List of COVID-19 simulation models
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COVID-19 simulation models are mathematical infectious disease models for the spread of COVID-19.{{cite journal | vauthors = Adam D | title = Special report: The simulations driving the world's response to COVID-19 | journal = Nature | volume = 580 | issue = 7803 | pages = 316–318 | date = April 2020 | pmid = 32242115 | doi = 10.1038/d41586-020-01003-6 | doi-access = | bibcode = 2020Natur.580..316A | s2cid = 256820433 }} The list should not be confused with COVID-19 apps used mainly for digital contact tracing.
Note that some of the applications listed are website-only models or simulators, and some of those rely on (or use) real-time data from other sources.
Models with the most scientific backing
The sub-list contains simulators that are based on theoretical models. Due to the high number of pre-print research created and driving by the COVID-19 pandemic,{{Cite journal| vauthors = Brierley L |title=The role of research preprints in the academic response to the COVID-19 epidemic | journal = Authorea Preprints |url= https://www.authorea.com/users/305982/articles/436710-the-role-of-research-preprints-in-the-academic-response-to-the-covid-19-epidemic?commit=bbd7a90d14b09c2d509b6abfb0fbf2a9882ef43b|language=en|doi=10.22541/au.158516578.89167184|doi-access=free}} especially newer models should only be considered with further scientific rigor.{{Cite web|title=Another 178,000 deaths? Scientists' latest virus projection is a warning|url=https://www.nbcnews.com/science/science-news/vindicated-covid-19-models-warn-pandemic-far-over-n1240934|access-date=2021-02-22|website=NBC News|date=24 September 2020 |language=en}}{{Cite web| vauthors = Tufekci Z |date=2020-04-02|title=Don't Believe the COVID-19 Models|url=https://www.theatlantic.com/technology/archive/2020/04/coronavirus-models-arent-supposed-be-right/609271/|access-date=2021-02-22|website=The Atlantic|language=en}}
=Simulations and models=
- Chen et al. simulation based on Bats-Hosts-Reservoir-People (BHRP) model (simplified to RP only){{cite journal | vauthors = Chen TM, Rui J, Wang QP, Zhao ZY, Cui JA, Yin L | title = A mathematical model for simulating the phase-based transmissibility of a novel coronavirus | journal = Infectious Diseases of Poverty | volume = 9 | issue = 1 | pages = 24 | date = February 2020 | pmid = 32111262 | pmc = 7047374 | doi = 10.1186/s40249-020-00640-3 | doi-access = free }}
- CoSim19{{cite web |title=CoSim Online |url=https://shiny.covid-simulator.com/covidsim/ |website=shiny.covid-simulator.com |access-date=14 December 2023}} – Prof Lehr, based on SEIRD model
- COVID-19 MOBILITY MODELING{{cite web |title=COVID-19 Mobility Network Modeling |url=https://covid-mobility.stanford.edu |website=covid-mobility.stanford.edu |access-date=14 December 2023}} – Stanford based on SEIR model{{cite journal | vauthors = Chang S, Pierson E, Koh PW, Gerardin J, Redbird B, Grusky D, Leskovec J | title = Mobility network models of COVID-19 explain inequities and inform reopening | journal = Nature | volume = 589 | issue = 7840 | pages = 82–87 | date = January 2021 | pmid = 33171481 | doi = 10.1038/s41586-020-2923-3 | doi-access = free | bibcode = 2021Natur.589...82C }}
- COVID-19 Simulator{{cite web |title=Home - COVID-19 Simulator |url=https://covid19sim.org/ |website=covid19sim.org |access-date=14 December 2023}} – Harvard Medical School based on a validated system dynamics (compartment) model{{Cite web|title=Policy Simulator Methodology|url=https://covid19sim.org/documents/policy-methods|access-date=2021-02-21|website=COVID-19 Simulator|language=en-gb}}
- COVID-19 Surge{{cite web |title=Healthcare Workers |url=https://www.cdc.gov/coronavirus/2019-ncov/hcp/COVIDSurge.html |website=Centers for Disease Control and Prevention |access-date=14 December 2023 |language=en-us |date=11 February 2020}} – CDC{{Cite web|last=CDC|date=2020-02-11|title=Healthcare Workers|url=https://www.cdc.gov/coronavirus/2019-ncov/hcp/COVIDSurge.html|access-date=2021-02-22|website=Centers for Disease Control and Prevention|language=en-us}}
- COVIDSIM{{cite web |last1=Ng |first1=Mark Kok Yew |title=SEIRS-based COVID-19 Simulation Package |url=https://www.markusng.com/COVIDSIM/ |website=markusng.com |access-date=14 December 2023 |language=en}} – by Mark Kok Yew Ng et al.
