Analyzing Covid-19

National Institute Of Allergy And Infectious Diseases

Investigators

Abstract

Many complex mathematical and epidemiological methods have been used to model the Covid-19 pandemic. Among other results from these models has been the view that closing schools had little impact on infection rates in several countries1. We took a different approach. Making one assumption, we simply plotted cases, hospitalizations and deaths, on a log2 Y axis and a linear date-based X axis, and analyzed them using segmented regression, a powerful method that has largely been overlooked during this pandemic. Here we show that the data fit straight lines with correlation coefficients ranging from 92% - 99%, and that these lines broke at interesting intervals, revealing that school closings dropped infection rates in half, lockdowns dropped the rates 3 to 4 fold, and other actions (such as closing bars and mandating masks) brought the rates even further down. Hospitalizations and deaths lagged, but paralleled cases. The graphs, which are easy to read, reveal changes in infection rates that are not obvious using other graphing methods, and have several implications for modeling and policy development during this and future pandemics. Overall, other than full lockdowns, three interventions had the most impact: closing schools, closing bars and wearing masks: a message easily understood by the public. in addition, people in the USA who self-identify as Latino/Hispanic have far lower death rates than other populations. we do not yet know why this is. Finally, the risk of dying of Covid-19 doubles with every decade of age over 30. Thus any underlying condition that predisposes to death is strictly correlated with age. This does not fit with most of the conditions currently thought to be risk factors.