key: cord-0721166-oumcmkco authors: Scott, Nick; Palmer, Anna; Delport, Dominic; Abeysuriya, Romesh; Stuart, Robyn M; Kerr, Cliff C; Mistry, Dina; Klein, Daniel J; Sacks‐Davis, Rachel; Heath, Katie; Hainsworth, Samuel W; Pedrana, Alisa; Stoove, Mark; Wilson, David; Hellard, Margaret E title: Modelling the impact of relaxing COVID‐19 control measures during a period of low viral transmission date: 2020-11-18 journal: Med J Aust DOI: 10.5694/mja2.50845 sha: 1c36c629fba9a6273ea291464344c96c09bfbbd3 doc_id: 721166 cord_uid: oumcmkco OBJECTIVES: To assess the risks associated with relaxing coronavirus disease 2019 (COVID‐19)‐related physical distancing restrictions and lockdown policies during a period of low viral transmission. DESIGN: Network‐based viral transmission risks in households, schools, workplaces, and a variety of community spaces and activities were simulated in an agent‐based model, Covasim. SETTING: The model was calibrated for a baseline scenario reflecting the epidemiological and policy environment in Victoria during March–May 2020, a period of low community viral transmission. INTERVENTION: Policy changes for easing COVID‐19‐related restrictions from May 2020 were simulated in the context of interventions that included testing, contact tracing (including with a smartphone app), and quarantine. MAIN OUTCOME MEASURE: Increase in detected COVID‐19 cases following relaxation of restrictions. RESULTS: Policy changes that facilitate contact of individuals with large numbers of unknown people (eg, opening bars, increased public transport use) were associated with the greatest risk of COVID‐19 case numbers increasing; changes leading to smaller, structured gatherings with known contacts (eg, small social gatherings, opening schools) were associated with lower risks. In our model, the rise in case numbers following some policy changes was notable only two months after their implementation. CONCLUSIONS: Removing several COVID‐19‐related restrictions within a short period of time should be undertaken with care, as the consequences may not be apparent for more than two months. Our findings support continuation of work from home policies (to reduce public transport use) and strategies that mitigate the risk associated with re‐opening of social venues. The model allows people to be a part of multiple independent contact networks. Within each network, a "contact" is a link between two people indicating that transmission would be possible if one of them were infected. The model is designed so that each individual can be a part of an arbitrary number of contact networks used to approximate transmission dynamics associated with different activities or specific public spaces. For this analysis, we considered networks and settings most likely to be subject to a policy change in Australia, with contact networks explicitly modelled for: households; schools; workplaces; social networks; cafés and restaurants; pubs and bars; public transport; places of worship; professional sport; community sport; beaches; entertainment (cinemas, performing arts venues etc); national parks; public parks; large events (concerts, festivals, sports games etc.); child care; and aged care. Each contact network is defined by a set of properties: the percentage (and age range) of the population who are a part of it; the mean number of contacts per day associated with these activities; whether the contacts are known or random; the type of network structure (random or cluster; for example, public transport is random while schools/workplaces are clustered); the risk of transmission relative to a household contact (scaled to account for frequency of some activities); the effectiveness of contact tracing that might occur; and the effectiveness of quarantine at reducing transmission (for example, quarantine may be effective for workplace transmission, not effective for household transmission, and partially effective for community transmission due to imperfect adherence). Details of the contact networks are provided in Supporting Information, D. The model population was initialised through the generation of households. Individual households were explicitly modelled based on the household size distribution for Australia [1] , with each person in the model assigned to a house. To assign people in the model an age, a single adult was selected for each household as an index, whose age was randomly sampled from a subset of the Victorian adult population (all adults 22 years and older and a percentage of 18-21 year olds -20%, 40%, 60%, and 80% of people aged 18, 19, 20 and 21, respectively) to ensure that at least one adult was in each household. The age of additional household members was then assigned according to Australian agespecific household contact estimates (from Prem et al. [2] , Figure 2 ), by drawing the age of the remaining members from a probability distribution based on the row corresponding to the age of the index member. The resulting age distribution of the model population, compared to the Victorian population, is provided in Figure 1 . School classrooms were explicitly modelled. Classroom sizes were drawn randomly from a Poisson distribution with mean 21, the Victorian mean [3] . People in the model aged 5-18 years were assigned to classrooms with people of the same age. Each classroom had one randomly selected adult (>21 years) assigned to it as a teacher. The school contact network was then created as a collection of disjoint, completely connected clusters (i.e. classrooms). Similarly, a work contact network was created as a collection of disjoint, completely connected clusters of people aged 18-65 years. The size of each cluster was drawn randomly from a Poisson distribution with mean equal to the estimated mean number of daily work contacts (Table 4) . Other clustered contact networks, such as places of worship, community sports, professional sports, child care and aged care were generated analogously (with transmissibility scaled to account for event frequency; Supporting Information, D). Random contact networks (e.g. public transport) were generated by allocating each person a number of contacts drawn from a Poisson distribution with mean as per Supporting Information, D. Unlike the clustered contact networks, the contacts in random contact networks were resampled at each time step in the model (representing days). Policy scenarios modelled were informed by the COVID-19 public health response in Victoria [4] and the COVIDSAFE Australia framework [5] , and included scenarios related to: the effectiveness of contact tracing; compliance with physical distancing; restricting access to hospitality and entertainment venues and other public spaces; restricting access to places of worship; restricting the size of social gathering; restricting community