Dataset Information

Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017.

ABSTRACT: With regard to fully harvesting the potential of big data, public health lags behind other fields. To determine this potential, we applied big data (air passenger volume from international areas with active chikungunya transmission, Twitter data, and vectorial capacity estimates of Aedes albopictus mosquitoes) to the 2017 chikungunya outbreaks in Europe to assess the risks for virus transmission, virus importation, and short-range dispersion from the outbreak foci. We found that indicators based on voluminous and velocious data can help identify virus dispersion from outbreak foci and that vector abundance and vectorial capacity estimates can provide information on local climate suitability for mosquitoborne outbreaks. In contrast, more established indicators based on Wikipedia and Google Trends search strings were less timely. We found that a combination of novel and disparate datasets can be used in real time to prevent and control emerging and reemerging infectious diseases.

SUBMITTER: Rocklov J

PROVIDER: S-EPMC6537727 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017.

Rocklöv Joacim J Tozan Yesim Y Ramadona Aditya A Sewe Maquines O MO Sudre Bertrand B Garrido Jon J de Saint Lary Chiara Bellegarde CB Lohr Wolfgang W Semenza Jan C JC

Emerging infectious diseases 20190601 6

With regard to fully harvesting the potential of big data, public health lags behind other fields. To determine this potential, we applied big data (air passenger volume from international areas with active chikungunya transmission, Twitter data, and vectorial capacity estimates of Aedes albopictus mosquitoes) to the 2017 chikungunya outbreaks in Europe to assess the risks for virus transmission, virus importation, and short-range dispersion from the outbreak foci. We found that indicators based ...[more]

PMID: 31107221

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Project description:Biological invasions have emerged as one of the main drivers of biodiversity change and decline, and numbers of species classed as alien in parts of their ranges are rapidly rising. The European Union established a dedicated regulation to limit the impacts of invasive alien species (IAS), which is focused on the species on a Union List of IAS of particular concern. However, no previous study has specifically addressed the ecology of invasive alien mammals included on the Union List.We performed a systematic review of published literature on these species. We retrieved 262 publications dealing with 16 species, and we complemented these with the most up-to-date information extracted from global databases on IAS.We show that most of the study species reached Europe as pets and then escaped from captivity or were intentionally released. On average each year in the period 1981-2020, 1.2 species were recorded for the first time as aliens in European countries, and most species are still expanding their alien ranges by colonising neighbouring territories. France is the most invaded nation, followed by Germany, Italy, and the Russian Federation, and the muskrat Ondatra zibethicus, the American mink Neovison vison, and the raccoon dog Nyctereutes procyonoides are the most widespread species, having invaded at least 27 countries each. Invasive mammals of European Union concern are threatening native biodiversity and human well-being: worryingly, 81% of the 16 study species are implicated in the epidemiological cycle of zoonotic pathogens.Containing secondary spread to further countries is of paramount importance to avoid the establishment of new populations of invasive mammals and the related impacts on native communities, ecosystem services, and human health.We present a compendium on the ecology and impacts of invasive mammals of European Union concern. It can be used to assist environmental policies, identify and subsequently fill knowledge gaps, and inform stakeholders.

Project description:BackgroundHuman movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global).ObjectiveBig data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local).MethodsWe will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems.ResultsThis project was funded as of May 2020. We have started the data collection, processing, and analysis for the project.ConclusionsResearch findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus.International registered report identifier (irrid)DERR1-10.2196/24432.

Dataset Information

Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017.

Publications

Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017.

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