Abstract
Time analysis of the course of an infectious disease epidemic is a critical way to understand the dynamics of pathogen transmission and the effect of population scale interventions. Computational methods have been applied to the progression of the COVID-19 outbreak in five different countries (Ireland, Germany, UK, South Korea and Iceland) using their reported daily infection data. A Gaussian convolution smoothing function constructed a continuous epidemic line profile that was segmented into longitudinal time series of mathematically fitted individual logistic curves. The time series of fitted curves allowed comparison of disease progression with differences in decreasing daily infection numbers following the epidemic peak being of specific interest. A positive relationship between the rate of declining infections and countries with comprehensive COVID-19 testing regimes existed. Insight into different rates of decline infection numbers following the wave peak was also possible which could be a useful tool to guide the reopening of societies. In contrast, extended epidemic timeframes were recorded for those least prepared for large-scale testing and contact tracing. As many countries continue to struggle to implement population wide testing it is prudent to explore additional measures that could be employed. Comparative analysis of healthcare worker (HCW) infection data from Ireland shows it closely related to that of the entire population with respect to trends of daily infection numbers and growth rates over a 57-day period. With 31.6% of all test-confirmed infections in healthcare workers (all employees of healthcare facilities), they represent a concentrated 3% subset of the national population which if exhaustively tested (regardless of symptom status) could provide valuable information on disease progression in the entire population (or set). Mathematically, national population and HCWs can be viewed as a set and subset with significant influences on each other, with solidarity between both an essential ingredient for ending this crisis.
Highlights
At the time of writing, the coronavirus disease 2019 (COVID-19) pandemic has passed its first peak in Europe and been suppressed in several Asian and Australasian countries, but is growing in the Americas [1]
Confirmed COVID-19 infection data for Germany, UK, South Korea and Iceland were taken from the open COVID-19 Data Repository provided by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University [10]
Five Global North countries (Ireland, Germany, UK, South Korea and Iceland) of varying population and epidemic scales were selected for data cross comparison
Summary
Confirmed COVID-19 infection data for Germany, UK, South Korea and Iceland were taken from the open COVID-19 Data Repository provided by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University [10]. Daily confirmed infected case data was processed in three stages. A moving average was calculated over a five-day window, allowing smaller windows at endpoints so that the Correlation of decline from peak and healthcare workers COVID-19 infection data total number of data points are preserved. A series of logistic curves were fitted to the epidemic profile line making it possible to divide the epidemic timeline into growth, peak and decline stages. Logistic functions occur regularly in the study of epidemics and fitting infection data to series of logistic functions have been utilized previously [8]
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