# Methods to Estimate the Effect of Interventions on the Incidence and Transmission of Tuberculosis

> **NIH NIH R01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2021 · $285,500

## Abstract

Tuberculosis (TB) is the leading cause of infectious disease deaths globally, recently surpassing HIV/AIDS.
The WHO has set goals for the eventual elimination of TB, but there are few, if any, reliable tools to monitor
progress toward this goal. Additionally our understanding of the spatial and demographic variability of TB
transmission and incidence is inadequate, making the allocation of limited resources challenging. This project
proposes to develop methods to estimate transmission and incidence of TB in real time using data sources that
are widely available. In Aim 1, we will develop improved estimates of the serial interval and duration of disease
at time of diagnosis by using existing data (ours and published). These estimates will make use of statistical
methods and novel modifications of these approaches to make them relevant to TB. These estimates provide
the foundation of monitoring TB, further modeling work to guide policy, and provide valuable insights into TB
epidemiology. Aim 2 will focus on developing methods to estimate the reproductive number of TB in real time,
providing insight on transmission patterns. We propose to modify methods we and others have developed for
diseases with short latency to TB, relying on routinely collected surveillance data. We will extend these
methods to estimate heterogeneities in the reproductive number spatially, across demographic characteristics,
and according the presence/absence of drug resistance. In Aim 3 we address the challenge of estimating
disease incidence. Current estimates are given on a countrywide basis and result from annual reports of newly
diagnosed individuals supplied to the WHO by individual countries. This data does not provide granularity in
incidence estimates and is hampered by substantial reporting issues, which are not well-understood. We
propose a novel Bayesian approach to estimate TB incidence from TB prevalence surveys, using our
estimates of duration derived in Aim 1. TB prevalence surveys are detailed cluster sampled surveys with
spatial, clinical, and demographic information on all participants. They are performed throughout areas with a
high burden of TB. This approach will provide more detailed information on TB incidence and its
heterogeneities, allowing for more targeted allocation of resources. We will create R packages to disseminate
the methods that we develop in all the aims for widespread use. This work will enable policy makers to better
understand the incidence of TB as well as groups and areas that are carrying the highest burden of disease.
This knowledge will guide intervention efforts, with the eventual goal of elimination.

## Key facts

- **NIH application ID:** 10124996
- **Project number:** 5R01GM122876-05
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Laura Forsberg White
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $285,500
- **Award type:** 5
- **Project period:** 2017-04-15 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10124996

## Citation

> US National Institutes of Health, RePORTER application 10124996, Methods to Estimate the Effect of Interventions on the Incidence and Transmission of Tuberculosis (5R01GM122876-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10124996. Licensed CC0.

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