COVID-19 Data Collection : Garbage In, Garbage Out

I analyze anomalies in the data collected by the major COVID-19 data repositories. These anomalies are due to how the data is aggregated. I find that the method used tends to understate the rise in the infection rate, overstate the fall after the peak, and generate spurious peaks and drops. As a result, relying on this data can lead to delaying taking action during the rise up to a peak and delaying normalization after the fall.I make recommendations for changing the aggregation methodology to correct for these problems. Moreover, rather than aggregating the data at all, making the raw data available would both improve the situation as well as make more accurate and detailed analysis possible