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The Spatiotemporal Characteristics and Climatic Factors of COVID-19 in Wuhan, China

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The Spatiotemporal Characteristics and Climatic Factors of COVID-19 in Wuhan, China sustainability Article The Spatiotemporal Characteristics and Climatic Factors of COVID-19 in Wuhan, China Qiaowen Lin 1, Guoliang Ou 2,*, Renyang Wang 3, Yanan Li 4, Yi Zhao 5 and Zijun Dong 2 1 School of Economic and Management, China University of Geosciences, Wuhan 430074, China; [email protected] 2 School of Construction and Environmental Engineering, Shenzhen Polytechnic, Shenzhen 518055, China; [email protected] 3 New Economic Research Institute, Ningbo University of Finance & Economics, Ningbo 315000, China; [email protected] 4 School of Management, Wuhan Institute of Technology, Wuhan 430205, China; [email protected] 5 School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China; [email protected] * Correspondence: [email protected]; Tel.: +86-755-26731157 Abstract: COVID-19 is threatening the whole world. This paper aims to explore the correlation between climatic factors and the morbidity of COVID-19 in Wuhan, China, mainly by using a geographic detector and GWR model. It was found that the response of the morbidity of COVID-19 to meteorological factors in Wuhan is different at different stages. On the whole, the morbidity of COVID-19 has a strong spatial aggregation, mainly concentrated in the central area of Wuhan City. There is a positive correlation between wind speed and the spread of COVID-19, while temperature has a negative correlation. There is a positive correlation between air pressure and the number of COVID-19 cases. Rainfall is not significantly correlated with the spread of COVID-19. It is concluded Citation: Lin, Q.; Ou, G.; Wang, R.; Li, Y.; Zhao, Y.; Dong, Z. The that wind speed, relative humidity, temperature, and air pressure are important meteorological Spatiotemporal Characteristics and factors affecting the spread of COVID-19 in Wuhan. Any two variables have greater interaction Climatic Factors of COVID-19 in with the spatial distribution of the incidence rate of COVID-19 than any one factor alone. Those Wuhan, China. Sustainability 2021, 13, findings not only provide a new insight for the key intervention measures and the optimal allocation 8112. https://doi.org/10.3390/ of health care resources accordingly but also lay a theoretical foundation for disease prevention, su13148112 disease intervention and health services. Academic Editor: Keywords: climatic factors; weather; COVID-19; morbidity; Wuhan Mohammad Valipour Received: 27 May 2021 Accepted: 17 July 2021 1. Introduction Published: 20 July 2021 A pneumonia outbreak of unknown cause, detected in Wuhan, China, was first Publisher’s Note: MDPI stays neutral reported to the World Health Organization (WHO) Country Office in China on 31 December with regard to jurisdictional claims in 2019. By 2 January 2020, 41 admitted patients with confirmed infections by this virus, published maps and institutional affil- now named Corona Virus Disease 2019 (COVID-19), had been identified in hospitals in iations. China [1]. At 10:00 on 23 January 2020, Wuhan New Coronavirus Epidemic Prevention and Control Headquarters announced that the city bus, subway, ferry and long-distance passenger transport were suspended. The airport and train stations were temporarily closed. Wuhan was put into lockdown. Case numbers in China stabilized at around 80,000 by mid-February [2]. On 12 March 2020, WHO declared COVID-19 to be a global Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. health emergency [3]. There have been 94,963,847 confirmed cases of COVID-19 globally, This article is an open access article including 2,050,857 deaths, reported to the WHO on 20 January 2021 [4]. distributed under the terms and Since the beginning of this pandemic, researchers across the world have shown great conditions of the Creative Commons interest in understanding its influences and driving mechanisms. It is widely acknowledged Attribution (CC BY) license (https:// that saliva, nasal discharge, or airborne particles lead to the spread of COVID-19 [5–7], creativecommons.org/licenses/by/ while person-to-person transmission has been proved to be the likely route [8–11]. Elderly 4.0/). people and children are at a higher risk of coronavirus infection [12–16]. In particular, it Sustainability 2021, 13, 8112. https://doi.org/10.3390/su13148112 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 8112 2 of 17 has also been found that weather factors, mainly including temperature, wind speed and humidity, are considered as the main predictors of coronavirus disease, due to the viability, transmission and range of spread of the virus [17]. However, most of those studies address the impact between a single climatic factor and COVID-19 incidences [18–20]. The interaction of those climatic parameters with COVID-19 is still unknown. Climatic factors are characterized by regional differences. Each climatic factor does not exist alone, and multiple factors often