A mobility-based approach to optimize pandemic lockdown strategies
New method for modeling COVID-19 spread incorporates real-time data on people's movements

Date:
August 12, 2021
Source:
PLOS
Summary:
A new strategy for modeling the spread of COVID-19 incorporates
smartphone-captured data on people's movements and shows promise
for aiding development of optimal lockdown policies.



FULL STORY ==========================================================================
A new strategy for modeling the spread of COVID-19 incorporates
smartphone- captured data on people's movements and shows promise for
aiding development of optimal lockdown policies. Ritabrata Dutta of
Warwick University, U.K., and colleagues present these findings in the open-access journal PLOS Computational Biology.


========================================================================== Evidence shows that lockdowns are effective in mitigating the spread of
COVID- 19. However, they do come at a high economic cost, and in practice,
not everybody follows government guidance on lockdowns. Thus, Dutta and colleagues propose, an optimal lockdown strategy would balance between controlling the ongoing COVID-19 pandemic and minimizing the economic
costs of lockdowns.

To help guide such a strategy, the researchers developed new mathematical models that simulate the spread of COVID-19. The models focus on England
and France and -- using a statistical approach known as approximate
Bayesian computation -- they incorporate both public health data and
data on changes in people's movements, as captured by Google via Android devices; this mobility data serves as a measure of the effectiveness of lockdown policies.

Then, the researchers demonstrated how their models could be applied
to design optimal lockdown strategies for England and France using
a mathematical technique called optimal control. They showed that it
is possible to design effective lockdown protocols that allow partial
reopening of workplaces and schools, while taking into account both
public health costs and economic costs.

The models can be updated in real time, and they can be adapted to any
country for which reliable public health and Google mobility data are available.

"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,"
Dutta says. "In a not- so-distant future, policy makers 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." Next, the researchers plan to refine their country-wide
models to work at smaller scales; specifically, each of the 348 local
district authorities of the U.K.

The researchers add, "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." ========================================================================== Story Source: Materials provided by PLOS. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Ritabrata Dutta, Susana N. Gomes, Dante Kalise, Lorenzo
Pacchiardi. Using
mobility data in the design of optimal lockdown strategies for
the COVID- 19 pandemic. PLOS Computational Biology, Aug. 12, 2021;
DOI: 10.1371/ journal.pcbi.1009236 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210812145057.htm

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