The COVID-19 pandemic has caused severe health and economic impacts globally. Strategies to safely reopen economies, travel and trade are a high priority. Until a reliable vaccine is available, non-pharmaceutical techniques are the only available means of disease control. In this paper, we aim to evaluate the extent to which social distancing (SD) and facemask (FM) use can mitigate the transmission of COVID-19 when restrictions are lifted. We used a microsimulation activity-based model for Sydney Greater Metropolitan Area, to evaluate the power of SD and FM in controlling the pandemic under numerous scenarios. The hypothetical scenarios are designed to picture feasible futures under different assumptions. Assuming that the isolation of infected cases and the quarantining of close contacts are in place, different numerical tests are conducted and a full factorial two-way MANOVA test is used to evaluate the effectiveness of the FM and SD control strategies. The main and interactive effects of the containment strategies are evaluated by the total number of infections, percentage of infections reduction, the time it takes to get the pandemic under control, and the intensity of active cases.

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Different agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate different control strategies to control outbreak of the infectious disease. While there are several estimation methods for the disease-specific parameters of COVID-19, they have been used for aggregate level models such as SIR and not for agent-based models. We propose a mathematical structure to determine parameter values of agent-based models considering the mutual effects of parameters. Then, we assess the extent to which different control strategies can intervene the transmission of COVID-19. Accordingly, we consider scenarios of easing social distancing restrictions, opening businesses, speed of enforcing control strategies and quarantining family members of isolated cases on the disease progression. We find the social distancing compliance level in the Sydney greater metropolitan area to be around 85%. Then we elaborate on consequences of easing the compliance level in the disease suppression. We also show that tight social distancing levels should be considered when the restrictions on businesses and activity participations are easing.

Bus system design is a difficult problem, and hence is usually decomposed into a series of sub-problems solved sequentially. Bus network design is foremost in this series of problems. The bus network design problem in this study is the problem of choosing a subset of interconnected bus routes from among a given set of such routes, which minimizes the total travel time of the users of the network, while being feasible in fleet requirements. The Ant System concept has been exploited to solve this problem. The algorithm has been applied to the problem and calibrated based on the network of Sioux Falls. For this purpose, several fleet assignment routines have been tested, some sensitivity analyses are made to estimate suitable parameter values, and alternative ways of laying pheromone on bus routes have been examined.Experiments are conducted to investigate the performance of the solution algorithm when the number of routes, or bus fleet size, increases. Moreover, other experiments help to determine the number of algorithmic iterations. These experiments prepared the algorithm to be applied to design the bus network of the City of Mashhad, with a population of over 2 million. The results have been compared with those of another solution to the same problem, obtained by another meta-heuristic, namely a Genetic Algorithm. Electronic supplementary materialThe online version of this article (

Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.

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