31 March 2020
(Reposted 1st April as due to human error — mine — this didn’t make it into the newsletter.)
The model uses the now-familiar method of looking at reported deaths and inferring the infections that must have existed several weeks prior. The conclusion?
We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of lockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.
It estimates the lives saved as a result:
With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.
The paper also tries to estimate the percentage of the population that is infected in these countries.
We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-2 up to 28th March, representing between 1.88% and 11.43% of the population.
Here is the key table, presenting ranged estimates at a 95% confidence interval:
I would be cautious with these figures as the study had to make a number of important assumptions (including that the impact of control measures was roughly consistent across these countries). Still, it gives us a sense of what the possible ranges might be.
A very different approach in the US also gives hope that control measures and behavioural changes are slowing the spread, reported in this New York Times article:
The article reports that a manufacturer of internet-connected thermometers, Kinsa Health, has been monitoring atypical fevers across the US, and identified a clear trend.
Kinsa has more than one million thermometers in circulation and has been getting up to 162,000 daily temperature readings since Covid-19 began spreading in the country.
The company normally uses that data to track the spread of influenza. Since 2018, when it had more than 500,000 thermometers distributed, its predictions have routinely been two to three weeks ahead of those of the Centers for Disease Control and Prevention, which gathers flu data on patient symptoms from doctors’ offices and hospitals.
Since 2018, when it had more than 500,000 thermometers distributed, its predictions have routinely been two to three weeks ahead of those of the Centers for Disease Control and Prevention, which gathers flu data on patient symptoms from doctors’ offices and hospitals.
To identify clusters of coronavirus infections, Kinsa recently adapted its software to detect spikes of “atypical fever” that do not correlate with historical flu patterns and are likely attributable to the coronavirus.
Here’s a link to the results, and here’s the key chart:
I’d be cautious about concluding that this represents a decrease in COVID-19 infections. “Atypical” here means simply “an unusual incidence of elevated flu-like illness levels.” It’s entirely possible that this trend primarily represents a decrease in seasonal flu, since social distancing and other control measures would also impact flu transmission. As the company says in its description of its technical approach, “It is also important to note that this method identifies anomalous ILI [Influenza-Like-Illnesses] events, not COVID-19 in particular.”
Still, it’s a fantastic example of the use of both big data and connected devices, and could be helpful in monitoring, predicting, and surgically responding to outbreaks going forward.