Modelling the impact of non-pharmaceutical interventions on workplace transmission of SARS-CoV-2 in the home-delivery sector
Authors: Whitfield Carl A. , van Tongeren Martie , Han Yang , Wei Hua , Daniels Sarah , Regan Martyn , Denning David W. , Verma Arpana , Pellis Lorenzo , Hall Ian ,   the University of Manchester COVID-19 Modelling Group
Published: March 2022

AbstractObjectiveWe aimed to use mathematical models of SARS-COV-2 to assess the potential efficacy of non-pharmaceutical interventions on transmission in the parcel delivery and logistics sector.MethodsWe developed a network-based model of workplace contacts based on data and consultations from companies in the parcel delivery and logistics sectors. We used these in stochastic simulations of disease transmission to predict the probability of workplace outbreaks in this settings. Individuals in the model have different viral load trajectories based on SARS-CoV-2 in-host dynamics, which couple to their infectiousness and test positive probability over time, in order to determine the impact of testing and isolation measures.ResultsThe baseline model (without any interventions) showed different workplace infection rates for staff in different job roles. Based on our assumptions of contact patterns in the parcel delivery work setting we found that when a delivery driver was the index case, on average they infect only 0.14 other employees, while for warehouse and office workers this went up to 0.65 and 2.24 respectively. In the LIDD setting this was predicted to be 1.40, 0.98, and 1.34 respectively. Nonetheless, the vast majority of simulations resulted in 0 secondary cases among customers (even without contact-free delivery). Our results showed that a combination of social distancing, office staff working from home, and fixed driver pairings (all interventions carried out by the companies we consulted) reduce the risk of workplace outbreaks by 3-4 times.ConclusionThis work suggests that, without interventions, significant transmission could have occured in these workplaces, but that these posed minimal risk to customers. We found that identifying and isolating regular close-contacts of infectious individuals (i.e. house-share, carpools, or delivery pairs) is an efficient measure for stopping workplace outbreaks. Regular testing can make these isolation measures even more effective but also increases the number of staff isolating at one time. It is therefore more efficient to use these isolation measures in addition to social distancing and contact reduction interventions, rather than instead of, as these reduce both transmission and the number of people needing to isolate at one time.Author summaryDuring the COVID-19 pandemic the home-delivery sector was vital to maintaining people’s access to certain goods, and sustaining levels of economic activity for a variety of businesses. However, this important work necessarily involved contact with a large number of customers as well as colleagues. This means that questions have often been raised about whether enough was being done to keep customers and staff safe. Estimating the potential risk to customers and staff is complex, but here we tackle this problem by building a model of workplace and customer contacts, from which we simulate SARS-CoV-2 transmission. By involving industry representatives in the development of this model, we have simulated interventions that have either been applied or considered, and so the findings of this study are relevant to decisions made in that sector. Furthermore, we can learn generic lessons from this specific case study which apply to many types of shared workplace as well as highlighting implications of the highly stochastic nature of disease transmission in small populations.