The Unexpected Coronavirus Lessons from Online Candle Reviews
Looking for Covid-19 in unusual places
Early on in the pandemic, I looked at Google Trends and found that you could see signs of the pandemic from Google searches such as “loss of smell.” Here is the most recent trend for the United States, where anosmia is the general topic for these types of searches.
You’ll notice that the topic peaked in March—around the time that news of this symptom became public—although there was a little rise before. But it is rising again. This is interesting but also noisy. Drill down to subregions and the data is harder to parse.
Then, the other day I was alerted to the following tweetstorm by economist Craig Palsson. He had come across another person, Kate Petrova, who was inspired by another tweet to delve into Amazon reviews of scented candles—in particular, those complaining that the scented candles they received were, in fact, unscented. That makes for interesting reading.
This is the most interesting graph.
Now, this all might not be perfect. There is a certain type of person who buys a candle, and mostly likely there are seasonal elements. But it is an interesting potential window on the pandemic. It shows that we have many more tools for pandemic surveillance, and there might be fruitful ways of having A.I. work through a ton of data to find other indicators. To be sure, this is less useful for Covid-19 than it may be to get early warnings of trends for the next pandemic.
Some of this work is already being done. A recent paper by Cornelia Ilin, Sébastien E. Annan-Phan, Xiao Hui Tai, Shikhar Mehra, Solomon M. Hsiang, and Joshua E. Blumenstock takes mobility data from Google, Facebook, Baidu, and SafeGraph. With this data, the researchers were able to generate a more accurate short-term forecast of Covid-19 cases than with epidemiological approaches alone. This is because mobility can cause infections (although it can also go the other way).
This type of analysis will likely be useful for the current pandemic. The point here is that in today’s world, there is an opportunity to greatly build up surveillance of public health-related information. Some years ago, Google thought it could do this for the flu. It turned out the company’s predictive algorithm wasn’t stable, but the idea was solid. We just need to continue to push to find the right approach.
