Incidence of COVID-19-Associated Mucormycosis in COVID-19 Patients after Discharge from the COVID-19 Hospital.

Background: There are studies available on the prevalence of coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM) in hospitalized patients but not on the incidence of CAM in post-discharge patients. The aim of our study was to find the incidence of CAM in the patients discharged from a COVID hospital. Material and Methods: Adult patients with COVID discharged between March 1, 2021 and June 30, 2021 were contacted and enquired about sign and symptoms of CAM. Data of all included patients were collected from electronic records. Results: A total of 850 patients responded, among which 59.4% were males, 66.4% patients had co-morbidities, and 24.2% had diabetes mellitus. Around 73% of patients had moderate to severe disease and were given steroids; however, only two patients developed CAM post discharge. Conclusion: The incidence of CAM post discharge was low in our study, which could be attributed to protocolized therapy and intensive monitoring.

Management of Allergic Fungal Rhinosinusitis Associated with Vision Loss During COVID-19 Era.

The ongoing COVID-19 pandemic has given rise to unique challenges related to healthcare management. The problems have arisen due to the direct effect of COVID 19 infection and treatment or as repercussions of administrative efforts being undertaken to check the rapid spread of the epidemic. The management of some of the diseases has been hampered with the implementation of the policies like lockdown and transportation difficulties. This paper presents a series of four patients (6 eyes with vision loss) of an otherwise benign entity, Allergic Fungal Rhinosinusitis (AFRS), causing visual deterioration, managed amid the pandemic. AFRS has been known to cause vision loss by pressure over the optic nerve or its blood supply; however, a timely surgical intervention in the form of functional endoscopic sinus surgery to remove the disease and decompress the optic nerve, results in favourable outcomes in most patients. A delay in diagnosis and treatment may result in irreparable damage with the resulting inability to salvage the vision. In our series, we observed that vision recovery could be achieved in 66.7% of the affected eyes (four out of six eyes), while a poor visual outcome was observed in two (33%). The poor visual outcome was observed for the eyes with a prolonged visual impairment (4-6 months) at the time of presentation. We would appeal to the physicians to be cognizant of the adverse outcomes associated with the delayed surgical intervention of AFRS in the current pandemic scenario.

Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network.

The outbreak of COVID-19 has engulfed the entire world since the end of 2019, causing tremendous loss of lives. It has also taken a toll on the healthcare sector due to the inability to accurately predict the spread of disease as the arrangements for the essential supply of medical items largely depend on prior predictions. The objective of the study is to train a reliable model for predicting the spread of Coronavirus. The prediction capabilities of various powerful models such as the Autoregression Model (AR), Global Autoregression (GAR), Stacked-LSTM (Long Short-Term Memory), ARIMA (Autoregressive Integrated Moving Average), Facebook Prophet (FBProphet), and Residual Recurrent Neural Network (Res-RNN) were taken into consideration for predicting COVID-19 using the historical data of daily confirmed cases along with Twitter data. The COVID-19 prediction results attained from these models were not up to the mark. To enhance the prediction results, a novel model is proposed that utilizes the power of Res-RNN with some modifications. Gated Recurrent Unit (GRU) and LSTM units are also introduced in the model to handle the long-term dependencies. Neural Networks being data-hungry, a merged layer was added before the linear layer to combine tweet volume as additional features to reach data augmentation. The residual links are used to handle the overfitting problem. The proposed model RNN Convolutional Residual Network (RNNCON-Res) showcases dominating capability in country-level prediction 20 days ahead with respect to existing State-Of-The-Art (SOTA) methods. Sufficient experimentation was performed to analyze the prediction capability of different models. It was found that the proposed model RNNCON-Res has achieved 91% accuracy, which is better than all other existing models.

Predictors of Mortality among Patients Hospitalized with COVID-19 during the First Wave in India: A Multisite Case-Control Study.

