ZIKV outbreak; this generated between 3.6 and 5.4 million cases (depending on analysis) over the time period 1 January 2015-23 May 2017. The association between ZIKV and microcephaly was statistically tested against models with alternative causes or with effect modifiers. We found no evidence for alternative non-ZIKV causes of the 2015-2017 microcephaly outbreak, nor that concurrent exposure to arbovirus infection or vaccination modified risk. We estimate an absolute risk of microcephaly of 40.8 (95% CI 34.2-49.3) per 10,000 births and a relative risk of 16.8 (95% CI 3.2-369.1) given ZIKV infection in the first or second trimester of pregnancy; however, because ZIKV infection rates were highly variable, most pregnant women in Brazil during the ZIKV outbreak will have been subject to lower risk levels. Statistically significant associations of ZIKV with other birth defects were also detected, but at lower relative risks than that of microcephaly (relative risk < 1.5). Our analysis was limited by missing data prior to the establishment of nationwide ZIKV surveillance, and its findings may be affected by unmeasured confounding causes of microcephaly not available in routinely collected surveillance data.

CONCLUSIONS

This study strengthens the evidence that congenital ZIKV infection, particularly in the first 2 trimesters of pregnancy, is associated with microcephaly and less frequently with other birth defects. The finding of no alternative causes for geographic differences in microcephaly rate leads us to hypothesize that the Northeast region was disproportionately affected by this Zika outbreak, with 94% of an estimated 8.5 million total cases occurring in this region, suggesting a need for seroprevalence surveys to determine the underlying reason.

Biased efficacy estimates in phase-III dengue vaccine trials due to heterogeneous exposure and differential detectability of primary infections across trial arms

Vaccine efficacy (VE) estimates are crucial for assessing the suitability of dengue vaccine candidates for public health implementation, but efficacy trials are subject to a known bias to estimate VE toward the null if heterogeneous exposure is not accounted for in the analysis of trial data. In light of many well-characterized sources of heterogeneity in dengue virus (DENV) transmission, our goal was to estimate the potential magnitude of this bias in VE estimates for a hypothetical dengue vaccine. To ensure that we realistically modeled heterogeneous exposure, we simulated city-wide DENV transmission and vaccine trial protocols using an agent-based model calibrated with entomological and epidemiological data from long-term field studies in Iquitos, Peru. By simulating a vaccine with a true VE of 0.8 in 1,000 replicate trials each designed to attain 90% power, we found that conventional methods underestimated VE by as much as 21% due to heterogeneous exposure. Accounting for the number of exposures in the vaccine and placebo arms eliminated this bias completely, and the more realistic option of including a frailty term to model exposure as a random effect reduced this bias partially. We also discovered a distinct bias in VE estimates away from the null due to lower detectability of primary DENV infections among seronegative individuals in the vaccinated group. This difference in detectability resulted from our assumption that primary infections in vaccinees who are seronegative at baseline resemble secondary infections, which experience a shorter window of detectable viremia due to a quicker immune response. This resulted in an artefactual finding that VE estimates for the seronegative group were approximately 1% greater than for the seropositive group. Simulation models of vaccine trials that account for these factors can be used to anticipate the extent of bias in field trials and to aid in their interpretation.

Practical unidentifiability of a simple vector-borne disease model: Implications for parameter estimation and intervention assessment

