What is it about?
When the COVID-19 pandemic first struck, governments adopted different measures to contain it. Some of these measures were more successful than others. To find out which measures were working, a team of researchers conducted a study in 2020. They had three simple goals: 1) to analyze COVID-19 metrics from different countries 2) to look at similar trends across countries, and 3) to identify countries with atypical trends. They developed a numerical method that could include multiple variables. Then, they used this method to study COVID-19 deaths and cases in 208 different countries on different days. This helped them understand the seriousness of the spread in different countries. Next, they studied the cases and deaths in conjunction. How many cases turned into deaths, over time? Which countries displayed atypical trends in their case and death counts? These questions helped them understand if the management strategies of these countries were effective. They found that in most countries, it took 16 days for a positive diagnosis of COVID-19 to turn into a death. The number of cases were ahead of the death counts by 32 days. This suggests that there is a clear lag between cases and deaths. In countries with inadequate healthcare systems, the number of deaths were found to be higher.
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Why is it important?
The new method can provide new insights into the case and death counts in different countries. It can prepare governments by helping them predict a surge in cases. Most importantly, it can help identify successful measures used to manage the pandemic. Implementing these measures can help countries minimize losses due to COVID-19. KEY TAKEAWAY: Authors developed a numerical method to analyze and compare COVID-19 data. This method is highly effective at delineating the trends in case and death counts due to the COVID-19 pandemic in different countries. It could help identify successful pandemic control measures.
Read the Original
This page is a summary of: Cluster-based dual evolution for multivariate time series: Analyzing COVID-19, Chaos An Interdisciplinary Journal of Nonlinear Science, June 2020, American Institute of Physics, DOI: 10.1063/5.0013156.
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