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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).
IRD, the developers of GAMA and their partners in Vietnam have been supporting Vietnamese authorities in their fight against the COVID-19 pandemic by developing a complete modelling platform named COMOKIT, which aims at assessing and comparing mitigation policies and interventions against the spread of the virus.
COMOKIT has now become completely generic and is available from this GitHub repository. A position paper is also available.
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 .
- The 60%-90% Issue: researchers working within SIMASSOC have tested the effects of a tracing app, finding that it becomes effective only with 90% coverage. Previous simulations (with the Imperial College model?) had a much more attainable figure of 60%. (source: Frank Dignum, invited presentation at MABS 2020)
The AU/NZ model and elimination
A group of Australian researchers led by Jason Thompson created a NetLogo model that points out to a good chance of obtaining elimination of the virus, as opposed to containment. Check the paper on SSRN and the model on GitHub
The findings highlight that it is possible to eliminate SARS-CoV-2 transmission within these two countries.
However, a high probability of elimination is contingent on maintaining current strict physical distancing restrictions for at least two months, before relaxing these restrictions and relying on border control, surveillance and contact tracing.
It is plausible that this period could be reduced with the implementation (as is happening in both NZ and Australia) of much improved testing and contact tracing (e.g. with digital technologies).
A collection of Systems Dynamics Society resources
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
- A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment - code available at https://github.com/covid19-model
Ornstein-Uhlenbeck Pandemic model with results posted at https://www.swarmprediction.com/about.html - source code available at https://github.com/microprediction/pandemic and supports crowd sourced simulation data collection via
docker run xtellurian/pandemic
- Paul Smaldino’s social distancing model
- Annamaria Berea’s CoViD19 model
- epiDEM Travel and control
- Social Interventions model by Jen Badham
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.
- How much variation should one assume in the virus itself? In a discussion on the simsoc list, Klaus Jaffe suggests that “infected hosts that die fast, disperse less infecting agents than hosts that survive, which disperse less virulent strains”. However, other researchers doubt it would apply to the current virus (“The 1918 … pandemic… went through three waves, and the second was considerably more virulent than the other two.”
- We have results from the large scale study of the town of Vo in Italy.
- 43.2% (95% CI 32.2-54.7%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic.
- The mean serial interval was 6.9 days (95% CI 2.6-13.4)
- no statistically significant difference in the viral load of symptomatic versus asymptomatic infections
- Contact tracing revealed that most new infections in the second survey were infected in the community before the lockdown or from asymptomatic infections living in the same household.
- 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.
- Unified COVID-19 Data Lake Available to Global Research Community. Made available by C3.ai
- Data science resources from the Academic Data Science Alliance
- Open Science Framework’s Coronavirus Collection
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 / Volunteer Opportunities
- 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
- Crowd Fight COVID-19 sends out a google form every few days with posts from researchers worldwide looking for help, recommendations, networking (if you know someone that could help) etc.
- The National Academies is hosting online meetings on how academics can get involved
- The Just One Giant Lab has a number of open source projects, from engineering to data: