Innovative Statistical Approaches for Public Health and Global Epidemiology

During an infectious disease outbreak, the growth rate (r) quantifies the relative change in cases over time. Integrated Brownian Motion (IBM)—the time integral of a Wiener process—can be modeled as a Markov process when modeled jointly with its derivative, which allows for a state space representation and therefore avoids potentially costly matrix inversions. This makes IBM a computationally efficient smoothing prior when the derivative (i.e., the growth rate) is of primary interest. Real-world case trajectories, however, often exhibit both gradual trends and abrupt shifts, like sudden surges driven by emerging variants or waning immunity. Standard Gaussian priors do not capture these mixed dynamics effectively, as they can either oversmooth rapid changes or produce overly noisy estimates during stable periods. To address this, we propose a locally adaptive extension of IBM that can flexibly model both gradual and abrupt changes in case incidence. We demonstrate the effectiveness of this method using simulated outbreak data and California county-level SARS-CoV-2 data.  

Drug adherence is key to the success of biomedical preventive interventions in HIV/AIDS research. Analysis of the drug adherences to identify their trend over time shall help researchers develop an essential understanding of the patterns involved. In this talk, we will present a semiparametric modeling framework to study the comparability of gap times and analyze the drug adherences as recurrent event data, where each individual patient may experience repeated clinical events. We apply the proposed method to the HIV Prevention Trial Network 052 study, a milestone clinical trial that established the benefit of the Treatment-as-Prevention strategy in HIV/AIDS prevention. 

Viral deep-sequencing technologies play a crucial role toward understanding disease transmission patterns, because the higher resolution of these data provide evidence on transmission direction. To better utilize these data and account for uncertainty in phylogenetic analysis, we propose a spatial Poisson process model to uncover HIV transmission flow patterns at the population level. We represent pairings of two individuals with viral sequence data as typed points, with coordinates representing covariates such as sex and age, and the point type representing the unobserved transmission statuses (linkage and direction). Points are associated with deep-sequence phylogenetic analysis summary scores that reflect the strength of evidence for each transmission status. Our method jointly infers the latent transmission status for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, our framework does not require pre-classification of the transmission statuses of data points, instead learning them probabilistically through fully Bayesian inference. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages over previous methods that discretize the covariate space. In a HIV transmission study from Rakai, Uganda, we demonstrate that our framework can capture age structures in HIV transmission at high resolution and bring valuable insights. (This is joint work with Kate Grabowski, Joseph Kagaayi, Oliver Ratmann, and Jason Xu.) 

Eastern and Southern Africa bear a disproportionately large share of the global HIV burden. Although this region comprises only about 6.2% of the world's population, it accounts for 45% of new HIV infections worldwide. Historically, HIV programs in these regions have focused almost exclusively on the general population, with limited attention to the distinct needs of key populations. With a recent shift towards more targeted interventions, our work leverages the unique PLACE datasets to produce granular size estimates of female sex workers, facilitating more effective and efficient HIV program planning. Because of social stigma and discrimination towards FSW, it is difficult to measure or estimate the size and location of FSW at any spatial resolution, especially a fine-scale resolution. In this study, we develop a generalized linear mixed-effect model to estimate the female sex worker population at the grid-cell level and propose a case-control sampling strategy to speed up the computation of the model fitting. This sampling approach broadly applies to zero-inflated models with large sample sizes. We demonstrate the approach's efficiency and accuracy through simulation studies and analyses of PLACE data, and we establish the theoretical properties of the optimal sampling procedure. With our proposed model and identified demographic variables, we obtain a reasonable estimation of female sex workers' distribution across four Eastern and Southern African countries. 

The Network Scale-up Method (NSUM) is a survey-based method to estimate the size of hard-to-reach populations. NSUM relies on questions of the form, "How many [X]'s do you know?" collected from the general population, where [X] refers to any subpopulation of interest. In this work, we estimate the number of migrant workers in Jordan at the governorate level using a NSUM survey. The traditional NSUM survey is supplemented with an additional survey from migrant workers, enabling us to better understand the social dynamics of this population. Results are compared to official records from the government of Jordan.