Economic evaluation of disease elimination ======================= **Human African Trypanosomiasis Modeling and Economic Predictions for Policy** ([HATMEPP](https://go.warwick.ac.uk/hatmepp)) Administered by Marina Antillon, PhD. Collaborating centers: ___Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER)___ University of Warwick Coventry, UK ___Swiss Tropical and Public Health (Swiss TPH) Institute___ (An affiliated institute of the University of Basel) Basel, Switzerland Contact: Marina Antillon marina.antillon@swisstph.ch. COPYRIGHT 2020, Swiss TPH and Warwick University --- @[toc](Contents) # Project Objective In the current study, we propose a modification to the net benefits framework to consider the implications of switching from an optimal strategy (in terms of cost-per-burden-averted) to a strategy with a higher likelihood of meeting the global target (i.e. elimination of transmission by a specified date). We illustrate the advantages of our framework by considering the economic case of efforts to eliminate transmission by 2030 of gambiense human African trypanosomiasis (gHAT) in three illustrative health zones. A preprint of the paper is available [here](https://doi.org/10.1101/2021.02.10.20181974). --- # Overview of analysis The analysis is broadly defined in three parts, each of these parts is executed in various R code files: I. Projecting health outcomes under alternative strategies of gHAT control. II. Costing: for clinical activities as well as screening and prevention (vector control) activities under alternative strategies. III. Cost-effectiveness analysis of alternative strategies. This model takes the output of the dynamic (SIR-type) model and projects the clinical outcomes and the accompanying costs of treating patients. The clinical and economic model is constituted by a probability tree model. The overall flow of models is diagrammed below and provided in high resolution [here](https://osf.io/qk6n2/):  ## Software considerations The tools for the economic model are coded in R, version 3.6.3. While we would highly recommend using the code within RStudio environment (in part because of it's features to manage the project with .RProj and packrat) this is not strictly necessary and the benefits of packrat are available from a classic R interface or a shell command line. Some of the results tables for the project are produced automatically within the code in the project via Rmarkdown (using knitr) and Latex. See the help links later in this document for more information. For detailed information, see: [Installation to-do list](https://osf.io/fh6ca/wiki/Installation%20to-do%20list/) (all free) ## Hardware considerations **Hardware needs:** A personal computer with at least 8 GB of RAM. **Time to run:** For reference, a single run of the code for all strategies in one place takes about 12-15 minutes in a MacBook Pro (Mid-2014 model) with a 3 GHz Intel Core i7 processor and 16 GB of RAM. **Memory needed:** Intermediate simulations and results produce about 1GB of output. --- # Preparation [Installation to-do list](https://osf.io/fh6ca/wiki/Installation%20to-do%20list/) (all free) --- # How to run this analysis The entire analysis for one health zone is run by running the code `code00a_master`. To change the health zone that will be run, change `z=1` in line 8 of `code00a_master`. `z` values correspond to regions 1-3 in the paper. Once the analysis has been run independently for all three health zones, then one must run `code07_summarize` to construct the tables and figures of results that appear in the manuscript and the supplement. For reference, the file structure of this repository is described here: [file structure](https://osf.io/9375x/wiki/File%20structure%2C%20inputs%2C%20and%20outputs/) --- # Troubleshooting [Troubleshooting](https://osf.io/9375x/wiki/Troubleshooting/)