Research

Research Focus

• Infectious Diseases

• Climatic and Environmental Impacts on Health

• Health Equity (Social Determinants of Health)

• Global Health

• Child and Maternal Health 

• Mental Health

General Descriptions of Some Research Projects 

Effect of climate change on household malaria risk and respective intervention and surveillance in western Kenya

• Malaria remains a significant public health challenge, particularly in regions like western Kenya, where climatic, ecological, and environmental factors influence transmission dynamics. Although interventions such as insecticide-treated nets (ITNs), indoor residual spraying (IRS), and artemisinin-based combination therapy have reduced malaria incidence in children under five years, a shift in malaria burden toward older age groups and fine-scale malaria transmission variation has been observed. However, this complex transmission dynamic has not been sufficiently studied, despite being critical to informing interventions and surveillance. School-aged children (6-15 years) often exhibit asymptomatic infections, which can serve as reservoirs for continued transmission, making them a critical focus for surveillance and intervention strategies. Climatic factors such as rainfall and temperature are known contributors to malaria outbreaks, but their interaction with environmental factors (e.g., distance to water sources, forests, and health facilities) and the vector population remains underexplored to inform interventions on the administrative blocks or county governments. Studies have shown that rainfall creates favorable breeding conditions for mosquitoes, often leading to a surge in malaria cases in subsequent months. However, the relationship between these environmental drivers and malaria transmission is not always linear, as factors such as extreme temperatures can reduce mosquito populations, while moderate rainfall may enhance vector abundance. This proposal aims to address these gaps by investigating how age-group-specific malaria risk correlates with climatic, ecological, and environmental factors within the intervention administration blocks or counties. By integrating vector data (e.g., mosquito density and infection prevalence), the goal of this proposal is to offer a more comprehensive understanding of malaria transmission dynamics. The overall goal is to improve the performance of the community health promoters and key decision-makers regarding household and county-wide malaria interventions and surveillance after considering the integration of different influencing factors.

Within-Host Bayesian Joint Modeling of Longitudinal and Time-to-Event Data of Leishmania Infection

1. • The host immune system plays a significant role in managing and clearing pathogen material during an infection, but this complex process presents numerous challenges from a modeling perspective. There are many mathematical and statistical models for these kinds of processes that take into account a wide range of events that happen within the host. In this work, we present a Bayesian joint model of longitudinal and time-to-event data of Leishmania infection that considers the interplay between key drivers of the disease process: pathogen load, antibody level, and disease. The longitudinal model also considers approximate inflammatory and regulatory immune factors. In addition to measuring antibody levels produced by the immune system, we adapt data from CD4+ and CD8+ T cell proliferation and expression of interleukin 10, interferon-gamma, and programmed cell death 1 as inflammatory or regulatory factors mediating the disease process. The model is developed using data collected from a cohort of dogs naturally exposed to Leishmania infantum. The cohort was chosen to start with healthy infected animals, and this is the majority of the data. The model also characterizes the relationship features of the longitudinal outcomes and time of death due to progressive Leishmania infection. In addition to describing the mechanisms causing disease progression and impacting the risk of death, we also present the model’s ability to predict individual trajectories of canine leishmaniosis (CanL) progression. The within-host model structure we present here provides a way forward to address vital research questions regarding the understanding progression of complex chronic diseases such as visceral leishmaniasis, a parasitic disease causing significant morbidity worldwide.

