Dengue importation into Europe: A network connectivity-based approach

Abstract

The spread of dengue through global human mobility is a major public health concern. A key challenge is understanding the transmission pathways and mediating factors that characterized the patterns of dengue importation into non-endemic areas. Utilizing a network connectivity-based approach, we analyze the importation patterns of dengue fever into European countries. Seven connectivity indices were developed to characterize the role of the air passenger traffic, seasonality, incidence rate, geographical proximity, epidemic vulnerability, and wealth of a source country, in facilitating the transport and importation of dengue fever. We used generalized linear mixed models (GLMMs) to examine the relationship between dengue importation and the connectivity indices while accounting for the air transport network structure. We also incorporated network autocorrelation within a GLMM framework to investigate the propensity of a European country to receive an imported case, by virtue of its position within the air transport network. The connectivity indices and dynamical processes of the air transport network were strong predictors of dengue importation in Europe. With more than 70% of the variation in dengue importation patterns explained. We found that transportation potential was higher for source countries with seasonal dengue activity, high passenger traffic, high incidence rates, high epidemic vulnerability, and in geographical proximity to a destination country in Europe. We also found that position of a European country within the air transport network was a strong predictor of the country's propensity to receive an imported case. Our findings provide evidence that the importation patterns of dengue into Europe can be largely explained by appropriately characterizing the heterogeneities of the source, and topology of the air transport network. This contributes to the foundational framework for building integrated predictive models for bio-surveillance of dengue importation.

Predicting dengue importation into Europe, using machine learning and model-agnostic methods

“…Hence, destination countries with higher connectivity within the network have an increased risk of importation. These findings are similar to other previous studies that characterize the role of the air transport network structure in mediating epidemic spread 10,11 and collaborates the results of our previous work 13 . The above explanations capture the relative contribution of the input variables in predicting the importation of dengue at an aggregated level for Europe.…”

“…On average our model predicted a higher probability of an imported case of dengue, from a source country with high dengue incidence rates, large population, and high air passenger volume. These findings support a priori expectation for these factors to increase the importation risk of dengue and are consistent with other studies 12,13,66 . Also, our model predicts a higher probability of an imported case for destination countries with high connectivity within the network (as measured by degree centrality), with a putative connection hub to other countries (betweenness centrality) and connects to other countries in a relatively short amount of time (closeness centrality).…”

“…Unlike conventional statistical modelling approaches, these methods account for the co-dynamics of the network structure and how they interact with other risk factors to mediate the importation of dengue [10][11][12] . Our previous work 13 integrates this modelling approach and offers a foundational understanding of the importation patterns of dengue in Europe. connectivity indices between a source and destination country.…”

“…Drawing on the underlining concept of spatial interaction modelling, that inflow between two locations is a function of the attributes of the source and destination and their corresponding interaction 27 . Connectivity indices between a source country and a destination country in Europe were previously developed 13 , using different factors that potentially mediate dengue importation risk. The indices were decomposed into components describing the source strength' (the risk of dengue infection) and the transport or importation potential (the connection between a source country and a potential destination country in Europe).…”

Sociocultural, behavioural and political factors shaping the COVID-19 pandemic: the need for a biocultural approach to understanding pandemics and (re)emerging pathogens

“…Several studies have examined the effects of network dynamics as a broad proxy for social dynamics and their effects on epidemics (Barrett et al, 2008;Cauchemez et al, 2011;Christley et al, 2005;Eubank et al, 2004;Stattner & Vidot, 2011). Within evolutionary epidemiology, genuinely insightful use has been made of 'big data', to combine behavioural data such as flight connections with phylogeographic data on pathogen behaviour (Grubaugh et al, 2017;Salami et al, 2020;Tian et al, 2017). Such work has also demonstrated how host shifts in network dynamics have the potential to create fundamental selective (Buckee et al, 2004;Leventhal et al, 2015) and epidemiological pressures (Pellis et al, 2015) on pathogens, being largely constrained within such networks.…”