- CovidSim – Imperial College London, MRC Centre for Global Infectious Disease Analysis, Neil Ferguson et al.
- CovidSim{{cite web |title=Research project - CovidSim - Modeling and simulation of covid infection spread in crowds in system relevant infrastructures - HM Hochschule München University of Applied Sciences |url=https://www.hm.edu/en/research/projects/project_details/koester_1/covidsim.en.html |website=Hochschule München |access-date=14 December 2023 |language=en-en}} – Research project by Munich University of Applied Sciences, Prof Köster
- COVIDSim{{cite web |last1=Ng |first1=Mark |title=COVIDSim — SEIRS-based COVID-19 Simulation Package |website=GitHub |url=https://github.com/nkymark/COVIDSim |access-date=14 December 2023 |date=24 September 2023}} – written in MATLAB{{Citation| vauthors = Ng M |title=nkymark/COVIDSim|date=2020-12-23|url=https://github.com/nkymark/COVIDSim|access-date=2021-03-09}} by Ng and Gui{{cite journal | vauthors = Ng KY, Gui MM | title = COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility | journal = Physica D: Nonlinear Phenomena | volume = 411 | pages = 132599 | date = October 2020 | pmid = 32536738 | pmc = 7282799 | doi = 10.1016/j.physd.2020.132599 | arxiv = 2004.01974 | bibcode = 2020PhyD..41132599N }}
- CovidSIM.eu{{Cite web |title=CovidSIM |url=http://covidsim.eu/|access-date=2021-03-13 |website=covidsim.eu}} – Martin Eichner, Markus Schwehm supported by University of Tübingen and sponsored by the German Federal Ministry of Education and Research.
- CovidSIM{{cite journal | vauthors = Schneider KA, Ngwa GA, Schwehm M, Eichner L, Eichner M | title = The COVID-19 pandemic preparedness simulation tool: CovidSIM | journal = BMC Infectious Diseases | volume = 20 | issue = 1 | pages = 859 | date = November 2020 | pmid = 33213360 | pmc = 7675392 | doi = 10.1186/s12879-020-05566-7 | doi-access = free }} – Schneider et al.
- CovRadar{{cite web |title=CovRadar |url=https://covradar.net/ |website=covradar.net |access-date=14 December 2023 |language=en}} – for molecular surveillance of the Corona spike protein{{cite bioRxiv| vauthors = Wittig A, Miranda F, Hölzer M, Altenburg T, Bartoszewicz JM, Dieckmann MA, Genske U, Giese SH, Nowicka M, Schiebenhoefer H, Schmachtenberg AJ | display-authors = 6 |date=2021-04-06|title=CovRadar: Continuously tracking and filtering SARS-CoV-2 mutations for molecular surveillance|biorxiv=10.1101/2021.02.03.429146v2}}{{Cite web|date=2021-02-07|title=Molecular surveillance of SARS-CoV-2 spike protein mutations using CovRadar|url=https://www.news-medical.net/news/20210207/Molecular-surveillance-of-SARS-CoV-2-spike-protein-mutations-using-CovRadar.aspx|access-date=2021-04-25|website=News-Medical.net|language=en}}
- De-Leon and Pederiva – A dynamic particle Monte Carlo algorithm based on the basic principles of statistical physics.{{cite journal | vauthors = De-Leon H, Pederiva F | title = Particle modeling of the spreading of coronavirus disease (COVID-19) | journal = Physics of Fluids | volume = 32 | issue = 8 | pages = 087113 | date = August 2020 | pmid = 32848352 | pmc = 7441410 | doi = 10.1063/5.0020565 | arxiv = 2005.10357 | bibcode = 2020PhFl...32h7113D }}{{cite journal | vauthors = De-Leon H, Pederiva F | title = Statistical mechanics study of the introduction of a vaccine against COVID-19 disease | journal = Physical Review E | volume = 104 | issue = 1 | pages = 014132 | date = July 2021 | pmid = 34412259 | doi = 10.1103/PhysRevE.104.014132 | arxiv = 2012.07306 | bibcode = 2021PhRvE.104a4132D | s2cid = 229155979 }}
