Abstracts
Dr Darren Dahly, UCC
The BMJ living review of prediction models for diagnosis and prognosis of covid-19: A case study in research waste
 
Dr James Sweeney, UL 
An age-structured SEIR model for COVID-19 incidence, with framework for evaluating health intervention cost
We propose a model to describe COVID–19 community spread incorporating the role of age-specific social interactions. Through a flexible parameterisation of an age-structured deterministic Susceptible Exposed Infectious Removed (SEIR) model, we provide a means for characterising different forms of lockdown which may impact specific age groups differently. Social interactions are represented through age group to age group contact matrices, which can be trained using available data and are thus locally adapted. This framework is easy to interpret and suitable for describing counterfactual scenarios, which could assist policy makers with regard to minimising morbidity balanced with the costs of prospective suppression strategies. Our work originates from an Irish context and we use disease monitoring data from February 29th 2020 to January 31st 2021 gathered by Irish governmental agencies. We demonstrate how Irish lockdown scenarios can be constructed using the proposed model formulation and show results of retrospective fitting to incidence rates and forward planning with relevant “what if / instead of” lockdown counterfactuals. 
 
Dr Tsukushi Kamiya, NUIG/College de France
Estimating time-varying infectious contact on the island of Ireland: a multi-strain epidemiological model of SARS-CoV-2
Mathematical modelling plays a key role in understanding and predicting epidemiological dynamics of infectious diseases. To estimate the changing infectious contact that leads to new infections during the COVID-19 pandemic, we construct a flexible discrete-time model that incorporates multiple strains of the virus with different transmissibilities. Using a Bayesian approach, we fit the model to to longitudinal data on hospitalisations with COVID-19 from the Republic of Ireland and Northern Ireland during the first year of the pandemic. We describe the estimated change in infectious contact in the context of government-mandated non-pharmaceutical interventions in the two jurisdictions on the island of Ireland. We also take advantage of the fitted model to conduct counterfactual analyses exploring the the impact of lockdown timing and a more transmissible new variant.
 
Dr Anthony Masters
The challenges of communicating Covid-19 statistics
Pandemic statistics have swamped news reports and our daily lives. This talk is about we can communicate with clarity, helping people to make sense of the pandemic.
 
Prof Adele Marshall, QUB
Modelling the Effectiveness of Non-Pharmaceutical Interventions on COVID-19 in Northern Ireland
 
Dr Catherine Timoney, HSE
Network Analysis of a COVID-19 Outbreak in the West of Ireland
Notification and contact tracing systems for COVID-19 hold a vast amount of information on the transmission chains of the virus. Network analysis is very useful in visualising the spread of COVID-19, getting better understanding of how cases are linked together and noticing how far reaching the initial infections were. Here the Irish infectious disease notification system, the Computerised Infectious Disease Reporting system (CIDR) and contact tracing system, the Covid Care Tracker (CCT),  are used to create networks of the spread of the virus. These two databases are not linked together and no unique identifier for entries is present across the two. I will discuss a method of linking the datasets, issues that arise in doing so, and look at the resulting network of an outbreak in the student population of Galway, Ireland.
 
Dr Lisa Domegan, HPSC
Mortality surveillance during the COVID-19 pandemic in the Republic of Ireland
 
The Health Protection Surveillance Centre (HPSC), in collaboration with Departments of Public Health, monitor and report on all notified COVID-19 deaths in Ireland. Notification data are collated on Ireland’s Computerised Infectious Disease Reporting System (CIDR). HPSC use the World Health Organisation’s definition for COVID-19 deaths. Epidemiological analysis of all notified COVID-19 deaths reported to HPSC during the COVID-19 pandemic, by core demographics and by pandemic wave will be presented. In addition, HPSC receives daily registered deaths data from the General Register Office (GRO) on all deaths from all causes registered in Ireland. These data have been used since 2009 to calculate and monitor excess all‐cause mortality in Ireland, as part of the EuroMOMO network. Excess mortality estimates are calculated using the standardised European EuroMOMO algorithm. The mortality baseline is modelled using a glm poisson corrected for over dispersion. A delay adjustment is built into the EuroMOMO models, correcting registered deaths data for reporting delays. Excess mortality data during the COVID-19 pandemic, by age group and pandemic wave will also be presented.
 
Dr Jos Ijpelaar, NISRA
Providing timely evidence of Covid-19 related deaths in Northern Ireland
Dr Jos Ijpelaar: Researcher at the Northern Ireland Statistics and Research Agency (NISRA) Administrative Research Unit and has been working on Covid-19 deaths in Northern Ireland, reporting on age-standardised mortality rates, excess mortality, pre-existing conditions of Covid-19 deaths, and equality/socio-demographic characteristics of Covid-19 deaths. Details of these publications can be found here: https://www.nisra.gov.uk/statistics/cause-death/covid-19-related-deaths.
 
Prof Cathal Walsh, UL
Reflections on Challenges and Successes while Modelling COVID-19

Workshop funded by the Irish Statistical Association, with support from the Royal Statistical Society

Organised by Nicola Fitz-Simon and Laura Boyle for the ISA and RSS Medical Section

Registration: This event is free to attend.  Please email crfbiostatistics@nuigalway.ie by Wednesday 13^th April; registration will be confirmed by return email.  If the event is oversubscribed priority will be given to PhD students, ISA and RSS members.