Severe acute respiratory syndrome coronavirus 2 disease (COVID-19) has caused more than 6 million deaths globally. Understanding predictors of mortality will help in prioritizing patient care and preventive approaches. This was a multicentric, unmatched, hospital-based case-control study conducted in nine teaching hospitals in India. Cases were microbiologically confirmed COVID-19 patients who died in the hospital during the period of study and controls were microbiologically confirmed COVID-19 patients who were discharged from the same hospital after recovery. Cases were recruited sequentially from March 2020 until December-March 2021. All information regarding cases and controls was extracted retrospectively from the medical records of patients by trained physicians. Univariable and multivariable logistic regression was done to assess the association between various predictor variables and deaths due to COVID-19. A total of 2,431 patients (1,137 cases and 1,294 controls) were included in the study. The mean age of patients was 52.8 years (SD: 16.5 years), and 32.1% were females. Breathlessness was the most common symptom at the time of admission (53.2%). Increasing age (adjusted odds ratio [aOR]: 46-59 years, 3.4 [95% CI: 1.5-7.7]; 60-74 years, 4.1 [95% CI: 1.7-9.5]; and ≥ 75 years, 11.0 [95% CI: 4.0-30.6]); preexisting diabetes mellitus (aOR: 1.9 [95% CI: 1.2-2.9]); malignancy (aOR: 3.1 [95% CI: 1.3-7.8]); pulmonary tuberculosis (aOR: 3.3 [95% CI: 1.2-8.8]); breathlessness at the time of admission (aOR: 2.2 [95% CI: 1.4-3.5]); high quick Sequential Organ Failure Assessment score at the time of admission (aOR: 5.6 [95% CI: 2.7-11.4]); and oxygen saturation < 94% at the time of admission (aOR: 2.5 [95% CI: 1.6-3.9]) were associated with mortality due to COVID-19. These results can be used to prioritize patients who are at increased risk of death and to rationalize therapy to reduce mortality due to COVID-19.

Modeling virus transport and dynamics in viscous flow medium.

In this paper, we developed a mathematical model to simulate virus transport through a viscous background flow driven by the natural pumping mechanism. Two types of respiratory pathogens viruses (SARS-Cov-2 and Influenza-A) are considered in this model. The Eulerian-Lagrangian approach is adopted to examine the virus spread in axial and transverse directions. The Basset-Boussinesq-Oseen equation is considered to study the effects of gravity, virtual mass, Basset force, and drag forces on the viruses transport velocity. The results indicate that forces acting on the spherical and non-spherical particles during the motion play a significant role in the transmission process of the viruses. It is observed that high viscosity is responsible for slowing the virus transport dynamics. Small sizes of viruses are found to be highly dangerous and propagate rapidly through the blood vessels. Furthermore, the present mathematical model can help to better understand the viruses spread dynamics in a blood flow.

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters.

The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.

Is safe distance enough to prevent COVID-19? Dispersion and tracking of aerosols in various artificial ventilation conditions using OpenFOAM.

The current COVID-19 pandemic has underlined the importance of learning more about aerosols and particles that migrate through the airways when a person sneezes, coughs and speaks. The coronavirus transmission is influenced by particle movement, which contributes to the emergence of regulations on social distance, use of masks and face shield, crowded assemblies, and daily social activity in domestic, public, and corporate areas. Understanding the transmission of aerosols under different micro-environmental conditions, closed, or ventilated, has become extremely important to regulate safe social distances. The present work attempts to simulate the airborne transmission of coronavirus-laden particles under different respiratory-related activities, i.e., coughing and speaking, using CFD modelling through OpenFOAM v8. The dispersion coupled with the Discrete Phase Method (DPM) has been simulated to develop a better understanding of virus carrier particles transmission processes and their path trailing under different ventilation scenarios. The preliminary results of this study with respect to flow fields were in close agreement with published literature, which was then extended under varied ventilation scenarios and respiratory-related activities. The study observed that improper wearing of mask leads to escape of SARS-CoV-2 containminated aerosols having a smaller aerodynamic diameter from the gap between face mask and face, infecting different surfaces in the vicinity. It was also observed that aerosol propagation infecting the area through coughing is a faster phenomenon compared to the propagation of coronavirus-laden particles during speaking. The study's findings will help decision-makers formulate common but differentiated guidelines for safe distancing under different micro-environmental conditions.

