Trends in psychiatric diagnoses by COVID-19 infection and hospitalization among patients with and without recent clinical psychiatric diagnoses in New York city from March 2020 to August 2021.

Determining emerging trends of clinical psychiatric diagnoses among patients infected with the SARS-CoV-2 virus is important to understand post-acute sequelae of SARS-CoV-2 infection or long COVID. However, published reports accounting for pre-COVID psychiatric diagnoses have usually relied on self-report rather than clinical diagnoses. Using electronic health records (EHRs) among 2,358,318 patients from the New York City (NYC) metropolitan region, this time series study examined changes in clinical psychiatric diagnoses between March 2020 and August 2021 with month as the unit of analysis. We compared trends in patients with and without recent pre-COVID clinical psychiatric diagnoses noted in the EHRs up to 3 years before the first COVID-19 test. Patients with recent clinical psychiatric diagnoses, as compared to those without, had more subsequent anxiety disorders, mood disorders, and psychosis throughout the study period. Substance use disorders were greater between March and August 2020 among patients without any recent clinical psychiatric diagnoses than those with. COVID-19 positive patients (both hospitalized and non-hospitalized) had greater post-COVID psychiatric diagnoses than COVID-19 negative patients. Among patients with recent clinical psychiatric diagnoses, psychiatric diagnoses have decreased since January 2021, regardless of COVID-19 infection/hospitalization. However, among patients without recent clinical psychiatric diagnoses, new anxiety disorders, mood disorders, and psychosis diagnoses increased between February and August 2021 among all patients (COVID-19 positive and negative). The greatest increases were anxiety disorders (378.7%) and mood disorders (269.0%) among COVID-19 positive non-hospitalized patients. New clinical psychosis diagnoses increased by 242.5% among COVID-19 negative patients. This study is the first to delineate the impact of COVID-19 on different clinical psychiatric diagnoses by pre-COVID psychiatric diagnoses and COVID-19 infections and hospitalizations across NYC, one of the hardest-hit US cities in the early pandemic. Our findings suggest the need for tailoring treatment and policies to meet the needs of individuals with pre-COVID psychiatric diagnoses.

Deep Learning Models to Predict Covid-19 Cases in India Using Air Pollution and Meteorological Data

A worldwide pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known as coronavirus disease 2019 (COVID-19), has killed many people. More than 31.6 million cases have been recorded in India alone till 2021. The main aim of this study is to identify the relationship between COVID-19 and air pollution concerning geographical location. Considerably air pollution also increases the cases, and COVID-19 disease causes damage to the respiratory system. Applying the Long short-term memory (LSTM) and Bidirectional Long short-term memory (BiLSTM) deep Learning model, this work attempts at giving insight into the connection between the various factors impacting COVID-19 mortality rates, i.e., the dispersion between the confirmed number of cases and the air pollution levels in major urban centres, namely Delhi, Bengaluru, Chennai, Mumbai, and Kolkata in India COVID-19 infections discovered that there is an association between high PM10 and PM2.5 pollution levels and having confirmed diseases are high. There is a concrete relationship between PM2.5 and COVID-19 mortality, which confirmed by the developed deep learning model that uses multiple regression analysis. The research model estimate, forecast and track COVID-19 case infections effects on air pollution, particularly in metropolitan cities. The BiLSTM model gives better score values between 0.903 and 0.951, whereas the LSTM model scores between 0.754 and 0.829. This research reveals a link between health and air pollutions parameters during this pandemic period. The results obtained from the research show a constructive co-relationship between the level of air pollution and diffusion of coronavirus.