How Long is the Worst Part of the COVID-19 Mortality Surge?
Authors: de Almeida Valmor F. 
Published: May 2020

Objectives: Define and quantify the critical surge period of existing on-line COVID-19 mortality data; this could be instrumental to decision makers to determine how long health controls should be in place, and how long to expect the public health system to be in great stress. Create and share on-line, open-source software to continuously monitor critical surge periods in various communities as the data set grows. Test the critical surge period computational method on hierarchical data sets of various communities: a) globally; b) countrywide (states in the US); and c) statewide (counties/towns in the US states).Study Design: This was an open-source, on-line, COVID-19 data analytics development with support for web-browsers on any current mobile device.Methods: On-line, timely data for COVID-19 mortality was automatically retrieved by a developed application using the programming Python language and Jupyter electronic notebooks. An on-line, version-controlled code repository was created for the application and also used for providing an open execution environment for the research community. The data was fitted to a modified sigmoid function via an automated non- linear least-squares method. Three optimal parameters were computed and the critical times for the maximum and minimum curvatures of the function were used to obtain the critical surge period. The goodness of the fit was measured by a standard coefficient of determination, and alternatively, by direct pointwise relative error. Results: The software developed (a Python package) is called covid-surge (repository URL: htttps://github.com/dpploy/covid-surge) and available for download from PyPI on-line (URL: https://pypi.org/project/covid-surge/). Using covid-surge, the critical surge period was computed for the communities with the most evolved COVID-19 outbreak. At the time of this writing, the critical surge period was calculated for all countries with fully evolved mortality (12 out of 188) and found to be in average 23 days with 3 days of standard deviation. Similarly, for all states/districts in the US (20 out of 56), the computed average was 25 days with 3 days of standard deviation. Likewise, for the same states in the US, counties/towns that had fully evolved mortality surge (91 total out of 981 that had death cases) had the overall average of the surge period equal to 23 days with a standard deviation of 3 days. The accuracy of these results can be reproduced on-line using the software provided. In addition, forward results using newer data can also be generated by the research community using covid-surge.Conclusions: The surge period for hierarchical data including 12 countries, 20 US states, and 91 US counties/towns is remarkably similar: a 24-day period with 3-day standard deviation for all communities across many countries and the US. All of these communities were under similar COVID-19 controls, including social distancing, improved hygiene, and isolation which helps explain the constancy of the surge period. That is, these communities experienced nearly the same amount of time during the most stressful period of the mortality surge. Although the extent to which epidemic controls are applied can vary from community to community, this study shows that the variability of the surge period is small. As the COVID-19 sweeps over the continents, additional data will be available for fully-evolved communities and a later comparison can be made with covid-surge on-line. This study suggests that communities not yet fully affected by the COVID-19 mortality may expect a period of high stress on their public health system of about 30 days (lower bound). That is, health care organizations should plan to operate at full capacity for a minimum of 30 days.