Category: Publications

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.

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

Nat Microbiol 2024 Jan;9(1):274-283

PMID: 38110699

Abstract

Although it is known that household infections drive the transmission of dengue virus (DENV), it is unclear how household composition and the immune status of inhabitants affect the individual risk of infection. Most population-based studies to date have focused on paediatric cohorts because more severe forms of dengue mainly occur in children, and the role of adults in dengue transmission is understudied. Here we analysed data from a multigenerational cohort study of 470 households, comprising 2,860 individuals, in Kamphaeng Phet, Thailand, to evaluate risk factors for DENV infection. Using a gradient-boosted regression model trained on annual haemagglutination inhibition antibody titre inputs, we identified 1,049 infections, 90% of which were subclinical. By analysing imputed infections, we found that individual antibody titres, household composition and antibody titres of other members in the same household affect an individual’s risk of DENV infection. Those individuals living in households with high average antibody titres, or households with more adults, had a reduced risk of infection. We propose that herd immunity to dengue acts at the household level and may provide insight into the drivers of the recent change in the shifting age distribution of dengue cases in Thailand.