Welcome to Coronavirus Government Global Briefing, Mandarin Premium’s morning update on everything in local and global government responses to the COVID-19 outbreak.

How data could guide us out safely


As Australia, New Zealand, and a host of other countries slowly begin to reopen, understanding the effectiveness of specific public health measures — and in turn, the impact of removing them — will become crucial to preventing future outbreaks.


With that in mind, Nature reports that researchers at the London School of Hygiene and Tropical Medicine have begun work on a WHO platform aggregating data from 10 groups currently tracking interventions, “including teams at the University of Oxford, UK, the Complexity Science Hub Vienna (CSH Vienna), and public-health organisations and non-profit organisations such as ACAPS, which analyses humanitarian crises.”


While researchers urge some caution in quantifying the effectiveness of a single measure — some policies may only prove effective in coordination with others, for example, while intercultural, socio-economic and geographical differences mean one control measure can create wildly different impacts across countries — the goal is to create the most comprehensive, standardised database of existing research, to be made free and staffed by 1,100 volunteers working on cleaning, combining and updating the information.


For example, CSH Vienna has captured details of about 170 interventions across 52 countries, “ranging from small measures such as floor stickers that mark a two-metre separation to major, restrictive policies such as school closures.” Oxford’s ‘COVID-19 Government Response Tracker’ also monitors 13 interventions across 100 countries, creating a “stringency” index from 7 of the 13 to capture and compare the overall severity of approaches.


The Vienna team is also examining how some countries are starting to reopen and institute other measures, including the mandatory use of face masks, and comparing patterns across countries, and comparing patterns though methods such as “clustering countries by how early in their epidemics they began interventions and by the total number of restrictions introduced.”


Ultimately, researchers hope the database could help forecast how adding and removing interventions could impact the number of infections, with two potential, imperfect methods:


“One approach involves using a machine-learning technique called a recurrent neural network to learn from patterns in the data and make predictions. Researchers can learn how important a given intervention is by looking at how predictions shift when they remove information about it from the network.”

“Another technique involves regression analysis, which estimates the strength of the relationship between a particular measure, such as school closure, and a metric, such as R, across all countries. Using a regression technique such as Lasso, for example, researchers can determine which measures reduce R most.”


On the domestic data front, QUT researchers earlier this week launched a model to predict the trajectory of the virus and its mortality, available at covidwave.org, suggesting that Australia’s death rate has — at least for this cycle — peaked.


The model compares a) the ratio of known infections to recoveries with b) the number of reported daily deaths in each country.