In a novel approach to analysing the impact of the COVID19 pandemic sweeping the world, the Brussels and Washington DC based healthcare consultancy Vital Transformation (VT) compared the strategies adopted by several countries currently managing the pandemic.
Seeking to clarify the conflicting opinions of experts about what are the drivers of mortality and rates of disease across Europe, the analysis focused on multiple demographic factors in a multi-variant statistical analysis. This included demographics measured as the percentage of the population over 65 years of age and population density; infrastructure measured as the number of hospital beds per 1,000 population; mobility patterns measured as the number of tourists and university students per 100,000 population; as well as the quantity and results of antibody tests compared to the confirmed cases of COVID19.
The research was based upon data collected at the region and city level of the spread of the pandemic, thus going beyond conventional methodology that tends to draw conclusions at aggregate country level. This is an important consideration baked into the analysis because local governments have been deploying different tactics in seeking to contain the spread of COVID19, and large errors and variances can be contained in national data.
“The problem that we see with much of the modelling is it tries to interpret the impact of hospital beds, testing, etc. at the country level. There is far too much variability in the data for that approach, so we’ve tracked the mitigating factors impacting the spread of the pandemic by city and region, and then captured all the variables at the local level. This minimizes the variability and provides us with what seems to be a surprisingly robust statistical model to test a bunch of variables at the same time,” said Duane Schulthess, Managing Director of Vital Transformation.
After running a series of statistical regressions to assess the impact of the many possible variables impacting the rate of mortality, the researchers found that the single strongest predictor was the number of antibody-detecting tests administered. Specifically, they found that a 1% increase in the number of tests accounted for a 0.56% decrease in mortality.
A 1% increase in testing for COVID19 leads to a 0.56% decrease in mortality rates.
Testing 500,000 subjects a week saves 2,500 lives.
The current quarantine is costing Italy and Spain each roughly €1.75 billion per day.
“The results are clear: more testing will save more lives than indiscriminate social distancing and it will cost less,” said Anja Schiel, a senior advisor at the Norwegian Medicines Agency and a co-author of the study.
The false trade-off between testing and social distancing
This research was underpinned by a stark assessment by the International Monetary Fund that a nationwide quarantine compelling the closing of nonessential services would translate into a 3% drop in annual GDP for each month those sectors remain closed (1). This translates into roughly €50 billion of lost GDP per month in both Spain and Italy, or €1.75 billion per day.
The modelling looked specifically at the management of the pandemic by Germany and Italy. Germany has run a coordinated mass testing strategy since the beginning of the pandemic, setting a goal of administering 500,000 tests per week. According to VT’s model, this will save 2,500 lives at a cost of €20 mil per week.
Italy opted for testing only persons who presented symptoms, even as it moved into a countrywide quarantine. Yet, as COVID19 extended from North to South and continued to batter Italy for weeks, a need to shift strategy became obvious.
The results of a pilot project initiated by the Red Cross days prior to the full national lockdown showed that population testing and selective isolation resulted in full eradication of the virus within 14 days. Had the testing approach been deployed earlier at the national level, the lockdown and all the macroeconomic consequences it implies down the line may have been averted or at least vastly reduced, and the economic impact of lost GDP would have been substantially mitigated.
Summary of methods
- Vital Transformation (VT) collected various population and health data from 53 cities in Europe, the USA, and South Korea.
- VT then ran a series of multiple regressions testing the statistical significance of these parameters to determine their statistical significance on the mortality rate of COVID19.
- Three variables showed high statistical significance: ‘Number of Tests’ (p < 0.0001), total ‘Confirmed Cases’ (p < 0.0001), and ‘Population Density per Km2 (p < 0.0001).
- To assess the impact or ‘elasticity’ of the key variables, our model was re-estimated with the key variables measured by their natural logarithm.
- Results of the statistical model were then compared to the current approaches to disease management in terms of both QALY and GDP.
Source: Vital Transformation