Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020).

INTRODUCTION: and Aims: No valid treatment or preventative strategy has evolved till date to counter the SARS CoV 2 (Novel Coronavirus) epidemic that originated in China in late 2019 and have since wrought havoc on millions across the world with illness, socioeconomic recession and death. This analysis was aimed at tracing a trend related to death counts expected at the 5th and 6th week of the COVID-19 in India. MATERIAL AND METHODS: Validated database was used to procure global and Indian data related to coronavirus and related outcomes. Multiple regression and linear regression analyses were used interchangeably. Since the week 6 death count data was not correlated significantly with any of the chosen inputs, an auto-regression technique was employed to improve the predictive ability of the regression model. RESULTS: A linear regression analysis predicted average week 5 death count to be 211 with a 95% CI: 1.31-2.60). Similarly, week 6 death count, in spite of a strong correlation with input variables, did not pass the test of statistical significance. Using auto-regression technique and using week 5 death count as input the linear regression model predicted week 6 death count in India to be 467, while keeping at the back of our mind the risk of over-estimation by most of the risk-based models. CONCLUSION: According to our analysis, if situation continue in present state; projected death rate (n) is 211 and467 at the end of the 5th and 6th week from now, respectively.

Reference equation for spirometry interpretation for Eastern India.

INTRODUCTION: Spirometry measurements are interpreted by comparing with reference values for healthy individuals that have been derived from multiple regression equations from earlier studies. There are only two such studies from Eastern India, both by Chatterjee et al., one each for males and females. These are however single center and approximately two decades old studies. AIMS: (1) to formulate a new regression equation for predicting FEV1 and FVC for eastern India and (2) to compare the results to the previous two studies by Chatterjee et al. MATERIALS AND METHODS: Healthy nonsmokers were recruited through health camps under the initiative of four large hospitals of Kolkata. Predicted equations were derived for FEV1, FVC and FEV1/FVC in males and females separately using multiple linear regression, which were then compared with the older equations using Bland-Altman method. RESULTS: The Bland-Altman analyses show that the mean bias for females for FVC was 0.39 L (95% limits of agreement 1.32 to -0.54 L) and for FEV1 was 0.334 L (95% limits of agreement of 1.08 to -0.41 L). For males the mean bias for FEV1 was -0.141 L, (95% limits of agreement 0.88 to -1.16 L) while that for FVC was -0.112 L (95% limits of agreement 0.80 to -1.08 L). CONCLUSION: New updated regression equations are needed for predicting reference values for spirometry interpretation. The regression equations proposed in this study may be considered appropriate for use in current practice for eastern India until further studies are available.