Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis.

BACKGROUND: The viability and virulence of COVID-19 are complex in nature. Although the relationship between environmental parameters and COVID-19 is well studied across the globe, in India, such studies are limited. This research aims to explore long-term exposure to weather conditions and the role of air pollution on the infection spread and mortality due to COVID-19 in India. METHOD: District-level COVID-19 data from April 26, 2020 to July 10, 2021 was used for the study. Environmental determinants such as land surface temperature, relative humidity (RH), Sulphur dioxide (SO2), Nitrogen dioxide (NO2), Ozone (O3), and Aerosol Optical Depth (AOD) were considered for analysis. The bivariate spatial association was used to explore the spatial relationship between Case Fatality Rate (CFR) and these environmental factors. Further, the Bayesian multivariate linear regression model was applied to observe the association between environmental factors and the CFR of COVID-19. RESULTS: Spatial shifting of COVID-19 cases from Western to Southern and then Eastern parts of India were well observed. The infection rate was highly concentrated in most of the Western and Southern regions of India, while the CFR shows more concentration in Northern India along with Maharashtra. Four main spatial clusters of infection were recognized during the study period. The time-series analysis indicates significantly more CFR with higher AOD, O3, and NO2 in India. CONCLUSIONS: COVID-19 is highly associated with environmental parameters and air pollution in India. The study provides evidence to warrant consideration of environmental parameters in health models to mediate potential solutions. Cleaner air is a must to mitigate COVID-19.

Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study.

BACKGROUND: Studies of the transmission dynamics of COVID-19 have depicted the rate, patterns, and predictions of cases of this pandemic disease. To combat transmission of the disease in India, the government declared a lockdown on March 25, 2020. Even after this strict lockdown was enacted nationwide, the number of COVID-19 cases increased and surpassed 450,000. A positive point to note is that the number of recovered cases began to slowly exceed that of active cases. The survival of patients, taking death as the event that varies by age group and sex, is noteworthy. OBJECTIVE: The aim of this study was to conduct a survival analysis to establish the variability in survivorship of patients with COVID-19 in India by age group and sex at different levels, that is, the national, state, and district levels. METHODS: The study period was taken from the date of the first reported case of COVID-19 in India, which was January 30, 2020, up to June 30, 2020. Due to the amount of underreported data and removal of missing columns, a total sample of 26,815 patients was considered. Kaplan-Meier survival estimation, the Cox proportional hazard model, and the multilevel survival model were used to perform the survival analysis. RESULTS: The Kaplan-Meier survival function showed that the probability of survival of patients with COVID-19 declined during the study period of 5 months, which was supplemented by the log rank test (P<.001) and Wilcoxon test (P<.001) to compare the survival functions. Significant variability was observed in the age groups, as evident from all the survival estimates; with increasing age, the risk of dying of COVID-19 increased. The Cox proportional hazard model reiterated that male patients with COVID-19 had a 1.14 times higher risk of dying than female patients (hazard ratio 1.14; SE 0.11; 95% CI 0.93-1.38). Western and Central India showed decreasing survival rates in the framed time period, while Eastern, North Eastern, and Southern India showed slightly better results in terms of survival. CONCLUSIONS: This study depicts a grave scenario of decreasing survival rates in various regions of India and shows variability in these rates by age and sex. In essence, we can safely conclude that the critical appraisal of the survival rate and thorough analysis of patient data in this study equipped us to identify risk groups and perform comparative studies of various segments in India. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2020.08.01.20162115.