The Nordic Pandemic Preparedness Modelling Network monthly webinars
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Here you will find information about upcoming webinars and previous webinars, including abstracts.
Upcoming webinars
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Webinars 2025
6 February 2025, Denmark
Which fecal indicator to use for normalizing SARS-CoV-2 concentrations in wastewater
- Lasse Engbo Christiansen, Senior Research, Statens Serum Institut
SARS-CoV-2 concentration multiplied by flow provides a simple estimate of the amount of virus that is excreted in a catchment. However, rainfall, infiltration, and water from industries are diluting the fecal matter that is flushed from toilets. PMMoV is commonly used as a fecal indicator but it shows seasonal variation so we are testing three alternatives. This gives rise to even more questions and thus analysis where I will present the current state.
Wastewater informs hospital admissions
- Lasse Engbo Christiansen, Senior Research, Statens Serum Institut
The reporting of admissions with and due to COVID-19 is delayed. Nowcasting and forecasting can improve the information but SARS-CoV-2 concentrations in wastewater can improve it even further.
6 March 2025, Finland
Communicating Deaths: Different Mortality Metrics
- Simopekka Vänskä, senior researcher, THL
(no abstract)
3 April 2025, bonus
The Politics of the Pandemic in Eastern Europe and Eurasia - Blame Game and Governance
- M. Zavadskaya, Finnish Institute of International Affairs (FIIA) and researcher at the Aleksanteri Institute, University of Helsinki
This talk provides a comprehensive overview of the political impact of the COVID-19 emergency in central and eastern Europe and Eurasia. Offering a theoretical framework linking the authoritarian, post-Soviet institutional legacy with patterns of political behavior, support and governments’ policies, the expert contributors argue that domestic political regimes mediate and shape citizens’ perceptions of public health crises, and the very regimes’ political survival. The authors explore how the pandemic affected regime change, government stability, business groups and civil societies in more than 15 countries of the region from the discovery of the virus to the vaccination rollout. The studies rely on a broad range of empirical evidence from the region – survey, state statistics, ethnography and interviews.
Formulating, explaining and empirically testing the causal mechanisms that drive political accountability and support through a cross-country comparison and in-depth case studies of popular and electoral support attempting to highlight any patterns specific to the region, this book contributes to studies of governance and political accountability in low-trust countries with authoritarian legacies and proclivities. Drawing on an interdisciplinary approach that brings together area studies, history, sociology and political science, it will also be of value to those interested in systematic effect of political regimes on handling public health crises.
8 May 2025, Sweden
Identifiability in an epidemic model with prior immunity and under-reporting
- Fanny Bergström, Stockholm U
Abstract: We examine the identifiability of a modified SIR model that accounts for under-reporting and pre-existing immunity in the population. We provide a mathematical proof of the unidentifiability and later show that we are able to identify the model parameters using properties of the deterministic SIR model and survey data of either the prior immunity or under-reporting. Our results show the limitations of parameter inference in partially observed epidemics and the importance of identifiability analysis in the development and application of models for public health decision-making.
Sideward Contact Tracing in an Epidemic Model with Mixing Groups
- Dongni Zhang, LInköping U
Abstract: We consider a stochastic epidemic model with sideward contact tracing, where infection occurs through interactions at gatherings involving two or more individuals (referred to as mixing events). In contrast to traditional tracing methods, which focus on identifying the infector or the infectees of an index case, sideward tracing targets those infected during the same event. The early stage of the epidemic is analysed using a branching process with sibling dependencies. To address the challenges given by the dependencies, we aggregate sibling groups (individuals who become infected at the same event) into macro-individuals and define a corresponding macro-branching process. This approach allows us to derive an expression for the effective macro-reproduction number, which is equivalent to the effective individual reproduction number, and serves as a threshold for the behaviour of the epidemic. Our numerical examples reveal that sideward tracing can substantially reduce the reproductive number, particularly when mixing events are larger or when super-spreading events occur. However, we also highlight its limitations: without restrictions on gathering size, the reproductive number may remain excessively high, making it infeasible to reduce it below one even with perfect tracing. These findings underscore the potential and the challenges of sideward tracing as a control measure, especially in scenarios where gathering size limitations are relaxed.
Webinars 2024
January 18th 2024, Norway
Estimating the household secondary attack rate with the Incomplete Chain Binomial model.
- Jonas Christoffer Lindstrøm, Norwegian Institute of Public Health
We use the Chain Binomial model to estimate the Secondary Attack Rate in household studies. We extend the model to allow for inference in incompletely observed outbreaks, and illustrate the methodology on two data sets. We have also implemented an R package for the model.
February 29 2024, Sweden
Analysis of the boundary between as simple model as possible and too simple model in studies of outbreaks of new types of diseases.
- Fredrik Liljeros, Department of Sociology, Stockholm University
(no abstract)
April 4 2024, Finland
Unifying incidence and prevalence under a time-varying general branching process
- Mikko Pakkanen, Imperial College London
In epidemiology, renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a time-varying variant of the Crump-Mode-Jagers branching process. This model accommodates a time-varying reproduction number and a time-varying distribution for the generation interval. We then derive renewal-like integral equations for incidence, cumulative incidence and prevalence under this model. We also investigate some of the salient theoretical properties of these equations. Finally, we present a numerical discretisation scheme to solve the equations and use this scheme to estimate rates of transmission from serological prevalence of SARS-CoV-2 in the UK and historical incidence data on Influenza, Measles, SARS and Smallpox. Joint work with X. Miscouridou, M. J. Penn, C. Whittaker, T. Berah, S. Mishra, T. A. Mellan and S. Bhatt.
