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Infants less than 1 y of age experience high rates of dengue disease in dengue virus (DENV) endemic countries. This burden is commonly attributed to antibody-dependent enhancement (ADE), whereby concentrations of maternally derived DENV antibodies become subneutralizing, and infection-enhancing. Understanding antibody-related mechanisms of enhanced infant dengue disease risk represents a significant challenge due to the dynamic nature of antibodies and their imperfect measurement processes. Further, key uncertainties exist regarding the impact of long-term shifts in birth rates, population-level infection risks, and maternal ages on the DENV immune landscape of newborns and their subsequent risks of severe dengue disease in infancy. Here, we analyze DENV antibody data from two infant cohorts (N = 142 infants with 605 blood draws) and 40 y of infant dengue hospitalization data from Thailand. We use mathematical models to reconstruct maternally derived antibody dynamics, accounting for discretized measurement processes and limits of assay detection. We then explore possible antibody-related mechanisms of enhanced infant dengue disease risk and their ability to reconstruct the observed age distribution of hospitalized infant dengue cases. We find that ADE mechanisms are best able to reconstruct the observed data. Finally, we describe how the shifting epidemiology of dengue in Thailand, combined with declining birth rates, have decreased the absolute risk of infant dengue disease by 88% over a 40-y period while having minimal impact on the mean age of infant hospitalized dengue disease.

BackgroundDengue virus (DENV) often circulates endemically. In such settings with high levels of transmission, it remains unclear whether there are risk factors that alter individual infection risk.MethodsWe tested blood taken from individuals living in multigenerational households in Kamphaeng Phet province, Thailand for DENV antibodies (N = 2364, mean age 31 years). Seropositivity ranged from 45.4% among those 1-5 years old to 99.5% for those >30 years. Using spatially explicit catalytic models, we estimated that 11.8% of the susceptible population gets infected annually.ResultsWe found that 37.5% of the variance in seropositivity was explained by unmeasured household-level effects with only 4.2% explained by spatial differences between households. The serostatus of individuals from the same household remained significantly correlated even when separated by up to 15 years in age.ConclusionsThese findings show that despite highly endemic transmission, persistent differences in infection risk exist across households, the reasons for which remain unclear.

Chikungunya virus (CHIKV) infections are distributed across the globe, causing significant and, often, lasting morbidity. CHIKV vaccines are in development, but their evaluation is limited by the unpredictability of CHIKV transmission, which classically manifests as explosive epidemics separated by variable interepidemic periods. A passive surveillance study for undifferentiated febrile illness was established in southern Thailand in 2012 and is ongoing. Among 1466 febrile individuals with acute and convalescent specimens, 398 (27.1%) had molecular or serological evidence of acute CHIKV infection. The proportions of participants confirmed to have CHIKV infection differed by year, being highest during epidemic periods (41.1% in 2018–2019, corresponding to a large regional CHIKV outbreak, compared to 19.3% in 2012–2017). These data suggest persistent circulation of CHIKV in the study area, though additional studies are needed to confirm these findings and to discern whether this persistence reflects widespread, low-level transmission or migrating bursts of focal epidemic activity. Author summary: Chikungunya virus (CHIKV) infections found worldwide and can lead to significant long-lasting morbidity due to chronic joint pain. Although CHIKV vaccines are in development (with one recently receiving accelerated approval based on a surrogate end-point), the transmission patterns of CHIKV are unpredictable, presenting as explosive epidemics separated by variable interepidemic periods, making clinical trials difficult. In southern Thailand, we monitored acute febrile illnesses via an enhanced passive surveillance system from 2012 to 2019 and by combining molecular and serological methods, acute CHIKV was identified in 27.1% of 1466 individuals. These findings suggest persistent circulation of chikungunya in the region and that there may be unknown reservoirs or low-level transmission occurring outside of detected outbreaks. [ABSTRACT FROM AUTHOR]

