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.
Background To prevent the viral transmission from higher infected to lower infected area, controlling the vehicular traffic, consequently public movement on roads is crucial. Containment strategies and local cognition regarding pandemic might be helpful to control vehicular movement. This study aimed to ascertain the effectiveness of containment strategies and local cognition for controlling traffic volume during COVID-19 pandemic in Dhaka, Bangladesh. Method Six containment strategies were considered to explore their influence on traffic condition, including declaration of general holiday, closure of educational institution, deployment of force, restriction on religious gathering, closure of commercial activities, and closure of garments factories. Newspaper coverage and public concern about COVID-19 were considered as local cognition in this research. The month of Ramadan as a potential event was also taken into account considering it might have an impact on the overall situation. Average daily journey speed (ADJS) was calculated from real-time traffic data of Google Map to understand the vehicular traffic scenario of Dhaka. A multiple linear regression method was developed to comprehend the findings. Results The results showed that among the containment strategies, declaration of general holiday and closure of educational institutions could increase the ADJS significantly, thereby referring to less traffic movement. Besides, local cognition could not significantly affect the traffic condition, although the month of Ramadan could increase the ADJS significantly. Conclusion It is expected that these findings would provide new insights into decision-making and help to take appropriate strategies to tackle the future pandemic situation.
and in the Global South are making wild investments in auto-oriented urban and transport infrastructures. Ahmed & Shi Ye (2008:126) show that such developments in Chinese and Pakistani cities in many cases compromise environmental sustainability, long term feasibility, social equity and favor "a minority of premium modes users over a majority who prefers walking, biking and conventional transit system". Public transport systems in Delhi, India, as well, exclude these users, who remain outside the formal planning process and is not "taking care of the slow vehicles [NMVs] on the road" and hence, is functioning in a sub-optimal condition (Tiwari, 2002:95). In case of African cities, Khayesi et al (2010) find similar levels of negligence towards pedestrians, cyclists and street vendors in transport policy and practice in Nairobi, Kenya. They show that this results in competing use of pavements and roads which ultimately exposes pedestrians, cyclists and street vendors to insecurity and harassment. In Bangkok, the Skytrain has contributed to increase social inequality by providing an alternative mode for an 'idealized' (new type) user, thus ignoring the unaffordable majority (Richardson & Jensen, 2008). This is a common feature in a growing generation of urban mega-projects in the Global South (ibid); more frequent are large investments in roads and elevated motorways than rail-transit systems or mass-transit systems like BRTs. Thus, urban 'soft mobilities' are eliminated in favour of auto-mobility resulting in a "spatial organization and mobility regime incongruent with widespread transit, pedestrian and bicycling spaces" (Henderson, 2004:203). NMT is "the neglected Cinderella of transport modes" (Gwilliam, 2003:212) and is "systematically underrecognized" (World Bank, hereafter WB, 2002:xiii). Rahman et al. (2009) have expressed serious doubt regarding the future of popular NMVs-rickshaw, becak, etc-in Asia. NMT detractors depict these NMVs as being degrading and slow while causing congestion and argue for bans. Hence, bicycles and rickshaws are banned in many Chinese cities (Zacharias, 2012) as well as in an increasing number of Indian cities:
In this study, we aimed to evaluate the performance of various machine learning (ML) classifiers to predict mode choice of movement-challenged persons (MCPs) based on data collected through a questionnaire survey of 384 respondents in Dhaka, Bangladesh. The mode choice set consisted of CNG-driven auto-rickshaw, bus, walking, motorized rickshaw, and non-motorized rickshaw, which was found as the most prominent mode used by MCPs. Age, sex, income, travel time, and supporting instrument (as an indicator of the level of disability) utilized by MCPs were explored as predictive variables. Results from the different split ratios with 10-fold cross-validation were compared to evaluate model outcomes. A split ratio of 60% demonstrates the optimum accuracy. It was found that Multi-nominal Logistic Regression (MNL), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) show higher accuracy for the split ratio of 60%. Overfitting of bus and walking as a travel mode was found as a source of classification error. Travel time was identified as the most important factor influencing the selection of walking, CNG, and rickshaw for MNL, KNN, and LDA. LDA and KNN depict the supporting instrument as a more important factor in mode choice than MNL. The selection of rickshaw as a mode follows a relatively normal probability distribution, while probability distribution is negatively skewed for the other three modes.
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