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Projects

Projects 2020-21

Project III (MATH3382)

Health Data Science: Understanding the local impact of epidemics

The novel coronavirus disease (Covid-19) was identified in China late 2019. It has since then developed into a global health emergency. From a global and national perspective, the development of the disease has, so far, followed the behaviour of a “textbook” epidemic with a classic exponential increase in cases and deaths if unmanaged. When investigating cases and deaths at smaller geographies (e.g. local authorities), we start to see the effect of demographic variations, geography, socio-economic factors and behaviours as the virus spread in small communities.

In this project, we will collect and analyse different sources of data related to the Covid-19 outbreak, assess the quality of these sources, and investigate the local nuances of outbreaks. Students may choose, for example, to look at risk factors and their statistical significance, time series analysis for cases and deaths, or derive a comparison to other epidemics.

Prerequisites

This project is recommended for students that have taken one or more of the following modules:

  • Statistical Concepts II (MATH2041)
  • Math Modelling II (MATH 2637)
  • Monte Carlo II (MATH 2667)
References
  • ICNARC (2020) ICNARC report on Covid-19 in critical care (June 19th, 2020), ICNARC Case Mix Programme Database
  • Birrell, P., Blake, J., van Leeuwen, E., de Angelis, D. (2020) Covid-19: nowcast and forecast Covid-19: nowcast and forecast
  • ONS (2020) ONS repository with latest data and analysis on coronavirus in the UK and its effect on the economy and society Coronavirus (Covid-19)

Project IV (MATH4072)

Health Data Science: Understanding the local impact of epidemics using agent-based models

The novel coronavirus disease (Covid-19) was identified in China late 2019. It has since then developed into a global health emergency. From a global and national perspective, the development of the disease has, so far, followed the behaviour of a “textbook” epidemic with a classic exponential increase in cases and deaths if unmanaged. When investigating cases and deaths at smaller geographies (e.g. local authorities), we start to see the effect of demographic variations, geography, socio-economic factors and behaviours as the virus spread in small communities.

In this project, we will collect and analyse different sources of data related to the Covid-19 outbreak, build agent-based models to simulate the dynamics of small populations and investigate the impact of different policies and interventions on the spread of a virus.

Prerequisites

This project is recommended for students that have taken one or more of the following modules:

  • Statistical Methods III (MATH3051)
  • Mathematical Biology III (Math3171)
  • Monte Carlo II (MATH 2667)
  • Math Modelling II (MATH 2637)
References
  • ICNARC (2020) ICNARC report on Covid-19 in critical care (June 19th, 2020), ICNARC Case Mix Programme Database
  • Birrell, P., Blake, J., van Leeuwen, E., de Angelis, D. (2020) Covid-19: nowcast and forecast Covid-19: nowcast and forecast
  • ONS (2020) ONS repository with latest data and analysis on coronavirus in the UK and its effect on the economy and society Coronavirus (Covid-19)
  • Adam, D. (2020) Special report: The simulations driving the world’s response to Covid-19, NatureLink