This repository includes simulation model and analysis scripts for modelling the COVID-19 pandemic in Illinois per covidregion.
1. Model overview
2. Software used
3. Postprocess and analyse simulation outputs
4. Data sources
5. Model updates
6. Resources
A basic SEIR model was extended to include symptom status (asymptomatic, presymptomatic, mild and severe symptoms), hospitalization, critical illness, and deaths, while allowing to track detected and undetected cases separately. In the model, the susceptible population is exposed (infected) at a constant rate described by the transmission probability and contact rate with the infectious population. After latent period of few days, the exposed population becomes infectious and moves either to the asymptomatic or pre-symptomatic compartments. At the end of the incubation period pre-symptomatic population develops either mild or severe symptoms. Mild symptomatic cases recover at a similar rate as asymptomatic cases, while all severe symptomatic cases that ‘should’ need professional care move to the hospitalization compartment. Hospitalized cases either recover or develop critical illness and then recover, or die. Once recovered, we assume that the population stays immune throughout the simulation period. The asymptomatic presymptomatic, mild symptomatic, severe symptomatic infections, undetected hospitalized are the infectious compartments with reduced infectiousness for the detected sub-compartments due to self-isolation. We assume that hospitalized cases that are detected are not infectious.
Simulations run per Emergency Medical Service Area (EMS) and are aggregated for restore regions, and for Illinois. As of the 22nd of July, the 'covid regions' are used. For simplicity, the term 'EMS' is kept in the modelling files.
Most of the parameters are derived from literature, local hospital data as well as doublechecked with other models used in Illinois (i.e. UChicago). The starting date, intervention effect size, and the transmission parameter "Ki"are fitted to death data.
Show parameter tables
All the parameters are sampled from a uniform distribution as specified in the experiment config (yaml) file
| parameter | name |
|---|---|
| Ki | Transmission rate (contact rate * infection probability) |
| Ks | Progression to presymtomatic ( fraction_symptomatic / time_to_infectious)) |
| Kl | Progression to asymptomatic ((1 - fraction_symptomatic ) / time_to_infectious)) |
| dAs | detection rate of asymptomatic infections |
| dSym | detection rate of mild symptomatic infections |
| dSys | Detection rate of severe symptomatic infections |
| Ksym | Progression to mild symptoms |
| Ksys | Progression to severe status ( fraction_severe * (1 / time_to_symptoms)) |
| Kh | Hospitalization rate |
| Kh_D | Hospitalization rate minus delay in detection |
| Kr_a | Recovery rate of asymptomatic infections |
| Kr_m | Recovery rate mild symptomatic infections |
| Kr_m_D | Recovery rate mild symptomatic infections minus delay in detection |
| Kr_h | Recovery rate of hospitalized cases |
| Kr_c | Recovery rate of critical cases |
| Kc | Progression to critical |
| Km | Deaths |
| Parameter | Description | Value | Unit | Source |
|---|---|---|---|---|
| Initial infectious population | Number of infectious population that initiates the local transmission | 10 | N | Assumed |
| Ki (transmission rate) | Rate at which susceptible become infectious (contact rate * probability of infection). | rate | Fit to data from pre March 21 (EMResource and line list) | |
| Ki multiplier (transmission rate multiplier) | This parameter adjusts the initial transmission rate over time to reflect changes in mitigation policies, lockdowns and mask wearing as well as other factors that affect transmission. | Fit to data every week with monthly time events for changing transmission. | ||
| Date of imported infection | Date when local transmission started | Feb 13 - Feb 27 | date | Fit to data from pre March 21 (EMResource and line list) |
