Understanding COVID-19

What is it about?

This paper supplements the findings of Jones and Strigul (2020) for COVID-19’s chaotic behavior. This study examined the SI influence on COVID-19 case counts both in country-wide and in finer granular geographical data (‘barangay’ or village level COVID-19 case counts in the Philippines). The study found a high SI correlation in majority of the COVID-19 country-wide analysis (Brunei, Cambodia, Singapore, Philippines, the US, UK, Malaysia, Japan, South Korea and Indonesia). On the other hand, by analyzing the finer granular geographical data, this study found that SI effects were reduced when the population density is increased.

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Photo by Martin Sanchez on Unsplash

Photo by Martin Sanchez on Unsplash

Why is it important?

Using the findings of the paper, the authors believe that the effect of population density, its potential influence on the SI and consequent impact on COVID-19 interventions in addition to understanding chaotic behaviour should be considered in the design of future non-linear models which hopefully will influence decision-making and ultimately result to better policies and interventions equally advantageous to both the country’s economy and health care system.