Mathematical modeling has an extensive history in vector-borne disease epidemiology, and is increasingly used for prediction, intervention design, and understanding mechanisms. Many studies rely on parameter estimation to link models and data, and to tailor predictions and counterfactuals to specific settings. However, few studies have formally evaluated whether vector-borne disease models can properly estimate the parameters of interest given the constraints of a particular dataset. Identifiability analysis allows us to examine whether model parameters can be estimated uniquely-a lack of consideration of such issues can result in misleading or incorrect parameter estimates and model predictions. Here, we evaluate both structural (theoretical) and practical identifiability of a commonly used compartmental model of mosquito-borne disease, using the 2010 dengue epidemic in Taiwan as a case study. We show that while the model is structurally identifiable, it is practically unidentifiable under a range of human and mosquito time series measurement scenarios. In particular, the transmission parameters form a practically identifiable combination and thus cannot be estimated separately, potentially leading to incorrect predictions of the effects of interventions. However, in spite of the unidentifiability of the individual parameters, the basic reproduction number was successfully estimated across the unidentifiable parameter ranges. These identifiability issues can be resolved by directly measuring several additional human and mosquito life-cycle parameters both experimentally and in the field. While we only consider the simplest case for the model, we show that a commonly used model of vector-borne disease is unidentifiable from human and mosquito incidence data, making it difficult or impossible to estimate parameters or assess intervention strategies. This work illustrates the importance of examining identifiability when linking models with data to make predictions and inferences, and particularly highlights the importance of combining laboratory, field, and case data if we are to successfully estimate epidemiological and ecological parameters using models.

Burden of dengue among febrile patients at the time of chikungunya introduction in Piedecuesta, Colombia

OBJECTIVE

To estimate the age-specific incidence of symptomatic dengue and chikungunya in Colombia.

METHOD

A passive facility-based fever surveillance study was conducted among individuals with undifferentiated fever. Confirmatory diagnostics included serological and molecular tests in paired samples, and surveillance's underreporting was assessed using capture-recapture methods.

RESULTS

Of 839 febrile participants 686 completed the study. There were 33.2% (295/839) dengue infections (51% primary infections), and 35.9% (191/532) of negative dengue cases there were chikungunya cases. On average, dengue cases were younger (median = 18 years) than chikungunya cases (median = 25 years). Thrombocytopaenia and abdominal pain were the main dengue predictors, while presence of rash was the main predictor for chikungunya diagnosis. Underreporting of dengue was 31%; the estimated expansion factors indicate an underreporting rate of dengue cases of threefold for all cases and of almost sixfold for inpatients.

CONCLUSIONS

These findings highlight the ongoing coexistence of both arboviruses, a distinct clinical profile of each condition in the study area that could be used by clinicians to generate a differential diagnosis, and the presence of underreporting, mostly among hospitalised cases.

Epidemiology of dengue and other arboviruses in a cohort of school children and their families in Yucatan, Mexico: Baseline and first year follow-up

Dengue is the most prevalent mosquito-borne viral disease of humans and is caused by the four serotypes of dengue virus. To estimate the incidence of dengue and other arboviruses, we analyzed the baseline and first year follow-up of a prospective school-based cohort study and their families in three cities in the state of Yucatan, Mexico. Through enhanced surveillance activities, acute febrile illnesses in the participants were detected and yearly blood samples were collected to evaluate dengue infection incidence. A Cox model was fitted to identify hazard ratios of arboviral infections in the first year of follow-up of the cohort. The incidence of dengue symptomatic infections observed during the first year of follow-up (2015-2016) was 3.5 cases per 1,000 person-years (95% CI: 1.9, 5.9). The incidence of dengue infections was 33.9 infections per 1,000 person-years (95% CI: 31.7, 48.0). The majority of dengue infections and seroconversions were observed in the younger age groups (≤ 14 years old). Other arboviruses were circulating in the state of Yucatan during the study period. The incidence of symptomatic chikungunya infections was 8.6 per 1,000 person-years (95% CI: 5.8, 12.3) and the incidence of symptomatic Zika infections was 2.3 per 1,000 person-years (95% CI: 0.9, 4.5). Our model shows that having a dengue infection during the first year of follow-up was significantly associated with being female, living in Ticul or Progreso, and being dengue naïve at baseline. Age was not significantly associated with the outcome, it was confounded by prior immunity to dengue that increases with age. This is the first report of a cohort in Latin America that provides incidence estimates of the three arboviruses co-circulating in all age groups. This study provides important information for understanding the epidemiology of dengue and other arboviruses and better informing public health policies.