- Dr. Ghaffarzadegan’s model{{Cite web|url=https://forio.com/app/navidg/covid-19-v2/index.html#dashboard~cmbo.html |title=Dr. Ghaffarzadegan's model - Simulate your university's covid-19 cases |website=forio.com |access-date=2 May 2021}}{{cite journal | vauthors = Ghaffarzadegan N | title = Simulation-based what-if analysis for controlling the spread of Covid-19 in universities | journal = PLOS ONE | volume = 16 | issue = 2 | pages = e0246323 | date = 2021-02-01 | pmid = 33524045 | pmc = 7850497 | doi = 10.1371/journal.pone.0246323 | doi-access = free | bibcode = 2021PLoSO..1646323G }}{{Cite web|title=COVID-19 simulation model creates scenarios|url=https://www.vtnews.vt.edu/content/vtnews_vt_edu/en/articles/2021/02/unirel-covid-simulation-model.html|access-date=2021-02-21|website=www.vtnews.vt.edu|language=en|archive-date=2021-03-06|archive-url=https://web.archive.org/web/20210306075649/https://www.vtnews.vt.edu/content/vtnews_vt_edu/en/articles/2021/02/unirel-covid-simulation-model.html|url-status=dead}}
- Event Horizon – COVID-19{{cite web |title=Event Horizon - COVID-19 |url=http://rocs.hu-berlin.de/corona/ |website=rocs.hu-berlin.de |access-date=14 December 2023 |language=en}} – HU Berlin based on SIR-X model{{cite journal | vauthors = Maier BF, Brockmann D | title = Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China | journal = Science | volume = 368 | issue = 6492 | pages = 742–746 | date = May 2020 | pmid = 32269067 | pmc = 7164388 | doi = 10.1126/science.abb4557 | arxiv = 2002.07572 | doi-access = free | bibcode = 2020Sci...368..742M }}
- Evolutionary AI{{cite web |title=Evolutionary AI |url=https://evolution.ml/ |website=evolution.ml |access-date=14 December 2023}} – "Non-pharmaceutical interventions (NPIs) that the AI generates for different countries and regions over time, their predicted effect."{{Cite web|title=Evolutionary AI|url=https://evolution.ml/|access-date=2021-04-26|website=evolution.ml}}{{cite arXiv| vauthors = Miikkulainen R, Francon O, Meyerson E, Qiu X, Sargent D, Canzani E, Hodjat B |date=2020-08-01|title=From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic|class=cs.NE|eprint=2005.13766}}{{Cite web|title=Can Computer Models Select the Best Public Health Interventions for COVID-19?|url=https://spectrum.ieee.org/can-computer-models-select-the-best-public-health-interventions-for-covid19|access-date=2021-04-26|website=IEEE Spectrum: Technology, Engineering, and Science News|date=5 January 2021|language=en}}
- From the index case to global spread – Dr. Siwiak, based on GLEAM framework embedding actual population densities, commute patterns and long-range travel networks.{{Cite journal |last1=Siwiak |first1=Marian |last2=Szczesny |first2=Pawel |last3=Siwiak |first3=Marlena |date=2020-07-10 |title=From the index case to global spread: the global mobility based modelling of the COVID-19 pandemic implies higher infection rate and lower detection ratio than current estimates |journal=PeerJ |language=en |volume=8 |pages=e9548 |doi=10.7717/peerj.9548 |doi-access=free |pmid=32728498 |issn=2167-8359|pmc=7357567 }}