interact with each other. In addition, Wuhan was the first city where the virus was discovered. However, the spatiotemporal characteristics and climatic factors of COVID-19 in Wuhan have not been explored. Therefore, to fill this research gap, this paper has analyzed in detail the correlation between multiple meteorological factors and COVID-19 in Wuhan, which can hopefully provide new insights for policymakers and medical institutions regarding the real-life situation, to better determine the key intervention measures and the optimal allocation of health care resources accordingly. The remainder of the paper is organized as follows. Firstly, the related literature is reviewed. Secondly, the spatial correlation and differences of the incidence rate of COVID- 19 in Wuhan are analyzed by using global spatial autocorrelation. Thirdly, local Getis–Ord ∗ GI is used to conduct hotspot detection, to analyze its local autocorrelation. Based on this detection, the influence of climatic factors on incidence rate is analyzed by comparing the traditional linear regression model with geographically weighted regression (GWR). Fourthly, the interaction between multiple meteorological factors and COVID-19 is further identified by a geographic detector. Finally, the conclusions of this study are given. 2. Climatic Indicators and COVID-19 Recent literature has demonstrated that different climatic indicators, such as tempera- ture, humidity, and wind speed, can significantly affect the number of COVID-19 cases and mortality [21,22]. The majority of studies show that the relationship between temperature and COVID-19 cases is mostly place- and facility-specific [18]. A significant relationship is found between average and minimum temperatures in COVID-19 cases in New York [23]. It was observed that the increase of one unit in the daytime temperature range resulted in a decrease in the number of COVID-19 cases by approximately 6.4 times in Italy [19]. Lower temperature levels are related to the higher number of COVID-19 cases on a particular day, according to the study in Turkey [21]. A study conducted in 17 different cities of China advocated that a 1 ◦C increase in ambient temperature and diurnal temperature range was associated with a decrease in the number of daily confirmed cases [22]. However, in one study conducted in Jakarta, Indonesia, no correlation between temperature and the number of COVID-19 cases was found [20]. There is also a body of literature that considers humidity as one of the crucial at- mospheric factors in predicting the transmission of COVID-19. Humidity is proved to have a negative relationship with the virus outbreak speed [19]. Another study shows that absolute humidity has significant negative effects on confirmed case counts for four cities in China [22]. It was suggested that the relation between humidity and the number of COVID-19 cases was the most significant on the particular day of the study in Turkey. This indicates that an increase in humidity results in a decline in the number of COVID-19 cases [21]); however, no significant association between COVID-19 cases and absolute humidity was found in China [24]. There is relatively less research on the relationship between wind speed and the spread of COVID-19. The low speed of the wind was observed to be significant [19]. One study showed that there was no significant correlation between wind speed and the number of COVID-19 cases [23]. However, it is interesting to find that the average wind speed over 14 days has the highest correlation with the number of COVID-19 cases in Turkey. Higher wind speed was also related to a greater number of cases [21]. Sustainability 2021, 13, 8112 3 of 17 The correlation between rainfall and the number of COVID-19 cases has also been examined in many studies. Evidence shows that rainfall is negatively and weakly correlated with the spread of COVID-19 [23]. However, some countries with higher rainfall showed an increase in the number of COVID-19 cases. It is advocated that an increase of one inch of rainfall per day was associated with an increase of 56 COVID-19 cases per day [25]. No correlation between rainfall and the number of COVID-19 cases was found in Iran [19], which is consistent with the study in Indonesia [20]. The literature mentioned above has improved our understanding of the spread of COVID-19. However, there are still some gaps that need to be addressed. On the one hand, the existing literature mainly used correlation and regression analysis methods, but geographically weighted regression, which can show spatial distribution characteristics, has been largely neglected. On the other hand, the interaction of those climatic parameters with COVID-19 has barely been studied, which falls short of revealing a deeper understanding of the dynamics of the pandemic. Therefore, in this study, the interaction of those climatic parameters is emphasized by using the geographic detector and GWR model, which contributes to addressing those gaps. 3. The Study Area Wuhan is the capital of Hubei Province in central China (Figure1).
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