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Imperial College ABM Model
Report on the Imperial College ABM model. The report makes a case in favor of suppression (making it so that R_0<1) against mitigation (“slowing but not necessarily stopping epidemic spread”) “Mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems… overwhelmed many times over”
- the report has been criticized in the review from Nassim Taleb et al., that states
- “… conclusions that there will be resurgent outbreaks are wrong.”
- “They ignore the possibility of superspreader events in gatherings by not including the fat tail distribution of contagion in their model”
- " a multiscale approach accelerates response efforts, reduces social impacts, allows for relaxing restrictions in areas earlier that are less affected, enables uninfected areas to assist in response in the ares that are infected, and is a much"
- details on the Imperial College ABM can be found in the supporting information of their PNAS 2008 paper. (source).
New England Complex Systems Institute Coronavirus initiatives
The SIMASSOC initiative
An agent-based model, in NetLogo, reporting on two possible interventions (testing, schools) providing also country comparison and economic scenario. Code available on github .
Systems Dynamics Society resources
Meta COVID-19 discussion
Politics of COVID 19 meta list: https://the-syllabus.com/politics-of-covid19-readings-part1/
Article in WIRED on the challenges of modeling and policy making: https://www.wired.com/story/the-mathematics-of-predicting-the-course-of-the-coronavirus/
- remarks on how the agent-based community regrets not having organized a response team (because we know the limits of the models)
- discusses and compares two models, the “Washington Post” (simple) and the Imperial College model (descriptive).
- Modeling tools related to COVID-19: https://threader.app/thread/1239072817678823425
- Marco’s thread on Harry Steven’s flattening the curve model published in the Washington Post: https://twitter.com/janssen_marco/status/1239265735219671040
Current Models related to COVID-19
- Alison Hill: https://alhill.shinyapps.io/COVID19seir/
- Kucharski et al. has published a work-in-progress stochastic SEIR model at https://github.com/adamkucharski/2020-ncov
- Paul Smaldino’s social distancing model
- Annamaria Berea’s CoViD19 model
- epiDEM Travel and control
Calibration / Validation
- Melinda Mills’ and co-authors “Demographic science aids in understanding the spread and fatality rates of COVID-19”. This paper examines the role of age structure in deaths thus far in Italy and South Korea.
- Li et al. Science paper, “Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)” uses observations from China in a dynamic metapopulation model (what is it?) for the estimate of undocumented infections. The estimate is a staggering 86%.
- Projections on resources needed and number of deceased in the US: COVID-19 Projections from The Institute for Health Metrics and Evaluation (IHME) (University of Washington). Paper here.
- Allen Institute’s COVID-19 Open Research Dataset (CORD-19)
- COVID-19 Data Repositories List (curated by Arizona State University, University of Arizona, Northern Arizona University)
- Cuebiq, an intelligence and measurement company, is providing evidence of mixing through large-scale phone tracking. This kind of individual traces could be precious for simulation calibration.
- 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE. Updated daily, csv tables with Confirmed Deaths Recovered Active values for geographical location.
Several IT groups are trying to come out with privacy-preserving applications for monitoring and community alerts. Data extracted from those apps (when and if possible) could inform simulation. Most (all?) of them use the idea of multiple, anonymous IDs.
- Covidalert: Anonymously monitor your interactions and find out if you have been exposed to the virus, with no GPS tracking. Open source. “DOESN’T track users’ GPS positions.DOESN’T need any login.DOESN’T collect any privat or sensible data, such as name, surname, mobile number.DOESN’T shows on a public map your health status together with the locations you visited or the place you live in.”
Hackathons / Competitions
- DevPost COVID-19 Global Hackathon
- Kaggle competition on the CORD-19 research dataset: https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
- HPC resources for COVID-19 (USA based): https://covid19-hpc.mybluemix.net/ with a call for research proposals to XSEDE