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The mean age of dengue hemorrhagic fever (DHF) cases increased considerably in Thailand from 8.1 to 24.3 y between 1981 and 2017 (mean annual increase of 0.45 y). Alternative proposed explanations for this trend, such as changes in surveillance practices, reduced mosquito–human contact, and shifts in population demographics, have different implications for global dengue epidemiology. To evaluate the contribution of each of these hypothesized mechanisms to the observed data, we developed 20 nested epidemiological models of dengue virus infection, allowing for variation over time in population demographics, infection hazards, and reporting rates. We also quantified the effect of removing or retaining each source of variation in simulations of the age trajectory. Shifts in the age structure of susceptibility explained 58% of the observed change in age. Adding heterogeneous reporting by age and reductions in per-serotype infection hazard to models with shifts in susceptibility explained an additional 42%. Reductions in infection hazards were mostly driven by changes in the number of infectious individuals at any time (another consequence of shifting age demographics) rather than changes in the transmissibility of individual infections. We conclude that the demographic transition drives the overwhelming majority of the observed change as it changes both the age structure of susceptibility and the number of infectious individuals. With the projected Thai population age structure, our results suggest a continuing increase in age of DHF cases, shifting the burden toward individuals with more comorbidity. These insights into dengue epidemiology may be relevant to many regions of the globe currently undergoing comparable changes in population demographics.

This study evaluates the efficacy of an intervention for persons living with HIV (PLH) and their family members in Thailand. A randomized controlled trial of 813 PLH and family members was carried out at four district hospitals in Thailand. Participants completed Computer Assisted Personal Interview assessments at baseline, 6, 12, 18, and 24 months. The primary outcome was quality of life (QoL); other measures included depressive symptoms and family functioning. Relative to the standard care condition, the intervention group reported significantly improved QoL at 6 months (P = 0.0014). When the intervention efficacy was stratified by baseline depressive symptoms (low vs. high), intervention efficacy was observed only among those with low depressive symptoms. Study findings suggest that the intervention was more efficacious for participants with less depressive symptoms and better family functioning. Extensive interventions may be optimal for those who have the capacity to learn the tools and skills.

ObjectivesWe examined findings from a randomized controlled intervention trial designed to improve the quality of life of people living with HIV in Thailand.MethodsA total of 507 people living with HIV were recruited from 4 district hospitals in northern and northeastern Thailand and were randomized to an intervention group (n = 260) or a standard care group (n = 247). Computer-assisted personal interviews were administered at baseline and at 6 and 12 months.ResultsAt baseline, the characteristics of participants in the intervention and standard care conditions were comparable. The mixed-effects models used to assess the impact of the intervention revealed significant improvements in general health (B = 2.51; P = .001) and mental health (B = 1.57; P = .02) among participants in the intervention condition over 12 months and declines among those in the standard care condition.ConclusionsOur results demonstrate that a behavioral intervention was successful in improving the quality of life of people living with HIV. Such interventions must be performed in a systematic, collaborative manner to ensure their cultural relevance, sustainability, and overall success.

Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000-2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010-2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe. [ABSTRACT FROM AUTHOR]

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. [ABSTRACT FROM AUTHOR]

Infectious diseases remain a significant health concern around the world. Mathematical modeling of these diseases can help us understand their dynamics and develop more effective control strategies. In this work, we show the capabilities of interior-point methods and nonlinear programming (NLP) formulations to efficiently estimate parameters in multiple discrete-time disease models using measles case count data from three cities. These models include multiplicative measurement noise and incorporate seasonality into multiple model parameters. Our results show that nearly identical patterns are estimated even when assuming seasonality in different model parameters, and that these patterns show strong correlation to school term holidays across very different social settings and holiday schedules. We show that interior-point methods provide a fast and flexible approach to parameterizing models that can be an alternative to more computationally intensive methods. [ABSTRACT FROM AUTHOR]