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Mathematical Modelling And The COVID-19 Pandemic

Travis Lux

Some scientists and health experts are racing to find treatments and to develop a vaccine to stop the deadly COVID-19 pandemic. And others are saving lives in a different way, by developing mathematical models.

But what is a mathematical model, and how can it save lives?
A mathematical model works like a weather forecast. Based on things we know about COVID-19 today, a model can predict how many people it will affect, where, and when. Models also help us predict how well different mitigation strategies like face masks will work.

LSU Assistant Professor of Biological Science Tad Dallas developed one such model by combining novel statistical techniques and a large dataset on pathogen biogeography, which required help from colleagues knowledgeable in community ecology and epidemiology.

"By combining aspects of community ecology into the study of human infectious disease, we were able to gain some insight into the distribution of pathogens at a global scale," Dallas said. "And to apply ideas across subfields, it helps to have a great team of collaborators from somewhat different fields, as I definitely found in Colin Carlson and Timothée Poisot."

But how do scientists accurately model a brand new virus like the new coronavirus?
It took experts some time to realize that people without any symptoms were spreading the virus to others. Only then did their models show that everyone should be wearing a face mask in public. As experts learn more about the virus, they have to change their models.

Some scientists are now proposing a radically more flexible approach to modeling COVID-19. They plan to start with older, simpler models that have successfully predicted the spread of other diseases. Simpler models can work better than complex ones, especially when we don’t have all the data we need. And when COVID-19 testing gets us better data, then we can switch to more complex models and get better predictions.

"Infectious disease outbreaks, whether they be widespread like Influenza or fairly geographically restricted like Ebola, may be difficult to prevent," Dallas said. "However, if we can forecast outbreak potential in time, public health officials and governments can preemptively prepare for a potential outbreak event."

Dallas and colleagues recognize that detailed information on countries and pathogens may be required to more accurately forecast what countries certain pathogens appear in, but the simple method they developed outperforms traditional null models.

"Our approach leverages data on the entire network of pathogens and countries in order to forecast potential pathogen outbreak, emergence and re-emergence events," Dallas said. "Emergence events, which are first records of a pathogen recorded in a given country, are incredibly difficult to predict, as they are sort of by definition unexpected."

Special thanks to Dr. Bret Elderd and Dr. Tad Dallas in LSU's Department of Biological Sciences. For more information on modelling the spread of diseases like COVID-19, click here.