The development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant challenge for vaccine development has been defining and measuring serotype-specific outcomes and correlates of protection. The plaque reduction neutralisation test (PRNT) is the gold standard assay for measuring serotype-specific antibodies, but this test cannot differentiate homotypic and heterotypic antibodies and characterising the infection history is challenging. To address this, we present an analysis of pre- and post-infection antibody titres measured using the PRNT, collected from a prospective cohort of Thai children. We applied four machine learning classifiers and multinomial logistic regression to the titre data to predict the infecting serotype. The models were validated against the true infecting serotype, identified using RT-PCR. Model performance was calculated using 100 bootstrap samples of the train and out-of-sample test sets. Our analysis showed that, on average, the greatest change in titre was against the infecting serotype. However, in 53.4% (109/204) of the subjects, the highest titre change did not correspond to the infecting serotype, including in 34.3% (11/35) of dengue-naïve individuals (although 8/11 of these seronegative individuals were seropositive to Japanese encephalitis virus prior to their infection). The highest post-infection titres of seropositive cases were more likely to match the serotype of the highest pre-infection titre than the infecting serotype, consistent with antigenic seniority or cross-reactive boosting of pre-infection titres. Despite these challenges, the best performing machine learning algorithm achieved 76.3% (95% CI 57.9–89.5%) accuracy on the out-of-sample test set in predicting the infecting serotype from PRNT data. Incorporating additional spatiotemporal data improved accuracy to 80.6% (95% CI 63.2–94.7%), while using only post-infection titres as predictor variables yielded an accuracy of 71.7% (95% CI 57.9–84.2%). These results show that machine learning classifiers can be used to overcome challenges in interpreting PRNT titres, making them useful tools in investigating dengue immune dynamics, infection history and identifying serotype-specific correlates of protection, which in turn can support the evaluation of clinical trial endpoints and vaccine development. Author summary: Dengue is a viral infection transmitted by mosquitoes that has rapidly spread worldwide over the past 50 years. It is caused by four distinct virus serotypes, and developing a vaccine that protects against all of them is a key goal. However, accurately measuring serotype-specific immune responses and identifying the serotype responsible for past infections remains a significant challenge. The plaque reduction neutralisation test (PRNT) is the gold standard method for measuring serotype-specific antibody responses. However, its cross-reactivity, combined with the complex short-term cross-protective nature of dengue antibodies, currently makes the identification of the infecting serotype challenging. To tackle this, we analysed antibody data from Thai children, collected both before and after dengue infection. By applying machine learning models to this data, we predicted the infecting serotype with an average accuracy of 71% to 80%. This approach improves our understanding of how the immune system responds to different dengue serotypes and has the potential to inform the evaluation of vaccine efficacy in future trials. [ABSTRACT FROM AUTHOR]

Background Dengue virus (DENV) nonstructural protein 1 (NS1) has multiple functions within infected cells, on the cell surface, and in secreted form, and is highly immunogenic. Immunity from previous DENV infections is known to exert both positive and negative effects on subsequent DENV infections, but the contribution of NS1-specific antibodies to these effects is incompletely understood. Methods We investigated the functions of NS1-specific antibodies and their significance in DENV infection. We analyzed plasma samples collected in a prospective cohort study prior to symptomatic or subclinical secondary DENV infection. We measured binding to purified recombinant NS1 protein and to NS1-expressing CEM cells, antibody-mediated natural killer (NK) cell activation by plate-bound NS1 protein, and antibody-dependent cellular cytotoxicity (ADCC) of NS1-expressing target cells. Results We found that antibody responses to NS1 were highly serotype cross-reactive and that subjects who experienced subclinical DENV infection had significantly higher antibody responses to NS1 in preinfection plasma than subjects who experienced symptomatic infection. We observed strong positive correlations between antibody binding and NK activation. Conclusions These findings demonstrate the involvement of NS1-specific antibodies in ADCC and provide evidence for a protective effect of NS1-specific antibodies in secondary DENV infection. [ABSTRACT FROM AUTHOR]

Dengue virus (DENV) causes an estimated 390 million infections each year, 96 million of which manifest as clinical disease (2). Dengue is endemic in >100 countries. An estimated 3.9 billion [...]

Dengue is of public health importance in tropical and sub-tropical regions. Dengue virus (DENV) transmission dynamics was studied in Kamphaeng Phet Province, Thailand, using an enhanced spatiotemporal surveillance of 93 hospitalized subjects with confirmed dengue (initiates) and associated cluster individuals (associates) with entomologic sampling. A total of 438 associates were enrolled from 208 houses with household members with a history of fever, located within a 200-m radius of an initiate case. Of 409 associates, 86 (21%) had laboratory-confirmed DENV infection. A total of 63 (1.8%) of the 3,565 mosquitoes collected were dengue polymerase chain reaction positive (PCR+). There was a significant relationship between spatial proximity to the initiate case and likelihood of detecting DENV from associate cases and Aedes mosquitoes. The viral detection rate from human hosts and mosquito vectors in this study was higher than previously observed by the study team in the same geographic area using different methodologies. We propose that the sampling strategy used in this study could support surveillance of DENV transmission and vector interactions.