- IHME model – Institute for Health Metrics and Evaluation COVID model
- MEmilio{{Cite journal |display-authors=6 |vauthors=Abele D, Kühn MJ, Koslow W, Rack K, Siggel M, Kleinert J, Lutz A |date=2022-01-01 |title=MEmilio - a high performance Modular EpideMIcs simuLatIOn software |url=https://github.com/DLR-SC/memilio |journal=GitHub |language=en}} – an open source high performance Modular EpideMIcs simuLatIOn software based on hybrid graph-SIR-type model{{cite journal |display-authors=6 |vauthors=Kühn MJ, Abele D, Mitra T, Koslow W, Abedi M, Rack K, Siggel M, Khailaie S, Klitz M, Binder S, Spataro L, Gilg J, Kleinert J, Häberle M, Plötzke L, Spinner CD, Stecher M, Zhu XX, Basermann A, Meyer-Hermann M |date=September 2021 |title=Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution |journal=Mathematical Biosciences |volume=339 |issue= |pages=108648 |doi=10.1016/j.mbs.2021.108648 |pmc=8243656 |pmid=34216635}} with commuter testing between regions{{cite journal | last1=Kühn | first1=Martin J. | last2=Abele | first2=Daniel | last3=Binder | first3=Sebastian | last4=Rack | first4=Kathrin | last5=Klitz | first5=Margrit | last6=Kleinert | first6=Jan | last7=Gilg | first7=Jonas | last8=Spataro | first8=Luca | last9=Koslow | first9=Wadim | last10=Siggel | first10=Martin | last11=Meyer-Hermann | first11=Michael | last12=Basermann | first12=Achim | title=Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany | journal=BMC Infectious Diseases | volume=22 | issue=1 | date=2022 | issn=1471-2334 | pmid=35379190 | pmc=8978163 | doi=10.1186/s12879-022-07302-9 | doi-access=free | page=333|medrxiv=10.1101/2021.04.23.21255995}} and vaccination strategies{{cite journal | last1=Koslow | first1=Wadim | last2=Kühn | first2=Martin J. | last3=Binder | first3=Sebastian | last4=Klitz | first4=Margrit | last5=Abele | first5=Daniel | last6=Basermann | first6=Achim | last7=Meyer-Hermann | first7=Michael | title=Appropriate relaxation of non-pharmaceutical interventions minimizes the risk of a resurgence in SARS-CoV-2 infections in spite of the Delta variant | journal=PLOS Computational Biology | volume=18 | issue=5 | date=2022-05-16 | issn=1553-7358 | pmid=35576211 | pmc=9135349 | doi=10.1371/journal.pcbi.1010054 | doi-access=free |page=e1010054| bibcode=2022PLSCB..18E0054K |medrxiv=10.1101/2021.07.09.21260257}} and agent-based models
- OpenCOVID{{cite journal | vauthors = Shattock AJ, Le Rutte EA, Dünner RP, Sen S, Kelly SL, Chitnis N, Penny MA | title = Impact of vaccination and non-pharmaceutical interventions on SARS-CoV-2 dynamics in Switzerland | journal = Epidemics | volume = 38 | issue = 7 | pages = 100535 | date = March 2022 | pmid = 34923396 | pmc = 8669952 | doi = 10.1016/j.epidem.2021.100535 | bibcode = 2021PLSCB..17E9146H }}{{cite web |url= https://github.com/SwissTPH/OpenCOVID |title=Git-repository with open access source-code for OpenCOVID. |author= |date=2022-01-31 |website=GitHub |publisher=Swiss TPH |access-date=2022-02-15}} – Swiss Tropical and Public Health Institute (Swiss TPH) – Open access individual-based transmission model of SARS-CoV-2 infection and COVID-19 disease dynamics implemented in R.
