Turning on a DyME: Incorporating time-use and health datasets into a Dynamic Microsimulation Epidemiological model for COVID-19
Join the IDSAI for its first seminar of 2021
Speakers: Professor Gavin Shaddick, Chair of Data Science & Statistics Professor Karyn Morrissey, European Centre for Environment & Human Health Dr Fiona Spooner, Research Fellow, IDSAI / ECEHH Dr Jesse Abrams, Research Fellow, IDSAI / GSI
|An Institute for Data Science and Artificial Intelligence seminar|
|Date||21 January 2021|
|Time||15:00 to 16:00|
|Place||By zoom - details will be sent when you register|
In December 2019, cases of pneumonia of unknown cause were identified in Wuhan city, China, and a novel enveloped RNA betacoronavirus was discovered, named as 2019-nCoV and known as SARS-CoV-2. On 30 January 2020, the WHO Director-General declared the novel coronavirus disease outbreak a public health emergency of international concern and designated SARS-CoV-2 type of pneumonia as coronavirus disease 2019 (COVID-19). As the number of cases increased, counties and governments introduced intervention strategies to try and control the spread of the disease. Those interventions include social distancing, isolation, wearing face masks, lockdowns and others. In March 2020, the UK government introduced the first nationwide lockdown, closing schools, pubs, restaurants, hotels, non-essential shops and with people working from home in all but a few exceptions.
The need to inform policies and mitigation measures aimed at reducing the spread of the coronavirus highlights the need to understand the complex links between our daily activities and opportunities for the virus to spread. The national lockdowns, and more localised measures, have aimed to reduce the number of contacts between susceptible members of the population and those with the disease. Here, we develop a micro-simulation modelling framework and methods for its computational implementation that brings together epidemiological modelling, urban analytics, spatial analysis and data integration to provide the ability to assess the effects of past interventions and forecast the effects of future policy decisions. This information will be crucial in gaining a greater understanding of the effects of future policy decisions in different areas and within different populations. We demonstrate the use of the model in a case study based on the county of Devon where we compare the effects of different lockdown strategies and present a computationally efficient approach to running complex simulation models of this type.