BackgroundLong-term homologous and temporary heterologous protection from dengue virus (DENV) infection may be mediated by neutralizing antibodies. However, neutralizing antibody titers (NTs) have not been clearly associated with protection from infection.Methodology/principal findingsData from two geographic cluster studies conducted in Kamphaeng Phet, Thailand were used for this analysis. In the first study (2004-2007), cluster investigations of 100-meter radius were triggered by DENV-infected index cases from a concurrent prospective cohort. Subjects between 6 months and 15 years old were evaluated for DENV infection at days 0 and 15 by DENV PCR and IgM ELISA. In the second study (2009-2012), clusters of 200-meter radius were triggered by DENV-infected index cases admitted to the provincial hospital. Subjects of any age ≥6 months were evaluated for DENV infection at days 0 and 14. In both studies, subjects who were DENV PCR positive at day 14/15 were considered to have been "susceptible" on day 0. Comparison subjects from houses in which someone had documented DENV infection, but the subject remained DENV negative at days 0 and 14/15, were considered "non-susceptible." Day 0 samples were presumed to be from just before virus exposure, and underwent plaque reduction neutralization testing (PRNT). Seventeen "susceptible" (six DENV-1, five DENV-2, and six DENV-4), and 32 "non-susceptible" (13 exposed to DENV-1, 10 DENV-2, and 9 DENV-4) subjects were evaluated. Comparing subjects exposed to the same serotype, receiver operating characteristic (ROC) curves identified homotypic PRNT titers of 11, 323 and 16 for DENV-1, -2 and -4, respectively, to differentiate "susceptible" from "non-susceptible" subjects.Conclusions/significancePRNT titers were associated with protection from infection by DENV-1, -2 and -4. Protective NTs appeared to be serotype-dependent and may be higher for DENV-2 than other serotypes. These findings are relevant for both dengue epidemiology studies and vaccine development efforts.

A four-year longitudinal cohort and geographic cluster study in rural Thailand was conducted to characterize the clinical spectrum of dengue virus (DENV) infection. Symptomatic DENV infections in the cohort were detected by active school absence-based surveillance that triggered cluster investigations around ill cohort children. Data from 189 cohort children with symptomatic DENV infection and 126 contact children in the clusters with DENV infection were analyzed. Of infected contacts, only 19% were asymptomatic; 81% were symptomatic, but only 65.9% reported fever. Symptom-based case definitions were unreliable for diagnosis. Symptomatic infections in contacts were milder with lower DENV RNA levels than the cohort. Infections in contacts with fever history were more likely to have detectable DENV RNA than infections without fever history. Mild infections identified by cluster investigations account for a major proportion of all DENV infections. These findings are relevant for disease burden assessments, transmission modeling, and determination of vaccine impact.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). RT-PCR detection of viral RNA represents the gold standard method for diagnosis of COVID-19. However, multiple diagnostic tests are needed for acute disease diagnosis and assessing immunity during the COVID-19 outbreak. Here, we developed in-house anti-RBD IgG and IgA enzyme-linked immunosorbent assays (ELISAs) using a well-defined serum sample panel for screening and identification of human SARS-CoV-2 infection. We found that our in-house anti-SARS-CoV-2 IgG ELISA displayed a 93.5% sensitivity and 98.8% specificity whereas our in-house anti-SARS-CoV-2 IgA ELISA provided assay sensitivity and specificity at 89.5% and 99.4%, respectively. The agreement kappa values of our in-house anti-SARS-CoV-2 IgG and IgA ELISA assays were deemed to be excellent and fair, respectively, when compared to RT-PCR and excellent for both assays when compared to Euroimmun anti-SARS-CoV-2 IgG and IgA ELISAs. These data indicate that our in-house anti-SARS-CoV-2 IgG and IgA ELISAs are compatible performing assays for the detection of SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

Background: Dengue virus (DENV) often circulates endemically. In such settings with high levels of transmission, it remains unclear whether there are risk factors that alter individual infection risk.Methods: We tested blood taken from individuals living in multigenerational households in Kamphaeng Phet province, Thailand for DENV antibodies (N = 2364, mean age 31 years). Seropositivity ranged from 45.4% among those 1-5 years old to 99.5% for those >30 years. Using spatially explicit catalytic models, we estimated that 11.8% of the susceptible population gets infected annually.Results: We found that 37.5% of the variance in seropositivity was explained by unmeasured household-level effects with only 4.2% explained by spatial differences between households. The serostatus of individuals from the same household remained significantly correlated even when separated by up to 15 years in age.Conclusions: These findings show that despite highly endemic transmission, persistent differences in infection risk exist across households, the reasons for which remain unclear. [ABSTRACT FROM AUTHOR]

