COVID-19 and the ‘what if machine’: How simulations and models help predict pandemic spread

COVID-19 and the ‘what if machine’: How simulations and models help predict pandemic spread


As the global response to COVID-19 unfolds in real time, public health officials are urging unprecedented measures in order to “flatten the curve.” But how do we know which actions are likely to slow the spread, and by how much?

The answer is pandemic models, and U of T Engineering professor Dionne Aleman (MIE) has first-hand experience in building these.

Aleman is an expert in the field of operations research, focusing primarily on applications in human health. Ten years ago, she created a model of a hypothetical pandemic and used it to explore how factors such as demographic variations in transmission rates or mitigation strategies affected the demand on health care services.

“These models can be used as ‘what if machines’ to help public health officials answer questions like ‘Is strategy A more effective than strategy B?’ or ‘Will this strategy have unexpected consequences on health-care providers?’” explains Aleman.

Writer Liz Do spoke to Aleman to learn more about her research, and how simulations and modelling play a vital role in pandemic preparedness.

Read the interview here:
news.engineering.utoronto.ca/covid-19-and-the-what-if-machine-how-simulations-and-models-help-predict-pandemic-spread/