Category: Publications

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.

Sanchez-Vargas LA, Mathew A, Salje H, Sousa D, Casale NA, Farmer A, Buddhari D, Anderson K, Iamsirithaworn S, Kaewhiran S, Friberg H, Currier JR, Rothman AL

J Infect Dis 2024 Nov;230(5):1147-1156

PMID: 38478732

Abstract

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.

Williams RJ, Brintz BJ, Ribeiro Dos Santos G, Huang AT, Buddhari D, Kaewhiran S, Iamsirithaworn S, Rothman AL, Thomas S, Farmer A, Fernandez S, Cummings DAT, Anderson KB, Salje H, Leung DT

Sci Adv 2024 Feb;10(7):eadj9786

PMID: 38363842

Abstract

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.