This section is all about practicing applying modeling and analytic concepts to realistic public health case studies.
These videos are designed to be watched after completing the four activities in this series. If you need to, pause the video now to locate and complete each activity, because these videos will walk through our proposed answers.
Activity 1 asks you to decide which analytic approach is best for a given case study and then determine how to communicate with a modeling and analytics team to get that output. The activity includes three case studies.
For Case Study 1, you reviewed and answered questions about what type of analytic approach would be best and why, when given the options of a qualitative assessment, nowcast, short-term forecast, and scenario model. You were asked: What information should you be prepared to provide to the modeling and analytics team? What other epidemiological information would be useful and how would you explain it? And what sources of uncertainty might affect the outputs the team develops?
Case Study 1 summarizes a question from a local long-term-care facility board to a county epidemiologist. The board is considering adopting a restricted visitor policy during the holidays to reduce the impact of seasonal respiratory diseases and wants to make a well-informed decision based on how effective the restricted visitor policy might be in preventing outbreaks of seasonal respiratory diseases. 
Sarah, who previously worked in a county epidemiology department, is here to help us decide which approach is best. Thanks, Mike. I think Scenario Modeling would be particularly helpful here. However, scenario models are not the only kind of models that could be useful here - for example, outputs from nowcasts or short-term forecasts might be helpful to estimate the level of immediate risk, especially if this was happening further into the respiratory virus season.
But why is scenario modeling so helpful here? Because we are interested in comparing the outcomes of hypothetical scenarios to assess the potential impact of an intervention - in this case, restricting visitors vs. not restricting visitors in long-term care facilities. Scenario modeling can be a helpful tool when: weighing multiple intervention options, deciding how to allocate scarce resources, making difficult decisions with social implications, trying to understand impact of factors beyond our control, and communicating different options or outcomes to leadership, the media, or the public.
Information relevant to share with the modeling and analytics team includes data such as: Historical data from previous years on respiratory illnesses in the community and in long-term care facilities, Size of the facilities, Number and pattern of visitors during the holidays, Whether there is ample supply of personal protective equipment, Staff vaccination rates, staff absenteeism rates, and Other prevention policies in place and estimated adherence. You can also share relevant epidemiologic information such as: transmission potential of respiratory viruses (including the probability of transmitting based on time visiting, especially if available for this population), and Patterns of transmissibility in people with or without symptoms. This is not exhaustive, and many other pieces of information might also be helpful based on the modeling approach - conversations with your team will be critical to help shape this. This concludes Activity 1, Case Study 1.