Simulator of interventions to address COVID-19

Simulator of interventions to address COVID-19

Coping with the COVID-19 epidemic requires a clear policy. There is a range of policy steps that can be taken to allow life alongside COVID-19. Incorrectly implementing or scheduling such measures can result in harsh prices in human life, economic aspects, and many other parameters.
We believe that the various entities that offer `exit strategies’ are based on data. However, the mathematical models they use and most of the data on which they are based are not transparent to the public. These models are mediated by the experts and readers of the reports can only see the bottom line. The one which supports the report’s conclusion. The lack of transparency does not allow a discursive dialogue between experts and the public and does not allow for a proper comparison of the various options on the table.

Will we experience a second wave? At what magnitude? How can it be suppressed?

To be truthful – we don’t have full answers to the above questions.  However, as time passes, the fog clears out, and we can better understand how the different observations combine together.  In the last few months, we have worked on the development of a simulator to understand and compare the short- and long-term impacts of various interventions.  The simulator helps understand the impact of the different factors, and highlights which information can change the arising picture.  The aim of the simulation is to guide the planning of a policy to address COVID-19, while putting emphasis on the following points:

• Full transparency: The mathematical model, parameters, and all deliverables including the software code will be open to the public.
• Availability: The model is accessible by a graphical interface. No special expertise or product licenses are required to run it.
• Joint teams with epidemiologists, economists, and policy planners.  However, we do not have an expert in mathematical epidemiology on board.

What does the tool give and what does it not?
The graphical interface allows for a simple exploration of the various possibilities while gaining an immediate sense of how different measures affect the long-term results in different parameters.  However, the tool in the current format does not contain any statistical elements, it does not perform sensitivity analysis and does not display error bars. As such, it is a convenient tool for examining and comparing different policy measures and for highlighting top choices. It does not substitute for a deeper examination of the emerging possibilities.

So, what is the bottom line?

Two parameters that have a high impact on the epidemic development arise while working with the simulator:

1. The public’s cooperation and compliance with healthcare regulations. Public compliance with the guidelines overwhelmingly influences the effectiveness of all policy measures examined. For example, the simulator shows that raising the portion of the public that is compliant by 10% is as effective as isolating all those aged 70 and over for three months and much more effective than closing the education system for four months. The simulator shows that even under optimal conditions, without very high compliance, the contact tracing system aimed at interrupting infection chains will not be effective enough to stop an outbreak. Since no (logical) regulation can be drafted for any situation and no regulation can be enforced, the public’s cooperation beyond blind obedience is required.
2. Percentage of confirmed invectives  There is uncertainty about the percentage of confirmed invectives or the number of unknown recoverers. The MALAL Advisory Committee acted upon the assumption that the vast majority of infected persons were identified, i.e., an order of magnitude of 0.2% of the population is currently carrying antibodies to the disease. The first serological survey in Israel indicates a significantly higher percentage, on the order of 2%. The percentage of confirmed invectives dramatically affects morbidity data. The simulator also shows the efficiency of a contact tracing system greatly decreases as the percentage of unknown invectives is higher, and that in a likely scenario contact tracing will not be sufficiently effective to inhibit an outbreak.

First glance and Simulator download

How can you help us? 

We would be happy to talk and cooperate with students and researchers. We are also considering upgrading the interface and looking for web developers and graphic designers.

Our email is:  simulator@technion.ac.il