Tool that models hospital demand during COVID-19 outbreak
Simulator | Source code repository | Data repository | Updates | ||
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This tool is based on an SIR model (see about page for details) that simulates a COVID19 outbreak. The population is initially mostly susceptible (other than for initial cases). Individuals that recover from COVID19 are subsequently immune. Currently, the parameters of the model are not fit to data but are simply defaults. These might fit better for some localities than others. In particular the initial cases counts are often only rough estimates.
The primary purpose of the tool is to explore the dynamics of COVID19 cases and the associated strain on the health care system in the near future. The outbreak is influenced by infection control measures such as school closures, lock-down etc. The effect of such measures can be included in the simulation by adjusting the mitigation parameters. Analogously, you can explore the effect of isolation on specific age groups in the column "Isolated" in the table on severity assumptions and age specific isolation.
Parameters fall into three different categories
- population parameters
- epidemiological parameters
- clinical parameters
Most parameters can be adjusted in the tool and for many of them we provide presets.
Input data for the tool and the basic parameters of the populations are collected in a separate repository neherlab/covid19_scenarios_data. Please add data on populations and parsers of publicly available case count data there.
- Node >= 10
- Yarn 1.x
This will run the application in development mode (with hot reloading):
git clone https://github.com/neherlab/covid19_scenarios
cd covid19_scenarios/
cp .env.example .env
yarn install
yarn dev
This will trigger the development server and build process. Wait for the build to finish, then navigate to
http://localhost:3000
in a browser (last 5 version of Chrome or Firefox are supported in dev mode)
Hit Ctrl+C in the terminal to shutdown.
โน๏ธ Hint: type "rs" in terminal to restart the build
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Copyright (c) 2020 neherlab