Category: Publications

Hamins-Puértolas M, Buddhari D, Salje H, Huang AT, Hunsawong T, Cummings DAT, Fernandez S, Farmer A, Kaewhiran S, Khampaen D, Srikiatkhachorn A, Iamsirithaworn S, Waickman A, Thomas SJ, Endy T, Rothman AL, Anderson KB, Rodriguez-Barraquer I

PLoS Comput Biol 2025 Nov;21(11):e1013708

PMID: 41289317

Abstract

Dengue virus (DENV) is an increasingly important human pathogen, with already half of the globe’s population living in environments with transmission potential. Since many cases are missed 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. 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.

Ashraf U, Chakraborty S, Scallan C, Lo NC, Alera MT, Farmer A, Cabalfin-Chua MN, Michael NL, Rothman AL, Wang TT

Sci Transl Med 2025 Sep;17(817):eadx7231

PMID: 40991727

Abstract

Dengue viruses (DENVs) cause 390 million infections annually, although only ~25% of these infections are symptomatic. Whereas antibody features linked to severe DENV disease are well studied, factors influencing infection susceptibility remain less clear. Here, we examined immunoglobulin G (IgG) characteristics before and after DENV vaccination (Dengvaxia) in individuals with a history of prior DENV exposure, comparing those who developed postvaccination infections to those who remained infection free. Elevated anti-DENV afucosylation, present before or after vaccination, was associated with increased likelihood of infection after vaccination. These data were further supported by mechanistic studies, which revealed that nonneutralizing, afucosylated, post-Dengvaxia IgG enhanced DENV replication in mice. This enhancement was dependent on CD16, the receptor for the afucosylated IgG Fc domain. Together, these findings support a model in which the presence of afucosylated IgG promotes virus replication, increasing the likelihood of productive infection upon DENV exposure. Moreover, these results highlight that IgG1 fucosylation is a predictor of risk for breakthrough DENV infection despite vaccination and support the importance of investigating strategies to regulate Fc fucosylation during vaccination.

Rapheal E, Kitro A, Imad H, Hamins-Peurtolas M, Olanwijitwong J, Chatapat L, Hunsawong T, Anderson K, Piyaphanee W

Emerg Infect Dis 2025 Jun;31(6):1149-1157

PMID: 40439444

Abstract

Dengue is a major cause of illness among local populations and travelers in dengue-endemic areas, particularly those who stay for an extended period. However, little is known about dengue risk among expatriates and other long-term travelers. We used catalytic models of force of infection to estimate time to 60% dengue virus (DENV) seropositivity for a cross-section of expatriates living in Bangkok and Pattaya, Thailand. Our model adjusted for daily time spent outside, years not exposed to DENV, sex, living environment, and use of mosquito repellent, nets, long sleeves, and air conditioning. We estimated an adjusted annual force of infection of 0.014 (95% CI 0.003-0.054) per year spent in dengue-endemic areas (67.3 years to 60% seropositivity), below that of local populations. Our findings suggest that expatriates have a DENV exposure profile distinct from locals and short-term travelers and should likely be considered independently when developing vaccine and prevention recommendations.

Lee H, Srikiatkhachorn A, Kalayanarooj S, Farmer AR, Park S

J Infect Dis 2025 Feb;231(1):241-250

PMID: 39078272

Abstract

BACKGROUND: This study aimed to compare the predictive performance of 3 statistical models-logistic regression, classification tree, and structural equation model (SEM)-in predicting severe dengue illness.

METHODS: We adopted a modified classification of dengue illness severity based on the World Health Organization’s 1997 guideline. We constructed predictive models using demographic factors and laboratory indicators on the day of fever occurrence, with data from 2 hospital cohorts in Thailand (257 Thai children). Different predictive models for each category of severe dengue illness were developed employing logistic regression, classification tree, and SEM. The model’s discrimination abilties were analyzed with external validation data sets from 55 and 700 patients not used in model development.

RESULTS: From external validation based on predictors on the day of presentation to the hospital, the area under the receiver operating characteristic curve was from 0.65 to 0.84 for the regression models from 0.73 to 0.85 for SEMs. Classification tree models showed good results of sensitivity (0.95 to 0.99) but poor specificity (0.10 to 0.44).

CONCLUSIONS: Our study showed that SEM is comparable to logistic regression or classification tree, which was widely used for predicting severe forms of dengue.

Huang AT, Buddhari D, Kaewhiran S, Iamsirithaworn S, Khampaen D, Farmer A, Fernandez S, Thomas SJ, Rodriguez-Barraquer I, Hunsawong T, Srikiatkhachorn A, Ribeiro Dos Santos G, O’Driscoll M, Hamins-Puertolas M, Endy T, Rothman AL, Cummings DAT, Anderson K, Salje H

Proc Natl Acad Sci U S A 2025 Jan;122(1):e2411768121

PMID: 39739790

Abstract

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.