Dynamic contagion spread modelling over emergent spatio-temporal contact networks
| dc.contributor.advisor | Ray, Suprio | |
| dc.contributor.advisor | Seahra, Sanjeev | |
| dc.contributor.author | Mistry, Avinaba | |
| dc.date.accessioned | 2023-11-02T19:08:26Z | |
| dc.date.available | 2023-11-02T19:08:26Z | |
| dc.date.issued | 2022-12 | |
| dc.description.abstract | Analysis, prediction and decision support for contagion spread and its associated risk with Ordinary Differential Equation models proved to be challenging in terms of incorporating the underlying heterogeneity of emergent spatio-temporal contact networks of the moving objects. These models reduce the granularity and scope of analysis of possible pharmacological and non-pharmacological intervention measures. To address these limitations, we propose to aggregate population level dynamics by modelling individual-level interactions. To that end, first we introduce an individual level agent-based stochastic contagion simulation modelling framework as a possible solution for adapting to rapidly changing parameters of a contagion including SARS-CoV-2. Second, we propose a spatio-temporal index to extract contact networks from distributed real-world mobility data stores. The framework has been implemented declaratively in R and functionally in Julia and the spatio-temporal index in Python. We conduct thorough experimental evaluation to show the viability and applicability of the proposed approaches. | |
| dc.description.copyright | © Avinaba Mistry, 2022 | |
| dc.format.extent | xvii, 132 | |
| dc.format.medium | electronic | |
| dc.identifier.oclc | (OCoLC)1426853109 | en |
| dc.identifier.other | Thesis 11261 | en |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/37522 | |
| dc.language.iso | en | |
| dc.publisher | University of New Brunswick | |
| dc.relation | MITACS | |
| dc.relation | The Black Arcs | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Computer Science | |
| dc.subject.lcsh | Biomathematics. | en |
| dc.subject.lcsh | Contagious distributions. | en |
| dc.subject.lcsh | Computer simulation. | en |
| dc.title | Dynamic contagion spread modelling over emergent spatio-temporal contact networks | |
| dc.type | master thesis | |
| oaire.license.condition | other | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.C.S. |