Modelling the impact of travel restrictions on COVID-19 cases in Newfoundland and Labrador

Abstract

In many jurisdictions, public health authorities have implemented travel restrictions to reduce coronavirus disease 2019 (COVID-19) spread. Policies that restrict travel within countries have been implemented, but the impact of these restrictions is not well known. On 4 May 2020, Newfoundland and Labrador (NL) implemented travel restrictions such that non-residents required exemptions to enter the province. We fit a stochastic epidemic model to data describing the number of active COVID-19 cases in NL from 14 March to 26 June. We predicted possible outbreaks over nine weeks, with and without the travel restrictions, and for contact rates 40-70% of pre-pandemic levels. Our results suggest that the travel restrictions reduced the mean number of clinical COVID-19 cases in NL by 92%. Furthermore, without the travel restrictions there is a substantial risk of very large outbreaks. Using epidemic modelling, we show how the NL COVID-19 outbreak could have unfolded had the travel restrictions not been implemented. Both physical distancing and travel restrictions affect the local dynamics of the epidemic. Our modelling shows that the travel restrictions are a plausible reason for the few reported COVID-19 cases in NL after 4 May.

Keywords: COVID-19; Newfoundland and Labrador; branching process; epidemic model; importations; travel restrictions.

Figure 1.

Model diagram. Uninfected individuals (white boxes) are either susceptible to infection, S, or recovered, R. Susceptible individuals become infected at mean rate, λ_S(t)Δt, where the event that an infection occurs is sampled from a distribution since the model is stochastic. Recovered individuals cannot be re-infected. Infected travellers that fail to self-isolate enter the population at a mean rate, λ_V(t)Δt. When a new infection occurs, a proportion, π, of these newly infected individuals are asymptomatic, where the number of individuals with asymptomatic infections at any time is I_A. Alternatively, a proportion, 1–π, of infected individuals will eventually develop clinical symptoms, although these individuals are initially pre-clinical (without symptoms), and the number of individuals that are pre-clinical at any time is I_P. At a mean rate, λ_P(t)Δt, individuals with pre-clinical infections develop clinical infections (with symptoms). Individuals with asymptomatic, pre-clinical, and clinical infections are infectious (blue boxes), and infectivity depends on the type of infection, and the number of days since the date of infection. Finally, both individuals with asymptomatic and clinical infections recover at mean rates λ_A(t)Δt and λ_C(t)Δt, respectively. See the electronic supplementary material for further details.

Figure 2.

The predicted mean number of active COVID-19 cases (lines) agrees well with the reported numbers of active COVID-19 cases in NL from 16 March to 26 June 2020 (dots) prior to the implementation of the travel restrictions on 4 May 2020. After 4 May 2020, we consider an alternative past scenario where no travel restrictions were implemented (b). Both with (a) and without (b) the travel restrictions, we consider different levels of physical distancing, represented as percentages of the daily contact rate at the pre-pandemic level (coloured lines). Each coloured line is the mean number of active clinical cases each day calculated from 1000 runs of the stochastic model, which considers variability in the timing and changes in the number of individuals with different COVID-19 infection statuses.

Figure 3.

The total predicted number of COVID-19 cases in NL occurring over nine weeks beginning on 4 May 2020 when travel restrictions are implemented (yellow boxes) is much less than the total number of cases occurring over this same period if the travel restrictions were not implemented (green boxes). The total number of COVID-19 cases occurring during the nine weeks subsequent to 4 May 2020 is highly variable, and without the implementation of the travel restrictions there is a higher risk of a large outbreak (also see table 3, 95% prediction intervals). When the travel restrictions are implemented, almost all of the cases occurring during the nine weeks subsequent to 4 May 2020 are due to infected individuals present in the community prior to 4 May 2020. Travel-related cases are all cases remaining after the ‘prior’ cases are removed (b). The contact rate is expressed as a percentage of the pre-pandemic contact rate. For each simulation, chance events affect the number of individuals that change COVID-19 infection statuses and the timing of these changes. The horizontal lines are medians, the coloured boxes are 1.58 times the interquartile range divided by the square root of n, the whiskers are 95% prediction intervals, and the dots are outliers for the n = 1000 simulation outcomes.

Figure 4.

The breakdown into three different sources of COVID-19 cases occurring in NL over nine weeks. We compare simulation results with travel restrictions (a) and without travel restrictions (b). The source of infections is either: an individual infected prior to 4 May 2020 (‘prior’, light blue); an individual that was infected prior to entering NL (‘travel’, green); or an NL resident that did not travel, but is part of an infection chain where the initial infectee is a traveller that entered NL after 4 May 2020 (‘local’, dark blue). Our model assumptions are reflected by the difference in the number of COVID-19 cases occurring in travellers over the nine weeks (green bars): approximately 0.5 with travel restrictions (a), as compared with 6.3 without travel restrictions (b). These infected travellers seed infection chains in the NL community resulting in a larger number of NL residents infected when the travel restrictions are not implemented (dark blue bars). Both with and without the travel restrictions, the number of cases due to prior infection in the NL community is similar (light blue bars). The contact rate is expressed as a percentage of the pre-pandemic contact rate.