| time_to_infectious | Time from being exposed to become infectious | (2.4 , 3.5) | days | Li et al 2020 |
| time_to_symptomatic | Time from becoming infectious to onset of symptoms | (3.0, 4.5) | days | Li et al 2020 and Jing et al 2020 |
| time_to_hospitalization | Time from possible detection to hospitalization | (3, 6) | days | Huang et al 2020 |
| Time to critical | Time between hospitalization and critical illness | (4, 6) | days | NMH, Huang et al 2020 |
| Time to death | Time between critical illness and deaths | (4, 6) | days | Yang et al 2020 |
| Recovery time asymptomatic | Time until an asymptomatic infection is cleared | (7, 10) | days | Assumed to be same as symptomatic |
| Recovery time mild symptomatic | Time until mildly symptomatic case recovers | (7, 10) | days | Wölfel et al 2020 |
| Recovery time hospitalized | Time until hospitalized cases (severe symptomatic) recover | (4, 6) | days | NMH, Lewnard et al 2020 and Wang et al 2020 |
| Recovery time critical | Time until critical cases (severe symptomatic) recover | (8,10) | Bi et al 2020 | |
| Fraction symptomatic | Fraction of infections that develop either mild or severe symptoms | (0.5, 0.7) | Oran and Topol et al 2020 | |
| Fraction severe symptomatic | Fraction of symptomatic that develop severe symptoms | (0.02, 0.1) | Salje et al 2020 | |
| Fraction critical | Fraction of severe symptomatic infections that require intensive care | (0.2, 0.35) | Lewnard et al 2020 | |
| CFR | Case fatality rate | (0.01, 0.04) | Wang et al 2020 | |
| Reduced infectiousness of detected cases | Fraction of detected cases that isolate and are removed from the infectious population | (0, 0.3) | Assumed | |
| Detection probability of asymptomatic case | Used for contact tracing simulations, per default asymtomatic cases are not detected | (0, 0) | Assumed | |
| Detection probability of mild symptomatic case | Initial value of the detection rate, which is increasing over time | (0.05, 0.2) | Assumed initial value, increase informed from Illinois specific data | |
| Detection probability of severe symptomatic case | Initial value of the detection rate, which is increasing over time | (0.2, 0.5) | Calculated from IL data | |
| Impact of transmission mitigation policies, lockdown and mask-wearing | reflected in transmission rate parameter | Fit to data and updated every week using monthly 'transmission changepoints' |
Note: List also on Box!
The time-event option in cms allows to change a paramter at a given time point (days) (which will stay constant afterwards if not resetted using a stop time-event). Time-event are used to define reduction in the transmission rate, reflecting a decrease in contact rates due to social distancing interventions (i.e. stay-at-home order). The time event can also be used to reflect increasing testing rates by increasing the detection of cases (i.e. dSym and dSys for increased testing at health facilities, or dAs and dSym for contact tracing)
Current scenarios include:
- No stay-at-home
- Continued stay-at-home
- Stop stay-at-home order - immediately
- Stop stay-at-home order - step-wise
- Contact tracing - immediately
- Contact tracing - step-wise
For details, see the cms implementation in one of the emodl generators
Currently the model includes 28 compartments of which 43 outcome variables are generated. The outcome variables or channels, as referred to in the py plotters, are different aggregates of the main compartments by type (i.e. all detected, all severe symptomatic) and includes cumulative counts for the calculation of incidences during postprocessing. A ranking 'observeLevel' was introduced to select subsets of the outcomes. The primary outcomes are those required for the weekly deliverables, the secondary are the related outcomes that are not required for the standard outputs and tertiary are those that can easily be calculated outside the model, such as prevalence, or outcomes rarely used such as infectiousness by symptomatic type and detection level.
