This paper aims to design a mathematical model for sustainable closed-loop supply chains during the coronavirus (COVID-19) outbreak. The proposed multi-objective model minimizes the total cost, carbon emission, and infection risk of the network and also maximizes social benefits. Some practical conditions such as stochastic demands, supplier selection, and facility disruptions are considered in the model. This stochastic model is transformed into a deterministic model by the Chance-Constrained Programming (CCP) approach. Thirty test datasets are applied to examine the effects of COVID-19 on the performance of the network. The problems are solved under two conditions (with and without COVID-19 considerations), and the results show that the total inventory of the model with COVID-19 is higher than the model without COVID-19. Moreover, the value of aggregated objectives of the model with COVID-19 is greater than the other model which indicates the greater distance from the optimal objectives. However, the computational efficiency of the model without COVID-19 is much higher than the other one. Some sensitivity analyses are also carried out, and several policy implications are proposed. According to the demand sensitivity analysis, when managers expect that the demands will increase, they should focus on the infection risk objective. On the contrary, when they expect decreased demands, they should pay more attention to environmental and economic objectives. The analysis of the objectives’ weights indicates that investing in the environmental and COVID-19 risk dimensions needs more financial resources compared to investing in the social dimension.

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