Impact of COVID-19 vaccination: a global perspective
by Priya Singh, Aditya Anand, Shweta Rana, Amit Kumar, Sujeet Kumar, Prabudh Goel, Krushna Chandra Gouda, Harpreet Singh
Introduction: The COVID-19 pandemic has caused widespread morbidity, mortality, and socio-economic disruptions worldwide. Vaccination has proven to be a crucial strategy in controlling the spread of the virus and mitigating its impact.
Objective: The study focuses on assessing the effectiveness of COVID-19 vaccination in reducing the incidence of positive cases, hospitalizations, and ICU admissions. The presented study is focused on the COVID-19 fully vaccinated population by considering the data from the first positive case reported until 20 September 2021.
Methods: Using data from multiple countries, time series analysis is deployed to investigate the variations in the COVID-19 positivity rates, hospitalization rates, and ICU requirements after successful vaccination campaigns at the country scale.
Results: Analysis of the COVID-19 positivity rates revealed a substantial decline in countries with high pre-vaccination rates. Within 1–3 months of vaccination campaigns, these rates decreased by 20–44%. However, certain countries experienced an increase in positivity rates with the emergence of the new Delta variant, emphasizing the importance of ongoing monitoring and adaptable vaccination strategies. Similarly, the analysis of hospitalization rates demonstrated a steady decline as vaccination drive rates rose in various countries. Within 90 days of vaccination, several countries achieved hospitalization rates below 200 per million. However, a slight increase in hospitalizations was observed in some countries after 180 days of vaccination, underscoring the need for continued vigilance. Furthermore, the ICU patient rates decreased as vaccination rates increased across most countries. Within 120 days, several countries achieved an ICU patient rate of 20 per million, highlighting the effectiveness of vaccination in preventing severe cases requiring intensive care.
Conclusion: COVID-19 vaccination has proven to be very much effective in reducing the incidence of cases, hospitalizations, and ICU admissions. However, ongoing surveillance, variant monitoring, and adaptive vaccination strategies are crucial for maximizing the benefits of vaccination and effectively controlling the spread of the virus.
Impact of comorbidity on patients with COVID-19 in India: A nationwide analysis
by Priya Singh, Yogendra Bhaskar, Pulkit Verma, Shweta Rana, Prabudh Goel, Sujeet Kumar, Krushna Chandra Gouda, Harpreet Singh
Background: The emergence of coronavirus disease (COVID-19) as a global pandemic has resulted in the loss of many lives and a significant decline in global economic losses. Thus, for a large country like India, there is a need to comprehend the dynamics of COVID-19 in a clustered way.
Objective: To evaluate the clinical characteristics of patients with COVID-19 according to age, gender, and preexisting comorbidity. Patients with COVID-19 were categorized according to comorbidity, and the data over a 2-year period (1 January 2020 to 31 January 2022) were considered to analyze the impact of comorbidity on severe COVID-19 outcomes.
Methods: For different age/gender groups, the distribution of COVID-19 positive, hospitalized, and mortality cases was estimated. The impact of comorbidity was assessed by computing incidence rate (IR), odds ratio (OR), and proportion analysis.
Results: The results indicated that COVID-19 caused an exponential growth in mortality. In patients over the age of 50, the mortality rate was found to be very high, ~80%. Moreover, based on the estimation of OR, it can be inferred that age and various preexisting comorbidities were found to be predictors of severe COVID-19 outcomes. The strongest risk factors for COVID-19 mortality were preexisting comorbidities like diabetes (OR: 2.39; 95% confidence interval (CI): 2.31–2.47; p < 0.0001), hypertension (OR: 2.31; 95% CI: 2.23–2.39; p < 0.0001), and heart disease (OR: 2.19; 95% CI: 2.08–2.30; p < 0.0001). The proportion of fatal cases among patients positive for COVID-19 increased with the number of comorbidities.
Conclusion: This study concluded that elderly patients with preexisting comorbidities were at an increased risk of COVID-19 mortality. Patients in the elderly age group with underlying medical conditions are recommended for preventive medical care or medical resources and vaccination against COVID-19.