Show table
| alphabet. No | name | description | observeLevel | compartments included |
|---|---|---|---|---|
| 1 | asymp_cumul | Number of all asymptomatic infections that happened (cumulative) | primary | asymptomatic, RA, RAs_det1 |
| 2 | asymp_det_cumul | Number of all detected asymptomatic infections that happened (cumulative) | secondary | As_det1, RAs_det1 |
| 3 | asymptomatic | Number of asymptomatic infections | secondary | As |
| 4 | asymptomatic_det | Number of detected asymptomatic infections | secondary | As_det1 |
| 5 | crit_cumul | Number of all critical cases that happened (cumulative) | primary | deaths, critical, RC2,RC2_det3 |
| 6 | crit_det | Number of critical cases that are detected | primary | C2_det3, C3_det3 |
| 7 | crit_det_cumul | Number of all detected critical cases that happened (cumulative) | primary | C2_det3, C3_det3, D3_det3, RC2_det3 |
| 8 | critical | Number of severe symptomatic infections that are hospitalized that are critical | primary | C2, C3, C2_det3, C3_det3 |
| 9 | death | Number of COVID-19 deaths in the population | primary | D3, D3_det3 |
| 11 | death_det_cumul | Number of all detected COVID-19 deaths in the population that happened | primary | D3_det3 |
| 12 | detected | Number of detected COVID-19 infections regardless of symptomaticity | primary | As_det1, Sym_det2, Sys_det3, H1_det3, H2_det3, H3_det3, C2_det3, C3_det3 |
| 13 | detected_cumul | Number of all detected COVID-19 infections regardless of symptomaticity (cumulative) | primary | As_det1, Sym_det2, Sys_det3, H1_det3, H2_det3, C2_det3, C3_det3, RAs_det1, RSym_det2, RH1_det3, RC2_det3, D3_det3 |
| 14 | exposed | Number of exposed (infected not yet infectious) in the population | secondary | E |
| 15 | hosp_cumul | Number of all hospitalizations due to COVID-19 that happened (cumulative) | primary | hospitalized, critical, deaths, RH1, RC2, RH1_det3, RC2_det3 |
| 16 | hosp_det | Number of detected COVID-19 hospitalizations | primary | H1_det3, H2_det3, H3_det3 |
| 17 | hosp_det_cumul | Number of all detected COVID-19 hospitalizations that happened (cumulative) | primary | H1_det3, H2_det3, H3_det3, C2_det3, C3_det3, D3_det3, RH1_det3, RC2_det3 |
| 18 | hospitalized | Number of severe symptomatic infections that are hospitalized | primary | H1, H2, H3, H1_det3, H2_det3, H3_det3 |
| 19 | infected | Number of all infected in the population | primary | all except susceptibles |
| 20 | infected_cumul | Number of all that were infected (cumulative) | secondary | infected, recovered, deaths |
| 21 | infected_det | Number of all infected that are detected | secondary | infectious_det, H1_det3, H2_det3, H3_det3, C2_det3, C3_det3 |
| 23 | infectious_det | Number of all infectious that are detected | tertiary | As_det1, P_det , Sym_det2, Sys_det3 |
| 24 | infectious_det_AsP | Number of all non-symptomatic that are infectious and detected | tertiary | As_det1, P_det |
| 25 | infectious_det_symp | Number of all symptomatic that are infectious and detected | tertiary | Sym_det2, Sys_det3 |
| 26 | infectious_undet | Number of infectious that are not detected | tertiary | As, P, Sym, Sys, H1, H2, H3, C2, C3 |
| 27 | presymptomatic | Number of presymptomatic infections | primary | P, Pdet |
| 28 | presymptomatic_det | Number of all detected presymptomatic infections | secondary | Pdet |
| 29 | prevalence | Number of infected (cumul) over total population | tertiary | infected / N |
| 30 | prevalence_det | Number of detected infected (cumul) over total population | tertiary | infected_det / N |
| 31 | recovered | Number of recovered COVID-19 cases in the population | primary | RAs,RSym, RH1, RC2, RAs_det1, RSym_det2, RH1_det3, RC2_det3 |
| 32 | recovered_det | Number of detected recovered COVID-19 cases in the population | primary | RAs_det1, RSym_det2, RH1_det3, RC2_det3 |
| 33 | seroprevalence | Number of recovered (cumul) over total population | tertiary | (infected + recovered) / N |
| 34 | seroprevalence_det | Number of detected recovered (cumul) over total population | tertiary | (infected_det + recovered_det) / N |
| 35 | susceptible | Number of susceptibles in the population | primary | S |
| 36 | symp_mild_cumul | Number of all mild symptomatic infections that happened (cumulative) | primary | symptomatic_mild, RSym, RSym_det2 |
| 37 | symp_mild_det_cumul | Number of all detected mild symptomatic infections that happened (cumulative) | primary | symptomatic_mild_det, RSym_det2 |
| 38 | symp_severe_cumul | Number of all severe symptomatic infections that happened (cumulative) | primary | symptomatic_severe, hospitalized, critical, deaths, RH1, RC2, RH1_det3, RC2_det3 |
| 39 | symp_severe_det_cumul | Number of all detected severe symptomatic infections that happened (cumulative) | primary | symptomatic_severe_det, hosp_det, crit_det, deaths_det |
| 40 | symptomatic_mild | Number of mild symptomatic infections | primary | Sym, Sym_det2; Sym, Sym_preD, Sym_det2 ; Sym, Sym_preD, Sym_det2a, Sym_det2b |
| 41 | symptomatic_mild_det | Number of detected mild infections in the population | secondary | symptomatic_mild_det |
| 42 | symptomatic_severe | Number of severe symptomatic infections | secondary | Sys, Sys_det3; Sys, Sys_preD, Sys_det3 ; Sys, Sys_preD, Sys_det3a, Sys_det3b |
| 43 | symptomatic_severe_det | Number of detected severe symptomatic infections | secondary | symptomatic_severe_det |
The "age_model" duplicates each compartment of the simple or the extended model for n age groups. To allow the age groups to get in contact with each other at different rates, the Ki (contact rate * probability of transmission) needs to be specified for a all age-combinations.
