Pandemic planning

Spatio-temporal clustering of multi-location time series to model seasonal influenza spread
Although seasonal influenza disease spread is a spatio-temporal phenomenon, public surveillance systems aggregate data only spatially, …
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Prediction of COVID-19 severity
Predicting of COVID-19 hospitalization, ICU, and death at the time of RT-PCR testing, days in advance of a patient seeking medical care, allowing for improved care and resource allocation
Pandemic modeling and planning
Predicting pandemic disease spread through very large agent-based simulations and optimizing mitigation strategies through graph theory and machine learning
How effective was Newfoundland & Labrador's travel ban to prevent the spread of COVID-19? An agent-based analysis
Background: To prevent the spread of COVID-19 in Newfoundland & Labrador (NL), NL implemented a wide travel ban in May 2020. We …
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Panel on simulation modeling for COVID-19
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Prediction of severe COVID-19 outcomes at the time testing: An anomaly detection approach
Early and effective detection of severe infection cases during a pandemic can significantly help patient prognosis and resource …
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morPOP: A fast and granular agent-based model of COVID-19 to examine school mitigation strategies in Newfoundland & Labrador
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Deriving pandemic disease mitigation strategies by mining social contact networks
In this chapter we propose a robust approach to deriving disease mitigation strategies from contact networks that are generated from …
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Efficiently identifying critical nodes in large complex networks
The critical node detection problem (CNDP) aims to fragment a graph G=(V,E) by removing a set of vertices R with cardinality |R|≤k, …
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A derandomized approximation algorithm for the critical node detection problem
In this paper we propose an efficient approximation algorithm for determining solutions to the critical node detection problem (CNDP) …
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