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Curated list of coronavirus modeling resources

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Imperial College ABM Model

Code for plotting results?
Model source code. The code is infamously complicated and there is abundant bug discussion.

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).

The COMOKIT initiative by IRD and the GAMA team

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

R/Shiny

Python

NetLogo

Calibration / Validation

Data Sources

Monitoring applications

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

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