A Bayesian Capture-Recapture model of vector-reservoir interaction in an ecological setting: a reservoir-targeted vaccine field study against Borrelia burgdorferi

1. • Lyme disease is a bacterial infection caused by Borrelia burgdorferi. In recent years, reservoir-targeted vaccines have emerged as an effective means to control the spread of zoonotic and vector-borne diseases by blocking the path of transmission to humans. In this work, we analyze data from a multi-site, multi-species longitudinal study of such a vaccine targeted at mice using a capture-recapture setting at six field sites in the state of Maryland. The primary species of interest are white-footed mice and black-legged ticks, and this preliminary work presents data collected between 2020 and 2022. We implement a joint Bayesian capture-recapture and infectious disease model to estimate the unknown population size of species for each site as well as ecological and epidemiological parameters. By deploying the reservoir-targeted vaccine in treatment sites, we estimated the impact of the oral-bait vaccine on the infection prevalence. Antibodies against Borrelia burgdorferi are known to be produced by the stimulation of the outer surface protein A, an abundant lipoprotein of the causative agent of Lyme disease, the Borrelia burgdorferi spirochete. Here, we also estimated the proportion of mice with levels of protective antibodies as a result of receiving the outer surface protein A oral bait immunization. Additionally, estimated parameters from the capture-recapture analysis, such as the behavioral effect of the subjects towards the trapping devices, the abundance per species, and the type of subjects that are more likely to get captured, are of interest to understand the current disease dynamic in the area and to improve the planning and execution of future epidemiological or ecological studies. Results obtained from this work could help reduce the risk of human exposure to Lyme disease.

Understanding the Determinants of Maternal Morbidity and Mortality

• Maternal mortality rates (MMR) in the United States (US) pose a pressing public health challenge, exceeding those rates from similarly developed nations. In addition, several concerning disparities exist among racial and ethnic groups, underserved rural populations, and individuals with limited socioeconomic means. The Centers for Disease Control and Prevention (CDC) reports that 50,000 women in the US suffer from pregnancy complications annually, where Black women are at least three times more likely to die due to a pregnancy-related cause when compared to White women. The estimated maternal mortality rate in the US by December of 2023 was 19.0 per 100,000 live births. For Black women, that rate was about 51.1 per 100,000 live births, the highest amongst any racial group. A concerning estimated MMR was observed for American Indian or Alaska Native women in January of 2022, of 110.8 per 100,000 live births. In addition to maternal mortality, severe maternal morbidity (SMM) is a critical issue, encompassing unexpected outcomes of labor and delivery that result in significant short- or long-term health consequences, which can lead to death. Other social determinants, such as access to quality healthcare, education, and income, along with biological factors like pre-existing health conditions, also play significant roles in these outcomes. Addressing these critical public health issues is imperative to ensure equitable healthcare and improve maternal health outcomes for all women. Leading Health Indicators (LHIs) are a select set of high-priority objectives from the Healthy People 2030 initiative, chosen to focus national efforts on improving health outcomes. These indicators span across life stages and address major causes of death and disease in the US. One key LHI in this initiative is “Reduce maternal deaths,” identified by MICH-04, which aims to lower the maternal mortality rate from 22.3 per 100,000 live births in 2022 to a target of 15.7 per 100,000 by 2030, reflecting ongoing challenges in maternal health and highlighting the need for effective interventions. This study, and future external proposals, will contribute to this objective by exploring the diverse factors behind SMM and MMR disparities and laying the groundwork for future research to develop strategies that enhance maternal health and achieve the LHI targets.

Creating County Health Rankings for the State of Iowa

• • For modern civilizations, understanding the population's overall health is crucial. In an effort to monitor the overall health of the people in the United States, the United States Department of Health and Human Services created the Healthy People program. Using the format of the latter, the Iowa Department of Public Health created the Healthy Iowans program. To take this healthy initiative one step further, we have seen the creation of the Healthiest State Initiative for the state of Iowa, with communities ranked according to health status. We participate in health assessment by ranking the 99 counties in Iowa from the most healthy to the least healthy according to a variety of health measures. Due to the rural nature of Iowa, many of these measures have small counts, which leads to highly variable estimates. We use Bayesian spatial smoothing techniques to arrive at reliable estimates of the chosen health measures. We then combine those measures in a statistical model to provide health status ranks for the 99 counties in Iowa, and we display the ranking on a map of the state. To see how the ranks of the counties change based on different health factors, a dynamic interface (Shiny application) was created.