The COVID-19 pandemic has caused incredible impacts on people’s travel behavior. Recent studies suggest that while the demand for public transport has decreased due to passengers’ inability to maintain physical distance inside this mode, the demand for private automobile and active transport modes (walking and cycling) has increased during the pandemic. Policymakers should take this opportunity given by the pandemic and encourage people to use active transport more in the new normal situation to achieve sustainable transportation outcomes. This study explores the expected change in active transport mode usage in the new normal situation in Bangladesh based on the data from a questionnaire survey. The study finds that 56% and 45% of the respondents were expected to increase travel by walking and cycling, respectively, during the new normal situation. On the other hand, 19% of the respondents were expected to do the opposite. The study further identifies the factors influencing the expected change in travel by active transport modes during the new normal situation by developing multinomial logistic regression models. Finally, this study proposes policies to increase active transport use beyond the pandemic and ensure sustainable mobility for city dwellers and their well-being.

Background: COVID-19 pandemic outbreak is an unprecedented shock throughout the world, which has generated a massive social, human, and economic crisis. Identification of risk factors is crucial to prevent the COVID-19 spread by taking appropriate countermeasures effectively. Therefore, this study aimed to identify the potential risk factors contributing to the COVID-19 incidence rates at the district-level in Bangladesh. Method: Spatial regression methods were applied in this study to fulfill the aim. Data related to 28 demographic, economic, built environment, health, and facilities related factors were collected from secondary sources and analyzed to explain the spatial variability of this disease incidence. Three global (ordinary least squares (OLS), spatial lag model (SLM), and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) regression models were developed in this study. Results: The results of the models identified four factors: percentage of the urban population, monthly consumption, number of health workers, and distance from the capital city, as significant risk factors affecting the COVID-19 incidence rates in Bangladesh. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh, with an R 2 value of 78.6%. Conclusion: Findings and discussions from this research offer a better insight into the COVID-19 situation, which helped discuss policy implications to negotiate the future epidemic crisis. The primary policy response would be to decentralize the urban population and economic activities from and around the capital city, Dhaka, to create self-sufficient regions throughout the country, especially in the northwestern region.

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Background The emergence of COVID-19 pandemic has not only shaken the global health sector, but also almost every other sector including the economic and education sectors. Newspapers are performing a significant role by featuring the news of COVID-19 from its very onset. The temporal fluctuation of COVID-19 related key themes presented in newspaper articles and the findings obtained from them could offer an effective lesson in dealing with future epidemics and pandemics. Aim and Method This paper intends to develop a pandemic management framework through an automated content analysis of local newspaper coverage of COVID-19 pandemic in Bangladesh. To fulfill the aim, 7,209 newspaper articles are assembled and analyzed from three popular local newspapers named “ bdnews24.com”, “New Age” , and “ Prothom Alo English” over the period from January 1, 2020 to October 31, 2020. Results Twelve key topics are identified: origin and outbreak of COVID-19, response of healthcare system, impact on economy, impact on lifestyle, government assistance to the crisis, regular updates, expert opinions, pharmaceutical measures, non-pharmaceutical measures, updates on vaccines, testing facilities, and local unusual activities within the system. Based on the identified topics, their timeline of discussion, and information flow in each topic, a four-stage pandemic management framework is developed for epidemic and pandemic management in future. The stages are preparedness, response, recovery, and mitigation. Conclusion This research would provide insights into stage-wise response to any biological hazard and contribute ideas to endure future outbreaks.

COVID-19 pandemic has caused adverse impacts on different aspects of life around the globe, including travelers’ mode choice behavior. To make their travel safe, transportation planners and policymakers need to understand people’s perceptions of the risk of COVID-19 transmission in different travel modes. This study aimed to estimate mode-wise perceived risk of viral transmission and identify the factors that influenced the perceived risk in Bangladesh. The study used a five-point Likert scale to measure the perceived risk of COVID-19 transmission in each travel mode. Using ordinal logistic regression models, the study explored the factors that influenced the perceived risk of COVID-19 transmission in different travel modes. The study found that people perceived a very high risk of viral transmission in public transport (bus), moderate risk in shared modes (rickshaw, auto-rickshaw, ridesharing), and very low risk in private modes (private car, motorcycle/scooter, walking, cycling). Such high-risk perception of viral transmission in public transport and shared modes might lead to a modal shift to private modes, which would worsen urban transport problems and undermine sustainable transportation goals. The study also found that socio-economic factors (gender, age, income) significantly influenced perceived risks in all travel modes. Contrarily, psychological factors (worry, care, and trust) were significant only for public and shared modes, but not for private modes. Lastly, travel behavior-related factors influenced perceived risk in shared and private modes.