youyanggu/yyg-seir-simulator
Underlying SEIR simulator for the YYG / covid19-projections.com model
| repo name | youyanggu/yyg-seir-simulator |
| repo link | https://github.com/youyanggu/yyg-seir-simulator |
| homepage | |
| language | Python |
| size (curr.) | 960 kB |
| stars (curr.) | 60 |
| created | 2020-06-23 |
| license | MIT License |
YYG / covid19-projections.com SEIR Simulator
We present the underlying SEIR model simulator behind the YYG / covid19-projections.com model, as well a summarized set of parameters that helped generate the projections.
If your system supports Python, you can generate your own simulations in under 5 minutes. No prior Python experience is needed to run this tool. Get started here.
Table of Contents
Introduction
To begin, we want to be clear that this is not the full model used by covid19-projections.com. This is the underlying SEIR model without the machine learning layer to learn the parameters. In fact, this simulator does not use any published data: it only simulates infections, hospitalizations, and deaths given a single set of parameters. As a result, this tool is meant to generate simulations, not projections.
Unlike traditional SEIR models, our simulator does not use differential equations - we use an abstracted state machine that tracks the probability transitions between the 4 states of SEIR: Susceptible-Exposed-Infectious-Recovered/deceased. Learn more about how our SEIR model works on our website.
Because this simulator has very little dependencies and does not rely on published data, it is fast to run, works right out of the box, and is easily modifiable. We’ve purposefully designed our simulator to be as lean and simple as possible.
The simulations produced by this program will not necessarily match the full model, but it is often be a close approximation. The full model for covid19-projections.com generates thousands of parameter sets for each region and weighs the parameters based on how the resulting simulations match the observed data. With that said, this is the full, unabridged SEIR model used to generate the simulations - no modifications have been made for this release.
In addition to the SEIR simulator, we provide the “best” set of parameters that our machine learning layer has learned to best fit the real-world observed data. This is done by taking a weighted mean (or median) of the individual parameters used in our full model. Because parameters are not independent, using only a single set of parameters may skew the simulation results when compared to the full model projections.
While this simulator may not be best suited to make projections (since it is not tuned on any published data), we believe this simulator is particular helpful for mapping out relative scenarios. For example, how much can we reduce infections/deaths if people started social distancing 7 days earlier. Or what if 20% of individuals self-quarantine after symptom onset. Or if there was no reopening.
Dependencies
You need Python 3 and NumPy. That’s it.
Note: This was built and tested on Python 3.8, so it’s possible that an older Python 3.x version may not be compatible.
Setup
- Make sure you have Python 3 and NumPy installed.
- Clone our repository:
git clone https://github.com/youyanggu/yyg-seir-simulator.git - You are now ready to run our simulator! See sample usage below.
Usage
To view a list of supported countries, states, and subregions, check covid19-projections.com.
List all command line flags/features:
python run_simulation.py --help
Basic Usage
Note: -v means verbose mode. Remove the flag and you will get fewer print statements.
US simulation:
python run_simulation.py -v --best_params_dir best_params/latest --country US
US state simulations:
python run_simulation.py -v --best_params_dir best_params/latest --country US --region NY
python run_simulation.py -v --best_params_dir best_params/latest --country US --region AZ
python run_simulation.py -v --best_params_dir best_params/latest --country US --region TX
US county simulations:
python run_simulation.py -v --best_params_dir best_params/latest --country US --region CA --subregion "Los Angeles"
python run_simulation.py -v --best_params_dir best_params/latest --country US --region NY --subregion "New York City"
python run_simulation.py -v --best_params_dir best_params/latest --country US --region FL --subregion Miami-Dade
Global country simulations:
python run_simulation.py -v --best_params_dir best_params/latest --country Brazil
python run_simulation.py -v --best_params_dir best_params/latest --country Sweden
python run_simulation.py -v --best_params_dir best_params/latest --country "United Kingdom"
Canadian province simulation:
python run_simulation.py -v --best_params_dir best_params/latest --country Canada --subregion Quebec
python run_simulation.py -v --best_params_dir best_params/latest --country Canada --subregion Ontario
python run_simulation.py -v --best_params_dir best_params/latest --country Canada --subregion "British Columbia"
Advanced Usage
Save outputs to CSV file
python run_simulation.py -v --best_params_dir best_params/latest --country US --save_csv_fname us_simulation.csv
Use a custom end date
python run_simulation.py -v --best_params_dir best_params/latest --country US --simulation_end_date 2020-11-01
Use a custom start date
python run_simulation.py -v --best_params_dir best_params/latest --country US --simulation_start_date 2020-02-01
Simulate effect of quarantine
In this scenario, we show how the course of the epidemic would be different if just 20% of infected individuals immediately self-quarantine after showing symptoms, reducing their own transmission by 25%. For the remaining 80% of infected individuals, we assume normal transmission. You can see a graph of this scenario here.
This can also be used as a proxy to simulate the effect of mask-wearing: if 20% of individuals wear masks, thereby reducing the overall transmission rate by 25%.
python run_simulation.py -v --best_params_dir best_params/latest --country US --quarantine_perc 0.2 --quarantine_effectiveness 0.25
Use an older set of best parameters
By default, we use the latest set of best parameters in best_params/latest, but you can also specify a different directory.
python run_simulation.py -v --best_params_dir best_params/2020-06-21 --country US
Use the top 10 best parameters rather than the mean
The four choices are: mean (default), median, top, top10
python run_simulation.py -v --best_params_dir best_params/latest --country US --best_params_type top10
Setting Parameters
We also provide support to customize your own parameters using the --set_param flag. The flag takes a pair of values: the name of the parameter to be changed and its value. You can use the flag multiple times.
See the available parameter options in the next section.
