Balancing Workforce Productivity Against Disease Risks for Epidemics

Balancing Workforce Productivity Against Disease Risks for Environmental and Infectious Epidemics Nina H. Fefferman, Ph.D. Rutgers Univ. fefferman@aesop.rutgers.edu

Direct threats: Well people Sick people Pathogens of all sorts Nothing terribly surprising about this

Sick Workers have a choice: Workers Being Productive Stay home (don’t be productive) Sick workers Lack of Productivity AND Sick People Go to work and maybe infect coworkers

Basic idea behind this research : Can we train or allocate our work force according to some algorithm in order to maintain a minimum efficiency? • Elements of the system : • Different tasks that need to be accomplished • Maybe each task has its own • 1) rate of production • (depends on having a minimum # of workers on each task) • 2) time to be trained to perform the task • 3) minimum number of workers needed • to accomplish anything

An assumption for today: • We will deal with all absence from work as “mortality” (permanent absence from the workforce once absent once for any reason) – • Depending on the specific disease/contaminant in question, this would definitely want to be changed to reflect “duration of symptoms causing absence from work” and “what is the probability of death from infection”

Based on this framework, we can ask whether or not infectious disease and environmental (or at least non-coworker mediated infectious disease) lead to different “successes” of task allocation methods? • We can simulate a population, with new workers being recruited into the system, staying in or learning and progressing through new tasks over time according to a variety of different allocation strategies • We measure success by amount of work produced (in each task and overall) and the survival of population (also in each task and overall) • (Today I’ll just show the “total” measures for the whole population, even though we measure everything in each task)

We’ll examine four different allocation strategies • Defined permanently : only trained for one thing • Allocated by seniority : progress through different tasks over time • Repertoire increases with seniority : build knowledge the longer you work • Completely random : just for comparison, everyone switches at random (Suggested by the most efficient working organizations of the natural world – social insects!) (Determined) (Discrete) (Repertoire) (Random)

Model formulation – (discrete) • Three basic counterbalancing parameters: • Disease/Mortality risks for each task Mt (this will change over time for the infectious disease, based on how many other coworkers are already sick) • Rate of production for each task Bt • The cost of switching to task t from some other task (either to learn how, or else to get to where the action is), St

We have individuals I and tasks (t) in iteration (x), so we writeIt,x • In each step of the Markov process, each individual It,x contributes to some Pt,xthe size of the population working on their task (t) in iteration (x) EXCEPT • 1) The individual doesn’t contribute if they are dead • 2) The individual doesn’t contribute if they are in the ‘learning phase’ • They’re in the learning phase if they’ve switched into their current task (t) for less than St iterations • In each iteration, for each living individual in Pt,x there is an associated probability Mt of dying (independent for each individual) • Individuals also die (deterministically) if they exceed a (iteration based)maximum life span

We also replenish the population periodically: every 30 iterations, we add 30 new individuals • This is arbitrary and can be changed, but think of it as a new “class year” graduating, or a new hiring cycle, or however else the workforce is recruited Then for each iteration (x), the total amount of work produced is And the total for all the iterations is just We also keep track of how much of the population is “left alive”, since there is a potential conflict between “work production” and population survival

Notice that we actually can write this in closed form – we don’t need to simulate anything stochastically to get meaningful results • HOWEVER – part of what we want to see is the range and distribution of the outcome when we incorporate stochasticity into the process

Now we can examine different relationships among the parameters: Suppose that we take all combinations of the following: IncreasingDecreasingConstant Bt = ρ1t Bt = ρ1(|T|-t) Bt = ρ1|T| St = ρ2t St = ρ2(|T|-t) St = ρ2|T| Mt = ρ3t Mt = ρ3(|T|-t) Mt = ρ3|T| ρ is some proportionality constant (in the examples shown, it’s just 1) Also in the examples shown the minimum number of individuals for each task is held constant for all t

So do we actually see differences in the produced amount of work? So even as the relationships among the parameters vary, we do see drastic differences in the amount of work produced

How about Survival? We also see differences in the survival probability of the population as the relationships among the parameters vary

If you want to be safest on average, via both metrics, Repertoire wins! So the full story as the relationships among the parameter values vary looks like:

But notice: In the examples you just saw, the mortality cost in each task was independent of the number of individuals in that task already affected • This is much more like an environmental exposure risk • What if we wanted to look at infectious disease risks? • Then the risk of mortality in each task would depend on the number of sick workers already performing that task • Mt = c + β(# Infectedt) • where β is the probability of becoming infected from contact with a sick coworker and c is any constant level of primary exposure

For simplicity now, let’s not let the other parameters vary in relation to each other – let’s just look at : Bt = ρ1t Increasing St = ρ2t Increasing Mt = c + β(# Infectedt) Constant primary + secondary And again a constant minimum number for each task And we will compare this with the narrower range of non-infectious scenarios by then keeping everything the same, but changing Mt back to just the constant primary exposure

So do we still actually see differences in the produced amount of work without infectious spread, but with the narrower range? Non-infectious Exposure

And when we introduce infectious spread, we still see differences among the allocation strategies Infectious Exposure

And in direct comparison? Non-infectious vs Infectious Mortality Risk? Total work Produced • Always better to have environmental disease • Makes sense • BUT – the difference in outcome is drastically different!

How about differences for overall survival? Non-infectious Exposure

So we also difference in survival Infectious Exposure

And again - Direct comparison? Population Left Alive Again, better to have only environmental exposure (makes sense again) But again, differences in delta between strategies

So, are the differences seen across strategies from environmental to infectious exposure the same for both survival and work? No! Work comparisons Survival comparisons Smaller delta Larger delta Larger delta Smaller delta

Take home messages: • YES! There are conflicts between productivity and disease risks, and the change depending on type of disease • It’s unlikely that these sorts of models will provide “easy” answers – but it IS likely that they could provide public policy makers with “likely disease-related repercussions” of societal organization policies • The more we look at the problem, the better the information to the decision makers can be

Balancing Workforce Productivity Against Disease Risks for Epidemics