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We quantify the impacts on consumption expenditure and the patterns following India’s initial sudden lockdown in response to the coronavirus disease pandemic and the gradual relaxation that followed. We use household survey data from a representative Indian state, Punjab. We separate the effects between rural versus urban households, and whether the households were female headed or had daily laborers. While the urban population cut back expenditure across all categories, rural households shifted toward basic commodities and cut back more on other expenditure. Rural households that included daily-wage laborers were the most severely affected.

Bangladesh has been combating the COVID-19 pandemic with limited financial resources and poor health infrastructure since March, 2020. Although the government has imposed several restricted measures to curb the progression of the outbreak, these arrays of measures are not sustainable in the long run. In this paper, we use a data-driven forecasting model considering susceptible, exposed, infected, recovered and deaths status through time to assess the impact of lift of flexible lockdown on the COVID-19 dynamics in Bangladesh. Our analysis demonstrates that the country might experience second infection peak in six to seven months after the withdrawal of current lockdown. Moreover, a prolonged restrictions until January, 2021 will shift the infection peak towards August, 2021 and reduce approximately 20% COVID-19 cases in Bangladesh.

We consider a compartmental model adapted to the case of COVID-19 that takes into account the detection of ill individuals and the cost of medical treatment and investigate the effect of a budget constraint. Our analysis shows how the collapse of the budget can drastically change the outcome of an epidemics. We see how with a good testing policy the epidemic might be controllable. By introducing a lockdown period, we show that the final number of deaths can be reduced substantially and observe that a budget collapse introduces rather extreme effects. We find that there exists a well-defined optimal starting point for a lockdown. We also looked at the consequences of a loss of immunity and found that then a budget collapse can occur for smaller costs of medical treatment.

In the new paradigm of health-centric governance, policymakers are in constant need of appropriate metrics to determine suitable policies in a non-arbitrary fashion. To this end, in this paper, a compartmentalized model for the transmission of COVID-19 is developed, with a socially distanced compartment added to the model. The modification allows for administrators to quantify the extent to which voluntary social distancing norms are followed, and address restrictions accordingly. Modifications are also made to incorporate inter-region migration, and suitable metrics are proposed to quantify the impact of migration on the rise of cases. The healthcare capacity is modeled and a method is developed to study the consequences of the saturation of the healthcare system. The model and related measures are used to study the nature of the transmission and spread of COVID-19 in India, and appropriate insights are drawn.

COVID-19 preventive measures have been a hindrance to millions of people over the globe not only affecting their daily routine but also affecting the mental stability. Among several preventive measures for COVID-19 spread, the lockdown is an important measure which helps considerably reduce the number of cases. The updated news about the COVID-19 is drastically spread in social media. Particularly, Twitter is widely used to share posts and opinions about the COVID-19 pandemic. Sentiment analysis (SA) on tweets can be used to determine different emotions such as anger, disgust, sadness, joy, and trust. But transparence is needed to understand how a given sentiment is evaluated with the black-box machine learning models. With this motivation, this paper presents a new explainable artificial intelligence (XAI)-based hybrid approach to analyze the sentiments of the tweets during different COVID-19 lockdowns. The proposed model attempted to understand the public’s emotions during the first, second, and third lockdowns in India by analyzing tweets on social media, and demonstrates the novelty of the work. A new hybrid model is derived by integrating surrogate model and local interpretable model-agnostic explanation (LIME) model to categorize and predict different human emotions. At the same time, the Top_jSimilarity evaluation metric is employed to determine the similarity between the original and surrogate models. Furthermore, top words using the feature importance are identified. Finally, the overall emotions during the first, second, and third lockdowns are also estimated. For validating the enhanced outcomes of the proposed method, a series of experimental analysis was performed on the IEEE port and Twitter API dataset. The simulation results highlighted the supremacy of the proposed model with higher average precision, recall, F-score, and accuracy of 95.69%, 96.80%, 95.04%, and 96.76%, respectively. The outcome of the study reported that the public initially had a negative feeling and then started experiencing positive emotions during the third lockdown.

This paper delineates the mathematical modeling and dynamics of a novel coronavirus (COVID-19) an outbreak, and it is a control measurement; the effect of lockdown in terms of lakhs of cases and deaths. The lockdown effect is studied with a different lockdown success rate and also describes the multiple transmission route in the infection dynamics, and pushes the role of the environmental reservoir in the transmission and the spread of this disease. In this situation, mathematical models are an important tool to assign an impressive strategy in order to fight against this pandemic. We exhibit the boundedness of the system, the local stability analysis and global stability analysis of the equilibrium to examine its epidemiological relevance. We have also carried out numerical simulations to validate the analytical results.

