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Background The aim of the study was to visualize the global spread of the COVID-19 pandemic over the first 90 days, through the principal component analysis approach of dimensionality reduction. Methods This study used data from the Global COVID-19 Index provided by PEMANDU Associates. The sample, representing 161 countries, comprised the number of confirmed cases, deaths, stringency indices, population density and GNI per capita (USD). Correlation matrices were computed to reveal the association between the variables at three time points: day-30, day-60 and day-90. Three separate principal component analyses were computed for similar time points, and several standardized plots were produced. Results Confirmed cases and deaths due to COVID-19 showed positive but weak correlation with stringency and GNI per capita. Through principal component analysis, the first two principal components captured close to 70% of the variance of the data. The first component can be viewed as the severity of the COVID-19 surge in countries, whereas the second component largely corresponded to population density, followed by GNI per capita of countries. Multivariate visualization of the two dominating principal components provided a standardized comparison of the situation in the161 countries, performed on day-30, day-60 and day-90 since the first confirmed cases in countries worldwide. Conclusion Visualization of the global spread of COVID-19 showed the unequal severity of the pandemic across continents and over time. Distinct patterns in clusters of countries, which separated many European countries from those in Africa, suggested a contrast in terms of stringency measures and wealth of a country. The African continent appeared to fare better in terms of the COVID-19 pandemic and the burden of mortality in the first 90 days. A noticeable worsening trend was observed in several countries in the same relative time frame of the disease’s first 90 days, especially in the United States of America.

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Abstract-Wavelet neural networks (WNNs) are a variant of artificial neural networks (ANNs), which are powerful mathematical modeling techniques that are used to model and study a variety of complex real-life problems. During the unsupervised learning stage, the translation vectors of the hidden nodes need to be determined. The conventional k-means and fuzzy c-means clustering algorithms have been used for this purpose. Nevertheless, these methods are prone and sensitive to initial cluster centers that have been randomly chosen. In this paper, a new hybrid clustering algorithm is presented. The type-2 fuzzy c-means clustering algorithm is hybridized with the metaheuristic harmony search algorithm. The new algorithm is then used to determine the translation vectors of WNNs. By incorporating the evolutionary harmony search algorithm in the clustering algorithm, the hybrid algorithm is able to escape from local minima while searching for the global minimum. To validate the effectiveness of the proposed algorithm, a real world problem of epileptic seizure classification problem is studied. Simulation results showed that the hybridized algorithm outperformed the stand-alone clustering algorithms.Index Terms-Wavelet neural networks, k-means clustering, fuzzy c-means clustering, harmony search algorithm, epileptic seizure classification.