Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015

Abstract

Due to worldwide increased human mobility, air-transportation data and mathematical models have been widely used to measure risks of global dispersal of pathogens. However, the seasonal and interannual risks of pathogens importation and onward transmission from endemic countries have rarely been quantified and validated. We constructed a modelling framework, integrating air travel, epidemiological, demographical, entomological and meteorological data, to measure the seasonal probability of dengue introduction from endemic countries. This framework has been applied retrospectively to elucidate spatiotemporal patterns and increasing seasonal risk of dengue importation from South-East Asia into China via air travel in multiple populations, Chinese travelers and local residents, over a decade of 2005–15. We found that the volume of airline travelers from South-East Asia into China has quadrupled from 2005 to 2015 with Chinese travelers increased rapidly. Following the growth of air traffic, the probability of dengue importation from South-East Asia into China has increased dramatically from 2005 to 2015. This study also revealed seasonal asymmetries of transmission routes: Sri Lanka and Maldives have emerged as origins; neglected cities at central and coastal China have been increasingly vulnerable to dengue importation and onward transmission. Compared to the monthly occurrence of dengue reported in China, our model performed robustly for importation and onward transmission risk estimates. The approach and evidence could facilitate to understand and mitigate the changing seasonal threat of arbovirus from endemic regions.

Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study

“…Mobile phone-based population movement data and air passenger itinerary data have been widely used to quantify the connectivity and transmission risk of pathogens via domestic and international human travel [18][19][20][21][22][23]. Given the rapidly growing number of confirmed 2019-nCoV infections, increasing evidence of human-to-human transmission within and beyond China [4,24], and our limited understanding of this novel virus [25,26], the findings here from travel patterns in historical data and spread risk estimation can help guide public health preparedness and intervention design across the world [27].…”

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

“…50 Data of global air traffic and itineraries have also been analysed to measure internal and international connectivity and its impact on the spread of pathogens and vectors at city or airport level. [3][4][5][6][7][8] Infrastructure data have also been used to define the connectivity between regions with the travel time as a proxy of human mobility and health accessibility. 51,52 Moreover, earth observation data, such as satellite imagery of night-time lights can help inform on the changing densities of populations within cities over the course of a year.…”

“…The pathogens introduced by travellers may lead to secondary transmission and local outbreaks, as has been observed in severe acute respiratory syndrome, influenza, Ebola, Zika, yellow fever and measles, among others, or to the appearance of diseases such as malaria in non-endemic areas following migration for work or travel to visit friends and relatives. [3][4][5][6][7][8][9][10][11][12][13] The spread of infectious diseases and their potential health risk in travellers has resulted in substantial concerns and challenges to global health systems and economies, [14][15][16][17] with a need to place more emphasis on understanding population mobility, infectious disease connectivity and the individual health risk of travellers.…”

Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China

“…Combining data augmentation methods 41 with hypotheses about ways in which reporting rates might vary through time could offer one way to relax this assumption. Unobserved DENV importation by people 42 , or potentially even mosquitoes, could explain some of the residual variation captured by % & (#). Second, we assumed that the population was immunologically naïve and remained so over time.…”