- OxCGRT{{cite web |title=COVID-19 Government Response Tracker |url=https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker |website=www.bsg.ox.ac.uk |access-date=14 December 2023 |language=en |date=18 March 2020}} – The Oxford COVID-19 Government Response Tracker{{Cite web|title=COVID-19 Government Response Tracker|url=https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker|access-date=2021-04-26|website=www.bsg.ox.ac.uk|date=18 March 2020 |language=en}}
- SC-COSMO{{cite web |title=sc-cosmo – Stanford-CIDE COronavirus Simulation MOdel |url=https://www.sc-cosmo.org/ |access-date=14 December 2023}} – Stanford-CIDE Coronavirus Simulation Model
- SDL-PAND: A digital Twin of the pandemic situation in Catalonia.{{cite web |title=SDL PAND - Mapa global |url=https://pand.sdlps.com |website=pand.sdlps.com |access-date=2023-05-20}}
- SECIR{{cite journal | last1=Khailaie | first1=Sahamoddin | last2=Mitra | first2=Tanmay | last3=Bandyopadhyay | first3=Arnab | last4=Schips | first4=Marta | last5=Mascheroni | first5=Pietro | last6=Vanella | first6=Patrizio | last7=Lange | first7=Berit | last8=Binder | first8=Sebastian C. | last9=Meyer-Hermann | first9=Michael | title=Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures | journal=BMC Medicine | volume=19 | issue=1 | date=2021 | issn=1741-7015 | pmid=33504336 | pmc=7840427 | doi=10.1186/s12916-020-01884-4 | doi-access=free | page=32|medrxiv=10.1101/2020.04.04.20053637}}{{Cite web|title=simm / covid19 / SECIR|url=https://gitlab.com/simm/covid19/secir|access-date=2021-07-05|website=GitLab|language=en}}{{Cite web|title=Report · Wiki · simm / covid19 / SECIR|url=https://gitlab.com/simm/covid19/secir/-/wikis/Report|access-date=2021-07-05|website=GitLab|language=en}}{{Cite web|title=Our research|url=https://www.helmholtz-hzi.de/en/news-events/stories/coronavirus-sars-cov-2/our-research/|access-date=2021-07-05|website=Helmholtz Centre for Infection Research|language=en}} – Model by Helmholtz Centre for Infection Research
- SEIR model on a small-world network used estimate the effect of non-pharmaceutical interventions on the structure of the transmission network{{cite journal | vauthors = Syga S, David-Rus D, Schälte Y, Hatzikirou H, Deutsch A | title = Inferring the effect of interventions on COVID-19 transmission networks | journal = Scientific Reports | volume = 11 | issue = 1 | pages = 21913 | date = November 2021 | pmid = 34754025 | pmc = 8578219 | doi = 10.1038/s41598-021-01407-y | arxiv = 2012.03846 | bibcode = 2021NatSR..1121913S }}
- SIAM's Epidemiology Collection{{cite web |title=Epidemiology Collection |url=https://epubs.siam.org/page/EpidemiologyCollection |website=epubs.siam.org |date= |access-date=}}
- SIRSS model that combines the dynamics of social stress with classical epidemic models.{{cite journal |vauthors=Kastalskiy IA, Pankratova EV, Mirkes EM, Kazantsev VB, Gorban AN |date=November 2021 |title=Social stress drives the multi-wave dynamics of COVID-19 outbreaks |journal=Scientific Reports |volume=11 |issue=1 |pages=22497 |arxiv=2106.08966 |bibcode=2021NatSR..1122497K |doi=10.1038/s41598-021-01317-z |pmc=8602246 |pmid=34795311 |doi-access=free}} Social stress is described by the tools of social physics.
- Smart Investment of Virus RNA Testing Resources to Enhance Covid-19 Mitigation{{cite web |title=COVID-19 Simulation Tool |url=https://corona-lab.ch |website=corona-lab.ch |access-date=28 April 2021}}{{cite journal | last1=Gorji | first1=Hossein | last2=Arnoldini | first2=Markus | last3=Jenny | first3=David F. | last4=Hardt | first4=Wolf-Dietrich | last5=Jenny | first5=Patrick | title=Dynamic modelling to identify mitigation strategies for the COVID-19 pandemic | journal=Swiss Medical Weekly | volume=151 | issue=1718 | date=2021-05-04 | issn=1424-3997 | doi=10.4414/smw.2021.20487 | page=w20487| pmid=33945149 |medrxiv=10.1101/2020.11.30.20239566| hdl=20.500.11850/484250 | hdl-access=free }}
- Youyang Gu COVID model
= Genome databases =
Several of these models make use of genome databases, including the following:
= Consortia, research clusters, other collections =
- CDC list of Forecast Inclusion and Assumptions{{cite web |title=CDC list of Forecast Inclusion and Assumptions |url=https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/forecasts-cases.htmll |website=www.cdc.gov/coronavirus}} – large list with different models, etc.