and professional sport; closing schools and childcare settings; closing non-essential workplaces, retail outlets and health care; and restricted travel across jurisdictional borders and domestic travel. Each policy change is linked to one or more networks, and can potentially influence the whole population. For example, if non-essential work begins, this would increase the size of the work network, as well as increasing transmissibility in public transport. See Supporting Information, E for full list of modelled scenarios. Epidemiological data for the daily number of tests conducted, new diagnoses and new severe cases, critical cases and deaths was obtained from the Victorian Department of Health [6, 7] . Newly diagnosed cases were classified as "imported" to Victoria if their mode of acquisition was listed as travel overseas. Disease specific parameters, including duration of incubation, infectious and symptomatic periods, and age-specific risks associated with disease severity and outcomes, were based on global published estimates (Tables 1 and 2) . Parameters for contact networks and the effect of policy changes were obtained from a combination of the literature and a modified Delphi process (Supporting Information, D). The modified Delphi process involved creation of a panel of 12 experts (a mixture of modellers, epidemiologists, qualitative researchers, social network researchers, infectious disease physicians and public health physicians), who participated in a video conference where they were introduced to the model and the interpretation of parameters. Panel members were then asked to make independent estimates of unknown parameters, which were collated and de-identified by the study team, and the median and range of each parameter was extracted. A follow-up video conference was held where the panel discussed the results and uncertainties and were provided an opportunity to revise any estimates. The distribution of responses for each parameter, as well as the final parameters used, are provided in Supporting Information, part D. The y-axis represents the age of the individual and the x-axis represents the age of their contacts. The colour represents the population-mean number of daily contacts with people of each age. Left: household mixing, based on estimates from Prem et al. [2] . Right: within schools, students aged 5-18 were in classrooms with a mean of 21 students (of the same age) and one teacher. Note that the mean number of contacts for the 15-19 age bracket is slightly lower as 19 year olds do not attend school; and also that contacts between students and teachers and between teachers and students are not visible due to the scale. The y-axis represents the age of the individual and the x-axis represents the age of their contacts. The colour represents the population-mean number of daily contacts with people of each age. Left: at workplaces, adults aged 18-65 could mix with adults of any other age. The higher mean number of contacts with people aged 25-35 (brighter vertical bands) is due to the disproportionate population age distribution in Victoria ( Figure 1 ). Right: in public spaces, all ages could mix together. Again, the higher mean number of contacts of ages 25-35 is due to the disproportionate population age distribution in Victoria; and the slightly higher mean number of contacts with the 75+ age bracket is because more it covers a greater age range. was modelled based on Australian household size distribution data, and was fixed throughout a simulation. Right: some the community transmission networks, such as public transport, were modelled such that each individual had a number of contacts that were randomly assigned, and were re-assigned each day. The parameters in this appendix were obtained from the literature where available, or through a modified Delphi process where studies were not available (a Delphi process modified to be possible during the COVID-19 pandemic). The Delphi method involves the creation of a group of experts, who anonymously reply to surveys and then receive feedback in the form of a statistical representation of the "group response". After seeing the group response, the process repeats itself and the group of experts are provided an opportunity to amend their responses, with the goal of subsequent iterations to reduce the range of responses and achieve an approximate expert consensus. The Delphi method is a widely accepted estimation technique, which has been applied across a number of areas of health and social science [19, 20] . For this study, a group of 12 experts (a mixture of modellers, epidemiologists, qualitative researchers, social network researchers and public health and infectious disease clinicians) were invited to participate. A video conference was held where they were introduced to the model and the interpretation of parameters, and participants were asked to make independent estimates of unknown parameters following the conference. Estimates were then collated by the study team, and the median and range of each parameter was extracted. A follow-up video conference was held where the panel discussed the results, uncertainties and were offered an opportunity to update any parameters. In this appendix, the distribution of responses are provided or each model parameter. Each contact network only applies to a subset of the model population; because not everyone participates in each activity, or attends each location, only a subset are able to be infected at these places or during these activities. The subset of the population that each network applies to is defined as a percentage of a given age range. Each network can have a different structure, with people either being connected to their contacts randomly ("random") or people being grouped into disconnected clusters ("clustered", e.g. schools, where the network consists of disjoint classrooms, with students in each classroom connected to one another). The differences between a random and clustered network are illustrated in Figure 4 . Each person in the model has a specified number of contacts in each network layer. The epidemiological definition of a contact between two people is used, where a contact is defined as having a 15-minute face-to-face conversation, or spending one hour or more in a room together. For those who have a non-zero number of contacts in a particular network (i.e. they are inside the applicable age range and randomly-selected population fraction defined in Table 3 ), if the contact network is "random" type, then their number of contacts is drawn from a Poisson distribution with mean as per Table 4 . If the contact network is "clustered", then the size of each cluster is drawn from a Poisson distribution with mean as per Table 4 . Networks can also be time-varying or not. For