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      Global trends in air travel: implications for connectivity and resilience to infectious disease threats

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          Abstract

          Background

          Increased connectivity via air travel can facilitate the geographic spread of infectious diseases. The number of travellers alone does not explain risk; passenger origin and destination will also influence risk of disease introduction and spread. We described trends in international air passenger numbers and connectivity between countries with different capacities to detect and respond to infectious disease threats.

          Methods

          We used the Fragile States Index (FSI) as an annual measure of country-level resilience and capacity to respond to infectious disease events. Countries are categorized as: Sustainable, Stable, Warning or Alert, in order of increasing fragility. We included data for 177 sovereign states for the years 2010 to 2019. Annual inbound and outbound international air passengers for each country were obtained for the same time period. We examined trends in FSI score, trends in worldwide air travel and the association between a state’s FSI score and air travel.

          Results

          Among countries included in the FSI rankings, the total number of outbound passengers increased from 0.865 billion to 1.58 billion between 2010 and 2019. Increasing fragility was associated with a decrease in travel volumes, with a 2.5% (95% CI: 2.0–3.1%) reduction in passengers per 1-unit increase in FSI score. Overall, travel between countries of different FSI categories either increased or remained stable.

          Conclusions

          The world’s connectivity via air travel has increased dramatically over the past decade. There has been notable growth in travel from Warning and Stable countries, which comprise more than three-quarters of international air travel passengers. These countries may have suboptimal capacity to detect and respond to infectious disease threats that emerge within their borders.

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          Most cited references13

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          Is Open Access

          The effect of human mobility and control measures on the COVID-19 epidemic in China

          The ongoing COVID-19 outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions have been undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
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            The role of the airline transportation network in the prediction and predictability of global epidemics

            The systematic study of large-scale networks has unveiled the ubiquitous presence of connectivity patterns characterized by large-scale heterogeneities and unbounded statistical fluctuations. These features affect dramatically the behavior of the diffusion processes occurring on networks, determining the ensuing statistical properties of their evolution pattern and dynamics. In this article, we present a stochastic computational framework for the forecast of global epidemics that considers the complete worldwide air travel infrastructure complemented with census population data. We address two basic issues in global epidemic modeling: (i) we study the role of the large scale properties of the airline transportation network in determining the global diffusion pattern of emerging diseases; and (ii) we evaluate the reliability of forecasts and outbreak scenarios with respect to the intrinsic stochasticity of disease transmission and traffic flows. To address these issues we define a set of quantitative measures able to characterize the level of heterogeneity and predictability of the epidemic pattern. These measures may be used for the analysis of containment policies and epidemic risk assessment.
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              • Article: not found

              Spread of a novel influenza A (H1N1) virus via global airline transportation.

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                Author and article information

                Journal
                Journal of Travel Medicine
                Oxford University Press (OUP)
                1195-1982
                1708-8305
                May 06 2020
                May 06 2020
                Affiliations
                [1 ]Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
                [2 ]BlueDot, Toronto, ON, Canada
                [3 ]Department of Family & Community Medicine, University of Toronto, Toronto, ON, Canada
                [4 ]Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, ON, Canada
                [5 ]Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
                Article
                10.1093/jtm/taaa070
                32374834
                4815d706-d2cf-45b4-9cb3-132eaa297b19
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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