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Hospital capacity expansion planning is critical for a healthcare authority, especially in regions with a growing diverse population. Policymaking to this end often requires satisfying two conflicting objectives, minimizing capacity expansion cost and minimizing the number of denial of service (DOS) for patients seeking hospital admission. The uncertainty in hospital demand, especially considering a pandemic event, makes expansion planning even more challenging. This work presents a multi-objective reinforcement learning (MORL) based solution for healthcare expansion planning to optimize expansion cost and DOS simultaneously for pandemic and non-pandemic scenarios. Importantly, our model provides a simple and intuitive way to set the balance between these two objectives by only determining their priority percentages, making it suitable across policymakers with different capabilities, preferences, and needs. Specifically, we propose a multiobjective adaptation of the popular Advantage Actor-Critic (A2C) algorithm to avoid forced conversion of DOS discomfort cost to a monetary cost. Our case study for the state of Florida illustrates the success of our MORL based approach compared to the existing benchmark policies, including a state-of-the-art deep RL policy that converts DOS to economic cost to optimize a single objective.

Novel coronavirus (nCoV) has created a new challenging situation all over the world. In Bangladesh, people are facing some difficulties to response the emergencies. There are so many people who are lacking of proper quarantine information and knowledge about prevention practices towards coronavirus disease 2019 (COVID-19). COVID-19 has created an experience of mental disorder like depression, anxiety, and stress. Although social media, newspaper, news, television has focused on this issue, still there is to be needed to identify the psychological effects like negative impact on our mind and behavioral changes during lockdown. An online survey of 248 respondents was conducted between April, 15 2020 and May, 15 2020. The aim of this study was to assess the relationship between higher knowledge of public regarding safety measures and depression among the adult population of Bangladesh during lockdown. This study focused on correlation between knowledge level and mental health condition like depression. About 50% respondents were felt high depression after the first announcement of lockdown in Bangladesh. Approximately 50% respondents stated that people of their locality were panicked, not panicked were 26.21% and the probability of getting panic were 23.79% due to COVID-19 during lockdown in Bangladesh. The respondents who had gathered a higher knowledge about precautions were associated with depression.