Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT’s Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.

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Student reflection is thought to be an important part of retaining and understanding knowledge gained in a course. Using natural language processing, we analyze and interpret student reflections from Massive Open Online Courses (MOOCs) to understand the students' sentiments and problem-solving procedures. The reflections are free text responses to questions from MIT 6.00.1x, an introductory programming MOOC. We compare different sentiment analysis methods, and conclude that the best-performing methods can robustly classify sentiment of student responses. In addition, we develop methods to analyze student problem-solving procedures using sentence parsing and topic modeling. We find our method can distinguish some common problem-solving procedures such as utilizing course resources.

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