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SMMEID – Projects
Project
1
#701113
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EIDM »
EIDM
EIDM ☜The Emerging Infectious Diseases Modelling Initiative (EIDM) – by the Public Health Agency of Canada and NSERC – aims to establish multi-disciplinary network(s) of specialists across the country in modelling infectious diseases to be applied to public needs associated with emerging infectious diseases and pandemics such as COVID-19. [1]☜F1CEB7
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Networks »
Networks
Networks☜Learn more about the five EIDM networks: CANMOD, MfPH, OMNI, OSN, and SMMEID.☜79B9B9
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SMMEID »
SMMEID
SMMEID☜This project assembles the top biostatisticians in Canada working on infectious diseases, and joins them with epidemiologists developing novel methods for data collection during the COVID-19 pandemic. Our group is developing methods and tools to get an accurate picture of the nature and extent of infectious disease transmission in the population, relying on real-world data from administrative sources and surveys. [2]☜79B9B9
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SMMEID – Projects
SMMEID – Projects☜☜70BEA3
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01 Imperfect testing and serosurveys »
01 Imperfect testing and serosurveys
01 Imperfect testing and serosurveys☜Projects in this node of the network involve developing statistical methods for analyzing infectious disease data, both in the context of the COVID-19 pandemic and other contexts. Goals include addressing various challenging issues associated with noisy data, including heterogeneity, varying incubation times, reporting errors, imperfect test specificity and sensitivity, and missing values.☜70BEA3
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02 COVID mortality forecasting »
02 COVID mortality forecasting
02 COVID mortality forecasting☜An existing forecasting model is made up of multiple waves, each of which follows the shape of a skew-normal density function, with model fitting done with Bayesian MCMC. A challenge of working with this model is the large number of latent variables which are not Gaussian. The model will be extended to make spatio-temporal forecasts, and improved algorithms for handling waves of infections must be built. Methods for handling censored, aggregated, and age-specific death counts are also required.☜70BEA3
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03 Modelling emerging infectious disease »
03 Modelling emerging infectious disease
03 Modelling emerging infectious disease☜The project will primarily focus on stochastic compartmental models and their applications in phylodynamics. A fundamental challenge for stochastic compartmental models is that the likelihood function is intractable. Our goal is to develop efficient methods for making inferences under these models.☜70BEA3
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04 Estimating COVID-19 prevalence in homeless populations »
04 Estimating COVID-19 prevalence in homeless populations
04 Estimating COVID-19 prevalence in homeless populations☜We will develop novel statistical models for estimating the abundance of hidden populations. Specifically, we will consider challenges associated with using Electronic Health Records to obtain estimates for populations at risk such as homeless populations under the COVID-19 pandemic.☜70BEA3
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05 Hidden Markov Individual-level Models of Disease Transmission »
05 Hidden Markov Individual-level Models of Disease Transmission
05 Hidden Markov Individual-level Models of Disease Transmission☜We will develop a hidden Markov modelling framework for individual-level models of disease transmission. Such an approach allows for the incorporation of individual-level covariates, including spatial or network-based distance, as well as delayed and under-reporting of cases. Models developed will be used to gain a greater understanding of the epidemiology of diseases such as COVID-19, influenza and Ebola.☜70BEA3
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06 Daily air pollution and mortality »
06 Daily air pollution and mortality
06 Daily air pollution and mortality☜The ultimate goal of the project is to create a new air quality warning system for Canada. [Note: this project is not funded by the Emerging Infectious Diseases network.]☜70BEA3
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07 Small-area estimation and serosurveys »
07 Small-area estimation and serosurveys
07 Small-area estimation and serosurveys☜This project will develop novel statistical models for infectious diseases to address some challenges in terms of prevalence rate of infection in different small areas and domains in Canada due to COVID-19 pandemic.☜70BEA3
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08 Spatial-Temporal Modeling of COVID-19 Adverse Outcomes in Canada »
08 Spatial-Temporal Modeling of COVID-19 Adverse Outcomes in Canada
08 Spatial-Temporal Modeling of COVID-19 Adverse Outcomes in Canada☜The aims of the research are to (1) identify localised risk factors for increased severe conditions for COVID-19 (2) identify localised areas in space–time at significantly higher risk, and (3) quantify the impact of changes in localised restriction policies on adverse outcomes and forecast the epidemic.☜70BEA3
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+About
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Gemaakt door:
David Price
NodeID:
#701113
Node type:
Project
Gemaakt op (GMT):
9/19/2022 9:21:00 PM
Laatste bewerking (GMT):
9/19/2022 9:21:00 PM
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