Project

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Statistical methods for spatio-temporal modelling and prediction of infectious diseases

Applicant Held Leonhard
Number 137919
Funding scheme Project funding (Div. I-III)
Research institution Institut für Epidemiologie, Biostatistik und Prävention Universität Zürich
Institution of higher education University of Zurich - ZH
Main discipline Mathematics
Start/End 01.03.2012 - 28.02.2015
Approved amount 314'433.00
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All Disciplines (4)

Discipline
Mathematics
Medical Statistics
Infectious Diseases
Methods of Epidemiology and Preventive Medicine

Lay Summary (English)

Lead
Lay summary

This research project aims to develop novel statistical methodology for

Both retro- and prospective analysis of space-time data on infectious disease

incidence. The new techniques will be applied in the particular context

of space-time surveillance data, but important parts of the methodology can

be used in a wider context.  The first sub-project is concerned with appropriate

integration of network data in statistical models to better describe the

spatio-temporal spread of infectious diseases. Both parameter- and

observation-driven models will be suitably extended and statistical

algorithms will be developed to incorporate the impact of network data in the

analysis.The second sub-project is concerned with the prospective analysis of

space-time count data. Statistical algorithms for sequential analysis of time

series models will be extended to the space-time setting with particular focus on

multivariate outbreak detection. Validation of both retro- and prospective analyses

will be based on the assessment of out-of-sample predictions based on proper

scoring rules.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Collaboration

Group / person Country
Types of collaboration
Prof. Tilmann Gneiting, Institut für Angewandte Mathematik, Universität Heidelberg Germany (Europe)
- in-depth/constructive exchanges on approaches, methods or results
Prof. Gertraud Schüpbach, VPH-Institut, Universität Bern Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Prof. Michael Höhle, Department of Mathematics, Stockholm University Sweden (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
35th Annual Conference of the International Society for Clinical Biostatistics Poster Time series analysis of Campylobacter incidence in Switzerland 25.08.2014 Vienna, Austria Wei Wei;
29th International Workshop on Statistical Modelling Talk given at a conference Flexible Estimation of Spatio-Temporal Interaction in a Point Process Model for Infectious Disease Spread 14.07.2014 Göttingen, Germany Meyer Sebastian;
Jahreskonferenz in Veterinary Public Health Institiute Talk given at a conference Campylobacter in Human and Chicken 04.12.2013 Bern, Switzerland Wei Wei;
International Hexa-Symposium on Biostatistics, Bioinformatics, and Epidemiology Talk given at a conference Modelling power-law spread of infectious diseases 15.11.2013 Hasselt University, Diepenbeek, Belgium Held Leonhard;
Workshop on "Evaluating probabilistic forecasts of multivariate quantities" Poster Evaluating Statistical Models for Multiple Time Series of Counts 30.10.2013 Heidelberg, Germany Wei Wei;
SMIDDY 2013 (Swiss Meeting for Infectious Disease Dynamics) Talk given at a conference Modelling power-law spread of infectious diseases 13.09.2013 Zürich, Switzerland Meyer Sebastian;
ROeS 2013 (Seminar of the Austro-Swiss Region of the International Biometric Society) Talk given at a conference Modelling power-law spread of infectious diseases 12.09.2013 Dornbirn, Austria Meyer Sebastian;
ROeS 2013 (Seminar of the Austro-Swiss Region of the International Biometric Society) Poster Campylobacter in human and chicken 12.09.2013 Dornbirn, Austria Wei Wei;
34th Annual Conference of the International Society for Clinical Biostatistics Talk given at a conference Statistical methods in infectious disease epidemiology 25.08.2013 München, Germany Held Leonhard;
Summer School on Topics in Space-Time Modelling and Inference Poster Power-law features in modelling the spatio-temporal spread of infectious diseases 28.05.2013 Department of Mathematical Sciences, Aalborg University, Aalborg, Denmark Meyer Sebastian;
3rd Joint Statistical Meeting DAGStat 2013 Talk given at a conference Spatial power-law perspectives in modelling infectious disease spread 21.03.2013 Freiburg, Germany Meyer Sebastian;
3rd Joint Statistical Meeting DAGStat 2013 Talk given at a conference Calibration test for count data 21.03.2013 Freiburg, Germany Wei Wei;
Workshop on "Early-warning systems in Switzerland" Talk given at a conference Modelling and monitoring of epidemic phenomena using the surveillance package in R 04.03.2013 Bern, Switzerland Meyer Sebastian; Wei Wei;
VI International Workshop on Spatio-Temporal Modelling (METMAVI) Talk given at a conference Movement Network Data and the Spatiotemporal Spread of Infectious Diseases 13.09.2012 University of Minho, Guimarães, Portugal Held Leonhard;
Invited seminar talk Individual talk A Space-Time Conditional Intensity Model for Invasive Meningococcal Disease Occurrence 07.09.2012 School of Biological and Chemical Sciences, Queen Mary, University of London, Great Britain and Northern Ireland Meyer Sebastian;
Open University conference on “Statistical Methods for Infectious Diseases” Poster A space-time conditional intensity model for invasive meningococcal disease occurrence 23.05.2012 Milton Keynes, Great Britain and Northern Ireland Meyer Sebastian;
58th Biometrical Colloquium, International Biometric Society (German Region) Talk given at a conference Spatio-Temporal Infectious Disease Epidemiology based on Point Processes 14.03.2012 Berlin, Germany Meyer Sebastian;


Awards

Title Year
Sebastian Meyer received a distinction from the Faculty of Science for his PhD thesis 2016

Use-inspired outputs

Associated projects

Number Title Start Funding scheme
116776 Multivariate analysis of dependent count and survival data 01.04.2007 Project funding (Div. I-III)
130002 Multivariate analysis of dependent count data 01.08.2010 Project funding (Div. I-III)
196247 SUSPend: Impact of Social distancing policies and Underreporting on the SPatio-temporal spread of COVID-19 01.06.2020 Special Call on Coronaviruses
124429 Multivariate analysis of dependent count data 01.04.2009 Project funding (Div. I-III)

Abstract

Summary of proposed researchThis research proposal aims to develop novel statistical methodology for both retro- and prospective analysis of space-time data on infectious disease incidence. The new techniques will be applied in the particular context of space-time surveillance data, but important parts of the methodology can be used in a wider context. The proposal is composed of three distinct projects, each of which will contribute significantly to the overall goals of this proposal. The first project is concerned withappropriate integration of network data in statistical models to better describe the spatio-temporal spread of infectious diseases. Both parameter- and observation-driven models will be suitably extended and statistical algorithms will be proposed to incorporate the impact of network data inthe analysis. Spatially-correlated overdispersion will be allowed for to achieve improved predictions of future disease incidence. The second project is concerned with the prospective analysis of space-time count data. Statistical algorithms for sequential analysis of time series models will beextended to the space-time setting with particular focus on multivariate outbreak detection. Validation of both retro- and prospective analysis will be based on the assessment of out-of-sample predictions based on proper scoring rules. The third project will develop general methodology tofacilitate this goal. In particular, new techniques for the assessment of multivariate predictions will be developed, which have wider relevance in other application areas.
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