Social and Environmental Factors Influencing COVID-19 Transmission and Mortalities in Developing and Developed Nations

Soheli Chowdhury, Majeedul Chowdhury
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Abstract

Background: The study sought to establish environmental and social factors that influenced the transmission and mortalities of COVID-19 in developing and developed nations. The factors that were assessed included temperature, average age of the population, urbanization, population density, and percentage of old-aged people in the population. The dependent variables were COVID-19 transmission and COVID-19-related deaths. Methods: The study employed a pragmatism research philosophy. It also relied on a deductive research approach and a descriptive research design. It adopted a mixed-method approach as it used both qualitative and quantitative data. It was a cross-sectional study, given its data measurement at a particular point in time. Data was analyzed and presented using descriptive techniques. Results: Statistical analyses were conducted to quantify the relationships between various factors and COVID-19 outcomes. A Kendall’s Tau test revealed a robust negative correlation between COVID-19 cases and temperature (Tb = -0.560, p<0.005). This result was further confirmed by Spearman’s rank correlation, showing a strong negative correlation with r(13) = -0.684, p<0.007. Similarly, a strong negative correlation was observed between COVID-19 deaths and average annual temperature using both Kendall’s Tau (Tb = -0.495, p<0.014) and Spearman’s rank correlation (r(13) = -0.648, p<0.012). Age emerged as a significant factor, with a strong positive correlation found between age and both COVID-19 infections (Tb = 0.516, p<0.010; r(13) = 0.670, p<0.009) and COVID-19-related mortalities (r(13) = 0.516, p<0.029). Urbanization was also positively correlated with COVID-19 infections (Tb = 0.530, p<0.008; r(13) = 0.640, p<0.014) and COVID-19 deaths (Tb = 0.398, p<0.048; r(13) = 0.561, p<0.037). Interestingly, no significant correlation was found between population density and COVID-19 infections or deaths in both developed and developing countries, as evidenced by Kendall’s Tau (TB = 0.331, p<0.1; Tb = 0.133, p<0.511) and Spearman’s rank correlation (r(13) = 0.425, p<0.130; r(13) = 0.161, p<0.583), respectively. Moreover, the percentage of elderly individuals in a country exhibited a strong positive correlation with both COVID-19 infections (Tb = 0.464, p<0.021; r(13) = 0.642, p<0.013) and COVID-19-related deaths (r(13) = 0.541, p<0.046).Conclusion: The study focused on social, demographic, and environmental factors influencing COVID-19 incidence and mortality in developing and developed nations. The study highlights significant COVID-19 transmission and mortality disparities between developed and developing countries. Developed countries exhibited higher infection and mortality rates, coupled with elevated death rates per million and infection rates per million, as compared to their developing counterparts. The research identified a correlation between lower average annual temperatures and increased mortality in developed countries. Contrary to this, high average annual temperatures were associated with a decline in COVID-19 infections.Moreover, developed countries, characterized by higher urbanization levels, population densities, and percentages of aged individuals, experienced elevated COVID-19 infection rates. The study also unveiled a positive correlation between age and COVID-19 infections, with developed countries hosting significantly older populations than their developing counterparts. However, population density did not clearly correlate with COVID-19 infections or deaths.
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影响发展中国家和发达国家 COVID-19 传播和死亡率的社会和环境因素
背景:该研究旨在确定影响发展中国家和发达国家 COVID-19 传播和死亡率的环境和社会因素。评估的因素包括气温、人口平均年龄、城市化程度、人口密度和人口中老年人的比例。因变量为 COVID-19 传播率和与 COVID-19 相关的死亡人数。研究方法研究采用了实用主义研究理念。它还依赖于演绎研究方法和描述性研究设计。由于采用了定性和定量数据,因此研究采用了混合方法。这是一项横断面研究,因为其数据测量是在一个特定的时间点进行的。数据采用描述性技术进行分析和展示。研究结果对各种因素与 COVID-19 结果之间的关系进行了量化统计分析。Kendall's Tau 检验显示,COVID-19 病例与温度之间存在显著的负相关(Tb = -0.560,p<0.005)。斯皮尔曼等级相关性进一步证实了这一结果,r(13) = -0.684,p<0.007,显示出很强的负相关。同样,通过 Kendall's Tau(Tb = -0.495,p<0.014)和 Spearman's rank correlation(r(13) = -0.648,p<0.012),也观察到 COVID-19 死亡数与年平均气温之间存在很强的负相关。年龄是一个重要因素,年龄与 COVID-19 感染(Tb = 0.516,p<0.010;r(13) = 0.670,p<0.009)和 COVID-19 相关死亡率(r(13) = 0.516,p<0.029)之间呈强正相关。城市化也与 COVID-19 感染(Tb = 0.530,p<0.008;r(13) = 0.640,p<0.014)和 COVID-19 死亡(Tb = 0.398,p<0.048;r(13) = 0.561,p<0.037)呈正相关。有趣的是,在发达国家和发展中国家,人口密度与 COVID-19 感染或死亡之间没有发现明显的相关性,这分别体现在 Kendall's Tau(TB = 0.331,p<0.1;Tb = 0.133,p<0.511)和 Spearman's rank correlation(r(13) = 0.425,p<0.130;r(13) = 0.161,p<0.583)上。此外,一个国家的老年人比例与 COVID-19 感染率(Tb = 0.464,p<0.021;r(13) = 0.642,p<0.013)和 COVID-19 相关死亡人数(r(13) = 0.541,p<0.046)都呈现出很强的正相关性:研究重点关注了影响发展中国家和发达国家 COVID-19 发病率和死亡率的社会、人口和环境因素。研究强调了发达国家和发展中国家在 COVID-19 传播和死亡率方面的显著差异。与发展中国家相比,发达国家的感染率和死亡率更高,每百万人的死亡率和每百万人的感染率也更高。研究发现,发达国家年平均气温较低与死亡率上升之间存在相关性。与此相反,年平均气温高与 COVID-19 感染率下降有关。此外,发达国家的城市化水平、人口密度和老龄人口比例较高,COVID-19 感染率也较高。研究还揭示了年龄与 COVID-19 感染之间的正相关关系,发达国家的人口年龄明显高于发展中国家。然而,人口密度与 COVID-19 感染或死亡并无明显关联。
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