Whose coronavirus strategy worked best? Scientists hunt most effective policies

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27th April 2020

Elizabeth Gibney


Researchers sift through data to compare nations’ vastly different containment measures.
Hong Kong seems to have given the world a lesson in how to effectively curb COVID-19. With a population of 7.5 million, it has reported just 4 deaths. Researchers studying Hong Kong’s approach have already found that swift surveillance, quarantine and social-distancing measures, such as the use of face masks and school closures, helped to cut coronavirus transmission — measured by the average number of people each infected person infects, or R — to close to the critical level of 1 by early February. But the paper, published1 this month, couldn’t tease apart the effects of the various measures and behavioural changes happening at the same time.

Working out the effectiveness of the unprecedented measures implemented worldwide to limit the spread of the coronavirus is now one of scientists’ most pressing questions. Researchers hope that, ultimately, they will be able to accurately predict how adding and removing control measures affects transmission rates and infection numbers. This information will be essential to governments as they design strategies to return life to normal, while keeping transmission low to prevent second waves of infection. “This is not about the next epidemic. It’s about ‘what do we do now’?” says Rosalind Eggo, a mathematical modeller at the London School of Hygiene and Tropical Medicine (LSHTM).

Researchers are already working on models that use data from individual countries to understand the effect of control measures. Models based on real data should be more nuanced than those that, at the start of the outbreak, necessarily predicted the effect of interventions mainly using assumptions. Combining data from around the world will allow researchers to compare countries’ responses. And compared with studies of individual countries, it should also allow them to design models that can make more accurate predictions about new phases of the pandemic and across many nations.

But untangling cause and effect is extremely challenging, in part because circumstances differ in each country and because there is uncertainty over how much people adhere to measures, cautions Eggo. “It’s really hard but it doesn’t mean we shouldn’t try,” she adds.

Pulling together

Efforts to tackle these questions will get a boost in the coming weeks from a database that brings together information on the hundreds of different interventions that have been introduced worldwide. The platform, being prepared for the World Health Organization (WHO) by a team at the LSHTM, gathers data collected by ten groups already tracking interventions — including teams at the University of Oxford, UK, the Complexity Science Hub Vienna (CSH Vienna), and public-health organizations and non-profit organizations such as ACAPS, which analyses humanitarian crises.

Read the full article on Nature