models.yml

basel:
  name: Covid19-Scenarios (Neher)
  origin: Neher Lab - Biozentrum Basel
  enabled: true
  imageURL: ghcr.io/covid-policy-modelling/basel-connector/basel-connector:latest
  metaURLs:
    code: https://github.com/neherlab/covid19_scenarios
    website: https://covid19-scenarios.org/about
  description: |
    We implement an age-stratified SEIR model with additional categories
    for individuals exposed to the virus that are not yet infectious, severely sick
    people in need of hospitalization, people in critical condition, and a fatal category.
    The model is currently under active development and the US state level parameters are under review.
  supportedParameters:
    - contactReduction
    - r0
  supportedRegions:
    AD:
    AE:
    AF:
    AG:
    AL:
    AM:
    AR:
    AT:
    AU:
    AZ:
    BA:
    BB:
    BD:
    BE:
    BF:
    BG:
    BH:
    BI:
    BJ:
    BN:
    BO:
    BR:
    BS:
    BT:
    BW:
    BY:
    BZ:
    CA:
      - CA
      - CA-AB
      - CA-BC
      - CA-MB
      - CA-NB
      - CA-NL
      - CA-NS
      - CA-NT
      - CA-NU
      - CA-ON
      - CA-PE
      - CA-QC
      - CA-SK
      - CA-YT
    CF:
    CH:
      - CH
      - CH-AG
      - CH-AI
      - CH-AR
      - CH-BE
      - CH-BL
      - CH-BS
      - CH-FR
      - CH-GE
      - CH-GL
      - CH-GR
      - CH-JU
      - CH-LU
      - CH-NE
      - CH-NW
      - CH-OW
      - CH-SG
      - CH-SH
      - CH-SO
      - CH-SZ
      - CH-TG
      - CH-TI
      - CH-UR
      - CH-VD
      - CH-VS
      - CH-ZG
      - CH-ZH
    CL:
    CM:
    CN:
    CO:
    CR:
    CU:
    CV:
    CY:
    CZ:
    DE:
      - DE
      - DE-BB
      - DE-BE
      - DE-BW
      - DE-BY
      - DE-HB
      - DE-HE
      - DE-HH
      - DE-MV
      - DE-NI
      - DE-NW
      - DE-RP
      - DE-SH
      - DE-SL
      - DE-SN
      - DE-ST
      - DE-TH
    DJ:
    DK:
    DM:
    DO:
    DZ:
    EC:
    EE:
    EG:
    ES:
      - ES
      - ES-AN
      - ES-AR
      - ES-AS
      - ES-CB
      - ES-CE
      - ES-CL
      - ES-CM
      - ES-CN
      - ES-CT
      - ES-EX
      - ES-GA
      - ES-IB
      - ES-MC
      - ES-MD
      - ES-ML
      - ES-NC
      - ES-PV
      - ES-RI
      - ES-VC
    ET:
    FI:
    FJ:
    FM:
    FO:
    FR:
      - FR
      - FR-ARA
      - FR-BFC
      - FR-BRE
      - FR-COR
      - FR-CVL
      - FR-GES
      - FR-GF
      - FR-GUA
      - FR-HDF
      - FR-IDF
      - FR-LRE
      - FR-MAY
      - FR-MQ
      - FR-NAQ
      - FR-NOR
      - FR-OCC
      - FR-PAC
      - FR-PDL
    GA:
    GB:
    GD:
    GE:
    GH:
    GI:
    GL:
    GM:
    GN:
    GQ:
    GR:
    GT:
    GW:
    GY:
    HK:
    HN:
    HR:
    HT:
    HU:
    ID:
    IE:
    IL:
    IN:
      - IN
      - IN-AN
      - IN-AP
      - IN-AR
      - IN-AS
      - IN-BR
      - IN-CH
      - IN-CT
      - IN-DD
      - IN-DL
      - IN-DN
      - IN-GA
      - IN-GJ
      - IN-HP
      - IN-HR
      - IN-JH
      - IN-JK
      - IN-KA
      - IN-KL
      - IN-LD
      - IN-MH
      - IN-ML
      - IN-MN
      - IN-MP
      - IN-MZ
      - IN-NL
      - IN-OR
      - IN-PB
      - IN-PY
      - IN-RJ
      - IN-SK
      - IN-TH
      - IN-TN
      - IN-TR
      - IN-UP
      - IN-UT
      - IN-WB
    IQ:
    IR:
    IS:
    IT:
      - IT
      - IT-21
      - IT-23
      - IT-25
      - IT-32
      - IT-34
      - IT-36
      - IT-42
      - IT-45
      - IT-52
      - IT-55
      - IT-57
      - IT-62
      - IT-65
      - IT-67
      - IT-72
      - IT-75
      - IT-77
      - IT-78
      - IT-82
      - IT-88
      - IT-BZ
      - IT-TN
    JM:
    JO:
    JP:
      - JP
      - JP-47
    KE:
    KG:
    KH:
    KI:
    KM:
    KN:
    KP:
    KR:
    KW:
    KZ:
    LA:
    LB:
    LC:
    LI:
    LK:
    LR:
    LT:
    LU:
    LV:
    LY:
    MA:
    MC:
    MD:
    ME:
    MG:
    MH:
    MK:
    ML:
    MM:
    MN:
    MT:
    MU:
    MV:
    MW:
    MX:
    MY:
    MZ:
    NA:
    NI:
    NL:
    NO:
    NP:
    NR:
    NZ:
    OM:
    PA:
    PE:
    PH:
      - PH
      - PH-00
      - PH-01
      - PH-02
      - PH-03
      - PH-05
      - PH-06
      - PH-07
      - PH-08
      - PH-09
      - PH-10
      - PH-11
      - PH-12
      - PH-13
      - PH-14
      - PH-15
      - PH-40
      - PH-41
    PK:
    PL:
    PT:
    PW:
    PY:
    QA:
    RO:
    RS:
    RU:
    SA:
    SB:
    SC:
    SD:
    SE:
      - SE
      - SE-AB
    SG:
    SI:
    SK:
    SM:
    SN:
    SO:
    SR:
    ST:
    SV:
    SY:
    SZ:
    TG:
    TH:
    TJ:
    TL:
    TM:
    TN:
    TO:
    TR:
    TT:
    TW:
    TZ:
    UA:
    UG:
    US:
      - US
      - US-AK
      - US-AL
      - US-AR
      - US-AZ
      - US-CA
      - US-CO
      - US-CT
      - US-DC
      - US-DE
      - US-FL
      - US-GA
      - US-HI
      - US-IA
      - US-ID
      - US-IL
      - US-IN
      - US-KS
      - US-KY
      - US-LA
      - US-MA
      - US-MD
      - US-ME
      - US-MI
      - US-MN
      - US-MO
      - US-MS
      - US-MT
      - US-NC
      - US-ND
      - US-NE
      - US-NH
      - US-NJ
      - US-NM
      - US-NV
      - US-NY
      - US-OH
      - US-OK
      - US-OR
      - US-PA
      - US-RI
      - US-SC
      - US-SD
      - US-TN
      - US-TX
      - US-UT
      - US-VA
      - US-VT
      - US-WA
      - US-WI
      - US-WV
      - US-WY
    UY:
    UZ:
    VC:
    VE:
    VN:
    YE:
    ZA:
    ZM:
    ZW:

