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Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. It can provide, psychological, social and cultural insights for understanding human behaviour in extreme events such as the COVID-19 pandemic. In this paper, we use prominent deep learning-based language models for COVID-19 topic modelling taking into account data from the emergence (Alpha) to the Omicron variant in India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situations during the COVID-19 pandemic. We also find a strong correlation between the major topics with news media prevalent during the respective time period. Hence, our framework has the potential to capture major issues arising during different phases of the COVID-19 pandemic which can be extended to other countries and regions. [ABSTRACT FROM AUTHOR]

The absence of reliable registration of Covid-19 deaths in India has prevented proper assessment and monitoring of the coronavirus pandemic. In addition, India's relatively young age structure tends to conceal the severity of Covid-19 mortality, which is concentrated in older age groups. In this paper, we present four different demographic samples of Indian populations for which we have information on both their demographic structures and death outcomes. We show that we can model the age distribution of Covid-19 mortality in India and use this modeling to estimate Covid-19 mortality in the country. Our findings point to a death toll of approximately 3.2–3.7 million persons by early November 2021. Once India's age structure is factored in, these figures correspond to one of the most severe cases of Covid-19 mortality in the world. India has recorded after February 2021 the second outbreak of coronavirus that has affected the entire country. The accuracy of official statistics of Covid-19 mortality has been questioned, and the real number of Covid-19 deaths is thought to be several times higher than reported. In this paper, we assembled four independent population samples to model and estimate the level of Covid-19 mortality in India. We first used a population sample with the age and sex of Covid-19 victims to develop a Gompertz model of Covid-19 mortality in India. We applied and adjusted this mortality model on two other national population samples after factoring in the demographic characteristics of these samples. We finally derive from these samples the most reasonable estimate of Covid-19 mortality level in India and confirm this result using a fourth population sample. Our findings point to a death toll of about 3.2–3.7 million persons by late May 2021. This is by far the largest number of Covid-19 deaths in the world. Once standardized for age and sex structure, India's Covid-19 mortality rate is above Brazil and the USA. Our analysis shows that existing population samples allow an alternative estimation of deaths due to Covid-19 in India. The results imply that only one out of 7–8 deaths appear to have been recorded as a Covid-19 death in India. The estimates also point to a very high Covid-19 mortality rate, which is even higher after age and sex standardization. The magnitude of the pandemic in India requires immediate attention. In the absence of effective remedies, this calls for a strong response based on a combination of non-pharmaceutical interventions and the scale-up of vaccination to make them accessible to all, with an improved surveillance system to monitor the progression of the pandemic and its spread across India's regions and social groups. [ABSTRACT FROM AUTHOR]

Objective: To determine the clinical manifestations, risk factors, treatment modalities and maternal outcomes in pregnant women with lab-confirmed COVID-19 and compare it with COVID-19 negative pregnant women in same age group. Design: Multicentric case-control study. Data sources: Ambispective primary data collection through paper-based forms from 20 tertiary care centres across India between April and November 2020. Study population: All pregnant women reporting to the centres with a lab-confirmed COVID-19 positive result matched with controls. Data quality: Dedicated research officers extracted hospital records, using modified WHO Case Record Forms (CRF) and verified for completeness and accuracy. Statistical analysis: Data converted to excel files and statistical analyses done using STATA 16 (StataCorp, TX, USA). Odds ratios (ORs) with 95% confidence intervals (CI) estimated using unconditional logistic regression. Results: A total of 76,264 women delivered across 20 centres during the study period. Data of 3723 COVID positive pregnant women and 3744 age-matched controls was analyzed. Of the positive cases 56·9% were asymptomatic. Antenatal complications like preeclampsia and abruptio placentae were seen more among the cases. Induction and caesarean delivery rates were also higher among Covid positive women. Pre-existing maternal co-morbidities increased need for supportive care. There were 34 maternal deaths out of the 3723(0.9%) positive mothers, while covid negative deaths reported from all the centres were 449 of 72,541 (0·6%). Conclusion: Covid-19 infection predisposed to adverse maternal outcomes in a large cohort of Covid positive pregnant women as compared to the negative controls. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has impacted almost all the sectors including agriculture in the country. The present paper investigates the impact of COVID-19 induced lockdown on both wholesale and retail prices of major pulses in India. The daily wholesale and retail price data on five major pulses namely Lentil, Moong, Arhar, Urad and Gram are collected for five major markets in India namely Delhi, Mumbai, Kolkata, Chennai and Hyderabad during the period January, 2019 to September, 2020 from Ministry of Consumer Affairs, Food & Public Distribution, Government of India. The Government of India declared nationwide lockdown since March, 24, to May, 31, 2020 in different phases in order to restrict the spread of the infection due to COVID-19. To see the impact of lockdown on price and price volatility, time series model namely Autoregressive integrated moving average (ARIMA) model with error following Generalized autoregressive conditional heteroscedastic (GARCH) model incorporating exogenous variable as lockdown dummy in both mean as well variance equations. It is observed that in almost all the markets, lockdown has significant impact on price of the pulses whereas in few cases, it has significant impact on price volatility. [ABSTRACT FROM AUTHOR]

