Editing Agent-based model (section)

=== In epidemiology ===
Agent-based models now complement traditional [[Compartmental models in epidemiology|compartmental]] models, the usual type of epidemiological models. ABMs have been shown to be superior to compartmental models in regard to the accuracy of predictions.<ref>{{Cite journal|last1=Eisinger|first1=Dirk|last2=Thulke|first2=Hans-Hermann|date=2008-04-01|title=Spatial pattern formation facilitates eradication of infectious diseases|journal=The Journal of Applied Ecology|volume=45|issue=2|pages=415–423|doi=10.1111/j.1365-2664.2007.01439.x|issn=0021-8901|pmc=2326892|pmid=18784795|bibcode=2008JApEc..45..415E }}</ref><ref>{{Cite book|url=https://press.princeton.edu/books/hardcover/9780691190822/agent-based-and-individual-based-modeling|title=Agent-Based and Individual-Based Modeling|date=2019-03-26|isbn=978-0-691-19082-2|language=en|last1=Railsback|first1=Steven F.|last2=Grimm|first2=Volker|publisher=Princeton University Press |access-date=October 19, 2020|archive-date=October 24, 2020|archive-url=https://web.archive.org/web/20201024163738/https://press.princeton.edu/books/hardcover/9780691190822/agent-based-and-individual-based-modeling|url-status=live}}</ref> Recently, ABMs such as [[CovidSim]] by epidemiologist [[Neil Ferguson (epidemiologist)|Neil Ferguson]], have been used to inform public health (nonpharmaceutical) interventions against the spread of [[Severe acute respiratory syndrome coronavirus 2|SARS-CoV-2]].<ref>{{Cite journal|last=Adam|first=David|date=2020-04-02|title=Special report: The simulations driving the world's response to COVID-19|journal=Nature|language=en|volume=580|issue=7803|pages=316–318|doi=10.1038/d41586-020-01003-6|pmid=32242115|bibcode=2020Natur.580..316A|s2cid=214771531|doi-access=}}</ref> Epidemiological ABMs have been criticized for simplifying and unrealistic assumptions.<ref>{{Cite journal|last1=Sridhar|first1=Devi|last2=Majumder|first2=Maimuna S.|date=2020-04-21|title=Modelling the pandemic|url=https://www.bmj.com/content/369/bmj.m1567|journal=BMJ|language=en|volume=369|pages=m1567|doi=10.1136/bmj.m1567|issn=1756-1833|pmid=32317328|s2cid=216074714|doi-access=free|access-date=October 19, 2020|archive-date=May 16, 2021|archive-url=https://web.archive.org/web/20210516061544/https://www.bmj.com/content/369/bmj.m1567|url-status=live|url-access=subscription}}</ref><ref>{{Cite journal|last1=Squazzoni|first1=Flaminio|last2=Polhill|first2=J. Gareth|last3=Edmonds|first3=Bruce|last4=Ahrweiler|first4=Petra|last5=Antosz|first5=Patrycja|last6=Scholz|first6=Geeske|last7=Chappin|first7=Émile|last8=Borit|first8=Melania|last9=Verhagen|first9=Harko|last10=Giardini|first10=Francesca|last11=Gilbert|first11=Nigel|date=2020|title=Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action|url=http://jasss.soc.surrey.ac.uk/23/2/10.html|journal=Journal of Artificial Societies and Social Simulation|volume=23|issue=2|pages=10|doi=10.18564/jasss.4298|s2cid=216426533|issn=1460-7425|doi-access=free|access-date=October 19, 2020|archive-date=February 24, 2021|archive-url=https://web.archive.org/web/20210224024334/http://jasss.soc.surrey.ac.uk/23/2/10.html|url-status=live|hdl=10037/19057|hdl-access=free}}</ref> Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases when ABMs are accurately calibrated.<ref>{{Cite journal|last1=Maziarz|first1=Mariusz|last2=Zach|first2=Martin|date=2020|title=Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal|url= |journal=Journal of Evaluation in Clinical Practice|language=en|volume=26|issue=5|pages=1352–1360|doi=10.1111/jep.13459|issn=1365-2753|pmc=7461315|pmid=32820573}}</ref> The ABMs for such simulations are mostly based on [[synthetic population]]s, since the data of the actual population is not always available.<ref>{{cite journal |last1=Manout |first1=O. |last2=Ciari |first2=F. |title=Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal With an Agent-Based Approach |journal=Frontiers in Built Environment |date=2021 |volume=7 |doi=10.3389/fbuil.2021.654279 |url=https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/pt/covidwho-1346397 |language=en|doi-access=free }}</ref>

{| class="wikitable"
|+ Examples of ABM use in epidemiology
|-
! Program !! Year !! Citation !! Description
|-
| Covasim || 2021 ||<ref>{{Citation |last1=Kerr |first1=Cliff |last2=Stuart |first2=Robyn |display-authors=1 |year=2021 |title=Covasim: an agent-based model of COVID-19 dynamics and interventions |work=medRxiv |volume=17 |issue=7 |pages=e1009149 |doi=10.1371/journal.pcbi.1009149 |doi-access=free |pmid=34310589 |pmc=8341708 |bibcode=2021PLSCB..17E9149K }}</ref> || SEIR model implemented in Python with an emphasis on features for studying the effects of interventions.
|-
| OpenABM-Covid19 || 2021 ||<ref>{{Citation |last1=Hinch |first1=Robert |last2=Probert |first2=William |display-authors=1 |year=2021 |title=OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing |journal=PLOS Computational Biology |volume=17 |issue=7 |pages=e1009146 |doi=10.1371/journal.pcbi.1009146 |pmid=34252083 |pmc=8328312 |bibcode=2021PLSCB..17E9146H |doi-access=free }}</ref> || Epidemic model of the spread of COVID-19, simulating every individual in a population with both R and Python interfaces but using C for heavy computation.
|-
| OpenCOVID || 2021 ||<ref>{{Citation |last1=Shattock |first1=Andrew |last2=Le Rutte |first2=Epke |last3=Duenner |first3=Robert |display-authors=2 |year=2021 |title=Impact of vaccination and non-pharmaceutical interventions on SARS-CoV-2 dynamics in Switzerland |journal=Epidemics |volume=38 |issue=7 |pages=100535 |doi=10.1016/j.epidem.2021.100535 |pmid=34923396 |pmc=8669952 |bibcode=2021PLSCB..17E9146H }}</ref><ref>{{cite web |url=https://github.com/SwissTPH/OpenCOVID |title=Git-repository with open access source-code for OpenCOVID. |author=<!--Not stated--> |date=2022-01-31 |website=GitHub |publisher=Swiss TPH |access-date=2022-02-15 |archive-date=February 15, 2022 |archive-url=https://web.archive.org/web/20220215120617/https://github.com/SwissTPH/OpenCOVID |url-status=live }}</ref> || An individual-based transmission model of SARS-CoV-2 infection and COVID-19 disease dynamics, developed at the [[Swiss Tropical and Public Health Institute]].
|}