This study examined the food insecurity and coping mechanisms among the indigenous Bangladeshi population of the Chittagong Hill Tracts (CHT) region to extract empirical evidence on the ongoing discussion on the COVID-19 pandemic-exacerbated food-insecurity situation. The study adopted a qualitative approach by interviewing 60 indigenous households. Data were collected in two phases between 15 June 2020, and 30 July 2021 in Bangladesh’s Chittagong Hill Tracts (CHT) region. Thematic data analyses were performed using the Granheim approach and NVivo-12 software. The authors used Huston’s social–ecological theory to explain the indigenous coping mechanisms. The research evidence revealed that most households experienced challenges over daily foods, manifesting in the decreasing consumption of them, the increased price of food items, a food crisis due to an income shock, malnutrition, the shifting to unhealthy food consumption, starvation and hunger, and food insufficiency, thereby leading to mental stress. This study further revealed that the indigenous population took crucial coping strategies to survive the pandemic. In response to COVID-19, they took loans and borrowed foods, reduced expenses, changed their food habits, avoided nutritional foods, relied on vegetables, sold domestic animals and properties, collected forest and hill foods, and depended on governmental and societal relief. This study also provides the in-depth policy actions for the urgent intervention of government, stakeholders, policymakers, NGOs, and development practitioners to take necessary initiatives to enhance the quality of life of the people that were affected by the post-pandemic recovery period.

This study intends to explore the responses of local government during the COVID-19-induced lockdown in the rural areas, with particular emphasis on Bangladesh. By adopting a qualitative phenomenological research approach and employing multi-method data collection techniques (for instance, Key Informant Interview (KII), Focus Group Discussion (FGD), participant observation, and content analysis), this study found that the local governments managed the crisis of the pandemic relatively well with its limited manpower and funding through adequate preparedness and prevention strategies; effective emergency responses; and consolidated post-lockdown measures. The study revealed that the Bangladesh local government promptly took some essential actions, such as preparedness and prevention, arrangement of home quarantine and isolation, the training program for readiness, and disseminated crucial information to the local people during the pandemic, such as using masks, hand washing and sanitizing, and social distancing. Besides, the local government delivered relief, such as food and non-food items and financial support. Furthermore, the rural local government took post-lockdown responses to tackle pandemic in rural Bangladesh. Nevertheless, the service delivery individuals from local governance encountered numerous challenges, like scarcity of manpower, less support, and superstition, while providing services during the pandemic.

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.