Effective and culturally appropriate hand-hygiene education is essential to promote health-related practices to control and prevent diseases such as Diarrhoea, Ebola and COVID-19. In this paper we outline and evaluate the Co-Creation processes underpinning a handwashing intervention for young children (A Germ's Journey) developed and delivered in India, Sierra Leone and the UK, and consider the implications surrounding Imperialist/Colonial discourse and the White Saviour Complex. The paper focuses both on the ways Co-Creation was conceptualised by our collaborators in all three countries and the catalysts and challenges encountered. Qualitative data have been drawn from in-depth interviews with five key stakeholders, focus group data from 37 teachers in Sierra Leone and responses to open-ended questionnaires completed by teachers in India (N = 66) and UK (N = 63). Data were analysed using thematic analysis and three themes, each with three constituent subthemes are presented. In the theme 'Representations of and Unique Approaches to Co-Creation' we explore the ways in which Co-Creation was constructed in relation to teamwork, innovative practice and more continuous models of evaluation. In 'Advantages of Co-Creation' we consider issues around shared ownership, improved outcomes and more meaningful insights alongside the mitigation of risks and short-circuiting of problems. In 'Challenges of Co-Creation' we discuss issues around timing and organisation, attracting and working with appropriate partners and understanding the importance of local context with inherent social, economic and structural barriers, especially in low-and-middle-income countries. We consider how theoretical elements of Co-Creation can inform effective international public health interventions; crucial during a global pandemic in which handwashing is the most effective method to control the transmission of COVID-19. Finally we reflect on some of the methodological challenges of our own work and in managing the potentially conflicting goals of the ethical and participatory values of Co-Creation with pragmatic considerations about ensuring an effective final 'product'. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle. [ABSTRACT FROM AUTHOR]

Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks where rise and fall of new cases affected lock-downs which directly affected the economy and employment. During the rise of COVID-19 cases with stricter lock-downs, people have been expressing their sentiments in social media. This can provide a deep understanding of human psychology during catastrophic events. In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India. The framework features LSTM language model with a global vector embedding and state-of-art BERT language model. We review the sentiments expressed for selective months in 2020 which covers the major peak of novel cases in India. Our framework utilises multi-label sentiment classification where more than one sentiment can be expressed at once. Our results indicate that the majority of the tweets have been positive with high levels of optimism during the rise of the novel COVID-19 cases and the number of tweets significantly lowered towards the peak. We find that the optimistic, annoyed and joking tweets mostly dominate the monthly tweets with much lower portion of negative sentiments. The predictions generally indicate that although the majority have been optimistic, a significant group of population has been annoyed towards the way the pandemic was handled by the authorities. [ABSTRACT FROM AUTHOR]