The impact of the coronavirus outbreak was seen all over the world in all sectors. In the case of Bangladesh, it was not free of threats. Like all other sectors, the economic, social, and educational sectors were under serious threat. This study examined the effects of COVID-19 on the lives of Bangladeshi students, with a particular focus on their idealized portrayals of plans, daily routines, social interactions, and mental well-being. This research also investigated the influence of COVID-19 on education, social life, and other sectors and how the government was dealing with this unprecedented situation and these elevation challenges. A mixed-methods approach was adopted for this research. A total of 90 students from Bangladeshi higher educational institutions were taken as a sample size using the random sampling method. SPSS software was used for data analysis. The study’s quantitative results showed that Bangladeshi students faced challenges related to teaching, learning, and social distancing during the COVID-19 pandemic. Additionally, the study revealed that the pandemic adversely affected higher education in Bangladesh. Rebels and concerned citizens from all parts of the state must work together to move forward. COVID-19 has had a natural effect on education and almost every other field. The need for social distancing has pushed the education system to change because of social distancing. Many educational institutions worldwide have shuttered their campuses and relocated their teaching and learning online.

The purpose of this study is to predict the frequency of mortality from urban traffic injuries for the most vulnerable road users before, during and after the confinement caused by COVID-19 in Santiago de Cali, Colombia. Descriptive statistical methods were applied to the frequency of traffic crash frequency to identify vulnerable road users. Spatial georeferencing was carried out to analyze the distribution of road crashes in the three moments, before, during, and after confinement, subsequently, the behavior of the most vulnerable road users at those three moments was predicted within the framework of the probabilistic random walk. The statistical results showed that the most vulnerable road user was the cyclist, followed by motorcyclist, motorcycle passenger, and pedestrian. Spatial georeferencing between the years 2019 and 2020 showed a change in the behavior of the crash density, while in 2021 a trend like the distribution of 2019 was observed. The predictions of the daily crash frequencies of these road users in the three moments were very close to the reported crash frequency. The predictions were strengthened by considering a descriptive analysis of a range of values that may indicate the possibility of underreporting in cases registered in the city’s official agency. These results provide new elements for policy makers to develop and implement preventive measures, allocate emergency resources, analyze the establishment of policies, plans and strategies aimed at the prevention and control of crashes due to traffic injuries in the face of extraordinary situations such as the COVID-19 pandemic or other similar events.