Part of the problem using the COVID-19 calculator is that it has too many parameters relative to available data, especially with the outbreak only 99% ended in China (for now). If we take only one data set, for example Chinese deaths/day, then we are better off fitting to a one or two parameter model. Quite amazingly, COVID data looks enough like the Popillia japonica seasonality data from iNaturalist. It is also well-fit by a logistic map (thanks Cris!). For reference: https://mathworld.wolfram.com/LogisticMapR=2.html With China data, we need to bin data 6 days into one bin, and then find 98.9% accuracy by overlap dot-product. From peak outbreak to 99.98% quell is six intervals, or 36 days. Depending on peak-flattening the extracted parameter 6 (days / bin) should be expected to change by region. This is a good way to compare distinct outbreaks, only when stated next to a distribution accuracy statistic. Another interesting data set is the iNaturalist seasonality for the Chaser and King Skimmer dragonflies of genus Libellula. Good news, there should be an outbreak soon! --Brad