Viridot: An automated virus plaque (immunofocus) counter for the measurement of serological neutralizing responses with application to dengue virus

The gold-standard method for quantifying neutralizing antibody responses to many viruses, including dengue virus (DENV), is the plaque reduction neutralization test (PRNT, also called the immunofocus reduction neutralization test). The PRNT conducted on 96-well plates is high-throughput and requires a smaller volume of antiserum than on 6- or 24-well plates, but manual plaque counting is challenging and existing automated plaque counters are expensive or difficult to optimize. We have developed Viridot (Viridot package), a program for R with a user interface in shiny, that counts viral plaques of a variety of phenotypes, estimates neutralizing antibody titers, and performs other calculations of use to virologists. The Viridot plaque counter includes an automatic parameter identification mode (misses <10 plaques/well for 87% of diverse DENV strains [n = 1521]) and a mode that allows the user to fine-tune the parameters used for counting plaques. We compared standardized manual and Viridot plaque counting methods applied to the same wells by two analyses and found that Viridot plaque counts were as similar to the same analyst's manual count (Lin’s concordance correlation coefficient, ρ_c = 0.99 [95% confidence interval: 0.99–1.00]) as manual counts between analysts (ρ_c = 0.99 [95% CI: 0.98–0.99]). The average ratio of neutralizing antibody titers based on manual counted plaques to Viridot counted plaques was 1.05 (95% CI: 0.98–1.14), similar to the average ratio of antibody titers based on manual plaque counts by the two analysts (1.06 [95% CI: 0.84–1.34]). Across diverse DENV and ZIKV strains (n = 14), manual and Viridot plaque counts were mostly consistent (range of ρ_c = 0.74 to 1.00) and the average ratio of antibody titers based on manual and Viridot counted plaques was close to 1 (0.94 [0.86–1.02]). Thus, Viridot can be used for plaque counting and neutralizing antibody titer estimation of diverse DENV strains and potentially other viruses on 96-well plates as well as for formalization of plaque-counting rules for standardization across experiments and analysts.

Although the plaque reduction neutralization test (PRNT) is an important assay for measuring neutralizing antibody responses against many viruses, no free, open-source programs specifically designed for virus plaque counting and neutralizing antibody titer estimation are available. We have developed Viridot, a package for R with a user-interface in shiny, which is designed for use by laboratory-based virologists and researchers with minimal coding experience. The program includes: automatic and user-specification of settings for plaque counting; saving of plaque counting settings; counting of many plates at once; and easy output of plaque counts, plaque sizes, and images with counted plaques circled. Viridot also includes programs to analyze plaque counts, including estimation of: neutralizing antibody titers, pfu/mL of a virus stock, and the dilution factor of virus needed for an experiment. Viridot can be used to standardize plaque-counting methods within and between laboratories, helping researchers formalize an important aspect of the PRNT method that is often subjective. Viridot thus provides laboratory researchers around the world with a free tool to improve the speed and consistency with which the PRNT is conducted, aiding the public health response to emerging viral diseases.

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.

Scoping review on vector-borne diseases in urban areas: transmission dynamics, vectorial capacity and co-infection

BACKGROUND

Transmission dynamics, vectorial capacity, and co-infections have substantial impacts on vector-borne diseases (VBDs) affecting urban and suburban populations. Reviewing key factors can provide insight into priority research areas and offer suggestions for potential interventions.

