A year upended: How governments (still) fight to keep Covid-19 in check — and what may work best
Organisation: The Straits Times
Organisation size: Big
Publication date: 30 Dec 2020
Credit: Rebecca Viviana Pazos,Stephanie Adeline, Tampus Charles Singson, Tin May Linn, Xaquin G. V.
Stay-at-home, social distancing – did they work?
This story explains the impact of movement restrictions in seven key places in Asia – Singapore, India, Indonesia, the Philippines, Japan, South Korea and Taiwan – and how the population complied with the measures.
Relying on a global study published in The Lancet medical journal that discovered the delayed impact of certain measures on the R-rate, which shows how long the outbreak has been spreading at a faster speed, we were able to use visual storytelling, graphics and personal profiles to highlight the wider consequences of such movement restrictions in each featured area.
We hope that it is used to explain the effectiveness of different movement restrictions used by policymakers in Asia. It is complicated and we hope we were able to deliver the message of nuance with clarity.
The project uses R for data analysis and outputs SVG charts using the ggplot2 library which has been set up to output different sizes for mobile and desktop using our in-house styles. The SVG charts are then adjusted in Adobe Illustrator to add annotations and refine styles. They are then inserted using the in-house Vue.js modular template which includes a ‘scrollytelling’ module among other key visual elements such as full-width parralax images and pull-out quotes.
As a result of this, we were able to update the latest data up to the most recent days before publishing with minimal effort. As it turned out, even though the analysis is not time-sensitive, at least till the final days of the year, South Korea’s third wave was still on the rise when we first began the story and had eventually overtaken its first peak in early 2020 by the time we were about to publish.
What was the hardest part of this project?
This project underwent a few rounds of editorial changes. The first concept was to use the government response index in much the same way as many reputable news organisations had been doing at the time (late October) but with a focus on Asia.
Upon our first pitch, our editors were keen to see how we could use the index to find out which measures worked better than others, as opposed to looking at countries. We were grateful to come across a different study (linked below) on movement restrictions by the University of Edinburgh which used the same index but took a global view at movement restriction effectiveness more specifically.
Our second pitch was approved but we then had to add additional locations – we originally proposed four including Singapore. This is, of course, fine, but it meant we needed to find a way to visually communicate the situations in seven countries quite high up in the beginning, but also have it display all in one mobile view. We resolved this issue after a day of staring at the 7 area charts to find a common pattern – the waves of each place’s daily new cases reflected to a certain extent the regularity/irregularity and number of movement restriction measures. We used ‘sparklines’ to show waves in one view that were then used later on in the story.
What can others learn from this project?
We think it is encouraging to not be disheartened when you get a negative response to a pitch. At the time it was frustrating as it was a dataset that was being used by many and well, and we had done a considerable load of work on it. However, in the end, I think the project turned out far better as a result of us being pushed to find something that suited the editorial needs.
We also think it is important to bring back human stories into data. We tried to do this by using personal profiles that represented the “voice of the many” as uncovered in the data. We ran the data analysis, suggested possible profiles that would fit the majority and co-ordinated this with our correspondents in the various locations. They all gave us exactly what we were looking for on their first attempt so this was heartening to confirm the intepretations we had made of data and ensured it wasn’t biased.