Strategy Consulting for Life Science

COVID-19 Trends, Analysis, and Primers

COVID-19 Overview

The pandemic of 2020 and 2021 has been a trying, uncomfortable, and confusing time for many Americans and citizens of the world. We’ve seen many instances of conflicting reporting from mainstream media, government leaders, CDC, and the US center for Infectious Disease. Moreover, there has been considerable misinformation and dis-information on the airwaves and the internet that has confused people as to what is the proper rules of engagement with other humans. Unfortunately, the poor and inaccurate reporting has created a division in the US about the impact of the virus and the strategy to reduce infections.

Around May of 2020, Bellwether Analytics began posting blogs and spreadsheets to help allay fears and inform people on the importance of isolation. The data at that time had several limitations, not the least of which was the self-reported nature of the information (e.g. each county reported back to the state and the states reported back to the CDC). Over time the availability of testing and definition of a COVID infection improved. Thus, at least one aspect of the limitations improved.

It is now one year later, and we have removed the documents from last year to present you with better insight into the pandemic. Over the next few weeks, we will be rolling out more insights based on the analysis we have conducted.

We recommend using this tool on a computer, rather than mobile or tablet.


Planned Roll-out:

June 15: Interactive trend tool (Built in Tableau)
June 24: Whitepaper style primers on the Virus, Diagnostics, and Vaccines
July 1: Comparative analysis on CDC model and our model on correlation between restrictions and infection growth
July 8: Analysis of the only state that turned on and off restrictions
July 22: Comparative analysis on CDC model and our model on correlation between restrictions and infection growth


Whitepapers


To kick things off we created a comprehensive tool to examine the pandemic trends across the globe, US states, and because Bellwether is located in Illinois, county level comparisons for Illinois. We chose to use infection rate, death rate, and growth rate instead of total number in order to normalize the results, in other words to make the data comparable.

As you will, see different countries lead in infections in different time periods. Some countries were able to contain the viral spread, while others saw ebbs and flows. These tools help us identify the periods of infectious growth vs stability vs decline. We can then dig deeper into the messages and behaviors within each country to understand the drivers of change.

Shortly after the launch of the tool, we will roll out mini whitepapers on virus, diagnostic, and vaccine. Our background is in life-science o we will provide fact-based unbiased information on the Covid virus, spread (based on epidemiology), and variants. In addition, we have been following the launch of testing globally and domestically. We have seen public health officials inaccurately categorize tests, so we hope to clarify antibody, antigen, and molecular testing. Then, we will explain vaccines (with some myth-busting) and the difference between anti-viral and vaccines.

Then for the data-oriented, we will report on our findings from statistical analysis we completed. There is a considerable amount of analysis to be found on the web. We wanted to ask the question: Did the various restrictions help reduce the spread of infection? If so, how much? The most complete data we have is for the US, so we have focused on the US impact. About a week after we completed our initial analysis, the CDC published their analysis. We had differing results, so we also replicated (based on their paper’s description) their analysis. That comparison led us to conduct a new analysis based on the only state to turn on and off their mandated restrictions.

All our work will have detailed descriptions on the methods, data sources, and conclusions we could make. The old adage of ‘correlation is not causation’ holds true as we will explain what we can glean vs only suspect.