MAIN BODY

Through a scoping review, we identify knowledge gaps on transmission dynamics, vectorial capacity, and co-infections regarding VBDs in urban areas. Peer-reviewed and grey literature published between 2000 and 2016 was searched. We screened abstracts and full texts to select studies. Using an extraction grid, we retrieved general data, results, lessons learned and recommendations, future research avenues, and practice implications. We classified studies by VBD and country/continent and identified relevant knowledge gaps. Of 773 articles selected for full-text screening, 50 were included in the review: 23 based on research in the Americas, 15 in Asia, 10 in Africa, and one each in Europe and Australia. The largest body of evidence concerning VBD epidemiology in urban areas concerned dengue and malaria. Other arboviruses covered included chikungunya and West Nile virus, other parasitic diseases such as leishmaniasis and trypanosomiasis, and bacterial rickettsiosis and plague. Most articles retrieved in our review combined transmission dynamics and vectorial capacity; only two combined transmission dynamics and co-infection. The review identified significant knowledge gaps on the role of asymptomatic individuals, the effects of co-infection and other host factors, and the impacts of climatic, environmental, and socioeconomic factors on VBD transmission in urban areas. Limitations included the trade-off from narrowing the search strategy (missing out on classical modelling studies), a lack of studies on co-infections, most studies being only descriptive, and few offering concrete public health recommendations. More research is needed on transmission risk in homes and workplaces, given increasingly dynamic and mobile populations. The lack of studies on co-infection hampers monitoring of infections transmitted by the same vector.

CONCLUSIONS

Strengthening VBD surveillance and control, particularly in asymptomatic cases and mobile populations, as well as using early warning tools to predict increasing transmission, were key strategies identified for public health policy and practice.

Stratified sero-prevalence revealed overall high disease burden of dengue but suboptimal immunity in younger age groups in Pune, India

BACKGROUND

In India, dengue disease is emerging as the most important vector borne public health problem due to rapid and unplanned urbanization, high human density and week management of the disease. Clinical cases are grossly underreported and not much information is available on prevalence and incidence of the disease.

METHODOLOGY

A cross sectional, stratified, facility based, multistage cluster sampling was conducted between May 4 and June 27, 2017 in Pune city. A total of 1,434 participants were enrolled. The serum samples were tested for detection of historical dengue IgG antibodies by ELISA using the commercial Panbio Dengue IgG Indirect ELISA kit. Anti-dengue IgG-capture Panbio ELISA was used for detection of high titered antibodies to detect recent secondary infection. We used this data to estimate key transmission parameters like force of infection and basic reproductive number. A subset of 120 indirect ELISA positive samples was also tested for Plaque Reduction Neutralizing Antibodies for determining serotype-specific prevalence.

FINDINGS

Overall, 81% participants were infected with dengue virus (DENV) at least once if not more. The positivity was significantly different in different age groups. All the adults above 70 years were positive for DENV antibodies. Over 69% participants were positive for neutralizing antibodies against all 4 serotypes suggesting intense transmission of all DENV serotypes in Pune. Age-specific seroprevalence was consistent with long-term, endemic circulation of DENV. There was an increasing trend with age, from 21.6% among <36 months to 59.4% in age group 10-12 years. We estimate that 8.68% of the susceptible population gets infected by DENV each year resulting into more than 3,00,000 infections and about 47,000 to 59,000 cases per year. This transmission intensity is similar to that reported from other known hyper-endemic settings in Southeast Asia and the Americas but significantly lower than report from Chennai.

CONCLUSIONS

Our study suggests that Pune city has high disease burden, all 4 serotypes are circulating, significant spatial heterogeneity in seroprevalence and suboptimal immunity in younger age groups. This would allow informed decisions to be made on management of dengue and introduction of upcoming dengue vaccines in the city.

Challenges and opportunities in controlling mosquito-borne infections

Mosquito-borne diseases remain a major cause of morbidity and mortality across the tropical regions. Despite much progress in the control of malaria, malaria-associated morbidity remains high, whereas arboviruses-most notably dengue-are responsible for a rising burden of disease, even in middle-income countries that have almost completely eliminated malaria. Here I discuss how new interventions offer the promise of considerable future reductions in disease burden. However, I emphasize that intervention programmes need to be underpinned by rigorous trials and quantitative epidemiological analyses. Such analyses suggest that the long-term goal of elimination is more feasible for dengue than for malaria, even if malaria elimination would offer greater overall health benefit to the public.

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.