mrc-ide-covid-sim:
  name: CovidSim (Imperial)
  origin: Imperial College
  enabled: true
  imageURL: ghcr.io/covid-policy-modelling/covid-sim-connector/covid-sim-connector:latest
  metaURLs:
    code: https://github.com/mrc-ide/covid-sim
    paper: https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf
  description: |
    We consider the feasibility and implications of strategies for COVID-19,
    looking at a range of NPI measures. It is important to note at the outset
    that given SARS-CoV-2 is a newly emergent virus, much remains to be
    understood about its transmission. In addition, the impact of many of the
    NPIs detailed here depends critically on how people respond to their
    introduction, which is highly likely to vary between countries and even
    communities.
  supportedParameters:
    - interventionStrategies
    - r0
  supportedRegions:
    AL:
    AT:
    BA:
    BE:
    BG:
    BY:
    CA:
    CH:
    CR:
    DE:
    DK:
    EE:
    ES:
    FI:
    FR:
    GB:
      - GB
      - GB-LND
    GI:
    GR:
    HR:
    HU:
    IR:
    IS:
    IT:
    LT:
    LU:
    LV:
    MD:
    ME:
    MK:
    MT:
    NG:
    NL:
    NO:
    PL:
    PT:
    RO:
    RS:
    RU:
    SE:
    SI:
    SK:
    UA:
    US:
      - US
      - US-AK
      - US-AL
      - US-AR
      - US-AS
      - US-AZ
      - US-CA
      - US-CO
      - US-CT
      - US-DC
      - US-DE
      - US-FL
      - US-GA
      - US-GU
      - US-HI
      - US-IA
      - US-ID
      - US-IL
      - US-IN
      - US-KS
      - US-KY
      - US-LA
      - US-MA
      - US-MD
      - US-ME
      - US-MI
      - US-MN
      - US-MO
      - US-MS
      - US-MT
      - US-NC
      - US-ND
      - US-NE
      - US-NH
      - US-NJ
      - US-NM
      - US-NV
      - US-NY
      - US-OH
      - US-OK
      - US-OR
      - US-PA
      - US-PR
      - US-RI
      - US-SC
      - US-SD
      - US-TN
      - US-TX
      - US-UT
      - US-VA
      - US-VI
      - US-VT
      - US-WA
      - US-WI
      - US-WV
      - US-WY

