Back

Can age-distribution be an indicator of the goodness of COVID-19 testing?

Hoseinpour Dehkordi, A.; Nemati, R.; Tavousi, P.

2020-12-28 infectious diseases
10.1101/2020.12.21.20248690 medRxiv
Show abstract

It has been evident that the faster, more accurate, and more comprehensive testing can help policymakers assess the real impact of COVID-19 and help them with when and how strict the mitigation policies should be. Nevertheless, the exact number of infected ones could not be measured due to the lack of comprehensive testing. In this paper, first of all, we will investigate the relation of transmission of COVID-19 with age by observing timed data in multiple countries. Then, we compare the COVID-19 CFR with the age-demography data. and as a result, we have proposed a method for estimating a lower bound for the number of positive cases by using the reported data on the oldest age group and the regions population age-distributions. The proposed estimation method improved the expected similarity between the age-distribution of positive cases and regions populations. Thus, using the publicly accessible data for several developed countries, we show how the improvement of testing over the course of several months has made it clear for the community that different age groups are equally prone to becoming COVID positive. The result shows that the age demography of COVID-19 gets similar to the age-demography of the population, together with the reduction of CFR over time. In addition, countries with less CFR have more similar COVID-19s age-distribution, which is caused by more comprehensive testing, than ones who have higher CFR. This leads us to a better estimation for positive cases in different testing strategies. Having knowledge of this fact helps policymakers enforce more effective policies for controlling the spread of the virus.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.