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Epidemiological modelling to provide data based COVID-19 policy options

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We are pleased to announce a collaboration between Oxford Policy Management, the University of Oxford, and the Covid-19 International Modelling Consortium. We aim to assist policy makers make evidence based decisions to contain the spread of COVID-19 based on mathematical and epidemiological modelling. Our group brings together top academic modellers, global public health specialists, local epidemiologists, and expert project management systems to provide whole package solutions.

The Challenge

COVID-19 is spreading across the world at an alarming rate. Timely decisions made by Governments in affected countries including Ministries of Health are essential to curb the epidemiological and economic impact on societies. At present, as there are no treatments for the disease or vaccines to prevent COVID-19, only behavioural change strategies (self-isolation for symptomatic individuals, increased hand hygiene, social distancing, working from home and school closure) are possible.

Our Approach

The CoMo consortium has developed an age-structured, compartmental Susceptible-Exposed-Infectious-Recovery (SEIR) 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 policy makers to see changes in real time.

Outcomes and wider impacts

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

  • 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;
  • the anticipated demand for hospital and ICU beds at various levels of the health system;
  • the quantity of tests, personal protective equipment, ventilators and other supportive tools needed in treating the diagnosis and treatment of patients, and
  • the cost of equipment needed.

The model can be useful in any context and country. For example, in Cameroon, our analysis has informed discussions of mitigation measures and health system response measures needed to curb the trajectory of the epidemic. In New York City, modellers from the CoMo consortium supported the forecasting of the number of hospital and ICU beds and estimated of the supplies and equipment that would be needed to respond to the surge in patients.