Research group SU-InfDisMod-group

The infectious disease modelling and analysis group at Stockholm University.

Group description

The Department of Mathematics at Stockholm University has a long history of mathematical modelling and statistical analysis of infectious diseases, dating back to the ground breaking paper by von Bahr and Martin-Löf (1980). The greater Stockholm region also hosts several groups and institutions dedicated to infectious disease research: we collaborate with scientists at Karolinska Institutet, Royal Institute of Technology (KTH), Swedish Public Health Agency (FHM) and the European Centre for Disease Control and prevention (ECDC). The group also has close ties to many international hubs including: Aalto University (Finland), Los Alamos NL (US), RIVM (Netherlands), RKI (Germany), University of Florida (US), University of Nottingham (UK), University of Oslo (Norway), and EpiConcept (France).

Scientific contents

The group covers many different areas of infectious disease modelling and analysis, both in terms of diseases and in terms of mathematical methodology. Methodologies used and developed include dynamic modelling using Susceptible-Infectious-Recovered models and its extensions, data mining in surveillance data to detect emerging outbreaks, real-time estimation of parameters during outbreaks.

Current projects include work on, e.g., Covid-19, HIV, Hepatitis C, Chlamydia. The types of questions being analysed include: effects of population heterogeneity, herd immunity, now-casting, surveillance, effects of prevention and the estimation of disease specific parameters such as incubation period, generation time and serial interval

Group members

See all current group members below.

The permanent members are Tom Britton (prof, group leader), Michael Höhle (prof) and Pieter Trapman (associate prof).

Former members (since 2018): Martina Favero (now Warwick University, UK), Federica Giardina (now Erasmus University, Netherlands), Disa Hansson (now FHM), Abid Lashari (now West-Sweden Public Health), KaYin Leung (now RIVM), Theresa Stocks (now private sector Germany), Yun Jun Zhang (now University of Peking)

Events

NordicMathCovid regular meetings. See NordicMathCovid for more details.

Our group has a journal club where we go through a research paper once every four weeks.

Funding

Current external funding:

Swedish Research Council (grant 2020-04744): 2021-2024. 3.6 Msek, PI: Tom Britton
Nordic Research Agency (NordForsk, grant 105572): 2020-2022. 8.8 Msek, PI: Tom Britton. See NordicMathCovid
Swedish Research Council: 2016-2020: 2 Msek, PI: Michael Höhle

Software with contributions from group members

"surveillance" – an R package containing statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena.

"epitweetr" – an R package for the Early Detection of Public Health Threats from Twitter Data.

"scanstatistics" – an R package for space-time anomaly detection using scan statistics.

Online Now-casting of ICU and case-fatalities of Covid in Sweden

Group members

Group managers

Tom Britton

Professor

Department of Mathematics (incl. Math. Statistics)

08-16 45 34

tom.britton@math.su.se

Members

Fanny Bergström

Phd Student

Department of Mathematics (incl. Math. Statistics)

fanny.bergstrom@math.su.se

Mohamed El Khalifi

Postdoc

Department of Mathematics (incl. Math. Statistics)

mohamed.elkhalifi@math.su.se

Dongni Zhang

PhD Student

Department of Mathematics (incl. Math. Statistics)

dongni.zhang@math.su.se

Publications

Recent publications by current and former members

Modelling preventive measures and their effect on generation times in emerging epidemics.

Favero M, Scalia Tomba GP and Britton T (2022). ArXiv.

Analysis of asymptomatic and presymptomatic transmission in SARS-CoV-2 outbreak, Germany, 2020.

Bender JK, Brandl M, Höhle M, Buchholz U, Zeitlmann N (2021). Emerging Infectious Diseases. 2021 Apr.

Quantifying the preventive effect of wearing face masks.

Britton T (2021). Proc Roy Soc A, 477: 20210151.

The risk for a new epidemic wave - and how it depends on R0, immunity level and restrictions.

Britton, T, Ball F, Trapman P. (2021). Royal Society Open Science.

Nowcasting the COVID-19 pandemic in Bavaria.

Günther F, Bender A, Katz K, Küchenhoff H, Höhle M (2021). Biometrical Journal, 63(3):490-502

Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection.

Jombart T, Ghozzi S, Schumacher D, Taylor TJ, Leclerc QJ, Jit M, Flasche S, Greaves F, Ward T, Eggo RM, Nightingale E, Meakin S, Brady OJ, Centre for Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Medley GF, Höhle M, Edmunds JW (2021). Royal Society Philosophical Transactions B, 376: 20200266.

Analysis of the early Covid-19 epidemic curve in Germany by regression models with change points.

Küchenhoff H, Günther F, Höhle M, Bender A (2021). Epidemiology and Infection, 149:e68.

The duration of a supercritical SIR epidemic on a configuration model.

Lashari AA, Serafimović A and Trapman P. Electronic Journal of Probability 26 (2021): 1-49.

Evaluating and optimizing COVID-19 vaccination policies: a case study of Sweden.

Sjödin H, Rocklöv J and Britton T (2021). MedRxiv.

Commentary on the use of the reproduction number R during the COVID-19 pandemic.

Vegvari C, Abbott S, Ball F, et al.(incl Trapman). Statistical Methods in Medical Research. (September 2021).

Analysing the Effect of Test-and-Trace Strategy in an SIR Epidemic Model.

D Zhang, T Britton (2021). arXiv preprint.

Inferring transmission heterogeneity using virus genealogies: Estimation and targeted prevention.

Y Zhang, T Leitner, J Albert, T Britton (2021). PLOS COMPUTATIONAL BIOLOGY 17 (9).

Epidemics on networks with preventive rewiring.

Ball F and Britton T (2020). ArXiv. (To appear in Rand Str Alg.)

Reproduktionstal, immunitet och vaccination.

Britton, T. (2020). Svepet, 38, 4:8-9 (in Swedish, klick nr 4, 2020).

Epidemic models on social networks - with inference.

Britton, T. (2020). Statistica Neerlandica, 74:3, 222-241. (Also arXiv:1908.05517.)

Basic stochastic transmission models and their inference.

Britton, T. (2020). In Handbook of Infectious Disease Data Analysis. CRC Press. Arxiv.

A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV2

Britton, T, Ball F, Trapman P. (2020). Science. 369 (6505), pp. 846-849.

Introducing pre-exposure prophylaxis to prevent HIV acquisition...

Full title: Introducing pre-exposure prophylaxis to prevent HIV acquisition among men who have sex with men in Sweden: insights from a mathematical pair-formation model. Hanson, D., Strömdahl, S., Leung, K-Y., Britton, T. (2020). British Medical Journal (BMJ Open), 10:2.

Inflow restrictions can prevent epidemics when contact tracing efforts are effective but...

Full title: Inflow restrictions can prevent epidemics when contact tracing efforts are effective but have limited capacity. Malmberg H. and Britton, T. (2020). Journal Royal Society: Interface, 17:170.