- CORSMA – EU consortium (COVID-19-Outbreak Response combining E-health, Serolomics, Modelling, Artificial Intelligence and Implementation Research){{Cite web|title=CORESMA|url=https://www.coresma.eu/en/|access-date=2021-07-05|website=CORESMA|language=en}}
- COVID-19 Forecast Hub{{cite web |title=Home - COVID 19 forecast hub |url=https://covid19forecasthub.org/ |website=covid19forecasthub.org |access-date=14 December 2023}} – Serves as a central repository of forecasts and predictions from over 50 international research groups.{{Cite web|date=2020-05-01|title=Where The Latest COVID-19 Models Think We're Headed — And Why They Disagree|url=https://projects.fivethirtyeight.com/covid-forecasts/|archive-url=https://archive.today/20200505032050/https://projects.fivethirtyeight.com/covid-forecasts/|url-status=dead|archive-date=May 5, 2020|access-date=2021-02-21|website=FiveThirtyEight|language=en|vauthors=Best R, Boice J}}{{Cite web|title=Home - COVID 19 forecast hub|url=https://covid19forecasthub.org/|access-date=2021-02-21|website=covid19forecasthub.org|language=en}}
- Nextstrain – Open-source project to harness the scientific and public health potential of pathogen genome data{{Cite web|title=Nextstrain|url=https://nextstrain.org/|access-date=2021-04-26|website=nextstrain.org|language=en}}
- See also Nextstrain SARS-CoV-2 resources{{cite web |title=Nextstrain |url=https://nextstrain.org/sars-cov-2 |website=nextstrain.org |access-date=14 December 2023 |language=en}}
- SIMID{{cite web |title=SIMID – Simulation Models of Infectious Diseases |url=https://www.simid.be/ |access-date=14 December 2023 |language=en}} – Simulation Models of Infectious Diseases – Belgium research consortium
- RAMP – Rapid Assistance in Modelling the Pandemic{{Cite web|title=Rapid Assistance in Modelling the Pandemic: RAMP {{!}} Royal Society|url=https://royalsociety.org/topics-policy/Health%20and%20wellbeing/ramp/|access-date=2021-03-09|website=royalsociety.org|language=en-gb}} (UK)
- UT Austin COVID-19 Modeling Consortium{{cite web |title=UT COVID-19 Modeling Consortium |url=https://covid-19.tacc.utexas.edu/ |website=covid-19.tacc.utexas.edu |access-date=14 December 2023}}
- [https://www.frontiersin.org/research-topics/13791/computational-approaches-to-foster-innovation-in-the-treatment-and-diagnosis-of-infectious-diseases Computational Approaches to Foster Innovation in the Treatment and Diagnosis of Infectious Diseases] by Frontiers
= Vaccination monitors, models or dashboards =
Note: The following (additional) resources are mostly based on actual data, not simulation. They might include predictive features, e. g. vaccination rate estimation, but in general are not based on theoretical or modeling grounds as the main list of this article. Nonetheless, forecasting remains important.{{Cite web|last=CDC|date=2020-02-11|title=Coronavirus Disease 2019 (COVID-19) - COVID-19 Forecasting: Background Information|url=https://www.cdc.gov/coronavirus/2019-ncov/science/forecasting/forecasting.html|access-date=2021-10-03|website=Centers for Disease Control and Prevention|language=en-us}} (See for example the [https://covid19forecasthub.org/ COVID-19 Forecast Hub]){{Cite web|title=About the Hub - COVID 19 forecast hub|url=https://covid19forecasthub.org/doc/|access-date=2021-10-03|website=covid19forecasthub.org|language=en}}
- COVID-19 Dashboard{{cite web |title=COVID-19 Map |url=https://coronavirus.jhu.edu/map.html |website=Johns Hopkins Coronavirus Resource Center |access-date=14 December 2023 |language=en}} - Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU){{Cite web|title=COVID-19 Map|url=https://coronavirus.jhu.edu/map.html|access-date=2021-04-26|website=Johns Hopkins Coronavirus Resource Center|language=en}}{{cite journal | vauthors = Dong E, Du H, Gardner L | title = An interactive web-based dashboard to track COVID-19 in real time | language = English | journal = The Lancet. Infectious Diseases | volume = 20 | issue = 5 | pages = 533–534 | date = May 2020 | pmid = 32087114 | pmc = 7159018 | doi = 10.1016/S1473-3099(20)30120-1 }}
- [https://www.cdc.gov/vaccines/imz-managers/coverage/covidvaxview/index.html COVIDVaxView] by the CDC{{Cite web|date=2021-09-23|title=COVIDVaxView {{!}} CDC|url=https://www.cdc.gov/vaccines/imz-managers/coverage/covidvaxview/index.html|access-date=2021-10-03|website=www.cdc.gov|language=en-us}}
- Datahub Novel Coronavirus 2019 dataset{{Cite web|last1=Datopian|last2=Open Knowledge International|title=Novel Coronavirus 2019|url=https://datahub.io/core/covid-19 |access-date=2021-05-02|website=DataHub.io|language=en}}{{Citation|title=datasets/covid-19|date=2021-05-02|url=https://github.com/datasets/covid-19|publisher=Data Packaged Core Datasets at GitHub|access-date=2021-05-02}} - COVID-19 dataset Coronavirus disease 2019 (COVID-19) time series listing confirmed cases, reported deaths and reported recoveries.
