A. James Clark School of Engineering

Background Meatpacking plants were major sources of COVID-19 outbreaks, posing unprecedented risks to employees, family members, and local communities. The effect on food availability during outbreaks was immediate and staggering: within 2 months, the price of beef increased by almost 7% with documented evidence of significant meat shortages. Meatpacking plant designs, in general, optimize on production; this design approach constrains the ability to enhance worker respiratory protection without reducing output. Methods Using agent-based modeling, we simulate the spread of COVID-19 within a typical meatpacking plant design under varying levels of mitigation measures, including combinations of social distancing and masking interventions. Results Simulations show an average infection rate of close to 99% with no mitigation, 99% with the policies that US companies ultimately adopted, 81% infected with the combination of surgical masks and distancing policies, and 71% infected with N95 masks and distancing. Estimated infection rates were high, reflecting the duration and exertion of the processing activities and lack of fresh airflow in an enclosed space. Conclusion Our results are consistent with anecdotal findings in a recent congressional report, and are much higher than US industry has reported. Our results suggest current processing plant designs made rapid transmission of the virus during the pandemic's early days almost inevitable, and implemented worker protections during COVID-19 did not significantly affect the spread of the virus. We argue current federal policies and regulations are insufficient to ensure the health and safety of workers, creating a justice issue, and jeopardizing food availability in a future pandemic.

According to the United Nation’s Department of Economic and Social Affairs Population Division, 66% of the world’s population will reside in urban areas by 2050; a boost from 30 % in 1950. Urbanization has indeed triumphed and its speed has brought innovation and economic growth. Its synergies within infrastructure systems are undeniable and have increased the demand for such systems. However, urbanization is one reason infrastructure systems are knocked out of equilibrium and show complex dynamical behavior. Most infrastructure systems have been designed without planning for this magnitude of potential demographic changes; thus redesigns are long overdue. Also, climate change looms. Resource scarcity and host of other factors leave their impacts; all pose some incidence of perturbation in the state of the infrastructure system. These perturbations can affect the system’s resilience, which is a defining property of each system for remaining functional in the midst of disruption from an adverse event. Therefore, it is essential to develop appropriate metrics and methods to enhance the resilience of infrastructures at the network level. Such enhancements are critical for sustainable infrastructure development that is capable of performing satisfactorily through intentional and/or stochastic disruptions. A resilience evaluation of a network typically entails assessing vulnerability and robustness as well as identifying strategies to increasing network efficiency and performance and offering recovery strategies ideally taken in a cost-effective manner. This dissertation uses complex network theory (CNT) as the theoretic basis to enhance the resilience of large-scale infrastructure networks, such as urban rail transit systems. Urban rail transit infrastructures are heterogeneous, complex systems consisting of a large number of interacting nodes and links, which can imitate a network paradigm. Any adverse event leading to a disruption in the interaction and connectivity of network components would dramatically affect the safety and wellbeing of commuters, as well as the direct and indirect costs associated with performance loss. Therefore, enhancing their resilience is necessary. Using the Washington D.C. Urban rail transit as a case study, this dissertation develops a methodology to analyze network topology, compute its efficiency, vulnerability, and robustness in addition to provide a unified metric for assessing the network resilience. The steps of methodology are applied to two models of weighted and unweighted networks. For the weighted model two novel algorithms are proposed to capture the general pattern of ridership in the network, and to reflect the weights on assessing network efficiency, respectively. This dissertation then proposes an effective strategy to increase the network resilience prior to a disruptive event, e.g., a natural disaster, by adding several loop lines in the network for topological enhancement. As such, adding a loop line can create redundancy to the vulnerable components and improve network resilience. Expanding on this, the dissertation offers comparative recovery strategies and cost model in the case of disruption. An effective recovery strategy must demonstrate rapid optimal restoration of a disrupted system performance while minimizing recovery costs. In summary, the systematic methodology described above, assesses and enhances the network resilience. The initial results rank the most vulnerable and robust components of the network. The algorithms developed throughout the study advance the weighted network analysis state of art. The topological enhancement strategy offered basis to justify capital improvement. Post failure recovery analysis and the cost model serves to inform decision makers in identifying best recover strategies with special attention not only to restoring performance of a system but also on reducing associated failure and recovery costs. The use of the methodology proposed in this dissertation may lead to significant societal benefits by reducing the risk of catastrophic failures, providing references for mitigation of disruption due to adverse events, and offering resilience- based strategies, and related pursuits.

A. James Clark School of Engineering

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