There have been a number of false notions about the use of case data to predict the course of the pandemic and that death data would be a better tool. The problem is that death data lags at least seven days and often up to a month, and that delays any needed interventions.
I have been using a calculus derivative function to predict the course of the pandemic in relation to cases, but decided to look at its predictive value in relation to deaths. While morbid, it’s clear that this approach provides a prediction of mortality.
The gray bars represent new DEATHS this time instead of cases. The black line is the seven day moving average of them.
The red line is my derivative function that predicts the future path of the pandemic. Given that deaths trail cases by a week, it predicts deaths in slightly over a week, in part due to getting deaths recorded. It’s a tighter match the further back in time.
The way to view it is to look at the red line when it crosses zero. The farther it is above zero, the faster the deaths will be climbing in a week. The further below zero, the faster the deaths will drop.
The steep rise in the average deaths is completely accounted for by the sharp initial rise in the red line.and the gradual decline is because the red line is hovering just below zero one week before.
This means that we are going to see a rapid rise in deaths for at least the next week from the data, but given my epidemiology experience, it’s going to climb sharply for quite a long time after that.