- Age groups: "0to9", "10to19", "20to29", "30to39", "40to49", "50to59", "60to69", "70to100" (emodl) To generate or modify the emodl files use the age specific emodl generator
The contacts between age groups were previously extracted for running an EMOD model from Prem et al 2017. Script that extracts the contact matrix values.
The "spatial_model" uses a special syntax as described here. To generate or modify the emodl files use the locale specific emmodl generator
- View the EMS specific emodl
A test verion is available under emodl file. To generate or modify the emodl files use the locale-age specific emmodl generator
The Compartmental Modeling Software (CMS) is used to simulate the COVID-19 transmission and disease progression. The CMS language defines 5 main type: species, observations, reactions, parameters and functions, in addition time-events can be added as well as state-events. Multiple compartments, called ‘species’ can be defined. The movement of populations between compartments is called reaction. The model runs with different solvers, including spatial solvers. The model is written in 'emodl' files and model configurations are written in 'cfg' files. The output is written into trajectories.csv files.
The latest model description in emodl format is written in the extendedmodel.emodl file (note original emodl with history in extendedmodel_cobey.emodl.
The runScenarios.py is used to run multiple simulations
given a configuration file with the parameters. The script builds off
a default configuration file extendedcobey.yaml
and substitutes parameters with the values/functions in the
user-provided configuration file using the @param@ placeholder. Multiple trajectories.csv that are produced per single simulation are combined into a trajectoriesDat.csv, used for postprocessing and plotting.
2.2 Configuration file:
The configuration file is in YAML format and is divided into 5
blocks: experiment_setup_parameters,
fixed_parameters_region_specific, fixed_parameters_global,
sampled_parameters, fitted_parameters. The sampled parameters need
the sampling function as well as the arguments to pass into that
function (function_kwargs). Currently, only a few
sampling/calculation functions are supported. More can be added by
allowing for more libraries in generateParameterSamples of runScenarios.py.
Note that the user-supplied configuration file is used to provide additional or updated parameters from the base configuration file.
- Master configuration: YAML file that defines the parameter input values for the model (if not specified uses the default
extendedcobey_200428.yaml) - Running location: Where the simulation is being run (either
LocalorNUCLUSTER) - Region: The region of interest. (e.g.
EMS_1, orILfor all EMS 1-11 inclued in the same model) - Configuration file: The configuration file with the parameters to use for the simulation. If a parameter is not provided, the value in the default configuration will be used. (e.g. sample_experiment.yaml)
- Emodl template (optional): The template emodl file to substitute in
parameter values. The default is extendedmodel.emodl. emodl
files are in the
./emodldirectory. - cfg template (optional): The default cfg file uses the Tau leaping solver (recommended B solver).
- Suffix for experiment name added as name_suffix (optional): The template emodl file to substitute in
parameter values. The default is test_randomnumber (e.g.
