About: A decision-support framework to optimize border control for global outbreak mitigation

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About: A decision-support framework to optimize border control for global outbreak mitigation Goto Sponge NotDistinct Permalink

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scientific article published on 18 February 2019

Attributes	Values
rdf:type	Item
description	article scientifique publié en 2019 (fr) im Februar 2019 veröffentlichter wissenschaftlicher Artikel (de) artículu científicu espublizáu en febreru de 2019 (ast) наукова стаття, опублікована в лютому 2019 (uk) scientific article published on 18 February 2019 (en)
publication date	wds:Q63246466-E63FEAAA-6A81-419F-BFC5-B96E93F17D9A
publication date	2019-02-18 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q63246466-DAC0B7FC-A2DD-4FF4-99D7-8E86DF3E245B
language of work or name	English
exact match	wds:Q63246466-1E304BE7-5EF0-4624-9CA3-EFB50764A182
exact match	https://scigraph.springernature.com/pub.10.1038/s41598-019-38665-w
cites work	wds:Q63246466-027BB8AA-ECEB-4F34-BFCB-9F26B8122135 wds:Q63246466-0915ECA8-DCDB-42F7-A056-C10D870CA634 wds:Q63246466-148E85D6-7B1A-4094-B1D4-6165F6CC2E01 wds:Q63246466-24BDF8A5-35A8-454C-AB47-9D5C10C1F9C2 wds:Q63246466-28735B33-94E5-4123-B2FE-C2B81B8F8B1D wds:Q63246466-3368FD76-3F17-44D4-BE44-2FE5B3BB3394 wds:Q63246466-3536187D-DB1F-41B7-A592-871FFBF8CD7A wds:Q63246466-36BB6124-B23A-4D64-A6D1-C88139169A21 wds:Q63246466-3FC9CEDE-0AC8-4B5F-9F03-AE04C14DE1F3 wds:Q63246466-42ABC386-2DDE-455D-A186-9E0B806835D3 wds:Q63246466-4370C732-3491-4891-AC71-4340CAFF3651 wds:Q63246466-46AAF2E4-133B-4FD9-BE9F-4293DB5124DE wds:Q63246466-4792D999-4E07-4ED8-BF4D-8F27A1837391 wds:Q63246466-4C481447-A46F-48F5-B4E2-A2617A85935A wds:Q63246466-4F56043C-FE34-45D4-A515-D05B80B32C17 wds:Q63246466-548C2E5B-2920-40E3-9227-107469C06F85 wds:Q63246466-56F123E6-1730-4C4C-B641-64DE7033C82C wds:Q63246466-5AF6B89E-271F-44C3-B834-D9B2AE917F3A wds:Q63246466-62BB4EBF-BED5-4922-B48C-2E632B4340E0 wds:Q63246466-691FA821-FBB4-4AEE-A3D4-2189DCE9C82F wds:Q63246466-6F8C7F59-AE0C-40B1-A056-F9F3C4CCE9FF wds:Q63246466-77BDE4FE-CCB1-4140-BDD7-FA9A667B7D19 wds:Q63246466-86D87FD7-1D38-4E9B-BD5A-3B0B60DA90A4 wds:Q63246466-894FFD54-2A41-4FC4-88C0-25D49BE42D30 wds:Q63246466-957046F7-1A0C-49B8-864C-663061F63E39 wds:Q63246466-96ABC128-C26A-46CD-89D9-F6FE7B618804 wds:Q63246466-9E507AFF-CFCE-46A7-8E89-33082692C595 wds:Q63246466-9EF8E257-7CF1-4E97-9B99-74392F773090 wds:Q63246466-A827B450-B8DD-4306-9187-1A7DF1395735 wds:Q63246466-ACA69D5A-357B-4592-8282-F83244DA4E2C wds:Q63246466-ADF0B143-73A2-44C3-884E-461CD7C52DB5 wds:Q63246466-B02C55E2-E15E-4FD4-8375-F4446565CE39 wds:Q63246466-B1D35FAE-2DB1-40EA-95C5-22FB037CAAB2 wds:Q63246466-B253B8A2-1788-4AFB-A147-4B786D6BF3BC wds:Q63246466-B387F474-AA8F-4C96-96F9-422AAC08B68D wds:Q63246466-B95F2669-9A98-4570-8790-CBBD1806A2BC wds:Q63246466-BD9587CC-EC8B-4BB8-9B05-CA8295EB0C12 wds:Q63246466-BFAB174A-E182-4CC3-B095-38548B5DDE34 wds:Q63246466-C43A1EC1-8278-462C-861B-40B6DB24802D wds:Q63246466-CB7E0C4F-D590-4717-944F-D77C6A0EC1EF wds:Q63246466-CC560476-DC31-47F5-9D0A-AF7F143D2163 wds:Q63246466-D01D0C52-BBF9-41A9-8D90-2D59BE76E644 