The Coalition for Applied Modeling for Prevention (CAMP) is dedicated to creating models that improve public health decision-making at the national, state, and local levels. We use statistical, epidemic simulation, and economic models to uncover new disease patterns and inform prevention policies in five areas: HIV, viral hepatitis, sexually transmitted infections, tuberculosis, and school/adolescent health.
We are made up of experts from a variety of fields - epidemiologists, economic and infectious disease modelers, physicians, economists, and health department representatives - working in partnership with leaders at the US Centers for Disease Control and Prevention.
CAMP has published a variety of high-impact scientific papers that tackle tough public health questions and has released interactive web tools that guide decision-making.
Explore our website to learn more about our work and the CAMP team. Thank you for visiting!This work is supported by The Centers for Disease Control and Prevention [Grant # 1 1 NU38PS004650]
Hamilton D,Rosenberg E,Jenness S,Sullivan P,Wang L,Dunville R,Barrios L,Aslam M,Goodreau S
Menzies N,Parriott A,Shrestha S,Dowdy D,Cohen T,Salomon J,Marks S,Hill A,Winston C,Asay G
Rationale: Mathematical modeling is used to understand disease dynamics, forecast trends, and inform public health prioritization. We conducted a comparative analysis of tuberculosis (TB) epidemiology and potential intervention effects in California, using three previously developed epidemiologic models of TB.
Objectives: To compare the influence of various modeling methods and assumptions on epidemiologic projections of domestic latent TB infection (LTBI) control interventions in California.
Methods: We compared model results between 2005 and 2050 under a base-case scenario representing current TB services and alternative scenarios including: 1) sustained interruption of Mycobacterium tuberculosis (Mtb) transmission, 2) sustained resolution of LTBI and TB prior to entry of new residents, and 3) one-time targeted testing and treatment of LTBI among 25% of non–U.S.-born individuals residing in California.
Measurements and Main Results: Model estimates of TB cases and deaths in California were in close agreement over the historical period but diverged for LTBI prevalence and new Mtb infections—outcomes for which definitive data are unavailable. Between 2018 and 2050, models projected average annual declines of 0.58–1.42% in TB cases, without additional interventions. A one-time LTBI testing and treatment intervention among non–U.S.-born residents was projected to produce sustained reductions in TB incidence. Models found prevalent Mtb infection and migration to be more significant drivers of future TB incidence than local transmission.
Conclusions: All models projected a stagnation in the decline of TB incidence, highlighting the need for additional interventions including greater access to LTBI diagnosis and treatment for non–U.S.-born individuals. Differences in model results reflect gaps in historical data and uncertainty in the trends of key parameters, demonstrating the need for high-quality, up-to-date data on TB determinants and outcomes.