Demographic and clinical factors associated with early hospital Coronovirus disease 2019 deaths in a low middle income setting: A record-based analysis of 20,641 deaths from India.

Understanding the demographic and clinical characteristics cases and deaths is essential for better clinical and public health management of coronavirus disease-2019 (COVID-19) in resource-limited settings. We analyzed the COVID-19 deaths reported from India, to describe the demographic and clinical characteristics and identify the factors associated with early hospital deaths (within one day of hospitalization) and survival duration. We conducted a record review of the publicly available data on COVID-19 deaths reported between January 30th and November 30th, 2020. After imputation for missing data, we calculated unadjusted and adjusted prevalence ratio, and regression coefficient for factors associated with early hospital death and survival duration. Of the 20,641 COVID-19 deaths analyzed: a) 14,684 (71.1%) were males; b) 10,134 (50.9%) were aged <65 years; c) 9,722 (47.1%) treated at public hospitals and d) 5405 (27.1%) were early hospital deaths. Breathlessness was the most common presenting complaint. Diabetes (11,075,53.7%), hypertension (95,77,46.5%) and coronary artery disease (2,821,13.7%) were the common comorbidities. After adjustment, early hospital death was significantly higher among patients aged <65 years, without severe acute respiratory illness (SARI) at admission, non-diabetics, and cared at public hospitals compared to their counterparts. Similarly, the survival duration was at least one day higher among patients presented with SARI, chronic liver disease and cared at a private hospital. The analysis covered >10% of India's COVID-19 deaths, providing essential information regarding the COVID-19 epidemiology. The characteristics associated with early hospital death and survival duration among the COVID-19 fatalities may be deliberated as markers for prognosis and compared with survivors.

Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis.

OBJECTIVE: Considering the rising menace of coronavirus disease 2019 (COVID-19), it is essential to explore the methods and resources that might predict the case numbers expected and identify the locations of outbreaks. Hence, we have done the following study to explore the potential use of Google Trends (GT) in predicting the COVID-19 outbreak in India. METHODS: The Google search terms used for the analysis were "coronavirus", "COVID", "COVID 19", "corona", and "virus". GTs for these terms in Google Web, News, and YouTube, and the data on COVID-19 case numbers were obtained. Spearman correlation and lag correlation were used to determine the correlation between COVID-19 cases and the Google search terms. RESULTS: "Coronavirus" and "corona" were the terms most commonly used by Internet surfers in India. Correlation for the GTs of the search terms "coronavirus" and "corona" was high (r > 0.7) with the daily cumulative and new COVID-19 cases for a lag period ranging from 9 to 21 days. The maximum lag period for predicting COVID-19 cases was found to be with the News search for the term "coronavirus", with 21 days, i.e., the search volume for "coronavirus" peaked 21 days before the peak number of cases reported by the disease surveillance system. CONCLUSION: Our study revealed that GTs may predict outbreaks of COVID-19, 2 to 3 weeks earlier than the routine disease surveillance, in India. Google search data may be considered as a supplementary tool in COVID-19 monitoring and planning in India.