KAUST researchers, led by Prof. Ibrahim Hoteit, have developed an advanced epidemic model that accounts for biological uncertainties and real-time data to enhance virus transmission predictions. The model builds on the traditional SEIR (susceptible-exposed-infected-recovered) framework, incorporating additional compartments such as quarantined, deaths, and vaccinated, to provide a more comprehensive view of the epidemic.

The model continuously refines its predictions by integrating a data assimilation process and uncertainty quantification, offering more reliable forecasts. Validated against real data from Saudi Arabia, it demonstrated reliable predictions up to 14 days in advance, highlighting its potential for informing effective public health responses.

This approach, initially designed for COVID-19 forecasting in Saudi Arabia, can be adapted to predict the spread of future pandemics.

🔗 Read more on KAUST Discovery

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