As the COVID-19 pandemic wreaks havoc and puts lives at risk, the advice experts provide to decision-makers has become essential. At the same time, researchers’ calculations led to differing conclusions on everything from masks to school closures, sparking heated debate. A new handbook on mathematical models has been produced jointly by Chalmers University of Technology, the University of Gothenburg in Sweden, and several Swedish government agencies. This handbook aims to pave the way for better decision-making and greater preparedness for the next pandemic.
Mathematical models are simplifications of reality that help us navigate a complex world. During the COVID-19 pandemic, mathematical models have been used to simulate the spread of the virus, predict medical needs, and assess the impact of various measures, from lockdowns to hand-washing routines and face coverings.
By translating a variety of factors into mathematical terms, including data on risk groups and demographics, and information on cases, recoveries, and deaths, researchers were able to use mathematical tools to create predictions and advise decision makers on important decisions.
Torbjörn Lundh is Professor of Biomathematics at Chalmers University of Technology and the University of Gothenburg. During the pandemic, he helped Sahlgrenska University Hospital in Gothenburg, Sweden, estimate the demand for intensive care beds on a weekly basis using mathematical modeling.
He is currently one of the authors of a new handbook produced jointly by Chalmers University of Technology, the Swedish Public Health Agency, the Swedish Defense Research Agency (FOI) and the Swedish Armed Forces. It provides practical guidance on how mathematical models can be used to inform decision-making and how results can be communicated during a crisis, when most things are uncertain and time is of the essence.
This is a book I wish I had gotten myself during the COVID-19 pandemic. That way, I would have been able to work more effectively and confidently. ”
Torbjörn Lundh, Professor of Biomathematics, Chalmers University of Technology
Models are not the final answer
Philip Gurley, professor of biomathematics at Chalmers University of Technology and the University of Gothenburg, is the handbook’s lead researcher. He hopes this will increase awareness of different models and how best to deal with them, thereby paving the way for better preparedness for future pandemics.
“No model can provide a definitive answer, but it can nevertheless be very useful. For us, this handbook was born out of frustration with the misunderstandings and sometimes harsh-toned exchanges between different groups that emerged in Sweden during the pandemic, and that also occurred in other countries. We believe that all models Although simplistic, we want to show that with the right assumptions, different models can complement each other. We hope this will improve collaboration between experts and provide more effective advice to decision-makers during the next pandemic,” said Philip Gurley.
Anders Tegnell, a senior advisor and former chief medical officer at the Swedish Public Health Agency, is also a co-author. He recalls the challenges faced during the COVID-19 pandemic, when many organizations wanted to help in a chaotic situation.
“Everything happened so quickly and so many people wanted to contribute their expertise that there was some confusion over terminology and even mistrust between different groups. One example of how this played out was the opinion pieces in the Swedish media, which were not particularly constructive,” says Anders Tegnell.
Various models offer a wider field of view
Chemists, mathematicians, and biologists often use completely different models in their work, based on AI, differential equations, different data models, etc. However, according to Torbjörn Lundh, the breadth of the tool is not the problem. In fact, quite the opposite.
“Different models and results give you a broader picture and a deeper understanding. It is rarely a good idea to rely on just one model, and not all models work the same at all stages. For example, at the beginning of the COVID-19 pandemic, it was difficult to use AI models because we didn’t have enough data yet,” he says.
Results are more reliable when multiple models point in the same direction. Another important conclusion is that there are risks in relying on overly complex models. As an example, Torbjorn Lund cited a controversial Imperial College London report from March 2020 that predicted hundreds of thousands of deaths and a strain on the health system unless strict restrictions were introduced. Since then, several researchers have criticized the way the model underlying the report was used.
“The more complex the model, the more difficult it is to explain and understand, and even the smallest change in the parameters you set can dramatically change the results,” he says.
Swedish data modelers are preparing for future pandemics
It is also important to “rehearse” together during periods when the virus is not circulating, as is currently being done in Sweden as part of the national SEMAFOR (Swedish Epidemic Modeling and Force Network).
“We are a group of pandemic preparedness modelers from Swedish government agencies and universities who come together to conduct realistic training. For example, for dengue reaching Stockholm, we held a mock press conference and Secretary Anders Tegnell played himself. This network has broadened our view of all the tools available for pandemic preparedness modeling and how we can work together to improve them,” says Lund.
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Chalmers University of Technology