Contributions from the silent majority dominate dengue virus transmission

Despite estimates that, each year, as many as 300 million dengue virus (DENV) infections result in either no perceptible symptoms (asymptomatic) or symptoms that are sufficiently mild to go undetected by surveillance systems (inapparent), it has been assumed that these infections contribute little to onward transmission. However, recent blood-feeding experiments with Aedes aegypti mosquitoes showed that people with asymptomatic and pre-symptomatic DENV infections are capable of infecting mosquitoes. To place those findings into context, we used models of within-host viral dynamics and human demographic projections to (1) quantify the net infectiousness of individuals across the spectrum of DENV infection severity and (2) estimate the fraction of transmission attributable to people with different severities of disease. Our results indicate that net infectiousness of people with asymptomatic infections is 80% (median) that of people with apparent or inapparent symptomatic infections (95% credible interval (CI): 0–146%). Due to their numerical prominence in the infectious reservoir, clinically inapparent infections in total could account for 84% (CI: 82–86%) of DENV transmission. Of infections that ultimately result in any level of symptoms, we estimate that 24% (95% CI: 0–79%) of onward transmission results from mosquitoes biting individuals during the pre-symptomatic phase of their infection. Only 1% (95% CI: 0.8–1.1%) of DENV transmission is attributable to people with clinically detected infections after they have developed symptoms. These findings emphasize the need to (1) reorient current practices for outbreak response to adoption of pre-emptive strategies that account for contributions of undetected infections and (2) apply methodologies that account for undetected infections in surveillance programs, when assessing intervention impact, and when modeling mosquito-borne virus transmission.

Most dengue virus infections result in either no perceptible symptoms or symptoms that are so mild that they go undetected by surveillance systems. It is unclear how much these infections contribute to the overall transmission and burden of dengue. At an individual level, we show that people with asymptomatic infections are approximately 80% as infectious to mosquitoes as their symptomatic counterparts. At a population level, we show that approximately 88% of infections result from people who display no apparent symptoms at the time of transmission. These results suggest that individuals undetected by surveillance systems may be the primary reservoir of dengue virus transmission and that policy for dengue control and prevention must be revised accordingly.

The Burden of Dengue Fever and Chikungunya in Southern Coastal Ecuador: Epidemiology, Clinical Presentation, and Phylogenetics from the First Two Years of a Prospective Study

Here, we report the findings from the first 2 years (2014-2015) of an arbovirus surveillance study conducted in Machala, Ecuador, a dengue-endemic region. Patients with suspected dengue virus (DENV) infections (index cases, N = 324) were referred from five Ministry of Health clinical sites. A subset of DENV-positive index cases (N = 44) were selected, and individuals from the index household and four neighboring homes within 200 m were recruited (N = 400). Individuals who entered the study, other than the index cases, are referred to as associates. In 2014, 70.9% of index cases and 35.6% of associates had acute or recent DENV infections. In 2015, 28.3% of index cases and 12.8% of associates had acute or recent DENV infections. For every DENV infection captured by passive surveillance, we detected an additional three acute or recent DENV infections in associates. Of associates with acute DENV infections, 68% reported dengue-like symptoms, with the highest prevalence of symptomatic acute infections in children aged less than 10 years. The first chikungunya virus (CHIKV) infections were detected on epidemiological week 12 in 2015; 43.1% of index cases and 3.5% of associates had acute CHIKV infections. No Zika virus infections were detected. Phylogenetic analyses of isolates of DENV from 2014 revealed genetic relatedness and shared ancestry of DENV1, DENV2, and DENV4 genomes from Ecuador with those from Venezuela and Colombia, indicating the presence of viral flow between Ecuador and surrounding countries. Enhanced surveillance studies, such as this, provide high-resolution data on symptomatic and inapparent infections across the population.

