In the second half of February 2020, millions of people around the globe found themselves stranded outside of their home countries or unable to undertake essential business or personal travel. This was due to airline travel bans that governments implemented worldwide in an attempt to limit the spread of a new coronavirus. Despite their best efforts in using travel restrictions to protect public health, by April 15, 2020, at least 175 countries worldwide had reported their first confirmed positive COVID-19 case. Cases then started to surge all over the world and it became evident that the pandemic was not going to be short-lived. Countries were forced to ask whether there might be alternative strategies that would accomplish the dual goal of protecting the populace while preserving as much of the global airline travel industry as possible for both business and humanitarian reasons.
COVID-19 is the first modern pandemic of this scale and duration. There are surprisingly few studies on how countries might regulate international airline travel in such a way as not to deteriorate the public health situation beyond that accomplished by shutting down borders. Many countries adopted a 14-day quarantine requirement for all international arrivals. Other countries such as U.K. and Portugal relaxed this restriction by only requiring quarantine for travelers from high-risk countries, where the risk status of a country was determined based on its incidence rate of confirmed cases. Despite these and other similar policies having been implemented, their effectiveness in balancing health outcomes and travel is not well understood.
Our team set to work in April 2020. We wanted to address two specific questions. First, how can we systematically quantify the effectiveness of different travel regulation policies at the global level? Second, are there less obvious but similarly rigorous policies that could be implemented to better achieve the dual goal of protecting health, society and the economy?
To address these questions, we used a network meta-population model to study the pandemic. In this framework, each country is represented by a local epidemiological model, and these local models are connected through the network representing global airline travel. The parameters of each local model need to be estimated from data, but because the compartmental model contains multiple unobserved states (such as unreported cases), standard statistical techniques, frequentist or Bayesian, cannot be used for this purpose. We decided to use an approach known as approximate Bayesian computation (ABC) to estimate the epidemiological parameters for each country. Simultaneously estimating these parameters for all countries is a difficult task, and it required developing a new estimation strategy -- based on the divide and conquer principle -- that would yield robust parameter estimates.
We then formulated several travel regulation policies. We defined the effectiveness of each policy in terms of public health outcomes and airline travel outcomes. We ultimately proposed two travel regulation policies, which we termed average control and probability control policy. The main idea behind these policies is allowing incoming international airline travel only up to a point where the number of cases in the country adopting the policy is -- on average or with a given probability -- not more than a fixed percentage (say, 5%) greater than the number of cases in the total shutdown scenario.
We used simulation to study these policies and provided theoretical results to support them. Our results appeared very encouraging. Simulations for the proposed average control policy suggest that it is possible to adopt airline travel volumes that are 40%–60% of the normal volume while achieving health outcomes that are comparable to complete shutdown. The results for empirical data were similarly promising, and the model achieved excellent fit to most countries. We found that in the summer of 2020, international airline travel could have resumed up to 58% of the pre-pandemic level with pandemic control comparable to that where all countries are fully closed. Finally, and perhaps most strikingly, our results show that if countries had followed our average control policy, opening 58% of airline travel in the manner proposed would actually have yielded better health outcomes than opening 33% of airline travel in the manner that was actually done (Figure 1). These findings suggest that lifting travel restrictions during the pandemic is possible if done appropriately, and they demonstrate how critical global coordination is for public health.
There are many exciting directions for extending our modeling framework, such as finding an optimized way to maximize the travel capacity, incorporating the effects of vaccination, and extending the model to deal with multiple virus strains. Our framework can be easily modified to use different local epidemiological models, and it can also be used to assess policies different from the ones we proposed. With new variants of the virus emerging, countries may resume travel restrictions. If that happens, we hope that evidence-based strategies aiming to preserve both public health and the economy would receive serious consideration.
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