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BACKGROUND As coronavirus disease 2019 (COVID-19) rapidly spreads worldwide, some attention has focused on studying the relationship between atmospheric pollution and the disease. Fine particulate matter with a diameter of 2.5 μm or less (PM2.5) is the most harmful form of atmospheric pollution because it can penetrate deep into the lungs and enter the blood stream unfiltered. OBJECTIVE To investigate the effects of risk factors associated with atmospheric pollution on the spread of COVID-19, taking Italy as an example. METHODS For descriptive statistical methods, the statistics of minimum, maximum, mean, median, standard deviation, rank, mean rank, and rank summation as well as time-series charts were used to depict the profiles of the relations among these input and output variables. For inferential statistical methods, Mann-Whitney U tests and Pearson, Kendall, and Spearman correlation tests were introduced to obtain more reliable conclusions regarding the study topic. RESULTS Statistical analysis showed that higher PM2.5 concentration and an industrial orientation may increase the spread of COVID-19. Moreover, 3-week time-shift and nonlinear relations may be optimal for use with PM2.5 concentration for predicting the spread of COVID-19. CONCLUSIONS Our results empirically proved that certain risk factors associated with atmospheric pollution (such as PM2.5 concentration and industrial orientation) affect the spread of COVID-19. In future analyses, additional factors that may affect the outcomes of COVID-19 as well as data from other countries will be included to obtain more complete conclusions. CLINICALTRIAL None. |