Uncovering rates at which susceptible individuals become infected with a pathogen, i.e., the force of infection (FOI), is essential for assessing transmission risk and reconstructing distribution of immunity in a population. For dengue, reconstructing exposure and susceptibility statuses from the measured FOI is of particular significance as prior exposure is a strong risk factor for severe disease. FOI can be measured via many study designs. Longitudinal serology is considered gold standard measurements, as they directly track the transition of seronegative individuals to seropositive due to incident infections (seroincidence). Cross-sectional serology can provide estimates of FOI by contrasting seroprevalence across ages. Age of reported cases can also be used to infer FOI. Agreement of these measurements, however, has not been assessed. Using 26 y of data from cohort studies and hospital-attended cases from Kamphaeng Phet province, Thailand, we found FOI estimates from the three sources to be highly inconsistent. Annual FOI estimates from seroincidence were 1.75 to 4.05 times higher than case-derived FOI. Seroprevalence-derived was moderately correlated with case-derived FOI (correlation coefficient = 0.47) with slightly lower estimates. Through extensive simulations and theoretical analysis, we show that incongruences between methods can result from failing to account for dengue antibody kinetics, assay noise, and heterogeneity in FOI across ages. Extending standard inference models to include these processes reconciled the FOI and susceptibility estimates. Our results highlight the importance of comparing inferences across multiple data types to uncover additional insights not attainable through a single data type/analysis., Competing Interests: Competing interests statement:The authors declare no competing interest.

Dengue virus (DENV) is an increasingly important human pathogen, with already half of the globe's population living in environments with transmission potential. Since only a minority of cases are captured by direct detection methods (RT-PCR or antigen tests), serological assays play an important role in the diagnostic process. However, individual assays can suffer from low sensitivity and specificity and interpreting results from multiple assays remains challenging, particularly because interpretations from multiple assays may differ, creating uncertainty over how to generate finalized interpretations. We develop a Bayesian mixture model that can jointly model data from multiple paired serological assays, to infer infection events from paired serological data. We first test the performance of our model using simulated data. We then apply our model to 677 pairs of acute and convalescent serum collected as a part of illness and household investigations across two longitudinal cohort studies in Kamphaeng Phet, Thailand, including data from 232 RT-PCR confirmed infections (gold standard). We compare the classification of the new model to prior standard interpretations that independently utilize information from either the hemagglutination inhibition assay (HAI) or the enzyme-linked immunosorbent assay (EIA). We find that additional serological assays improve accuracy of infection detection for both simulated and real world data. Models incorporating paired IgG and IgM data as well as those incorporating IgG, IgM, and HAI data consistently have higher accuracy when using PCR confirmed infections as a gold standard (87-90% F1 scores, a combined metric of sensitivity and specificity) than currently implemented cut-point approaches (82-84% F1 scores). Our results provide a probabilistic framework through which multiple serological assays across different platforms can be leveraged across sequential serum samples to provide insight into whether individuals have recently experienced a DENV infection. These methods are applicable to other pathogen systems where multiple serological assays can be leveraged to quantify infection history.

Uncovering rates at which susceptible individuals become infected with a pathogen, i.e. the force of infection (FOI), is essential for assessing transmission risk and reconstructing distribution of immunity in a population. For dengue, reconstructing exposure and susceptibility statuses from the measured FOI is of particular significance as prior exposure is a strong risk factor for severe disease. FOI can be measured via many study designs. Longitudinal serology are considered gold standard measurements, as they directly track the transition of seronegative individuals to seropositive due to incident infections (seroincidence). Cross-sectional serology can provide estimates of FOI by contrasting seroprevalence across ages. Age of reported cases can also be used to infer FOI. Agreement of these measurements, however, have not been assessed. Using 26 years of data from cohort studies and hospital-attended cases from Kamphaeng Phet province, Thailand, we found FOI estimates from the three sources to be highly inconsistent. Annual FOI estimates from seroincidence was 2.46 to 4.33-times higher than case-derived FOI. Correlation between seroprevalence-derived and case-derived FOI was moderate (correlation coefficient=0.46) and no systematic bias. Through extensive simulations and theoretical analysis, we show that incongruences between methods can result from failing to account for dengue antibody kinetics, assay noise, and heterogeneity in FOI across ages. Extending standard inference models to include these processes reconciled the FOI and susceptibility estimates. Our results highlight the importance of comparing inferences across multiple data types to uncover additional insights not attainable through a single data type/analysis.

The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric, significantly improved model performance., Competing Interests: Conflicts of Interest: The authors have declared no conflicts of interest.