The Social and Spatial Ecology of Dengue Presence and Burden during an Outbreak in Guayaquil, Ecuador, 2012

Dengue fever, a mosquito-borne arbovirus, is a major public health concern in Ecuador. In this study, we aimed to describe the spatial distribution of dengue risk and identify local social-ecological factors associated with an outbreak of dengue fever in the city of Guayaquil, Ecuador. We examined georeferenced dengue cases (n = 4248) and block-level census data variables to identify social-ecological risk factors associated with the presence/absence and burden of dengue in Guayaquil in 2012. Local Indicators of Spatial Association (LISA), specifically Anselin’s Local Moran’s I, and Moran’s I tests were used to locate hotspots of dengue transmission, and multimodel selection was used to identify covariates associated with dengue presence and burden at the census block level. We identified significant dengue transmission hotspots near the North Central and Southern portions of Guayaquil. Significant risk factors for presence of dengue included poor housing conditions, access to paved roads, and receipt of remittances. Counterintuitive positive correlations with dengue presence were observed with several municipal services such as garbage collection and access to piped water. Risk factors for increased burden of dengue included poor housing conditions, garbage collection, receipt of remittances, and sharing a property with more than one household. Social factors such as education and household demographics were negatively correlated with increased dengue burden. These findings elucidate underlying differences with dengue presence versus burden, and suggest that vulnerability and risk maps could be developed to inform dengue prevention and control; this is information that is also relevant for emerging epidemics of chikungunya and Zika viruses.

Seroprevalence of Dengue Antibodies in Three Urban Settings in Yucatan, Mexico

Dengue transmission in Mexico has become a major public health problem. Few epidemiological studies have examined the seroprevalence of dengue in Mexico, and recent estimates are needed to better understand dengue transmission dynamics. We conducted a dengue seroprevalence survey among 1,668 individuals including all age groups in three urban settings in Yucatan, Mexico. Children (< 19 years old) were selected randomly from schools. The adults (≥ 19 years old) were selected from healthcare facilities. Participants were asked to provide a venous blood sample and to answer a brief questionnaire with demographic information. Previous exposure to dengue was determined using indirect immunoglobulin G enzyme-linked immunosorbent assay. The overall seroprevalence was 73.6%. The age-specific seroprevalence increased with age, going from 51.4% (95% confidence interval [CI] = 45.0-57.9%) in children ≤ 8 years to 72% (95% CI = 66.3-77.2%) in the 9- to 14-years old. The highest seroprevalence was 83.4% (95% CI = 77-82.2%) in adults greater than 50 years. The seroprevalence in Merida was 68.6% (95% CI = 65-72%), in Progreso 68.7% (95% CI = 64.2-72.8%), and in Ticul 85.3% (95% CI = 81.9-88.3%). Ticul had the highest seroprevalence in all age groups. Logistic regression analysis showed that age and city of residence were associated with greater risk of prior dengue exposure. The results highlight the level of past exposure to dengue virus including young children. Similar studies should be conducted elsewhere in Mexico and other endemic countries to better understand the transmission dynamics of dengue.

The use of longitudinal cohorts for studies of dengue viral pathogenesis and protection

In this review, we describe how longitudinal prospective community-based, school-based, and household-based cohort studies contribute to improving our knowledge of viral disease, focusing specifically on contributions to understanding and preventing dengue. We describe how longitudinal cohorts enable measurement of essential disease parameters and risk factors; provide insights into biological correlates of protection and disease risk; enable rapid application of novel biological and statistical technologies; lead to development of new interventions and inform vaccine trial design; serve as sentinels in outbreak conditions and facilitate development of critical diagnostic assays; enable holistic studies on disease in the context of other infections, comorbidities, and environmental risk factors; and build research capacity that strengthens national and global public health response and disease surveillance.

Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji

Dengue is a major health burden, but it can be challenging to examine transmission and evaluate control measures because outbreaks depend on multiple factors, including human population structure, prior immunity and climate. We combined population-representative paired sera collected before and after the 2013/14 dengue-3 outbreak in Fiji with surveillance data to determine how such factors influence transmission and control in island settings. Our results suggested the 10-19 year-old age group had the highest risk of infection, but we did not find strong evidence that other demographic or environmental risk factors were linked to seroconversion. A mathematical model jointly fitted to surveillance and serological data suggested that herd immunity and seasonally varying transmission could not explain observed dynamics. However, the model showed evidence of an additional reduction in transmission coinciding with a vector clean-up campaign, which may have contributed to the decline in cases in the later stages of the outbreak.