Today’s total – 331 new confirmed cases, 6518 overall and 6604 including probable cases. The trend continues downward, but it isn’t very fast.  If you want to understand why and what that means, keep reading.
When I analyze the data, I look at 3 pieces of information to understand where the data is leading us. Before sharing those, I want to address death numbers. Obviously the goal of this effort is to prevent deaths, but as long as stay under the threshold of overwhelming our hospitals we’ll see some % of infected population pass away from this 10-20 days after contracting the virus no matter what we do. Deaths are a trailing indicator, cases are the leading indicator and where we want to focus.
 
1. The raw daily total of confirmed cases is the primary metric and the one we are trying to understand where it is heading. This number is fairly noisy on a daily basis. I realize we are undertesting to some extent but unless we big change in availability or methodology, we can draw comparisons over time to understand the direction even if don’t know the true number of infected.  Undertesting is important to interpreting death rates, not to this effort.
 
2. The next is the trend of cases over time. For any given date, we can determine if the trend is up or down. There are various ways to accomplish this, but I’m using a method called least squares fit. This calculates the line where we minimize the square of the errors (plus or minus) for each data point. If the slope of this line is positive, cases are increasing over that time period. If the slope is negative, the cases are decreasing. The longer the time period we use, the less sensitive the slope is to noise in the data, but we may miss seeing the impact of short-term trends. I’ve used a two-week trend to increase the certainty of the direction. I also ignore the 3 most recent days of data as we are still identifying those cases and will have big updates over the next few days. The current slope from March 26th to April 9th is -6.9. Each day we trend to have just under 7 fewer new cases per day.
 
3. The last one and most important is the trend of the trend. In calculus, we call this the derivative. Think of this as hitting the accelerator or applying the brakes to the case trend. If the derivative is positive, the rate of growth is increasing over time. If the derivative is negative, the rate of growth is decreasing over time. The latest date I can calculate the derivative for is April 6th and it was -1.4.
 
Based on the above, we can draw some interesting conclusions. Ohio’s rate of growth peaked on March 18th and 19th as that’s when the derivative flipped to negative. At that point, the rate of growth was +16.3. Unfortunately, the derivative isn’t strongly negative so it’s taken time to reduce the rate of growth to the current -6.9. Schools began to close in Ohio on 3/13, and bars and restaurants followed on the evening of 3/15. I don’t think it’s a coincidence that 3/15 was the peak of the derivative.
 
Additional restrictions were put in place over the week and the stay at home order went into effect on 3/23 which is now extended through May 1st. Since then, the derivative has stayed between -2.08 on 4/3 and -0.06 on 3/31. What that tells me is that the additional restrictions around the closure of non-essential businesses didn’t have much impact after we closed down schools and bars/restaurants and people began practicing social distancing in their interactions. That makes me optimistic that a reopening strategy can be pursued while continuing to social distance and keep the virus under control.