The good news is that COVID-19 is in decline in most parts of the world. We are not at the end, but we are at the end of the beginning. What I find most inspiring is how much research has been conducted to fight the disease, to understand citizen-consumer perceptions and to anticipate the economic implications.
In terms of the infection rate, we are past the peak. In terms of the economy, we are no longer in the Phase 1 “Guns, Germs and Beer Economy“. Yet, having cleared the infection peak in most parts of the world, and having cleared Phase 1 of the economic fallout, it would be wrong to say COVID-19 is in our rearview mirror. Instead, we are at a breakpoint — a fork in the road. A good path is declining infections and improving economic prospects. A bad path is increasing infections and a wobbling economy. I contend that whether we may continue on a good path depends on how well we apply data to our decisions.
Data Driven Decisions:
Thanks to data analysis ingenuity and a cooperative global spirit of seeking answers, we’ve learned a lot in the last five months. The coronavirus DNA has been sequenced. Over 100 vaccine candidates have been identified, several have passed the first set of efficacy tests. We’ve effectively deployed the blunt instrument of #stayhome and my analysis shows the efforts reduced the death trend by about 50% in the US.
We’ve learned to deploy less blunt force measures to reduce the transmission of the coronavirus. We’ve learned the role of humidity and temperature in reducing transmission. Closed population exposures, where everyone was tested (e.g. Diamond Princess Cruise and Roosevelt aircraft carrier), provide insights on the proportion of asymptomatic cases, and the dangers of unmitigated spread. Research has shown the value of wearing masks and hand washing. We’ve traced cases and determined exposure to indoor environments has more risk than exposure to outdoor environments. Research has shown how more time exposed to those who are infected increases risk. Serologic testing, while not perfect, provides insights on how wide the virus spread in communities.
We’ve found ways to apply market research to map consumer behavior in response to the virus. We’ve found data sets to give us clues on social distancing trends, such as mobile phone location data, transportation turnstyle records, road traffic meters, TSA airport travel counts, Zoom call streams, open table reservations, streaming video service usage — even pornhub has provided country level trend data on usage to help understand changes in consumer behavior during the COVID pandemic. The point is, there is more data available than in any pandemic or recession the world has ever faced. We know so much more and so much earlier — and the data is often broadly available to citizens. I’ve applied consumer index type analysis to evaluate the age distribution of who is most at risk of dying of COVID. I applied postal code analysis to determine the neighborhoods most likely to be infected with COVID-19. ESOMAR has gathered the list of leading market research firms that are tracking consumer perceptions to better quantify the impact of COVID-19 . McKinsey, Accenture, Deloitte, IPSOS, Kantar and many others are doing a great service in sharing their data and analysis. The amount of data available to better understand trends is indicative of a powerful trend in the democratization of data.
Future Research
There are many questions we don’t yet have answered because they require more time to elapse. For example, we don’t know the longer-term effects of being infected. We don’t know how many of the job losses are temporary (though a consumer survey shows 78% of those recently unemployed expect to return back to work shortly, but economists forecast only about 60 percent of jobs will return immediately). Perhaps the most critical question at this breakpoint is whether the virus remains on the decline or not. One of the more innovative approaches to answering that question comes from a MIT supported start-up called Biobot.
Every flush of the toilet is data. It is possible to measure the current level of virus in the community and identify when it is decreasing versus increasing. Biobot analyzes sewage effluent to determine the amount of infection in the community served by the wastewater treatment plant. I don’t personally have an investment in Biobot, or firsthand knowledge of exactly how they do what they do, but, when I was a teen, I worked in a water/wastewater lab. Most of the work was cleaning test-tubes and beakers, but there were a few tests I got to perform. I gained appreciation for the science of passive measurement. When I saw news in February, based on cases in Hong Kong that may have been linked to sewage, I wondered if it would be possible to passively measure the parts per million of virus in a community at the Wastewater treatment plant. Based on the pre-print paper from Biobot, MIT and Harvard, it appears they have an effective method to measure COVID in the community.
If all wastewater treatment plants applied the test, it would cover 75% of the US with measurement of the COVID infection rate (16,000 treatment plants in total, with a ratio of about 1 per 15,000 people) and over 80% of Europe (18,000 treatment plants – with a ratio of about one per every 20,000 people).
What wastewater measurement can’t do is tell you who in the community has it. Pipes flow together and often there are thousands and thousands of homes served by each wastewater treatment plant. Septic systems are not connected, so that’s a limitation as well. Projecting from parts per million of the virus in the sample to the number of people infected with great precision is a work in progress. Nonetheless, it can provide a non-biased trended measurement of where infections are heading for 75 to 80 percent of households in developed economies. Such measurement would not be subject to who gets tested and who doesn’t. I’ve encouraged my local government leaders participate, and we should get initial results soon. While the source of measurement is pretty dirty, the measurement is pretty clean.
In terms of economic measurement – we need more measurement and less lag time in measurement of consumer behaviors and intentions. Consumer spending is about 70% of developed economies, and recent analysis from McKinsey & Company shows consumers are reluctant to spend. Research from credit card companies show more savings and less spending as consumers worry they may not have a job in the future, or may not have a job to come back to if they were recently furloughed. Businesses are feeling the need for speed in their data collection and analysis.
I predict the businesses and governments that lean into the trend for more data driven decisions will do better. The businesses and governments that reduce the lag time from data to action will do the best.
