Models in Hep-C-Modelling

/*
Forecasting the effects of having a Hep C human infection model on prospects for vaccine approval and future infections
*/

/*
Part 1: FDA Approval
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// Baseline: Two vaccine candidates have reached clinical trials in approximately 35 years since Hep C was discovered. Treating this as an observation of a Poisson variable, the 90% confidence interval of the rate is (0.355,6.296). Use this to build a model of the number of new candidates expected annually. For the probability of candidates progressing all the way to FDA approval, use historic rates as the expected probability and introduce a subjective amount of uncertainty

/*
The second of two parts of a forecasting contest by 1Day Sooner, 2024
*/

// This model estimates the counterfactual effect on Hepatitis C chronic infection rates in the US of a vaccine being approved by the FDA.

// The overall method is to construct a number of models for the effects of treatments for HIV (HAART, 1995) and Hep A (vaccine, 1995) on the number of infections of those viruses, then take an average of these weights according to their relevance to Hep C.

// All models will be subjectively adjusted to reduce very high or negative predictions for the number of infections averted. I think the annual number of acute infections could not rise far beyond 2x its current level, since approximately 50% of injecting drug users are infected or immune already, and the IDU population can only grow so much. This group probably accounts for at least half of all infections. Hence, for models of "infections averted in the first 25 years as a multiple of the number of infections in the baseline year" I will crudely impose 25 as a maximum value. I think that it is very unlikely that a vaccine would have a negative effect (by making at-risk people behave less cautiously, perhaps), so I take the square root of any negative estimates for "infections averted in the first 25 years as a multiple of the number of infections in the baseline year"

/*
The first of two parts of a forecasting contest by 1Day Sooner, 2024.
*/

// This model estimates the counterfactual effect of a Human Challenge Trial (HCT) that develops a human infection model for Hepatitis C. The basic theory is that such a trial makes it possible that the FDA will confirm that it could approve a vaccine based on HCT clinical trials, without the need for a population study. Such confirmation may increase the number of candidates in clinical trials, and increase the probability of such candidates gaining FDA approval.

// The theory is outlined in this diagram: https://miro.com/welcomeonboard/d3ZSbWFsa2JVSDNMb3hmazBLRWN5enQ5NWx4SkpLcmNJM0ltV0tSekVkWHpVajJTOVp5Y09LdEYyWEJPVUZrcnwzNDU4NzY0NTIwODY2Mjk5NDU4fDI=?share_link_id=57245770260

// Transition probabilities are each given a code (eg. A1) for easy reference.