example, contact networks for public spaces (e.g. public transport) are regenerated each day, to simulate once-off mixing, compared to work networks in which specific individuals remain connected to one another. *Not size of large event but number of actual contacts during event Transmission of COVID-19 is likely to be highly variable depending on network. As well as an overall daily risk of transmission per contact (the calibration parameter for the model), the risk of transmission per contact per day is different for each network. Table 5 shows these estimated differences relative to the transmission risk per contact per day within households. Table 5 : Relative risk of transmission through a contact, compared to a household contact. No studies were available for these parameters, meaning that they were all are based on the median of the expert panel's estimates shown in Figure 9 below. People may not typically interact with the activities and public spaces corresponding to each network on a daily frequency; for example, community sport might be played once per week. The model currently does not include simulation of each activity with different frequencies, and so the impact of this was approximated by reducing the relative transmission risk in each contact network. The relative transmissibility (Table 5 ) was divided by the activity frequencies/365 to develop a proxy for per-day transmission risk. People who are asked to self-isolate are likely to change their behaviour in ways that reduce their likelihood of transmission through different contact networks. For people in quarantine, their relative transmissibility in each contact network (Table 5) is reduced by the factors shown in Table 7 . For example, quarantine is modelled to have no impact on household transmission, to completely stop workplace and school transmission, and reduce (but not stop) other forms of community transmission due to imperfect adherence. When a person is diagnosed, there is a probability of tracing the people they are connected to in different contact networks, and an associated time to trace them. For example, we assume that household members would be notified on the day of diagnosis, while workplace contacts would have a 70% chance of being traced within 2 days. The effectiveness of quarantine, contact tracing probabilities and tracing time were estimated from the expert panel. There were no studies available to estimate the impact of policy changes on each network. However, for many polices, the impact is based on turning on / off transmission within a particular network, and so the impact is derived from the network properties in Tables 3-6. For some policies, there are logical impacts that extend beyond their specific network; for example, if non-essential work is cancelled, then the transmission risk on public transport would be expected to decrease. For these auxiliary effects, the actual impact size is unknown, and so has been estimated by the panel of experts. reduction [29] . Interventions can be modelled by changing parameters dynamically throughout a simulation. At any time point in a simulation, parameters can be varied to:  Change the number of imported infections (from other Australian jurisdictions or internationally)  Change the number of tests per day  Change adherence to quarantine after diagnosis  Scale the overall probability of transmission per contact (e.g. due to general hand hygiene)  Scale the relative transmission risk for specific contact layers (e.g. a policy closing cafes and restaurants would set the transmission risk for the cafe/restaurant network to be zero)  Remove a proportion of people from a network (e.g. a policy stopping non-essential work would remove some people from the work contact network)  Change the effectiveness of contact tracing for a particular contact network (e.g. the COVIDSafe app makes contact tracing possible for community transmission only if both the infected and susceptible person have the app) Policy changes are linked to one or more networks, and can potentially influence the whole population. For example, if non-essential work begins, this would increase the size of the work network, as well as increasing transmissibility in public transport. Policy scenarios modelled were informed by the COVID-19 public health response and the COVIDSAFE Australia framework [5] . The following are examples of policies that can be simulated: Figure 2 , except with people of all ages having equal susceptibility to infection. Note that clinical outcomes were still assumed to vary by age. Compared to baseline estimates, opening schools has slightly worse outcomes, but still minimal compared to other policies due to contacts being known and contact tracing being effective. Minimal impact is seen for scenarios that apply directly to adults (e.g. pubs and bars opening or working from home stopping). Contact tracing (including the use of COVIDSafe app for different coverages) Communication and enforcement of physical distancing (e.g. signs, advertisements, policing) Any set of interventions can be run in combination, or staged according to policy change dates. References 1. Idcommunity. Australian household size Projecting social contact matrices in 152 countries using contact surveys and demographic data Victorian State Government. Victorian school system information Department of Health and Human Services (DHHS) Australian Department of Health. 3-Step framework for a COVIDSafe Australia Australian COVID-19 data The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application The serial interval of COVID-19 from publicly reported confirmed cases Serial interval of novel coronavirus (COVID-19) infections Investigation of three clusters of COVID-19 in Singapore: implications for surveillance and response measures Temporal dynamics in viral shedding and transmissibility of COVID-19 Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures Virological assessment of hospitalized patients with COVID-2019 Estimates of the severity of coronavirus disease 2019: a model-based analysis Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China COVID-19 Response Team. Severe outcomes among patients with coronavirus disease 2019 (COVID-19)-United States Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand The Delphi method and health research Current validity of the Delphi method in social sciences Local churches in Australia: research findings from NCLS Research The Sport Participation Research Project: sport participation rates, aggregation of 12 sports Census estimates on method of travel to work Australian Government Director of National Parks Australian Childcare Alliance. Pre-budget submission Australian Institute of Health and Welfare. Aged care information website Victorian Department of Health and Human Services