An LSTM-Based Forecast Of COVID-19 For Bangladesh

Preoperative events can be predicted using deep learning-based forecasting techniques. It can help to improve future decision-making. Deep learning has traditionally been used to identify and evaluate adverse risks in a variety of major applications. Numerous prediction approaches are commonly applied to deal with forecasting challenges. The number of infected people, as well as the mortality rate of COVID-19, is increasing every day. Many countries, including India, Brazil, and the United States, were severely affected;however, since the very first case was identified, the transmission rate has decreased dramatically after a set time period. Bangladesh, on the other hand, was unable to keep the rate of infection low. In this situation, several methods have been developed to forecast the number of affected, time to recover, and the number of deaths. This research illustrates the ability of DL models to forecast the number of affected and dead people as a result of COVID-19, which is now regarded as a possible threat to humanity. As part of this study, we developed an LSTM based method to predict the next 100 days of death and newly identified COVID-19 cases in Bangladesh. To do this experiment we collect data on death and newly detected COVID-19 cases through Bangladesh’s national COVID-19 help desk website. After collecting data we processed it to make a dataset for training our LSTM model. After completing the training, we predict our model with the test dataset. The result of our model is very robust on the basis of the training and testing dataset. Finally, we forecast the subsequent 100 days of deaths and newly infected COVID-19 cases in Bangladesh. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The implications of COVID-19 for concepts and practices of citizenship

Based on a review of citizenship and citizen participation in politics and policy studies, this article reveals diverse concepts of citizens and citizenship and their changing roles within the context of the COVID-19 pandemic. It argues that the pandemic will result in bringing citizens back into the policy process, given that active participation of citizens in solving wicked social problems has been emphasised. Our results suggest that the pandemic will result in a return of public citizens as their voluntary, active participation and coproduction practices are expected to increase. © 2022 Policy Press. All Rights Reserved.

Sustaining Asia's development amidst the COVID-19 pandemic: capacity development and governance innovation

Resilience is of paramount importance in dealing with a prolonged pandemic such as COVID-19, in which all countries inevitable suffer through multiple stages of adversity. Many Asian countries were initially hard hit by the pandemic, but some of them displayed the remarkable ability to withstand these shocks, overcome despair, and bounce back quickly. This special issue examines two aspects of resilience building in policy responses to crises such as COVID-19 - capacity development and governance innovation. Capacity can be a key factor in determining the effectiveness of health emergency preparedness, surveillance, response, and recovery systems for unprecedented public health crises like COVID-19, and governance innovation also plays a key role in resilience building by strengthening the roles of non-government actors in public health crises, the efficacy of science-policymaking interactions, and the uses of disruptive technologies.