- Impfdashboard.de - Germany's vaccination monitor{{Cite web | author = Bundesministerium für Gesundheit |title=Das offizielle Dashboard zur Impfkampagne der Bundesrepublik Deutschland|url=https://impfdashboard.de/|access-date=2021-06-07|website=impfdashboard.de|language=de}}
- Simulation der COVID19-Impfkampagne{{cite web |title=Simulation der COVID19-Impfkampagne |trans-title= |url=https://www.zidatasciencelab.de/cov19vaccsim/ |website=www.zidatasciencelab.de |access-date=14 December 2023}} - Monitor for vaccination-campaign in Germany by Zi Data Science Lab
- The Institute for Health Metrics and Evaluation (IHME) COVID-19 Projections{{cite web |title=IHME {{!}} COVID-19 Projections |url=https://covid19.healthdata.org/global?view=total-deaths&tab=trend |website=Institute for Health Metrics and Evaluation |access-date=14 December 2023 |language=en}}
- See also Institute for Health Metrics and Evaluation COVID model
Models with less scientific backing
The following models are purely for educational purposes only.
- Cellular Defense Automata model{{cite web |title=Cellular Defense Automata model |url=https://thememeticist.github.io/Cellular-Defense-Automata/ |website=thememeticist.github.io}}
- Overview of SARS-CoV-2 variants and mutations that are of interest{{cite web |title=CoVariants (CoVariants] - Overview of SARS-CoV-2 variants and mutations that are of interest) |url=https://covariants.org/ |website=covariants.org |access-date=14 December 2023 |language=en}}
- Covid-19 Simulator{{cite web |title=Covid-19 Simulator |url=https://www.coronasimulator.com/ |website=www.coronasimulator.com |access-date=28 April 2021}}
- COVID19: Top 7 - A curated list{{cite web |last1=Prabowo |first1=Arian |title=COVID19: Top 7 online interactive simulations, curated. |url=https://towardsdatascience.com/covid19-top-7-online-interactive-simulations-curated-fa4282889875 |website=Medium |access-date=2021-02-21 |language=en |date=3 May 2020}} posted on Medium
- github.com/topics: covid-19{{cite web |title=Build software better, together (topics/covid-19) |url=https://github.com/topics/covid-19 |website=GitHub |access-date=14 December 2023 |language=en}}
- ISEE Systems COVID-19 Simulator{{cite web |title=COVID-19 Simulator |url=https://exchange.iseesystems.com/public/isee/covid-19-simulator |website=exchange.iseesystems.com |access-date=14 December 2023}}
- nCoV2019.live{{cite web |title=Coronavirus Dashboard |url=https://ncov2019.live |website=ncov2019.live |access-date=14 December 2023 |language=en}} - "Numbers you need at a quick glance" by Schiffmann/Conlon
- cov19.cc- by Conlon{{Cite web|url=https://cov19.cc/ |title=cov19.cc |access-date=18 August 2023}}
- Simul8 - COVID-19 Simulation Resources{{Cite web |title=COVID-19 Simulation Resources |url=https://www.simul8.com/covid-19-simulation-resources |access-date=2020-02-21}}
- Simulating coronavirus with the SIR model{{Cite web |url=https://fatiherikli.github.io/coronavirus-simulation/ |title=Simulating coronavirus with the SIR model |work=fatiherikli.github.io |access-date=2021-02-21 |archive-date=2021-04-19 |archive-url=https://web.archive.org/web/20210419152720/https://fatiherikli.github.io/coronavirus-simulation/ |url-status=dead }}
- Virus Spread Simulation{{cite web |title=Covid-19 Spread Simulation |url=https://c19model.com/ |website=c19model.com |access-date=14 December 2023}}
Trainings and other resources
- [https://www.coursera.org/specializations/infectious-disease-modelling Infectious Disease Modelling Specialization] - provided on Coursera by Imperial College London
