Without testing there is no data

“Why is data on testing important? No country knows the total number of people infected with COVID-19. All we know is the infection status of those who have been tested. All those who have a lab-confirmed infection are counted as confirmed cases. This means that the counts of confirmed cases depend on how much a country actually tests. Testing is our window onto the pandemic and how it is spreading. Without data on who is infected by the virus we have no way of understanding the pandemic. Without this data we can not know which countries are doing well, and which are just underreporting cases and deaths.” - Our World in Data

Group testing vs individual testing

Many countries have used reverse transcriptase polymerase chain reaction (RT-PCR) tests for the diagnosis of SARS-CoV-2. An important feature of RT-PCR assay is rapid and highly sensitive. However, most testing strategies rely on individual tests of cases with restrictive diagnostic criteria. Now, attentions also turn to tests of cases who have mild or moderate symptoms, or are asymptomatic although tests on such cases are limited. Testing these cases is important to learn the actual number of people infected and slow the spread of COVID-19. Because the amount of cases to test is relatively large, an alternative strategy called group testing has been proposed. Group testing is well-suited to these cases since it is considered most efficient when the infection level is low. This interactive web is designed for understanding the power of group testing for COVID-19. Three kinds of group testing strategies are compared, including Dorfman’s pooling method, the grid method and the triple grid method. For details of the methodology, please refer to our work.

(moving your mouse to interact)

Sensitivity and specificity can be confusing terms that may lead to misunderstanding. This figure explains these notions of statistical measures of test accuracy, including FPR (false positive rate) and FNR (false negative rate). Although (sensitivity, FNR) and (specificity, FPR) are simply the inverse of each other, in order to distinguish the notions on tests or strategies that we are talking about, sensitivity and specificity are only used for strategy outcomes while FNR and FPR are used for test outcomes.