# 6 Frequently asked questions

## 6.1 How often should we expect at least one employee to arrive at work infected from the outside community?

The model does not provide insight into the answer of this question. The number and frequency of people arriving at work previously infected from outside contacts is a function of Covid-19 progression in the wider community. At any given time, only a small fraction of the population is infected with Covid-19, even during an outbreak. Thus, it is unlikely that multiple contagious employees infected from outside will arrive at work. In fact, given the conditions required for return-to-work policies in the wider community, it is unlikely that even one person will arrive infected in any given workspace. If there is a substantial risk that employees will arrive infected, that means a large outbreak is underway in the community and other public health initiatives, such as lock-downs, will take become the central policy concern. For these reasons, the model focuses on one infected person arriving in the workplace and that all secondary infections start from that single infected person.

## 6.2 What are factors to consider in deciding on the acceptable floor occupancy?

Floor occupancy in a building can be thought of as the first step in a natural firebreak. If an undetected outbreak occurs on a floor we want a policy that limits the total number infected. Using the model’s time slice feature, the user can see how fast a population becomes infected starting from a single infected person. The user can compare how large the infected population becomes at a fix time window, such as two weeks, which can represent a time spans for eventual detection. If the parameter settings lead to a large infected population after two weeks of an undetected outbreak, the decision maker can make a risk assessement and suggest lower occupancy levels.

## 6.3 What are “good” values for $$R_0$$?

$$R_0$$ is the basic reproduction number of the virus. It is not a biological constant, a rate over time ($$R_0$$ has no time units), or a measure of disease severity. $$R_0$$ is the average number of people an infected person will infect during the communicable period and is therefore controlled by complicated interactions between biology, behaviour, and other environmental factors. The literature suggests that depending on the circumstances $$R_0$$ for Covid-19 ranges between 2 and 6 with 6 being part of an extreme upper range. The CDC suggest a best central value of 2.5. In exploring the risks associated with parameter inputs, the base scenario without any mitigation having an $$R_0$$ in the 2 to 4 range makes for a good starting point.

## 6.4 How to select a mitigation factor value that corresponds to a given policy for protection, and social distancing measures?

The mitigation factor requires the most judgment. In the manual section describing the feature, there is a plot which shows the range of effectiveness of different mitigation tools. How each tool interacts is largely unknown. The user can build risk scenarios that are conservative or liberal by focusing on the range of each tool and which tools will feature most dominantly in the decision maker’s policy.

## 6.5 What counts as an “interaction”?

It is not common to catch Covid-19 from contaminated surfaces or fleeting encounters. The major mechanism for spreading the virus is person-to-person interactions over an extended period of time—that is, conversations that last several minutes, especially in noisy crowded environments which require loud speaking. In a work environment, the user will have to make judgments on what constitutes an interaction. Again, the user can build risk conservative and liberal risk scenarios by changing the inclusion of interaction types. The main driver will involve the nature of the work of the employees.