MAS6061 Epidemiology and Time Series

Both semesters, 2017/18 20 Credits
Lecturer: Dr Kostas Triantafyllopoulos uses MOLE Timetable
Aims Outcomes Assessment Full Syllabus

The module aims to equip students with the knowledge and skills necessary to design, analyse and report a variety of studies used in epidemiology, including both prospective and retrospective studies. It will also introduce statistical models that can be used to understand and forecast changes in medical and epidemiological phenomena that evolve through time. The students will gain a knowledge of the strengths and limitations of different study designs and of different sources of epidemiological data. Besides describing the basic concepts of epidemiology the module will also cover some aspects of genetic epidemiology.

There are no prerequisites for this module.
No other modules have this module as a prerequisite.


Outline syllabus

  • Semester 1: Epidemiology
    • Introduction to epidemiology
    • Confounding and causality.
    • Comparing populations and comparing individuals
    • Population genetics
    • Spatial epidemiology
  • Semester 2: Time Series
    • Preliminary material
      examples; purposes of analysis; components (trend, cycle, seasonal, irregular); stationarity, autocorrelation; approaches to time series analysis.
    • Simple descriptive methods
      smoothing; decomposition; differencing; autocorrelation. Probability models for stationary series: autoregressive models; moving average models; partial autocorrelation; invertibility; ARMA processes; ARIMA models for non-stationary series. Inference: identification and fitting; diagnostics; Ljung-Box statistic; choice of models; AIC.
    • Introduction to forecasting
      updating and errors; linear predictions; heuristic forecasting methods.
    • State space models
      formulation; filtering, prediction and smoothing; Kalman recursions; Bayesian inference; Bayesian forecasting; local level model; linear trend model; seasonal model.



Aims

  • To equip students with the knowledge and skills necessary to design, analyse and report a variety of studies used in epidemiology and medical research, including both prospective and retrospective studies
  • To introduce the strengths and weaknesses of different study designs and the strengths and limitations of different sources of epidemiological data
  • To introduce methods to uncover structure in series of observations made through time.
  • To illustrate how models for time series may be constructed and studied.
  • To develop methods to analyse and forecast time series.
  • To show how these methods are applied to data, and what kinds of conclusion are possible.

Learning outcomes

  • Know about different types of study design used in Epidemiology
  • Understand the strengths and weakenesses of different study designs
  • Understand the strengths and limitations of different sources of epidemiological data
  • Know how to investigate causality in non-randomised studies
  • Be able to calculate and apply different measures of health and disease incidence; prevalence and measures of effect; and understand issues around standardisation
  • Know how to identify forms of analysis appropriate to particular study designs and data, and understand their limitations.
  • Understand some aspects of genetic epidemiology
  • Plan and undertake an analysis of data arising from epidemiological studies
  • Appropriately interpret the results of analyses, taking into account particular study design issues
  • Be able to write a clear report of the results of an analysis of epidemiological data
  • understand general terms used in time series analysis, such as stationarity, autocorrelation function (ACF), and partial autocorrelation function (PACF).
  • understand the structure of ARMA and ARIMA models and be able to derive the ACF and PACF for simple ARMA models.
  • understand the approximate sampling behaviour of estimators of the ACF and PACF.
  • be able to fit models to time series data, assess the fit, and if necessary modify the model, and be able to use the fitted models for forecasting.
  • have undertaken an extended analysis of a problem involving a variety of multivariate methods and of a practical time series problem.

40 lectures, no tutorials

Assessment

Semester 1: Two projects worth 30% and 70%, each.

Semester 2: One project worth 15% and an 85% exam in May.

Full syllabus

Epidemiology

  • Introduction to Epidemiology; introduction to epidemiological study design; introduction to genetic epidemiology. (3 sessions)
  • Sensitivity/specificity; simple summary measures; relative risk odds ratio. (2 sessions)
  • Confounding; causality. (2 sessions)
  • Comparing populations: ecological studies and standardisation; comparing individuals: cohort, case-control and cross-sectional studies. (4 sessions)
  • Introduction to population genetics; kinship and identity by descent; components of genetic variance; genetic linkage mapping; linkage disequilibrium; genetic association studies. (7 sessions)
  • Spatial epidemiology; small area spatial analyses in public health. (2 sessions)
Time Series preliminary material
  • examples; purposes of analysis; components (trend, cycle, seasonal, irregular); stationarity, autocorrelation; approaches to time series analysis. (2 sessions)
Simple descriptive methods
  • smoothing; decomposition; differencing; autocorrelation. (1 session) Probability models for stationary series: autoregressive models; (1 session)
  • moving average models; partial autocorrelation; invertibility; (1 session)
  • ARMA processes; (2 sessions)
  • ARIMA models for non-stationary series. (1 session)
Inference
  • identification and fitting; (1 session)
  • diagnostics; Ljung-Box statistic; choice of models; AIC. (1 session)
Introduction to forecasting
  • updating and errors; (2 sessions)
  • linear predictions; heuristic forecasting methods. (2 sessions)
State space models
  • formulation; filtering, prediction and smoothing; Kalman recursions. (2 sessions)
  • Bayesian inference; Bayesian forecasting. (1 session)
  • local level model; linear trend model; seasonal model. (3 sessions)

Timetable (semester 1)

Mon 15:00 - 15:50 lecture   Hicks Lecture Theatre A
Wed 12:00 - 12:50 lecture   Dainton Building Lecture Theatre 1