Ted Cohen, DPH, MD, MPH

NIH R01 AI112438-01

Multiple PI (Ted Cohen lead PI, Josh Salomon co-PI)

In this multiple-PI application, we propose to develop and analyze mathematical models of tuberculosis (TB) and HIV epidemics to examine whether targeted use of preventive therapy for latent TB infection (TLTBI) and active TB case-finding may prove more effective, and more cost-effective, than non-targeted use of interventions. We propose to use this model to evaluate the comparative effectiveness and efficiency of alternative choices in the design and implementation of targeted strategies. Toward this goal our project has three specific aims: (1) To develop a detailed simulation model of TB/HIV co-epidemics and calibrate the model to 9 high burden countries in sub-Saharan Africa; (2) To use this model to identify strategies for effective and cost-effective use of targeted screening for active tuberculosis; and (3) To use this model to identify strategies for effective and cost-effective use of targeted TLTBI.

2014-2019

MIDAS Center for Communicable Disease Dynamics

NIH/NIGMS U54 GM088558

Co-Investigator

The goal of this project is to advance the quantitative study of communicable diseases through training/education, transdisciplinary research, and public health policy. The center develops statistical and novel modeling methods, trains mathematical modelers, performs outreach, and develops software for the analysis of communicable disease data.

2013-2015

TB Modeling and Analysis Consortium

Bill and Melinda Gates Foundation

Co-Investigator

The overall aim of TB MAC is to improve global TB control by coordinating and promoting mathematical modeling and other quantitative research relevant to support evidence-based policy making and implementation. Its objectives were to (1) Review high-priority research questions concerning TB control that require mathematical modeling and other quantitative research, (2) Review data, information, and expertise to achieve consensus on current knowledge and knowledge gaps, methodological standards, and current best practice for TB control decision making, and (3) Disseminate information and tools to key stakeholders, including TB control programmers and donors

2014-2018

Mathematical models to improve drug dosing for limiting persistence in M. tuberculosis

Gates Foundation

Principal Investigator

New modeling approaches that integrate readily available data on both pharmacokinetics and bacterial killing in vivo and in vitro, would be an attractive tool to inform the development of new anti-tuberculosis regimens. We hypothesize that more effective treatment strategies can be identified by considering chemical reaction kinetics in bacterial cells. Chemical parameters can be easily measured, such that predictions on antibacterial activity could be made very early in development. This would not only benefit TB therapy, but likely the development of antibiotics in general. Here, we propose to use mathematical models describing both reaction kinetics and growth and death of bacterial populations to achieve the following two aims: 1) Predict optimal treatment regimens (duration, dose size and frequency) for clearing persisting mycobacteria and 2) Develop a mechanistic understanding of dose-response curves

2014-2015

Improving estimates of the incidence of pediatric tuberculosis

TB Alliance

Principal Investigator

The objective of this work is to develop a method to utilize multiple sources of available data to improve current estimates of the incidence of pediatric TB at country, regional, and global levels.