Warwick University has come up with a new strategy for modelling the spread of Covid-19. The strategy incorporates smartphone-captured data on people’s movements, and it is also likely to aid the development of optimal lockdown policies.
Warwick University’s Ritabrata Dutta and his colleagues have presented these findings in an open-access journal called PLOS Computational Biology.
Evidence that came to the fore has revealed that lockdowns can effectively mitigate the spread of the virus, but in truth, they come at a very high economic cost. Besides, a section of the population refuses to follow the government guidelines.
Therefore, Dutta and his colleagues came up with a strategy to ensure that the spread of the virus is curbed while minimizing the economic costs of lockdowns.
Researchers developed new mathematical models to help guide the strategy.
The models focus on England and France. As part of the model, a statistical approach known as approximate Bayesian computation incorporates public health data and data on changes in people’s movements, which Google captures via Android devices. The mobility data is a measure of how effective lockdown policies are.
The researchers showed how it is possible to design a lockdown that would partially allow the reopening of schools and workplaces while considering the importance of public health costs and economic costs. The models can be updated in real-time and adapted to any country equipped with reliable public health and Google mobility data.
“Our work opens the door to a larger integration between epidemiological models and real-world data to, through the use of supercomputers, determine best public policies to mitigate the effects of a pandemic,” Ritabrata Dutta was quoted as saying.
“In a not-so-distant future, policymakers may be able to express certain prioritization criteria, and a computational engine, with an extensive use of different datasets, could determine the best course of action,” he added.
The researchers are now planning to refine their country-wide models to work at comparatively smaller scales.
“The integration of big data, epidemiological models and supercomputers can help us design an optimal lockdown strategy in real-time while balancing both public health and economic costs,” the researchers said.