wds:Q63246466-D0FF744E-778C-44B4-B47F-392B97275F24 wds:Q63246466-D2CCCCF0-6926-40B7-B196-F7FE7C69C2DB wds:Q63246466-D3502C85-4466-4328-851F-F22229E053EC wds:Q63246466-D3695A4D-4CF8-4080-8BF4-BB092CDA08ED wds:Q63246466-D47DE08E-C49B-42DB-91C7-3A5CD6CED035 wds:Q63246466-D919E841-1245-44C1-9D3C-1FC6A83CC724 wds:Q63246466-E22103EA-7D98-4C03-8415-2DCC3DA0F739 wds:Q63246466-EBEABF34-4E07-4BC1-97A2-DE0D4DBB7FEE wds:Q63246466-EEA8E394-C517-47B4-9B29-B20C7AB75565 wds:Q63246466-EEBBDB9D-FE35-4FB6-8B15-F3F531E648F7 wds:Q63246466-EF758534-650E-4BCA-97EC-A1B3A6DEDFAC
cites work	Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model Strategies for mitigating an influenza pandemic Strategies for containing an emerging influenza pandemic in Southeast Asia Containing pandemic influenza at the source. A simple explanation for the low impact of border control as a countermeasure to the spread of an infectious disease. Reaction–diffusion processes and metapopulation models in heterogeneous networks The challenge of emerging and re-emerging infectious diseases Empirical evidence for the effect of airline travel on inter-regional influenza spread in the United States Delaying the international spread of pandemic influenza. Modeling the spread of annual influenza epidemics in the U.S.: the potential role of air travel Strategies for containing a global influenza pandemic. Multiscale mobility networks and the spatial spreading of infectious diseases Social contacts and mixing patterns relevant to the spread of infectious diseases The role of the airline transportation network in the prediction and predictability of global epidemics Large-scale spatial-transmission models of infectious disease. Forecast and control of epidemics in a globalized world Outbreak of swine-origin influenza A (H1N1) virus infection - Mexico, March-April 2009. A Contribution to the Mathematical Theory of Epidemics Global risk of Zika virus depends critically on vector status of Aedes albopictus Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions Human mobility networks, travel restrictions, and the global spread of 2009 H1N1 pandemic The hidden geometry of complex, network-driven contagion phenomena. Modeling infectious disease dynamics in the complex landscape of global health Global disease spread: statistics and estimation of arrival times. Will travel restrictions control the international spread of pandemic influenza? Assessing the impact of travel restrictions on international spread of the 2014 West African Ebola epidemic Controlling pandemic flu: the value of international air travel restrictions. Epidemic processes in complex networks Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations Thresholds for epidemic spreading in networks. Stochastic processes are key determinants of short-term evolution in influenza a virus The global transmission and control of influenza Responding to global infectious disease outbreaks: lessons from SARS on the role of risk perception, communication and management Epidemic modelling: aspects where stochasticity matters. Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.

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