Category: Publications

Haltaufderhyde K, Srikiatkhachorn A, Green S, Macareo L, Park S, Kalayanarooj S, Rothman AL, Mathew A

J. Infect. Dis. 2018 Oct;218(10):1675-1685

PMID: 29917084

Abstract

Background: Follicular helper T cells (TFH) are specialized CD4 T cells required for B-cell help and antibody production.

Methods: Given the postulated role of immune activation in dengue disease, we measured the expansion and activation of TFH in the circulation (peripheral TFH [pTFH]) collected from Thai children with laboratory-confirmed acute dengue virus (DENV) infection.

Results: We found significant expansion and activation of pTFH subsets during acute infection with the highest frequencies of activated pTFH (PD1hi pTFH and PD1+CD38+ pTFH) detected during the critical phase of illness. Numbers of activated pTFH were higher in patients with secondary compared with primary infections and in patients with more severe disease. We also found a positive correlation between the frequencies of activated pTFH and the frequencies of plasmablasts.

Conclusions: To our knowledge, this is the first ex vivo analysis of pTFH activation during acute DENV infection. Overall, our study supports the model that pTFH contribute to disease evolution during the critical stage of illness.

Kang JY, Aldstadt J

Int J Geogr Inf Sci 2019;33(1):193-213

PMID: 31695574

Abstract

Spatially explicit agent-based models (ABMs) have been widely utilized to simulate the dynamics of spatial processes that involve the interactions of individual agents. The assumptions embedded in the ABMs may be responsible for uncertainty in the model outcomes. To ensure the reliability of the outcomes in terms of their space-time patterns, model validation should be performed. In this paper, we propose the use of multiple scale spatio-temporal patterns for validating spatially explicit ABMs. We evaluated several specifications of vector-borne disease transmission models by comparing space-time patterns of model outcomes to observations at multiple scales via the sum of root mean square error (RMSE) measurement. The results indicate that specifications of the spatial configurations of residential area and immunity status of individual humans are of importance to reproduce observed patterns of dengue outbreaks at multiple space-time scales. Our approach to using multiple scale spatio-temporal patterns can help not only to understand the dynamic associations between model specifications and model outcomes, but also to validate spatially explicit ABMs.

Park S, Srikiatkhachorn A, Kalayanarooj S, Macareo L, Green S, Friedman JF, Rothman AL

PLoS Negl Trop Dis 2018 Oct;12(10):e0006799

PMID: 30273334

Abstract

BACKGROUND: Early recognition of dengue, particularly patients at risk for plasma leakage, is important to clinical management. The objective of this study was to build predictive models for dengue, dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS) using structural equation modelling (SEM), a statistical method that evaluates mechanistic pathways.

METHODS/FINDINGS: We performed SEM using data from 257 Thai children enrolled within 72 h of febrile illness onset, 156 with dengue and 101 with non-dengue febrile illnesses. Models for dengue, DHF, and DSS were developed based on data obtained three and one day(s) prior to fever resolution (fever days -3 and -1, respectively). Models were validated using data from 897 subjects who were not used for model development. Predictors for dengue and DSS included age, tourniquet test, aspartate aminotransferase, and white blood cell, % lymphocytes, and platelet counts. Predictors for DHF included age, aspartate aminotransferase, hematocrit, tourniquet test, and white blood cell and platelet counts. The models showed good predictive performances in the validation set, with area under the receiver operating characteristic curves (AUC) at fever day -3 of 0.84, 0.67, and 0.70 for prediction of dengue, DHF, and DSS, respectively. Predictive performance was comparable using data based on the timing relative to enrollment or illness onset, and improved closer to the critical phase (AUC 0.73 to 0.94, 0.61 to 0.93, and 0.70 to 0.96 for dengue, DHF, and DSS, respectively).

CONCLUSIONS: Predictive models developed using SEM have potential use in guiding clinical management of suspected dengue prior to the critical phase of illness.

Salje H, Cummings DAT, Rodriguez-Barraquer I, Katzelnick LC, Lessler J, Klungthong C, Thaisomboonsuk B, Nisalak A, Weg A, Ellison D, Macareo L, Yoon IK, Jarman R, Thomas S, Rothman AL, Endy T, Cauchemez S

Nature 2018 May;557(7707):719-723

PMID: 29795354

Abstract

As with many pathogens, most dengue infections are subclinical and therefore unobserved . Coupled with limited understanding of the dynamic behaviour of potential serological markers of infection, this observational problem has wide-ranging implications, including hampering our understanding of individual- and population-level correlates of infection and disease risk and how these change over time, between assay interpretations and with cohort design. Here we develop a framework that simultaneously characterizes antibody dynamics and identifies subclinical infections via Bayesian augmentation from detailed cohort data (3,451 individuals with blood draws every 91 days, 143,548 haemagglutination inhibition assay titre measurements). We identify 1,149 infections (95% confidence interval, 1,135-1,163) that were not detected by active surveillance and estimate that 65% of infections are subclinical. After infection, individuals develop a stable set point antibody load after one year that places them within or outside a risk window. Individuals with pre-existing titres of ≤1:40 develop haemorrhagic fever 7.4 (95% confidence interval, 2.5-8.2) times more often than naive individuals compared to 0.0 times for individuals with titres >1:40 (95% confidence interval: 0.0-1.3). Plaque reduction neutralization test titres ≤1:100 were similarly associated with severe disease. Across the population, variability in the size of epidemics results in large-scale temporal changes in infection and disease risk that correlate poorly with age.

Kalayanarooj S, Rothman AL, Srikiatkhachorn A

J. Infect. Dis. 2017 Mar;215(suppl_2):S79-S88

PMID: 28403440

Abstract

The global burden of dengue and its geographic distribution have increased over the past several decades. The introduction of dengue in new areas has often been accompanied by high case-fatality rates. Drawing on the experience in managing dengue cases at the Queen Sirikit National Institute of Child Health in Bangkok, Thailand, this article provides the authors’ perspectives on key clinical lessons to improve dengue-related outcomes. Parallels between this clinical experience and outcomes reported in randomized controlled trials, results of efforts to disseminate practice recommendations, and suggestions for areas for further research are also discussed.