Impacts of COVID-19-Related Non-Pharmaceutical Interventions on Mobility and Accidents in Bangladesh.

Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown-including on the transport sector-to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra- and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making.

Face mask mandates and risk compensation: an analysis of mobility data during the COVID-19 pandemic in Bangladesh.

INTRODUCTION: Concerns have been raised about the potential for risk compensation in the context of mask mandates for mitigating the spread of COVID-19. However, the debate about the presence or absence of risk compensation for universal mandatory mask-wearing rules-especially in the context of COVID-19-is not settled yet. METHODS: Mobility is used as a proxy for risky behaviour before and after the mask mandates. Two sets of regressions are estimated to decipher (any) risk-compensating effect of mask mandate in Bangladesh. These include: (1) intervention regression analysis of daily activities at six types of locations, using pre-mask-mandate and post-mandate data; and (2) multiple regression analysis of daily new COVID-19 cases on daily mobility (lagged) to establish mobility as a valid proxy. RESULTS: (1) Statistically, mobility increased at all five non-residential locations, while home stays decreased after the mask mandate was issued; (2) daily mobility had a statistically significant association on daily new cases (with around 10 days of lag). Both significances were calculated at 95% confidence level. CONCLUSION: Community mobility had increased (and stay at home decreased) after the mandatory mask-wearing rule, and given mobility is associated with increases in new COVID-19 cases, there is evidence of risk compensation effect of the mask mandate-at least partially-in Bangladesh.

Modelling multiple occurrences of activities during a day: an extension of the MDCEV model.

The increased interest in time use among transport researchers has led to a search for flexible but tractable models of time use, such as Bhat's Multiple Discrete Continuous Extreme Value (MDCEV) model. MDCEV formulations typically model aggregate time allocation into different activity types during a given period, such as the amount of time spent working and shopping in a day. While these applications provide valuable insights into activity participation, they ignore disaggregate activity-episodes, that is the fact that people might split their total time spent working in multiple separate blocks, with breaks or other activities in between. Insights into this splitting into episodes are necessary for predicting trips and understanding time use satiation. We propose a modified MDCEV model where an activity-episode, rather than an activity type, is the basic choice alternative, using a modified utility function to capture the reduced likelihood of individuals performing a very large number of episodes of the same activity. Results from two large revealed preference datasets exhibit equivalent forecast accuracy between the traditional and proposed approach at an aggregate level, but the latter also provides insights on the number and duration of activity-episodes with significant accuracy.

Exploring the heterogeneity in the cyclone evacuation decision in the context of developing economy: A case study of costal Bangladesh.

The critical role of evacuation, particularly for the communities in developing countries exposed to cyclones, has only been realized after some disastrous evacuation experiences in recent cyclones. A profound understanding of the factors influencing the evacuation behavior is necessary to reduce the loss of lives, especially in the cyclone prone communities. The purpose of this research is to identify the key factors influencing households' evacuation decision during a cyclone in developing economies. To this end, the research employs state of the art discrete choice modeling techniques referred to as mixed logit models. The study builds on the data collected in 1991 from the coastal areas of Bangladesh following the cyclone Sidr. The analysis result reveals that the evacuees of developing countries like Bangladesh resort to nearby tall buildings during cyclone due to the insufficient facilities provided by the cyclone centers. In case of mandatory evacuation and for temporary house owner, the households' decision to evacuate is found to be uniform. However, significant heterogeneity is found in the households' decision to evacuate whenever they receive a voluntary notice and also if they live sufficiently far from the sea shore. The factors that are identified to be influencing households' evacuation decision during a cyclone will enable potential evacuees to better evaluate their decision and consequently make more informed decision about the timing as well as the necessity of evacuation. The analysis result will also help emergency managers to decide on the timing and the type of evacuation orders they need to provide for reducing causalities due to landfall caused by cyclones.