Artificial Intelligence to Improve the Diagnosis of Pediatric Tuberculosis
- Investigator: Meredith Brooks
- Funding Source: William F. Milton Fund, Harvard University
Tuberculosis is a preventable infectious disease, yet more than one million children fall sick with TB every year. Under-diagnosis remains a major challenge, because (1) children present with diverse and non-specific symptoms and (2) tests that diagnose TB in adults have low sensitivity in children, who often have paucibacillary TB or cannot produce sputum. Other challenges include complex diagnostic algorithms. Coupled with limited laboratory capacity in high Tb-burden settings and costly tests, these factors often preclude microbiological confirmation of TB in children. As a result, each year millions of children with Tb or sub-clinical TB infection are missed by TB services and never receive life-saving treatment. Effective diagnostic algorithms tailored for children need to be developed, ideally using large datasets to ensure validity. To address this knowledge gap, we leverage an existing dataset of children screened for TB in Kotri, Pakistan, providing a unique opportunity to assess age-specific barriers to TB diagnosis in children. We aim to: (1) identify age-specific gaps in the sequence of steps required for screening children for TB disease; and (2) refine diagnostic algorithms for classification of TB using age-specific predictors through machine-learning methods.
Adaptive Design to Aid in the Planning of community-based Tuberculosis screening services (ADAPT-TB)
- Investigator: Meredith Brooks
- Funding Source: Carlin Foundation Award for Public Health Innovation
Community-based screening via mobile units can close gaps in missed diagnoses by bringing screening services into communities, making screening more convenient for individuals with limited access to appropriate services. Questions remain, however, about how to efficiently operate these mobile units. Leveraging longstanding relationships in Lima, Peru, including existing collaborations involving mobile screening units, I will collect data from health facilities and mobile screening units to [Aim 1] establish spatial and temporal trends of the local tuberculosis burden and [Aim 2] build neighborhood-level models reflecting local risk of tuberculosis. I will then [Aim 3] develop a baseline decision model via a restless multi-armed bandit framework to make data-driven decisions about where, when, and how long to place the mobile units in the community. The overall goal is to optimize the real-time movement of these units throughout a community to increase the detection of individuals with TB and allocate resources more efficiently.
- Investigator: Laura White
- Funding Source: National Institute of General Medical Sciences
The “Tools for Transmission of Agents and Conditions (TRAC)” program will synergize statistical and mathematical modeling work in three areas of application: 1) Tuberculosis (TB) incidence and transmission; 2) monitoring substance use disorder (SUD) patterns; and 3) SARS CoV-2 transmission modeling. These three conditions are major public health problems, with TB being a leading cause of infectious disease death globally, SUD causing more deaths in the United States than HIV/AIDS in its peak, and SARS CoV-2 causing a pandemic with societal disruption and mortality exceeding anything we have experienced in the last century. We need improved analytical tools that leverage existing data to monitor these diseases, infer transmission hot spots, determine the efficacy of interventions, and understand the burden of these conditions. The goal of this work is to develop a suite of analytical tools that leverage rapidly emerging rich data sets to improve our understanding of disease transmission patterns, monitor changing dynamics of these conditions, and understand intervention strategies that are most effective. This work will inform public health practice for these diseases and create reproducible tools that can be used in an ongoing way.
- Investigator: Helen Jenkins
- Funding Source: National Institute of Allergy and Infectious Diseases