The situation is very dire worldwide as the COVID-19 pandemic continues to worsen.Corona Virus Disease 2019 (COVID-19) is growing rapidly that has infected more than 93 million people and 2.01 million dead worldwide as of January 17, 2021. According to the prior research done by Centers for Disease Control and Prevention direct or indirect contact with an infected subject, droplets emitted by coughing, sneezing or talking or the inhalation of small airborne particles remaining in the air are the root causes of transmitting COVID-19 disease between individuals.
Domingoet al. (2020) stated that, “a number of epidemiological studies have showed that exposure to air pollution is associated with several adverse outcomes, such as acute lower respiratory infections, chronic obstructive pulmonary disease, asthma, cardiovascular diseases, and lung cancer among other serious diseases.
Air pollutants such as sulfur oxides, nitrogen oxides, carbon monoxide and dioxide, particulate matter (PM), ozone and volatile organic compounds (VOCs) are commonly found at high levels in big cities and/or in the vicinity of different chemical industries. An association between air concentrations of these pollutants and human respiratory viruses interacting to adversely affect the respiratory system has been also notified”. The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) was identified in the United States that shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density.
Ding et al. (2020) mapping out the research that is published in The Journal of Infection in Developing Countries. In this research, the nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition.
In summation, the nonlinear relationship was measured by GAMM, which is significant in the sensitive test. In US, the interval effect of TD reminds us that it is crucial to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.
Keywords:
Temperature difference (TD); COVID-19; Population density; Nonlinearity; Generalized Additive Mixed Model (GAMM).
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