MRC Transition Support: A mathematical modelling framework for tuberculosis burden estimation and economic evaluation of pharmaceutical interventions.

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Tuberculosis (TB) is a major cause of disease and death globally, sickening around 10 million people and killing around 1.2 people annually (excluding the 200,000 or so deaths due to TB in people with HIV infections). Global TB rates have only been declining at a couple of percent per year for the last two decades. Around 500,000 people developed TB that was resistant to rifampicin - one of the key drugs used to treat TB. New drugs, and combinations of drugs, are being developed to treat tuberculosis, as are new vaccines that may protect against disease in adults.Quantifying the burden of TB is fundamental to understanding its global epidemiology and for making appropriate resource allocation decisions. Most estimates of new TB case numbers each year rely strongly on the number of cases reported by countries in that year to WHO. Unfortunately, one in three TB cases are thought to go either undetected or unreported, so the number of cases reported underestimates the number of new cases. While one can correct for this, it is hard to know exactly how much to adjust the reported numbers. Even if TB is diagnosed, drug resistance is often missed, leading to inappropriate treatment that can let drug-resistant strains to transmit to others, as well as allowing TB strains to acquire resistance to additional drugs. This complexity, combined with the low rate of identification, makes estimating the burden of drug-resistant TB, and understanding how much drug resistance is driven by transmission as opposed to acquired resistance on treatment, even more challenging.A major goal of this work is to use mathematical transmission models for burden estimation and provide a unified framework for all data. These models produce the number of new cases, deaths, and also the prevalence of disease. They can explicitly represent disease transmission (including drug-resistant strains) and so introduce a dependence between the number of new cases in different years. These models involve parameters derived from previous epidemiological work, but must be calibrated to learn from data on TB reports, deaths and prevalence. Calibration means adjusting imperfectly known model parameters in order to match observed model outputs to the data. This process provides a model that may be used to make predictions about burden, but may also teach us something about the underlying processes. Many of the parameters concerning the epidemiology and disease course of TB are quite uncertain, and this uncertainty is rarely represented fully in models.