20200417_EMS_10_test_rn29)
sample_age4grp_experiment.yaml(https://github.com/numalariamodeling/covid-chicago/blob/master/experiment_configs/sample_age4grp_experiment.yaml) Note: this extension works for any sub-group as it duplicates the parameter names for the defined group names, which need to be defined in the same way in the corresponding emodl file.
The simulation_submission_template.txt shows example command lines and scenarios that are currently being used.
Show examples
- Using the default emodl template:
python runScenarios.py --running_location Local --region IL --experiment_config sample_experiment.yaml - Using a different emodl template:
python runScenarios.py --running_location Local --region IL --experiment_config sample_experiment.yaml --emodl_template simplemodel_testing.emodl - Specifying experiment name suffix and changing running_location :
python runScenarios.py --running_location NUCLUSTER --region IL --experiment_config sample_experiment.yaml --emodl_template simplemodel_testing.emodl --name_suffix "testrun_userinitials" - Specifiying cms configuration file and solver:
python runScenarios.py --running_location Local --region IL --experiment_config sample_experiment.yaml --emodl_template simplemodel_testing.emodl --cfg_template model_Tau.cfg - Specifiying master configuration file and using short form of arguments:`python runScenarios.py -mc config_param_delay7.yaml -rl Local -r IL -c spatial_EMS_experiment.yaml -e extendedmodel_EMS_criticaldet_triggeredrollbackdelay.emodl -cfg model_B.cfg
As described in 2.1. and 2.2 parameters are sampled from the base configuration files when running python runScenarios.py.
The sample_parameters.py script handles only the sampled_parameters.csv, it allows to:
(1) generate csv file from configuration files without running simulations
(2) load and modify an existing sampled_parameters.csv (change or add single or multiple parameter) (default location experiment_configs\input_csv)
(3) replace single values for one or more parameter use python dictionary --param_dic {\"capacity_multiplier\":\"0.5\"}
(4) combine with multiple values for one or more parameters define additional csv file --csv_name_combo startdate_Ki_sets.csv
Show examples
Running examples:
- nsamples: optional, if specified if overwrites the nsamples in the base configuration, if loading an existing csv the first n samples will be selected (i.e. when selecting samples from an excisting csv file, could be modified to be random if needed)
- emodl_template: the emodl template is required to test whether the parameter csv table includes all required parameters defined in the desired emodl file to run
- example 1:
python sample_parameters.py -rl Local -r IL --experiment_config spatial_EMS_experiment.yaml --emodl_template extendedmodel_EMS.emodl -save sampled_parameters2.csv - example 2:
python sample_parameters.py -rl Local -save sampled_parameters_1000.csv --nsamples 1000 - example 3:
python sample_parameters.py -rl Local -load sampled_parameters_1000.csv -save sampled_parameters_1000_v2.csv --param_dic {\"capacity_multiplier\":\"0.5\"} - example 4:
python sample_parameters.py --csv_name_combo sampled_parameters_sm7.csv -save sampled_parameters_sm7_combo.csv-(sampled_parameters_sm7.csv not under version control, but would for example include 10 values for social multiplier 7 for all 11 regions, the base sample parameters are repeated for each of the 10 rows of the additional csv)
When running simulations with an pre-existing csv file, specify
--load_sample_parameters(boolean) and--sample_csv(name of csv file inexperiment_configs\input_csv).
Note: except the loaded "sampled_parameters.csv" and "sampled_parameters_1000.csv", csv files should not be added to version control on git.
Running simulation requires the CMS software and python. Additional software includes R and Rstudio for some of the postprocessing steps. The model runs on Windows and Linux as well as the Northwestern high performance computing cluster (Quest). The detailes are described below.
Show setup description
Use a .env file in the same directory as your runScenarios script to define paths to directories and files on your own computer.
Copy the sample.env file to .env and edit so that paths point as needed for your system.
The CMS software is provided as a compiled Windows executable, but can be run on Unix-like systems via wine.
If you do not have wine installed on your system, you can use the provided Dockerfile, which has wine baked in.
To build the Docker image, run docker build -t cms. Set DOCKER_IMAGE=cms in your environment or your .env file to use it.
A cloned version of the git repository can be found under /projects/p30781/covidproject/covid-chicago/.