- [https://aws.amazon.com/blogs/machine-learning/introducing-the-covid-19-simulator-and-machine-learning-toolkit-for-predicting-covid-19-spread/ Introducing the COVID-19 Simulator and Machine Learning Toolkit for Predicting COVID-19 Spread] - AWS Machine Learning Blog
See also
References
{{Reflist}}
Further reading
= Articles =
{{refbegin}}
- {{cite journal | vauthors = Adam D | title = Special report: The simulations driving the world's response to COVID-19 | journal = Nature | volume = 580 | issue = 7803 | pages = 316–318 | date = April 2020 | pmid = 32242115 | doi = 10.1038/d41586-020-01003-6 | s2cid = 214771531 | bibcode = 2020Natur.580..316A | doi-access = }}
- {{cite web | url = https://sites.math.washington.edu/~jarod/wxml2020.html | title = WXML 2020 covid-modeling learning guide | vauthors = Alper J | work = Department of Mathematics | publisher = University of Washington | location = Seattle, Washington }}
- {{cite journal | vauthors = Fuller J | title = What are the COVID-19 models modeling (philosophically speaking)? | journal = History and Philosophy of the Life Sciences | volume = 43 | issue = 2 | pages = 47 | date = March 2021 | pmid = 33770267 | pmc = 7994354 | doi = 10.1007/s40656-021-00407-5 }}
- {{cite web | vauthors = Roberts M, Driggs D, Selby I, Sala E, Schönlieb CB | url = https://sinews.siam.org/Details-Page/fighting-a-pandemic-with-medical-imaging-and-machine-learning-lessons-learned-from-covid-19 | title = Fighting a Pandemic with Medical Imaging and Machine Learning: Lessons Learned from COVID-19 | work = SIAM News | date = 1 June 2021}}
{{refend}}
= Books =
{{refbegin}}
- {{Cite book | vauthors = Basavarajaiah D, Murthy BN |url=https://www.taylorfrancis.com/books/9781003204794 |title=COVID Transmission Modeling: An Insight into Infectious Diseases Mechanism |date=2022-04-25 |publisher=Chapman and Hall/CRC |isbn=978-1-00-320479-4 |edition=1 |location=Boca Raton |doi=10.1201/9781003204794 |accessdate=2022-07-23}}
- {{cite book |url=https://www.taylorfrancis.com/books/9781003142584 |title=Computational Modelling and Imaging for SARS-CoV-2 and COVID-19 |vauthors= Prabha S, Karthikeyan P, Kamalanand K, Selvaganesan N |date=7 July 2021 |publisher=CRC Press |isbn=978-1-00-314258-4 |edition=1st |doi=10.1201/9781003142584|s2cid=237802484 }}
- {{Cite book |url=https://link.springer.com/10.1007/978-3-030-72834-2 |title=Modeling, Control and Drug Development for COVID-19 Outbreak Prevention |date=2022 |publisher=Springer International Publishing | veditors = Azar AT, Hassanien AE |isbn=978-3-030-72833-5 |series=Studies in Systems, Decision and Control |volume=366 |location=Cham |doi=10.1007/978-3-030-72834-2 |s2cid=240429840 |accessdate=2022-07-23}}
- {{Cite book | doi = 10.1007/978-3-030-79753-9 |title=Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis |date=2022 |publisher=Springer International Publishing | veditors = Pani SK, Dash S, dos Santos WP, Bukhari SA, Flammini F |isbn=978-3-030-79752-2 |location=Cham |s2cid=245119014 }}
- {{Cite book |last=Martcheva |first=Maia |url=http://link.springer.com/10.1007/978-1-4899-7612-3 |title=An Introduction to Mathematical Epidemiology |date=2015 |publisher=Springer US |isbn=978-1-4899-7611-6 |series=Texts in Applied Mathematics |volume=61 |location=Boston, MA |doi=10.1007/978-1-4899-7612-3 |accessdate=2022-09-12}}
- {{Cite book |last=Li |first=Michael Y. |url=http://link.springer.com/10.1007/978-3-319-72122-4 |title=An Introduction to Mathematical Modeling of Infectious Diseases |date=2018 |publisher=Springer International Publishing |isbn=978-3-319-72121-7 |location=Cham |doi=10.1007/978-3-319-72122-4 |accessdate=2022-09-12}}
{{refend}}