Making sense of the Global Coronavirus Data: The role of testing rates in understanding the pandemic and our exit strategy

Rahul Potluri; Deepthi Lavu

The Coronavirus disease 2019(COVID-19) outbreak has caused havoc across the world after it was first reported in Wuhan, China 1,2 . Subsequently, research on COVID-19 has exploded to understand the new disease and its impact on mankind [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] . However, the number of baseless articles resulting in fake news articles has also gone up exponentially [17] [18] [19] . A number of models have been adapted by policymakers to predict the course of COVID-19 across the world 4, 6, 13, 20 . The reason for such models is to ensure that healthcare systems can plan services to help them cope with the demands of this new disease which is resulting in serious cases leading to hospitalisation 3, 8 . Core elements of the prediction models have been the number of cases and deaths reported and these studies extrapolated the numbers forward to the population over time 4, 6, 13, 20 . Given the pandemic course of COVID-19, it has become common practice to compare its spread in different countries using case fatality rates 3, 4, 7, 13 . However, such methods only tell us part of the story. Vast differences amongst countries in their testing policies for varied reasons including availability of testing equipment, infrastructure, resources and local governing policies affect case fatality rates. In addition, comparing case fatality rates between countries which are at different stages of the epidemic in their region would be erroneous as rates at the beginning and end would be lower compared to rates at the peak when healthcare services are stretched to their limits. Therefore, the search for a common yardstick or denominator is necessary to compare different countries so that the data can be extrapolated for global comparison. Over the past four weeks, as COVID-19 spread further around the world, testing rates have picked up in most countries. We propose that analysis of the number of diagnostic tests performed in proportion to the number of cases and subsequently deaths in the underlying populations of different countries is the best way to predict what might happen next. We analysed this from the ACALM Big Data research unit.