idm-covasim:
  name: Covasim
  origin: Institute for Disease Modeling
  imageURL: ghcr.io/covid-policy-modelling/covasim-connector/covasim-connector:latest
  enabled: false
  metaURLs:
    code: https://github.com/InstituteforDiseaseModeling/covasim
    paper: http://paper.covasim.org/
    website: http://covasim.org/
  description: |
    Covasim is a stochastic agent-based simulator designed to be used for COVID-19 (novel coronavirus, SARS-CoV-2) epidemic analyses.
    These include projections of indicators such as numbers of infections and peak hospital demand.
    Covasim can also be used to explore the potential impact of different interventions, including social distancing, school closures, testing, contact tracing, and quarantine.
  supportedParameters:
    - interventionStrategies
    - contactReduction
  supportedRegions:
    GB:
      - GB-ENG
      - GB-NIR
      - GB-SCT
      - GB-WLS
    US:
      - US-AL
      - US-AK
      - US-AZ
      - US-AR
      - US-CA
      - US-CO
      - US-CT
      - US-DC
      - US-DE
      - US-FL
      - US-GA
      - US-HI
      - US-ID
      - US-IL
      - US-IN
      - US-IA
      - US-KS
      - US-KY
      - US-LA
      - US-ME
      - US-MD
      - US-MA
      - US-MI
      - US-MN
      - US-MS
      - US-MO
      - US-MT
      - US-NE
      - US-NV
      - US-NH
      - US-NJ
      - US-NM
      - US-NY
      - US-NC
      - US-ND
      - US-OH
      - US-OK
      - US-OR
      - US-PA
      - US-RI
      - US-SC
      - US-SD
      - US-TN
      - US-TX
      - US-UT
      - US-VT
      - US-VA
      - US-WA
      - US-WV
      - US-WI
      - US-WY

wss:
  name: WSS
  origin: University of Edinburgh / University of Cambridge
  imageURL: docker.pkg.github.com/gjackland/wss/wss-connector:latest
  enabled: true
  metaURLs:
    code: https://github.com/gjackland/WSS
    paper: https://doi.org/10.1101/2021.04.14.21255385
  description: |
    Weight scale and shift code for covid epidemic modelling.
  supportedRegions:
    GB:
      - GB-ENG
      - GB-SCT
      - E40000003
      - E40000005
      - E40000006
      - E40000007
      - E40000008
      - E40000009
      - E40000010

sir-ode-python:
  name: SIR ODE Python
  origin: Covid Policy Modelling
  enabled: true
  imageURL: ghcr.io/covid-policy-modelling/sir-ode-python-connector/sir-ode-python-connector:latest
  description: |
    This is a simple example of an SIR model that is run to steady state.
    The final size, the peak infected, and the time of the peak infected is returned, alongside the times and states of the model.
  supportedSchemas:
    input: MinimalModelInputBatch
    output: MinimalModelOutputBatch

micromob-bh-rm:
  name: MicroMoB (Beverton-Holt Aquatic and Ross-Macdonald Adult Mosquitoes Only)
  origin: dd-harp (Disease Dynamics - Health Analytics for Robust Policy)
  enabled: true
  imageURL: ghcr.io/dd-harp/micromob-connector/micromob-connector:latest
  description: |
    This is a simple aquatic and adult mosquito model that is defined
    on a set of points in space and most parameters are allowed to vary over
    space and time.
  supportedSchemas:
    input: MicroMoBBHRMInputBatch
    output: MicroMoBBHRMOutputBatch

delineo: 
  name: Delineo Disease Modeling Project
  origin: Johns Hopkins University
  enabled: true 
  imageURL: ghcr.io/delineo-disease-modeling/covid-ui/covid-ui:0.0.1
  metaURLs: 
    code: https://github.com/Delineo-Disease-Modeling/Covid-UI.git
    website: https://covidweb.isi.jhu.edu/
  description: |
    The Delineo Simulator attempts to realistically capture the mechanisms of disease spread within a diverse range of 
    geographies, ranging from small rural communities to large cities. The simulator works in two parts. Fundamentally, 
    the question of disease movement is a question of population movement. Using geolocation mobility data from SafeGraph,
    realistic population movement dynamics are created. Then, a statistical disease driver uses the population movement 
    information to predict disease spread.
  supportedParameters:
    - interventionStrategies
  supportedRegions: 
    US:
      - US-OK