This paper presents a novel analysis of the global spread of the SARS-CoV-2 virus that causes the COVID-19 disease using the R package “nCov2019”, with an emphasis on the global spread and forecasts of the disease, and the rate of transmission in individual countries at two different points in time, namely, March and September 2020. This throws in sharp relief the relative effectiveness of the attempts to risk manage the spread of the virus by “flattening the curve” (aka planking the curve) of the speed of transmission, and the efficacy of lockdowns in terms of the spread of the disease and death rates. Simple cross-sectional regressions are able to predict quite well both the total number of cases and deaths, which cast doubt on the above measures. The algorithmic techniques, results and analysis presented in the paper should prove useful to the medical and health professions, science advisers and risk management and decision making of healthcare by state, regional and national governments in all countries.

The paper presents a critical analysis of the European spread of the SARS-CoV-2 virus that causes the COVID-19 disease across 48 European countries and territories, including the Monaco and Andorra principalities and Vatican City. Simple cross-sectional regressions, using country populations, are able to predict quite accurately both the total number of cases and deaths, which cast doubt on measures aimed at controlling the disease via lockdowns. This throws into sharp contrast the relative effectiveness of the attempts to risk manage the spread of the virus by ‘flattening the curve’ of the speed of transmission, and the efficacy of lockdowns in terms of the spread of the disease and death rates. The algorithmic techniques, results and analysis presented in the paper should prove useful to the medical and health professions, science advisers and risk management and decision making of healthcare by state, regional and national governments in all countries in Europe.

In the absence of preventive therapies or effective treatment for most cases of coronavirus disease 2019 (COVID-19), governments worldwide have sought to minimize person-to-person severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission through a variety of lock-down measures and social distancing policies. Extreme events like the COVID-19 pandemic present a tremendous opportunity to make quantitative connections between changes in anthropogenic forcing, social and economic activity, and the related Earth system response. In this paper, we examine the air quality impacts associated with the pandemic response measures in the Northeastern United States.

When coronavirus disease 2019 (COVID-19) became a national health crisis, the local government of the Cagayan de Oro City (CDOC) did not implement total lockdown. The COVID-19 Adjustment Measure Program adopted by the local government probably affected the April 2020 Labor Force Survey that showed that Region 10 posted an employment rate of 88.9%, which is higher than the national average of 82.3% (Department of Labor and Employment, Region Office No. X (DOLE-X). NorMin secures highest employment rate amid COVID 19. 2020. Available from: https://pia.gov.ph/news/articles/1044898 [Accessed 9th May 2021]). Despite the regional figure being 6.6 percentage points higher than the national one, there is a decrease in employed persons by around 400,000 from 2.302 million persons employed in April 2019 to 1.883 million in April 2020 (Department of Labor and Employment, Region Office No. X (DOLE-X). NorMin secures highest employment rate amid COVID 19. 2020. Available from: https://pia.gov.ph/news/articles/1044898 [Accessed 9th May 2021]). Hence, the study determines the effect of COVID-19 protective measures implemented by the government on the economy of CDOC. Using the barangay-level and selected sectoral-level data on business registration, and employment data between 2010 and 2019, the study estimates that one-week lockdown means a ₱1,825 loss of income for a minimum-wage employee. One-month lockdown costs ₱7,300 foregone income, while one-quarter lockdown (or a half of six months) is equivalent to ₱21,900 income loss. We recommend 10 policy interventions, but the government should also think big and invest its resources into programs that create a multiplier effect on the economy. Multipliers are interventions that create ripples or positive impacts on other sectors and/or economic participants.

Infectious diseases have become a potential threat to public health over the last decade. This trend is possibly due to the emergence of highly pathogenic infections like Ebola, Influenza, West Nile virus, SARS, and very recently COVID-19, etc. These diseases are affecting the public health and have triggered significant economic damages worldwide. In this present study, we develop a stochastic epidemic model to study the effect of lockdown on infectious disease dynamics. The quarantined and un-quarantined susceptible and symptomatic and asymptomatic individuals are put into separate classes. The rate of susceptible to quarantine and quarantine to susceptible are assumed to be functions of time to capture the non-uniformity in the said two rates during the lockdown phase and unlock phase. These two parameters are also assumed to be dependent on the period of complete lockdown. A new approach termed as maximum stability index is coined to see the effect of the period of complete lockdown on the mean persistence time, which is surprisingly difficult to achieve in case the dimension of the model is high. However, the mean persistence time of the total infected population including symptomatic and asymptomatic cases is obtained by taking the average of the observed time to extinctions based on simulation.