COVID-19 vaccination does not affect male sexual functions.

BACKGROUND: COVID-19 infection has been linked with erectile dysfunction, which has also raised apprehensions about the impact of COVID-19 vaccination on male sexual functions. The purpose of this study was to investigate the impact of COVID-19 vaccination on male sexual functions, such as erectile function, orgasmic function, sexual desire, intercourse satisfaction, and overall satisfaction. METHODS: We used International Index of Erectile Function (IIEF) questionnaire for data collection. Mixed methods were adopted for this study, which consisted of Google online form distribution and the distribution of hard copies of the form to those who were not internet friendly. All data were entered in a spreadsheet and scores were assigned to each response according to the standard scores given in the IIEF questionnaire. Fifteen questions, one corresponding to each question in the IIEF questionnaire, were included to assess the impact of COVID-19 vaccination on each sexual function. RESULTS: In the first part of analysis, we calculated sexual function scores and men reporting low sexual function scores (~ 15%) were excluded, providing us with 465 individuals for further analysis. Regarding the impact of COVID-19 vaccination on male sexual functions, 71% individuals reported no impact, 3% reported a decline, 2.7% reported an improvement, and 23.3% could not assess the impact. We also performed analysis on the basis of age-groups of the participants and the duration after vaccination, finding that there was no impact irrespective of the age of subjects or the length of period after vaccination. CONCLUSIONS: COVID-19 vaccination does not affect male sexual functions, including erectile function, orgasmic function, sexual desire, intercourse satisfaction, and overall sexual satisfaction.

Epidemiology, Clinical Profile, Intensive Care Needs and Outcome in Children with SARS-CoV-2 Infection Admitted to a Tertiary Hospital During the First and Second Waves of the COVID-19 Pandemic in India.

OBJECTIVES: To compare the epidemiological, clinical profile, intensive care needs and outcome of children hospitalized with SARS-CoV-2 infection during the first and second waves of the pandemic. METHODS: This was a retrospective study of all children between 1 mo and 14 y, admitted to a dedicated COVID-19 hospital (DCH) during the first (1st June to 31st December 2020) and second waves (1st March to 30th June 2021). RESULTS: Of 217 children, 104 (48%) and 113 (52%) were admitted during the first and second waves respectively. One hundred fifty-two (70%) had incidentally detected SARS-CoV-2 infection, while 65 (30%) had symptomatic COVID-19. Comorbidities were noted in 137 (63%) children. Fifty-nine (27%) and 66 (30%) children required high-dependency unit (HDU) and ICU care respectively. Severity of infection and ICU needs were similar during both waves. High-flow oxygen (n = 5, 2%), noninvasive ventilation [CPAP (n = 34, 16%) and BiPAP (n = 8, 5%)] and invasive ventilation (n = 45, 21%) were respiratory support therapies needed. NIV use was more during the second wave (26% vs. 13%; p = 0.02). The median (IQR) length (days) of DCH stay among survivors was longer during the first wave [8 (6-10) vs. 5.5 (3-8); p = 0.0001]. CONCLUSIONS: Disease severity, associated comorbidities, PICU and organ support need and mortality were similar in the first and second waves of the pandemic. Children admitted during the second wave were younger, had higher proportion of NIV use and shorter length of COVID-19 hospital stay.

Natural Language Processing CAM Algorithm Improves Delirium Detection Compared With Conventional Methods.

Delirium is known to be underdiagnosed and underdocumented. Delirium detection in retrospective studies occurs mostly by clinician diagnosis or nursing documentation. This study aims to assess the effectiveness of natural language processing-confusion assessment method (NLP-CAM) algorithm when compared to conventional modalities of delirium detection. A multicenter retrospective study analyzed 4351 COVID-19 hospitalized patient records to identify delirium occurrence utilizing three different delirium detection modalities namely clinician diagnosis, nursing documentation, and the NLP-CAM algorithm. Delirium detection by any of the 3 methods is considered positive for delirium occurrence as a comparison. NLP-CAM captured 80% of overall delirium, followed by clinician diagnosis at 55%, and nursing flowsheet documentation at 43%. Increase in age, Charlson comorbidity score, and length of hospitalization had increased delirium detection odds regardless of the detection method. Artificial intelligence-based NLP-CAM algorithm, compared to conventional methods, improved delirium detection from electronic health records and holds promise in delirium diagnostics.

