How Climate Variables Influence the Spread of SARS-CoV-19 in the United States

Abstract

During the 2020 Coronavirus pandemic, several scientific types of research investigated the causes of high transmissibility and deaths caused by SARS-CoV-2. Among the spreading factors of the disease, it is known that there is an association between temperature and infected people. However, the studies that identified this phenomenon explored an association relationship, which is weaker and does not allow the identification of which variable would be the cause. This study aimed to analyze the impact of temperature variations and other climatic variables on the infection rate of COVID-19. Data were extracted from weather stations in the United States, which were segregated by county and day. Daily COVID-19 infections and deaths per county were also collected. Two models were used: the first model to analyze the temperature and the number of infected cases and the second model to evaluate the variables of temperature, precipitation, and snow in relation to COVID-19 infection. Model 1 shows that an increase in temperature at time zero caused a decrease in the number of infected cases. Meanwhile, a decrease in temperature after the temperature shock was associated with an increase in the number of cases, which tended to zero overall. A 1% increase in temperature caused a 0.002% decrease in the number of cases. The results suggested a causal relationship between the average temperature and number of CODIV-19 cases. Model 2, which includes temperature, precipitation, and snow shows that an increase in temperature resulted in a 0.00154% decrease response. There was no significant effect of increased precipitation and snow on the infection rate with COVID-19.

The Impact of the COVID-19 on Economic Sustainability—A Case Study of Fluctuation in Stock Prices for China and South Korea

“…As can be seen in Figure 2, specific past day q obtained similar p-values corresponding to the six models. Adjacent q values, such as (12,13,14) or (19,20,21), were also located in a similar range. Compared to the Korean model in Figure 3, p-values of the Chinese model in the past days were less concentrated but still continuous and coherent along past days in a global vision.…”

“…Furthermore, Hsieh et al [14] found that the volatility of stock prices increased substantially during crisis periods (e.g., the SARS outbreak, the 2002 anti-terror war, and the Enron scandal). To date, the humanitarian COVID-19 crisis has lasted for one year, and no one knows what the world economy will look like in 3 or 6 months [15].…”

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