Impact of assimilation of microwave imager radiance data on simulation of tropical cyclones over Bay of Bengal using National Centre for Medium Range Weather Forecasting Unified Model modelling system
by Ashish Routray, Shivaji Singh Patel, Devajyoti Dutta, K. B. R. R. Hari Prasad, K. C. Gouda, Rajeev Bhatla, V. S. Prasad
An effort was made to investigate the impact of the assimilation of microwave imager (MI) radiance observations on the simulation of tropical cyclones (TCs) using the high-resolution NCUM-4DVAR assimilation method. Two numerical experiments were performed: a control experiment (CTL) (assimilated global telecommunication system [GTS] observations) and satellite observations (SAT) (assimilated GTS plus global microwave imager [GMI] and special sensor microwave imager and sounder [SSMIS] satellite MI radiance). Assimilation of MI radiance is observed to be capable of depicting the structure, track and intensity of storms. The analysis increments of temperature and geopotential height suggested that the SAT experiment effectively adjusted the core region of TCs and systematically corrected the position in the first guess of the model. The strength of large-scale moisture transport from the underlying oceanic surface and helicity around the storms are well simulated by the SAT with the axisymmetric eye of TCs. The premature intensification of TCs is simulated by CTL, whereas the progression of the intensity of storms is relatively well captured in the SAT experiment. Dry air invading the inner core of a TC can disrupt the energy cycle and reduce storm intensity, as seen in the CTL simulations. Similarly, the negative value of diabatic heating appears around the centre of the storm with an increase of altitudes in CTL simulations, which impedes the intensity of TCs. This feature suggested that CTL simulations do not adequately bring out the intensity and vertical structure of the TCs. The track forecast of storms is considerably improved in SAT simulations compared to CTL. The forecast skill of rainfall is also relatively improved in SAT simulations. Overall assimilation of MI radiances enhanced the model's forecast skill to simulate structure, movement, intensity and precipitation associated with the storms.
Impact of assimilating GNSS ground-based and Space-based observations on Extreme rainfall prediction over Karnataka, India
by Smrati Purwar, Ajay Bankar, V Rakesh, Suelynn Choy, Haobo Li, G N Mohapatra & Yuriy Kuleshov
This study delves into the assessment of the impact of assimilating two kinds of Global Navigation Satellite System (GNSS) observations (ground-based (GB) and space-based) in the prediction of extreme rainfall events (EREs). We consider seven distinct sources of GNSS radio occultation (GNSS-RO) observations as GNSS space-based observations, with six of them (COSMIC1, KOMPSAT5, MetOPA, MetOPB, TerraSARX, TanDEMX) being combined to form a group of Global Positioning System (GPS)-RO observations, while COSMIC-2 (C2), which receives signals from GPS and GLObal NAvigation Satellite System (GLONASS), is handled independently. The study is conducted over the southern state of India, Karnataka, for 16 EREs that occurred between October 2019 and December 2020. The zenith tropospheric delay (ZTD) from International GNSS Service (IGS) GB stations and refractivity data from GNSS-RO (GPS-RO and C2) are assimilated in the Weather Research and Forecasting (WRF) model for forecasting these EREs. The results indicate that the assimilation of ZTD from GNSS-GB observations does not significantly improve the forecast due to an insufficient number of observations, whereas the improvement through the assimilation of refractivity profiles from GPS-RO observations is significant even with a smaller number of observations, due to the high vertical resolution of the profiles. The assimilation of GNSS-GB and GPS-RO observations improves the humidity, temperature, and winds at different vertical levels which leads to improvement in the rainfall prediction, particularly in the interior part of Karnataka. Furthermore, the assimilation of C2 observations proves highly advantageous, resulting in significant improvements across forecasted weather parameters and rainfall when compared to GNSS-GB and GPS-RO. These improvements in weather parameters due to assimilation result in a substantial improvement in rainfall by 17% for assimilation of C2 and 21% for assimilation of all GNSS observations (GNSS-GB, GPS-RO, and C2) across most of the regions, including coastal and south interior parts of Karnataka. This study delves into the assessment of the impact of assimilating two kinds of Global Navigation Satellite System (GNSS) observations (ground-based (GB) and space-based) in the prediction of extreme rainfall events (EREs). We consider seven distinct sources of GNSS radio occultation (GNSS-RO) observations as GNSS space-based observations, with six of them (COSMIC1, KOMPSAT5, MetOPA, MetOPB, TerraSARX, TanDEMX) being combined to form a group of Global Positioning System (GPS)-RO observations, while COSMIC-2 (C2), which receives signals from GPS and GLObal NAvigation Satellite System (GLONASS), is handled independently. The study is conducted over the southern state of India, Karnataka, for 16 EREs that occurred between October 2019 and December 2020. The zenith tropospheric delay (ZTD) from International GNSS Service (IGS) GB stations and refractivity data from GNSS-RO (GPS-RO and C2) are assimilated in the Weather Research and Forecasting (WRF) model for forecasting these EREs. The results indicate that the assimilation of ZTD from GNSS-GB observations does not significantly improve the forecast due to an insufficient number of observations, whereas the improvement through the assimilation of refractivity profiles from GPS-RO observations is significant even with a smaller number of observations, due to the high vertical resolution of the profiles. The assimilation of GNSS-GB and GPS-RO observations improves the humidity, temperature, and winds at different vertical levels which leads to improvement in the rainfall prediction, particularly in the interior part of Karnataka. Furthermore, the assimilation of C2 observations proves highly advantageous, resulting in significant improvements across forecasted weather parameters and rainfall when compared to GNSS-GB and GPS-RO. These improvements in weather parameters due to assimilation result in a substantial improvement in rainfall by 17% for assimilation of C2 and 21% for assimilation of all GNSS observations (GNSS-GB, GPS-RO, and C2) across most of the regions, including coastal and south interior parts of Karnataka.
Page 17 of 33