All the modules need to be installed on the personal quest environment
- use pip install ... in your terminal
- install
dotenvandyamlordereddictloaderconda create --name dotenv-py37 -c conda-forge python-yamlordereddictloader python=3.7 --yessource activate dotenv-py37conda install -c conda-forge yamlordereddictloader
cd /projects/p30781/covidproject/covid-chicago/
python runScenarios.py --running_location NUCLUSTER --region EMS_11 --experiment_config extendedcobey_200428.yaml --emodl_template extendedmodel.emodl --name_suffix "quest_run_<your initial>"
The experiments will go to the _temp folder on the quest gitrepository. To avoid confusion on owner of the simulations it is recommended to include the initials in the experiment name using the name_suffix argument
Next step copy the content of the submit_runSimulations.sh (should be a simple txt file) to the terminal to run:
cd /projects/p30781/covidproject/covid-chicago/_temp/<exp_name>/trajectories/dos2unix runSimulations.sh# converts windows format to linux formatsbatch runSimulations.sh# submits the simulations as an array job, note maximum array <5000 scenarios.
The requirements.txt includes name and version of required python and R modules.
Via the --post_process argument in the runScenarios command plotting processes can be directly attached to after simulations finished.
A sample plot is produced automatically, can be disabled via --noSamplePlot.
Even if no postprocess is specified, default batch files are generated for data comparison, process for civis steps and basic plotter (age, locale emodl).
Additional batch files or postprocesses can be linked to runScenarios of needed, otherwise the plotters folder provides a range of python files that do different visualizations (see readme in folder for details).
Per default a master_sample_plot.png is generated for every simulation regardless of type (base, age, spatial) for all Illinois.
locale_age_postprocessing.bat- generates trajectories for pre-specified outcome channels per age group using locale_age_postprocessing.py.
0_runDataComparison.batcomparing model predictions to data per region over time
- runFittingProcess.bat
In the experiment folder is per default a
0_runFittingProcess.batfile created, which run the fitting script. Note, currently hardcoded for the spatial model. The fitting script estimates the effect size multiplier and effect size change time event as parameters to estimate and write out in csv files. Per default the social multiplier and time event number from the exoeriment name suffix is taken, which needs to be in the form offitki9(fitki is removed and 9 is used in the parameter name). Example experiment name:20201006_IL_mr_local_fitki9, example submission command:python runScenarios.py -rl Local -r IL -mc masterconfig_forFitting.yaml -c spatial_EMS_forFitting.yaml -e extendedmodel_EMS_forFitting.emodl -n "mr_local_fitki9"
Several batch files are automatically generated when running the spatial model using the spatial_EMS_experiment.yaml in the runScenario submission command.
When adding the flag --post_process "processForCivis" in the runScenarios.py submission command, the files are automatically executed.
The postprocessing steps include 1) aggregation of the model predictions 2) probability of exceeding capacities, 3) estimate time varying reproductive number, and additional desciptove plots.
