ABSTRACT
The exponential model is a commonly used epidemic model for the analysis of initial outbreak data due to an infectious disease. But there have been questions about its validity in practice. This article examines this issue through statistical analysis on 22 countries' initial COVID-19 outbreak data provided by the World Health Organization. For each of 22 countries, a general regression analysis is conducted for the cumulative confirmed cases. Our regression function is a 3-5 piecewise fitted functions which are obtained via regression analysis
ABSTRACT
Big data are emerging paradigm that can be applied to huge volume of valuable data, which are often generated or collected at a fast velocity from a wide variety of rich data sources. These data can be of a wide variety of formats and/or type;they can be at different levels of veracity. Embedded in these data is implicit, previously unknown and useful information and knowledge that can be discovered by data science. Healthcare and medical data such as epidemiological data for disease like coronavirus disease 2019 (COVID-19) are examples of big data. Analyzing and mining these data led to discovery of knowledge and information about the disease, which in turn help people to get better understanding of the disease so that they could take parts in preventing or slowing down the spread of the disease, and/or protecting themselves from the disease. Hence, in this paper, we present a data science engine to analyze and mine COVID-19 data. As COVID-19 cases may not evenly distributed among spatial locations and/or evenly distributed throughout the entire period of pandemic, our engine conducts spatial-temporal data science to reveal important information and knowledge about epidemiological characteristics of the disease across different spatial locations and its temporal trends. Evaluation on real-life COVID-19 data demonstrates the effectiveness of our engine in conducting spatial-temporal data science of COVID-19 data.