Dataset Information

Hazardous infectious waste collection and government aid distribution during COVID-19: A robust mathematical leader-follower model approach.

ABSTRACT: The outbreak of COVID-19 has posed significant challenges to governments across the world. The increase in hazardous infectious waste (HIW) caused by the pandemic is associated with the risk of transmitting the virus. In this study, hazardous waste includes infectious waste generated both by individuals and by hospitals during the COVID-19 pandemic. To control the outbreak by maintaining social distance and home quarantine protocols, daily necessities and health supplies must be provided to the people affected. Governments play an essential role in the management of the crisis, creating an elaborate plan for collecting HIW and providing necessities and health supplies. This paper proposes a leader-follower approach for hazardous infectious waste collection and government aid distribution to control COVID-19. At the top level of the model, government policies are designed to support people by distributing daily necessities and health supplies, and to support contractors by waste collection. The lower level of the model is related to the operational decisions of contractors with limited capacities. Due to the potential risk of virus transmission via contaminated waste, the proposed model considers the complications imposed on contractors at the lower level. Applying a stochastic programming approach, four possible scenarios are examined, dependent of the severity of the outbreak. As a solution approach, the Benders decomposition method is combined with Karush-Kuhn-Tucker conditions. The results show that government support, in addition to much better management of citizen demand, can control the spread of the virus by implementing quarantine decisions.

SUBMITTER: Valizadeh J

PROVIDER: S-EPMC7908920 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Cells of epithelial tissue proliferate and pack together to attain an eventual density homeostasis. As the cell density increases, spatial distribution of velocity and force show striking similarity to the dynamic heterogeneity observed elsewhere in dense granular matter. While the physical nature of this heterogeneity is somewhat known in the epithelial cell monolayer, its biological relevance and precise connection to cell density remain elusive. Relevantly, we had demonstrated how large-scale dynamic heterogeneity in the monolayer stress field in the bulk could critically influence the emergence of leader cells at the wound margin during wound closure, but did not connect the observation to the corresponding cell density. In fact, numerous previous reports had essentially associated long-range force and velocity correlation with either cell density or dynamic heterogeneity, without any generalization. Here, we attempted to unify these two parameters under a single framework and explored their consequence on the dynamics of leader cells, which eventually affected the efficacy of collective migration and wound closure. To this end, we first quantified the dynamic heterogeneity by the peak height of four-point susceptibility. Remarkably, this quantity showed a linear relationship with cell density over many experimental samples. We then varied the heterogeneity, by changing cell density, and found this change altered the number of leader cells at the wound margin. At low heterogeneity, wound closure was slower, with decreased persistence, reduced coordination and disruptive leader-follower interactions. Finally, microscopic characterization of cell-substrate adhesions illustrated how heterogeneity influenced orientations of focal adhesions, affecting coordinated cell movements. Together, these results demonstrate the importance of dynamic heterogeneity in epithelial wound healing. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

Dataset Information

Hazardous infectious waste collection and government aid distribution during COVID-19: A robust mathematical leader-follower model approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets