About the model

What is mathematical modeling and how can it help control the Covid-19 pandemic?

T

he model framework was developed by researchers from the University of Oxford and Cornell. The model continues to evolve based on ideas from all members of the Consortium and implemented by the CoMo technical team. The CoMo Consortium has developed an age-structured, compartmental SEIRS (susceptible-exposed-infectious-recovered-susceptible) model to estimate the trajectory of COVID-19 based on different scenarios, and assess the potential impact of the various behavioural change strategies as well as treatment and vaccines, when they become available. A user-friendly interface enables widespread use, while dashboards and visualisation tools allow policymakers to see changes in real time.


How can the model
be used?

The outputs of the model can enable policymakers to make data based decisions to inform the public health response, such as:

1

The impact of the various mitigation strategies on transmission of the virus and mechanisms for “flattening of the curve” and which interventions will be more effective in their specific contexts.

2

The anticipated demand for hospital and ICU beds at various levels of the health system.

3

The quantity of tests, personal protective equipment, ventilators and other supportive tools needed in treating the diagnosis and treatment of patients, and the impact of vaccines.

4

The cost of equipment needed.


Continuous improvement

The model is continually being updated and refined based on feedback from members of the CoMo Consortium and the developing analytical needs. We have incorporated as much complexity as possible within a relatively simple framework, thus ensuring the Application’s functionality remains uncompromised.

The Application will be updated using the most parsimonious version of the model that we believe will be most suitable for all users. We intend to keep updates to a minimum and will in due course document all updates to the model in any upcoming releases. Any discordance in expected model behaviour between two model versions will be thoroughly investigated and critically presented to users.

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