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DC Field | Value | Language |
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dc.contributor.author | Mahmood, I | - |
dc.contributor.author | Jahan, M | - |
dc.contributor.author | Groen, D | - |
dc.contributor.author | Javed, A | - |
dc.contributor.author | Shafait, F | - |
dc.coverage.spatial | Amsterdam, Netherlands (cancelled due to COVID-19 pandemic) | - |
dc.date.accessioned | 2023-02-27T15:49:48Z | - |
dc.date.available | 2020-06-15 | - |
dc.date.available | 2023-02-27T15:49:48Z | - |
dc.date.issued | 2020-06-15 | - |
dc.identifier | ORCID iDs: Imran Mahmood https://orcid.org/0000-0003-0138-7510; Derek Groen https://orcid.org/0000-0001-7463-3765. | - |
dc.identifier.citation | Mahmood, I. et al. (2020) '', Computational Science – ICCS 2020. Lecture Notes in Computer Science() (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS 12139 (Part III), pp. 103 - 117. doi: 10.1007/978-3-030-50420-5_8. | en_US |
dc.identifier.isbn | 978-3-030-50419-9 (hbk) | - |
dc.identifier.isbn | 978-3-030-50420-5 (ebk) | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26017 | - |
dc.description.abstract | Vector-borne diseases (VBDs) account for more than 17% of all infectious diseases, causing more than 700,000 annual deaths. Lack of a robust infrastructure for timely collection, reporting, and analysis of epidemic data undermines necessary preparedness and thus posing serious health challenges to the general public. By developing a simulation framework that models population dynamics and the interactions of both humans and mosquitoes, we may enable epidemiologists to analyze and forecast the transmission and spread of an infectious disease in specific areas. We extend the traditional SEIR (Susceptible, Exposed, Infectious, Recovered) mathematical model and propose an Agent-based model to analyze the interactions between the host and the vector using: (i) our proposed algorithm to compute vector density, based on the reproductive behavior of the vector; and (ii) agent interactions to simulate transmission of virus in a spatio-temporal environment, and forecast the spread of the disease in a given area over a period of time. Our simulation results identify several expected dengue cases and their direction of spread, which can help in detecting epidemic outbreaks. Our proposed framework provides visualization and forecasting capabilities to study the epidemiology of a certain region and aid public health departments in emergency preparedness. | en_US |
dc.description.sponsorship | This work was supported by the HiDALGO project, which has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 824115. | en_US |
dc.format.extent | 103 - 117 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Springer Nature | en_US |
dc.rights | Copyright © 2020 Springer Nature. This is a pre-copyedited, author-produced version of an article accepted for publication in Computational Science – ICCS 2020. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) following peer review. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-50420-5_8 (see: https://www.springernature.com/gp/open-research/policies/journal-policies). | - |
dc.rights.uri | https://www.springernature.com/gp/open-research/policies/journal-policies | - |
dc.source | 20th International Conference on Computational Science, ICCS 2020 | - |
dc.source | 20th International Conference on Computational Science, ICCS 2020 | - |
dc.subject | dengue epidemiology | en_US |
dc.subject | agent-based modeling | en_US |
dc.subject | validation | en_US |
dc.subject | Anylogic | en_US |
dc.subject | host-vector interaction | en_US |
dc.title | An agent-based simulation of the spread of dengue fever | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | https://doi.org/10.1007/978-3-030-50420-5_8 | - |
dc.relation.isPartOf | Computational Science – ICCS 2020. Lecture Notes in Computer Science() (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
pubs.finish-date | 2020-06-05 | - |
pubs.finish-date | 2020-06-05 | - |
pubs.issue | Part III | - |
pubs.publication-status | Published | - |
pubs.start-date | 2020-06-03 | - |
pubs.start-date | 2020-06-03 | - |
pubs.volume | LNCS 12139 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.rights.holder | Springer Nature | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © 2020 Springer Nature. This is a pre-copyedited, author-produced version of an article accepted for publication in Computational Science – ICCS 2020. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) following peer review. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-50420-5_8 (see: https://www.springernature.com/gp/open-research/policies/journal-policies). | 1.08 MB | Adobe PDF | View/Open |
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