Main content
Home
Menu
Cost-effectiveness of end-game strategies against sleeping sickness across the Democratic Republic of Congo
Human African Trypanosomiasis Modeling and Economic Predictions for Policy (HATMEPP)
Collaborating centers:
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER)
University of Warwick
Coventry, UK
Epidemiology and Public Health
Swiss Tropical and Public Health (Swiss TPH) Institute
(An affiliated institute of the University of Basel)
Allschwil, Switzerland
Contact:
Marina Antillon
marina.antillon@aya.yale.edu
COPYRIGHT 2024, Swiss TPH and Warwick University
Contents
- Cost-effectiveness of end-game strategies against sleeping sickness across the Democratic Republic of Congo
- Project Objective
- Overview of analysis
- Preparation
- How to run this analysis
- Demo and sample results
- Troubleshooting
Project Objective
This project aims to evaluate the feasibility and cost-effectiveness of achieving elimination of gHAT in the DRC. The study focuses on assessing six plausible strategies for gHAT control and elimination at the health zone level, considering various funding levels and elimination targets. Building upon prior research, which concentrated on five health zones with different risk levels, this study expands the analysis to encompass health zones across the DRC which have reported cases .
To enhance the accuracy and reliability of the findings, the model fitting and projections are refined and compared to previous work. This refinement involves calibration to an additional four years of gHAT case and screening data . Furthermore, the model accounts for epidemiological uncertainties related to potential non-human animal transmission by utilizing an “ensemble model” approach. Stochastic projections are employed to better estimate variations in the timing of achieving elimination. By employing a comprehensive modeling framework, this study examines the intricate interplay among epidemiological dynamics, economic considerations, and temporal factors to inform effective decision-making regarding gHAT strategies for achieving EoT.
The pre-print manuscript is found here: https://doi.org/10.1101/2024.03.29.24305066
The GUI (companion website) is found here: https://hatmepp.warwick.ac.uk/DRCCEA/v6/
A guide on how to use the companion website is found here.
Overview of analysis
The analysis is broadly defined in four 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 within each health zone.
IV. Aggregate analysis of the impact of different objectives (WTP values and EOT goals) at the country level: a) how many health zones are on track and how many need a change in strategy for different objectives, including EOT by 2030; b) aggregate cases, deaths, costs, DALYs under different objectives; c) these results including and excluding the Bas-Uele region, which has a high level of uncertainty due to past history.
Click here for a high-resolution pdf
This model takes the output of the dynamic (SIR-type) model developed and operated by the Warwick team and projects the clinical outcomes and the accompanying costs of treating patients. The overall flow of models is diagrammed below and provided in high resolution here for most of the country:
Alternative decision tree is available here for Bas Uélé region, which had 8 different strategies modeled to account for the differing circumstances in that region (see the Supplementary Methods of the manuscript).
Software considerations
The tools for the economic model are coded in R. 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 renv) the use of RStudio is not strictly necessary and the benefits of renv 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 (all free)
Hardware considerations
Hardware needs: For optimal performance, the model was run for different places and different scenarios using a high-performance computing cluster at the University of Basel (scicore, http://scicore.unibas.ch/). Scicore is run with a slurm scheduler, and the bash file is included in this repository. However, a user could use a parallel computing package in R or any other automated task management solution, but no such implementation is presented here.
Duration: For reference, a single run of the code for all strategies in one place takes about 1-2 minutes in a MacBook Air (2020 model) with an Apple M1 chip and 16 GB of RAM.
Memory needed: Intermediate simulations and graphs need about 187 GB of storage for all outputs.
Preparation
Installation to-do list (all free)
How to run this analysis
Instructions to run the analysis
For reference, the file structure of this repository is described here: file structure
Demo and sample results
Troubleshooting
Start managing your projects on the OSF today.
Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.