分析 Uttarakhand 的 COVID-19 爆发:人口学见解和未来预防大流行病的战略

Gaurav Joshi, Akshara Pande, Omdeep Gupta, Anoop Nautiyal, Sanjay Jasola, Prashant Gahtori
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摘要

印度的北阿坎德邦(Uttarakhand)又称 "Dev Bhoomi "或 "神的居所",依偎在喜马拉雅山的怀抱中。它拥有丰富的自然丘陵环境,人口总数超过以色列、瑞士和香港等。在本报告中,我们将仔细研究 COVID-19 对全邦高地/丘陵和低地/平原 bhabar 地区的影响。 我们使用 Python 命令行从北阿坎德邦政府的 Covid-19 健康公报中获取了 12 个月的数据。数据分析包括阳性百分比/COVID-19 阳性率、康复率、死亡人数和加倍率,以及详细的统计分析。 在第一轮分析中,发现居住在德拉敦、哈里瓦、U. S. 纳加尔和奈尼塔尔四个地区的低地居民更易感染,阳性病例高峰期为第 21-26 周。另一方面,查莫利(Chamoli)、保利加瓦尔(Pauri Garhwal)和鲁德拉普拉亚格(Rudraprayag)等地形为丘陵的地区最不容易受到感染,报告的阳性病例多发生在第 30 至 31 周。鲁德拉普拉亚格丘陵地区的恢复率最高。确诊病例的多元回归与死亡病例、康复病例、其他病例和检测病例变量的关系得到了解释(R2 adj=0.99)。分析还显示,从 2020 年 5 月的最后一周到 6 月的第一周,翻倍率非常高。 高海拔丘陵地区 COVID-19 病例的减少可能与通气功能增强、动脉氧输送改善和组织氧合增加等因素有关。这项研究的结果为今后的大流行病预防工作提供了启示。总结当前的研究,我们提出了预防下一次大流行的 5 点解决方案。值得注意的是,虽然这项研究表明这些因素与 COVID-19 病例的减少之间存在潜在联系,但要确定两者之间的联系还需要进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing Uttarakhand's COVID-19 Outbreak: Demographic Insights and Strategies for Future Pandemic Prevention
The Indian state of Uttarakhand, also known as "Dev Bhoomi" or the Abode of Gods, is snuggled in the lap of the Himalayas. It is endowed with an abundant natural hilly environment and occupies more people in total than Israel, Switzerland, Hong Kong, etc. In this report, we look closely at the impact of COVID-19 on both high land/ hilly and low land/ plain bhabar zones across the state. The data was retrieved from the Uttarakhand Government Covid-19 health bulletin for 12 months using the Python command line. The data analysis covers percentage positivity/COVID-19 positivity rate, recovery, deceased and doubling rate, along with a detailed statistical analysis. In the first wave, low-land- inhabitants residing in 4 districts, Dehradun, Haridwar, U. S. Nagar and Nainital, were found more vulnerable, with a peak positive case during the 21st – 26th week. On the other hand, the districts with exclusive hilly terrains, including Chamoli, Pauri Garhwal, and Rudraprayag, were found to be the least susceptible and reported a high number of positive cases between the 30th and 31st week. The highest recovery rate was found to be in the hilly district of Rudraprayag. The multiple regression with confirmed cases was explained in relation to deceased, recovered, other, and tested variables (R2 adj= 0.99). The analysis also revealed a very high doubling rate from the last week of May to the first week of Jun 2020. The reduced number of COVID-19 cases in high-altitude hilly districts may be associated with factors such as enhanced ventilation, improved arterial oxygen transport, and increased tissue oxygenation. The findings from this study offer insights that can contribute to future pandemic prevention efforts. Summarising the current study, we have suggested 5-point solutions for preventing the next pandemic. It's important to note that while this study suggests a potential link between these factors and lower COVID-19 cases, further research is needed to establish a conclusive connection.
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