Assessing the spread of the novel Coronavirus in the absence of mass testing

Abstract

Background: Assessing why the spread of the COVID 19 virus slowed down in many countries in March through to May of 2020 is of great significance. The relative role of restrictions on behaviour (”lockdowns”) and of a natural slowing for other reasons is difficult to asses when mass testing was not widely done. This paper assesses the evolution of the spread of the COVID 19 virus over this period when there was not data on test results for a large, random sample of the population. Method: We estimate a version of the SIR model applied to data on the numbers who were tested positive in several countries over the period when the virus spread very fast and then its spread slowed sharply. Up to the end of April 2020 test data came from non random samples of populations who were overwhelmingly those who displayed symptoms. Using data from a period when the criteria used for testing (which was that people had clear symptoms) was relatively consistent is important in drawing out the message from test results. We use this data to assess two things: how large might be the group of those infected who were not recorded; how effective were lockdown measures in slowing the spread of the infection. Results: We find that to match data on daily new cases of the virus, the estimated model favours high values for the number of people infected but not recorded. Conclusions: Our findings suggest that the infection may have spread far enough in many countries by April 2020 to have been a significant factor behind the fall in measured new cases. Government restrictions on behaviour - lockdowns - were only one factor behind slowing in the spread of the virus.