A Comparison of the Leading Indicators of COVID-19 Deceased Rates in New Delhi, India during the First Quarter of the Novel Coronavirus Pandemic in 2020

Rian Puri
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Abstract

This paper analyzes the 5-, 14- and 21-day cumulative positivity rate vis-à-vis the COVID-19 deceased rate of each time period for the first four months of COVID-19 from April 2020 to September 2020 in New Delhi, India with the intention of getting insight into the relationship between the two and to evaluate the leading indicators of COVID-19 deceased rates using MATLAB programming language.Most news reports and media typically quote the 14-day positivity rate to know where Delhi is on the “curve” of corona virus COVID-19 cases. The 5-day positivity rate is marginally to slightly positively correlated with the deceased rate at +0.02 whereas the 14-day positivity rate has a negative correlation of -0.06 with the deceased rate. The higher negative correlation of the 21-day positivity rate of –0.12 with the deceased rate indicates that there are more recoveries over the subsequent 7-day period after 14 days which is also in-line with medical and health professionals who advocate a 14-day quarantine to recover from the virus if the population has tested positive. Statistical correlation and regression Analysis of Variants (ANOVA) analysis indicates that the 21-day positivity rate is negatively correlated to the deceased rate at -0.12 with an R2 (coefficient of determination) of 1.3% compared to correlation coefficients of –0.06 and +0.02 and R2 of 0.28% and 0.03% respectively for the 14-day and 5-day cumulative positivity rates.The 21-day rate is most relevant leading indicator in comparison with the 14-day and 5-day rate and is statistically significant at the 75% Confidence Interval. This implies a Regression equation of Deceased rate over 21 days = 0.0248 - 6.66% x 21 Day Positivity Rate + ErrorThis implies that corona hotspots should ideally quarantine for a longer 21-day period rather than the 14-day period typically advocated especially in areas where there is a stress on the healthcare facilities to avoid burdening the hospitals for non-critical cases.The paper suggests further avenues to explore this relationship including a split of the type of tests namely RT-PCR vs RAT antigen tests as RAT yields a higher 53% of false negatives vis-a-vis the COVID-19 deceased rate and an analysis over a longer time period of say 12 months to analyze the relationship of the delta (change) of the positivity rate and the delta of the deceased rate with statistical significance testing at the 95% and 99% confidence intervals. Longer period analysis would give further insight into leading indicators of rising and decreasing infections which could be used by government and health practitioners so as to proactively increase number of vaccines, health practitioners and hospital ICU beds in advance of a surge in infections and deceased rates.
2020年新型冠状病毒大流行第一季度印度新德里COVID-19死亡率领先指标比较
本文分析了印度新德里2020年4月至2020年9月新冠肺炎疫情前4个月各时间段的5天、14天和21天累计阳性率与-à-vis COVID-19病死率,旨在深入了解两者之间的关系,并利用MATLAB编程语言评估COVID-19病死率的领先指标。大多数新闻报道和媒体通常引用14天的阳性率来了解德里在冠状病毒COVID-19病例“曲线”上的位置。5天的阳性率与死亡率呈微正相关(+0.02),而14天的阳性率与死亡率呈负相关(-0.06)。21天的阳性率(-0.12)与死亡率的负相关性较高,表明14天后的7天内康复率较高,这也符合医疗卫生专业人士的观点,即如果人群检测呈阳性,则应进行14天的隔离以恢复病毒。统计相关和变异回归分析(ANOVA)分析表明,21天的阳性率与死亡率呈负相关(-0.12),R2(决定系数)为1.3%,而14天和5天的累积阳性率的相关系数分别为-0.06和+0.02,R2分别为0.28%和0.03%。与14天和5天的价格相比,21天的价格是最相关的领先指标,在75%的置信区间内具有统计学意义。这意味着21天内的死亡率= 0.0248 - 6.66% x 21天的阳性率+错误的回归方程。这意味着,理想情况下,冠状病毒热点地区应该隔离更长时间的21天,而不是通常提倡的14天,特别是在医疗机构压力较大的地区,以避免医院因非关键病例而增加负担。本文提出了进一步探索这种关系的途径,包括将检测类型分开,即RT-PCR与RAT抗原检测,因为与COVID-19死亡率相比,RAT产生更高的53%的假阴性,并在更长的时间段(例如12个月)内进行分析,以分析阳性率的增量(变化)与死亡率的增量之间的关系,并在95%和99%的置信区间进行统计显著性测试。长期分析将进一步深入了解感染上升和下降的主要指标,政府和卫生从业人员可以使用这些指标,以便在感染率和死亡率激增之前主动增加疫苗、卫生从业人员和医院重症监护病房床位的数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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