Set initial R_0 to 2.0
python run_simulation.py -v --best_params_dir best_params/latest --country US --set_param INITIAL_R_0 2.0
Set initial R_0 to 2.0 and lockdown R_t to 0.9
python run_simulation.py -v --best_params_dir best_params/latest --country US --set_param INITIAL_R_0 2.0 --set_param LOCKDOWN_R_0 0.9
Set the reopen date to June 1, 2020
python run_simulation.py -v --best_params_dir best_params/latest --country US --set_param REOPEN_DATE 2020-06-01
Assume no reopening
python run_simulation.py -v --best_params_dir best_params/latest --country US --set_param REOPEN_R_MULT 1
Use a custom infection fatality rate (IFR)
python run_simulation.py -v --best_params_dir best_params/latest --country US --set_param MORTALITY_RATE 0.008
Changing parameters
Sometimes you don’t want to replace an existing parameter, but rather just change it. The --change_param flag takes a pair of values: the name of the parameter to be changed and the amount to change the value by. You can use the flag multiple times.
See the available parameter options in the next section.
Increase initial R_0 by 0.1
python run_simulation.py -v --best_params_dir best_params/latest --country US --change_param INITIAL_R_0 0.1
Increase initial R_0 by 0.1 and decrease lockdown R_t by 0.1
python run_simulation.py -v --best_params_dir best_params/latest --country US --change_param INITIAL_R_0 0.1 --change_param LOCKDOWN_R_0 -0.1
Increase reopening date by 7 days
This simulates what would happen if people in the US reopened one week later.
python run_simulation.py -v --best_params_dir best_params/latest --country US --change_param REOPEN_DATE 7
Decrease inflection date by 7 days
This simulates what would happen if people in the US started social distancing one week earlier. You can see a graph of the results here.
python run_simulation.py -v --best_params_dir best_params/latest --country US --change_param INFLECTION_DAY -7
Set initial_R_0 to 2.2
You can also set the R_0 using ---set_param, and then change it via --change_param. See below for an alternate way to set the initial R_0 to 2.2:
python run_simulation.py -v --best_params_dir best_params/latest --country US --set_param INITIAL_R_0 2.0 --change_param INITIAL_R_0 0.2
Using Your Own Parameters
Don’t want to use our parameter set? Want to run a simulation for a region/country we do not support? No problem! You can simply modify run_simulations.py (in the main() function) and specify your own default parameters (list of parameters below). Afterwards, you can simply run:
python run_simulation.py -v
Note: You can still use the --set_param and --change_param flags from above, as well as the other flags explained in Advanced Usage.
You are also welcome to change the default parameters in fixed_params.py, though we would recommend caution when doing so as many of those parameters have already been optimized.
Parameters
There are several parameters that we need to provide the simulator before it can run. We describe them below:
INITIAL_R_0
This is the initial basic reproduction number (R_0). This value is usually between 0.8-6. Read more about our R_t estimates here.
LOCKDOWN_R_0
This is the post-mitigation effective reproduction number (R_t). This value is usually between 0.3-1.5.
INFLECTION_DAY
This is the date of the inflection point when INITIAL_R_0 transitions to LOCKDOWN_R_0. This is usually an indicator of when people in a region began social distancing. For example, in New York, the inflection day is around March 14.
RATE_OF_INFLECTION
This is the rate at which the INITIAL_R_0 transitions to LOCKDOWN_R_0. A number closer to 1 indicates a faster transition (i.e. faster lockdown), while a number closer to 0 indicates a slower transition. For most region, this value is between 0.15-0.5. The more localized a region, the higher the rate of inflection. So for example, we estimate New York City has a RATE_OF_INFLECTION between 0.4-0.5, while the US as an entire country has a RATE_OF_INFLECTION of approximately 0.2-0.3.
LOCKDOWN_FATIGUE
We incorporate a lockdown fatigue multiplier that is applied to the R_t. This is to simulate the increase in mobility after several weeks of lockdown, despite the order not being lifted. The default value is 1. If this value is greater than 1, then it will contribute to an increase in infections in the weeks following the lockdown/mitigation. We do not use this for the majority of our projections.
DAILY_IMPORTS
To begin our simulation, we must initialize / bootstrap our model with an initial number of infected individuals. Any epidemic in a region must begin with a number of imported individuals. This value gradually goes to 0 as community spread overtakes imported cases as the major source of spread. This value typically ranges from 10-1000.
MORTALITY_RATE
This is the initial estimate of the infection fatality rate (IFR). This value is usually between 0.005-0.0125. Read more about our IFR estimates here.
REOPEN_DATE
This is the date we estimate the region to reopen. Read more about our reopening assumptions here.
REOPEN_SHIFT_DAYS
Even though some regions open on the same date, infections can begin increasing faster or slower than we’d normally expect. We use the REOPEN_SHIFT_DAYS to account for that. For example, a value of 7 means that we are shifting the REOPEN_DATE to be 7 days later, while a value of -7 means we are shifting it to be 7 days earlier. The default value is 0.
REOPEN_R_MULT
This is the multiplier we apply to the LOCKDOWN_R_0 to generate the reopening R_t value. We assume this value must be greater or equal to 1. For a typical region, we expect the R_t to increase by around 0-30%, so this multiplier is 1-1.3.
POST_REOPENING_R_DECAY
This is the daily multiplier we apply to the R_t approximately 15-30 days after reopening. After some time has passed following a reopening, we assume that the spread will decrease over time due to improvements in contact testing and greater awareness within the population. The default value is 0.9975. Read more about our post-reopening assumptions here.
Question? Bug? Feedback? Suggestions?
We welcome questions, bug reports, feedback and suggestions. You can find a lot of information on covid19-projections.com. But feel free to open an issue ticket if your question was not answered.