Exploratory analysis of the suitability of data from the civil registration system for estimating excess mortality due to COVID-19 in Faridabad district of India.

Background & objectives: Due to shortcomings in death registration and medical certification, the excess death approach is recommended for COVID-19 mortality burden estimation. In this study the data from the civil registration system (CRS) from one district in India was explored for its suitability in the estimation of excess deaths, both directly and indirectly attributable to COVID-19. Methods: All deaths registered on the CRS portal at the selected registrar's office of Faridabad district in Haryana between January 2016 and September 2021 were included. The deaths registered in 2020 and 2021 were compared to previous years (2016-2019), and excess mortality in both years was estimated by gender and age groups as the difference between the registered deaths and historical average month wise during 2016-2019 using three approaches - mean and 95 per cent confidence interval, FORECAST.ETS function in Microsoft Excel and linear regression. To assess the completeness of registration in the district, 150 deaths were sampled from crematoria and graveyards during 2020 and checked for registration in the CRS portal. Agreement in the cause of death (CoD) in CRS with the International Classification of Diseases-10 codes assigned for a subset of 585 deaths after verbal autopsy was calculated. Results: A total of 7017 deaths were registered in 2020, whereas 6792 deaths were registered till 30 September 2021 which represent a 9 and 44 per cent increase, respectively, from the historical average for that period. The highest increase was seen in the age group >60 yr (19% in 2020 and 56% in 2021). All deaths identified in crematoria and graveyards in 2020 had been registered. Observed peaks of all-cause excess deaths corresponded temporally and in magnitude to infection surges in the district. All three approaches gave overlapping estimates of the ratio of excess mortality to reported COVID-19 deaths of 1.8-4 in 2020 and 10.9-13.9 in 2021. There was poor agreement (κ<0.4) between CoD in CRS and that assigned after physician review for most causes, except tuberculosis and injuries. Interpretation & conclusions: CRS data, despite the limitations, appeared to be appropriate for all-cause excess mortality estimation by age and sex but not by cause. There was an increase in death registration in 2020 and 2021 in the district.

Community practices in COVID-19 outbreak in rural Ballabgarh, Haryana - A mixed methods study.

Background: More than 43 million cases and 5.2 lakhs death have occurred due to COVID-19 in India. Approximately 1 lakh people (cumulative) have been infected by COVID-19 in Faridabad district alone as of 4 April 2022. To understand the effects of COVID-19 on community practices this study was conducted. Methods: A community-based cross-sectional study was conducted in Intensive Field Practice Area of Comprehensive Rural health Services Project (CRHSP), Ballabgarh, Haryana. Five hundred participants (≥18 years) were selected by using simple random sampling from Health Management Information System (HMIS) maintained at Centre for Community Medicine, AIIMS, New Delhi. Participants were informed regarding study and consent was taken. A semi-structured interview schedule was administered. Results: Study participants included 500 adults (52.2% Male). Mean age (S.D.) of participants were 39.1 (14.9) years. Almost all participants started practicing hand sanitisation (496, 99.2%), avoiding crowd (488, 97.6%), and covering face with cloth/handkerchief (459, 91.8%). More than 80% (428, 85.6%) started using mask, and following cough etiquettes (405, 81.0%). More than three-fourth (389, 77.8%) participants were very unsatisfied with lockdown. Majority faced financial difficulties (322, 64.4%), followed by difficulty in their entertainment/recreational activity (158, 31.6%), difficulty in acquiring ration/food items (87, 17.4%) and mental stress (46, 9.2%) during lockdown. Conclusions: Rural community of Ballabgarh showed positive practices with respect to prevention of COVID-19. Financial distress and job loss due to lockdown were widely reported from the rural community. Majority of the community was displeased with lockdown as intervention for COVID-19.