Show batch file description
0_runTrimTrajectories.batcalls a Python script trims the trajectories and per default keeps only dates after 2020-06-12 (timesteps >120) and selected outcome measures.0_createAdditionalPlots.batcalls two Python scripts I and II, requires to run 0_runTrimTrajectories.bat before (or change name of trajectories.csv). It generates additional plots, such as recent + nearest predictions on hospitalizations, ICU and deaths per region. Could be extended to include for example the prevalence plotter or other plots.0_runDataComparison.batcalls a Python script comparing model predictions to data per region over time1_runProcessForCivis.batcalls a Python script generates the result csv dataframe (i.e. nu_20201005.csv) and generates descriptive trajectories per channel and region2_runProcessForCivis.batcalls a Python script calculates the probability of hospital overflow and produces the (i.e. nu_hospitaloverflow_20201005.csv), also adds total number of beds additional script3_runProcessForCivis.batcalls a Rscript that runs the Rt estimation, the Rt columns are added to the result csv dataframe (i.e. nu_20201005.csv), produces descriptive plots4_runProcessForCivis.batcalls a Python script that generates the NU_civis_outputs subfolder and copies all relevant files and adds the changelog.txt. Only the changelog.txt will need manual editing to reflect the new changes every week.5_runProcessFor_CDPH.batcalls a Rscript that generates region Rt timelines for current and previous week and copies selected plots from0_createAdditionalPlots.batto the cdph folder5_runProcessForCivis_optional.batcalls a Rscript that generates the iteration comparison plot (last 3 weeks)
- Populaton estimates per county (2018): datahub.cmap.illinois.gov
- Region boundaries and operational units
- Emergency Medical Service Areas (EMS) regions (not used anymore): https://www.dph.illinois.gov/sites/default/files/publications/emsjuly2016small.pdf
- Covid regions: http://dph.illinois.gov/regionmetrics?regionID=1
- Restore regions: https://coronavirus.illinois.gov/s/restore-illinois-regional-dashboard
- Hospital census data: daily occupancy of intensive care unit (ICU) and non_ICU beds (EMResource)
- Hospital capacity limits for hospital and intensive care unit beds (from IDPH)
- Positive case line list data: Of each individual tested positive for COVID-19: age, sex, race, ethnicity, date of specimen collection, date of hospital admission (if admitted), date of death (if died)
- Covid like illness (CLI) admissions is obtained from IDPH and included in weekly model calibration.
The model is updated every week to fit to latest hospitalisation and deaths reports.
Show history of updates
- 20210106 added region specific recovery time critical
- 20201216 updated parameter fit
- 20201204 updated parameter fit, use muliplier 11 for decrease in trend
- 20201201 updated parameter fit
- 20201130 added transmission multiplier 12
- 20201124 updated parameter fit
- 20201119 updated parameter fit, added transmission multiplier 11
- 20201110 updated parameter fit
- 20201104 updated parameter fit
- 20201027 updated parameter fit, added transmission multiplier 10 (previously called social multiplier)
- 20201020 updated parameter fit
- 20201015 updated parameter fit, reset fitting method
- 20201007 updated parameter fit
- 20200929 updated parameter fit, changed fitting method
- 20200922 updated parameter fit, changed d_Sym parameters (generic)
- 20200915 updated parameter fit, added social multiplier 7 (time event Aug 25)
- 20200909 updated parameter fit, updated evolution of CFR
- 20200825 updated parameter fit
- 20200818 updated parameter fit, updated evolution of dSys and region-specific evolution of dSym
- 20200812 updated parameter fit
- 20200807 updated parameter fit
- 20200804 updated parameter fit
- 20200729 updated parameter fit, added region-specific evolution of dSym over time
- 20200722 updated parameter fit, use covid regions instead of EMS regions for fitting (same numbering 1-11)
- 20200715 updated parameter fit, added fifth social distancing multiplier (time event June 21st)
- 20200706 added time-varying fraction_critical
- 20200624 updated parameter fit
- 20200622 adjusted increase in detection for severe and mild symptomatic cases
- 20200622 updated model structure, added test delay in Asymptomatics and detections in presymptomatic
- 20200616 updated parameter fit
- 20200610 updated parameter fit
- 20200609 separat time delay for dSym and dSys, added d_Sym_incr 1-5 proportional to d_Sys_incr
- 20200602 updated parameter fit
- 20200523 added d_Sys_incr4 and d_Sys_incr5, parameter fitting, including test delay per default
- 20200521 added s_m_4, parameter fitting
- 20200515 parameter fitting (also 20200512, 20200501)
- 20200428 updated model disease and transmission parameters (previously 20200421, 20200419)
- 20200428 added d_Sys_incr1-3
- 20200421 adding scale-invariant Ki
- 20200407 add more detected observables
- 20200402 cobey model alignment (including presymptomatic)
- 20200321 initial model development including (S,E, Sym, Sys, As, H, C, D, R)
- CMS software publication; online documentation
- Chicago Covid Coalition website
- Modeling COVID 19 Transmission and Containment in Illinois (IPHAM Webinar) by Dr Jaline Gerardin.
- Modeling COVID-19 Transmission and Containment Efforts at Northwestern
- Use of the model for demonstrating statistical data assimilation by Dr Armstrong et al.