ACCDMi Symposium

Details

Date: Wednesday December 10

Location: A-1043 (Memorial University) and online (email ahurford@mun.ca for the link)

Times refer to Newfoundland (which is a time zone that is 30 mins offset from most places).

Schedule

Time Speaker Title
9.25- Amy Hurford Introduction to ACCDMi
9.40- Carsten Kirkeby Avian Influenza Epidemiology in Denmark and Europe: Dynamics, Risk and Early Warning
9.55- Joshua Mack Estimating the Annual Number of Humans Infected with Highly Pathogenic Avian Influenza Viruses from Historical Pandemic Rates
10.10- Wentao Meng Optimal control in a multi-structured model with imperfect vaccination

10.25- 5 min break

10.30- Dandan Hu Dynamical analysis in a nonlocal delayed reaction–diffusion tumor model with therapy
10.45- Sumit Jyoti Harnessing publicly available salmon data through epidemiological and statistical tools
11.00- Abdelmonem Mohamed Control and mitigation of infectious salmon anaemia virus in farmed Atlantic salmon: results from a scoping review
11.15- Ahsan Raquib Space–time clustering and multiple-membership survival analysis of time to first detection of infectious salmon anemia virus in Atlantic salmon farms

11.30- 5 min break

11.35- Thu Nguyen Sparse Bayesian Random Feature Models for Delay-Embedded Time Series in Epidemiology
11.50- Jingyu Li Computationally Efficient Bayesian Inference for Change Points Detection in Infectious Disease Model
12.05- Michael WZ Li The need to continue what we started – Evergreen problems post COVID-19 pandemic

12.20- 10 min break

12.30- Keynote A separate link was provided over email

Keynote

Modelling to support public health decisions

Nicholas Ogden, 12.30-1.30pm

The COVID-19 pandemic served as a catalyst for the development and use of infectious disease modelling to support public health decisions in Canada. In a post-pandemic environment, modelling has now become established as a key public health function. In this talk I will discuss the range of uses and applications for modelling in public health decision-making, as well as considerations of model complexity, realism, uncertainty, communication, and skillset and data needs.