Circular economy based approach for green energy transitions and climate change benefits

Concerning rapid resource depletion and the negative effects of climate change, the adaptation of Circular Economy (CE) strategies in the environmental sector is gaining global recognition and application. This study forecasts the energy demand and emissions scenarios for a circular economy-dependent green energy transition, based on learnings from the forced situation caused by the COVID-19 pandemic. This study focuses on the industrial, domestic, and transportation sectors of the National Capital Territory (NCT) of India i.e., Delhi. Implementation of circular economy strategies helps in limiting the impacts of rising energy demand by shifting towards green fuels and biofuels. The data related to energy demand, consumption, percentage share and growth, etc., was gathered and incorporated in Low Emission Analysis Platform model to generate three scenarios i.e., business as usual, circular economy (CE), and pandemic scenario to compare the outputs of energy demand and emissions in terms of CO2 and particulate matter. The results for the CE scenario, for the year 2020 to 2040 shows that there will be a reduction of 158.2 kt PM2.5 emissions (24.3%) and 540 Mt CO2 emissions (49%) as well as a reduction of 203 Mtoe total energy usage (49.3%) as compared to the business-as-usual scenario. The COVID-19 pandemic had a significant impact on societal and commercial activities in the concerned city. Activities came to a standstill during the COVID-19 pandemic, but the same had significantly improved the environment. This unusual forced situation of COVID-19 lockdown produced a unique scenario which shows that the total extra reduction in energy demand of 46%, CO2 and PM2.5 emissions of 45–60% could be achieved by 2040, as compared to CE scenario. Graphical Supplementary Information The online version contains supplementary material available at 10.1007/s43538-022-00137-7.

Do children with severe acute respiratory infection need cohorting & isolation before screening for COVID-19?

This retrospective analysis was done to ascertain the SARS-CoV-2-positivity rate in children (0-12 yr) with severe acute respiratory infection (SARI) and compare it to those without SARI to determine the need for running a dedicated SARI isolation facility for paediatric COVID-19 care. The case records of 8780 children (0-12 yr) admitted and/or tested for SARS-CoV-2 between June 2020 and May 2021 at a tertiary care centre in north India were analyzed. The overall SARS-CoV-2 reverse transcription (RT)-PCR positivity rate was 3.0 per cent (262/8780). There were 1155 (13.15%) children with SARI. Fifty of these 1155 (4.3%) children with SARI, as against 212 of the 7625 (2.8%) children without SARI, tested positive for COVID-19. The absolute difference in the positivity rate among SARI and non-SARI groups was only 1.54 per cent which translates to cohorting and isolating 65 children with SARI to pick up one extra SARS-CoV-2-positive child (compared to those without SARI). The positive predictive value of SARI as a screening test was 4.3 per cent. Our findings suggest that isolation of children with SARI as a transmission-prevention strategy for COVID-19 may not be required. This is particularly relevant in resource-limited settings.

Air pollution aggravating COVID-19 lethality? Exploration in Asian cities using statistical models.

The present work estimates the increased risk of coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 by establishing the linkage between the mortality rate in the infected cases and the air pollution, specifically Particulate Matters (PM) with aerodynamic diameters ≤ 10 µm and ≤ 2.5 µm. Data related to nine Asian cities are analyzed using statistical approaches, including the analysis of variance and regression model. The present work suggests that there exists a positive correlation between the level of air pollution of a region and the lethality related to COVID-19, indicating air pollution to be an elemental and concealed factor in aggravating the global burden of deaths related to COVID-19. Past exposures to high level of PM2.5 over a long period, is found to significantly correlate with present COVID-19 mortality per unit reported cases (p < 0.05) compared to PM10, with non-significant correlation (p = 0.118). The finding of the study can help government agencies, health ministries and policymakers globally to take proactive steps by promoting immunity-boosting supplements and appropriate masks to reduce the risks associated with COVID-19 in highly polluted areas.