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The outbreak of coronavirus disease 2019 (COVID-19) has led to significant challenges for schools and communities during the pandemic, requiring policy makers to ensure both safety and operational feasibility. In this paper, we develop mixed-integer programming models and simulation tools to redesign routes and bus schedules for operating a real university campus bus system during the COVID-19 pandemic. We propose a hub-and-spoke design and utilize real data of student activities to identify hub locations and bus stops to be used in the new routes. To reduce disease transmission via expiratory aerosol, we design new bus routes that are shorter than 15 minutes to travel and operate using at most 50% seat capacity and the same number of buses before the pandemic. We sample a variety of scenarios that cover variations of peak demand, social distancing requirements, and bus breakdowns to demonstrate the system resiliency of the new routes and schedules via simulation. The new bus routes were implemented and used during the academic year 2020–2021 to ensure social distancing and short travel time. Our approach can be generalized to redesign public transit systems with a social distancing requirement to reduce passengers’ infection risk. History: This paper was refereed. This article has been selected for inclusion in the Special Issue on Analytics Remedies to COVID-19. Funding: This work was supported by the National Science Foundation [Grant CMMI-2041745] and the University of Michigan, College of Engineering. 

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average.