May 2024
(replaced by workshop in Denmark)
12 September 2024, Denmark
Robust estimation of seasonal onset and intensity of viral respiratory infections
- Sofia Myrup Otero, Data Scientist at SSI
The aim of this study is to develop a more robust alternative to the Moving Epidemic Method (MEM), which is commonly used in the surveillance of viral respiratory viruses.
The MEM has shown to be overly sensitive to fluctuations between seasons and relies on an arbitrary parameter, making it difficult to interpret, compare and apply consistently.
Therefore, we propose an alternative method that utilizes timely detection of seasonal onset and adaptive calculations of intensity levels.
Waning of multiple outcomes in ODE compartmental models
- Rasmus Skytte Randløv, PhD, Special Consultant at SSI
Inspired by Mohamed El Khalifi and Tom Brittons work on extending SIRS models with gradual waning of immunity, we implement generalized numerical methods to account for waning of multiple outcomes in ODE compartmental models. Notably, our extension allows for simultaneous waning of immunity against infection and against hospitalization when these wane at different time scales.
10 October 2024, Finland
Vaccine effectiveness and herd immunity for HPV16 in partly open population
- Simopekka Vänskä, senior researcher, THL
Often the predictions of vaccine effectiveness are estimated by models with a closed population. In this talk, considering HPV16, the openness of population is included the analysis. We show how the openness is implemented for the sexual contact structure through which HPV (human papillomavirus) transmits, and how the level of openness changes the vaccine effectiveness, depending on the vaccination strategy (girls-only, or boys-and-girls vaccination).
7 Novembre 2024, Norway
Contact Tracing in ABMs
- Ida-Marie Fauske Johansson
The COVID-19 contact tracing data from Oslo municipality provides valuable insights into the contact patterns among various age groups and districts within the city. Utilizing this data, we developed an agent-based model to simulate the first two years of the COVID-19 pandemic. Our goal is to evaluate the effectiveness of the contact tracing strategy in Oslo. In this presentation, I will introduce our agent-based model, outline the key assumptions underlying our simulations, and discuss how we will proceed with model calibration and scenario analysis.
A modelling study of hepatitis C infection among people who inject drugs and immigrants
- Jørgen Eriksson Midtbø
The global incidence target for the elimination of hepatitis C among people who inject drugs (PWID) is <2/100. In Norway, the hepatitis C epidemic is concentrated in PWID. Immigrants are the second most important risk group for chronic infection. Using a stochastic compartmental model, we estimated the incidence of hepatitis C among active PWID, and the prevalence of chronic infection among active PWID, ex-PWID, and immigrants in Norway up to 2022. I will present the model and discuss some of the particular challenges we faced, including a non-constant size of the PWID population over time. The work is published in The Journal of Infectious Diseases: https://doi.org/10.1093/infdis/jiae147
5 December 2024, Sweden
Modeling of Vector-Borne Diseases accounting for Climate Chang
- Jonas Wallin, Lund University
A significant concern in Europe due to potential climate change is the introduction of new vectors, particularly mosquitoes. This development can lead to the emergence of diseases previously considered tropical, such as Dengue fever and West Nile virus. In this talk, I will present recent projects involving the statistical modeling of vector-borne diseases.
The primary applications are generating scenarios under climate change (long-term forecasting) and developing early warning systems (filtering or just-in-time prediction). To model the vectors, we have utilized two main classes of models: mechanistic process-based models and data-driven models. I will discuss the limitations of these approaches and suggest possible extensions to enhance state-of-the-art modeling for both filtering and long-term forecasting.
Webinars 2023
March 23 2023
An epidemic model with short-lived mixing groups
- Frank Ball, University of Nottingham
Almost all epidemic models make the assumption that infection is driven by the interaction between pairs of individuals, one of whom is infectious and the other of whom is susceptible. However, in society individuals mix in groups of varying sizes, at varying times, allowing one or more infectives to be in close contact with one or more susceptible individuals at a given point in time. In this talk, which is based on joint work with Peter Neal (University of Nottingham), we study the effect of mixing groups beyond pairs on the transmission of an infectious disease in a stochastic SIR model. We show that, for a given basic reproduction number $R_0$, the distribution of the size of mixing groups can have a significant impact on epidemic properties, such as the probability and size of a major outbreak.
Webinars 2022
27 Jan 2022
Trade-offs between mobility restrictions and transmission of SARS-CoV-2
- Martijn Gösgens, Eindhoven University of Technology
In mitigating COVID-19 outbreaks, governments face the dilemma to balance public health and societal impact. Mobility plays a central role in this dilemma because the movement of people enables both economic activity and virus spread. We use mobility data in the form of counts of travellers between regions, to extend the often-used SEIR models to include mobility between regions. We then formulate the trade-off between mobility and infection spread as an optimization problem. We consider restrictions where the country is divided into regions, and study scenarios where mobility is allowed within these regions, and disallowed between them. We propose heuristic methods to approximate optimal choices for these regions. We evaluate the obtained restrictions based on our trade-off. The results show that our methods are especially effective when the infections are highly concentrated, e.g. around a few municipalities, as resulting from superspreading events that play an important role in the spread of COVID-19. We introduce a measure to quantify this ‘concentration’ of infections and demonstrate our methods in the example of the Netherlands.
3 March 2022
- Ongoing research activities in the NordicMathCovid collaboration
- Optimal intervention strategies for minimizing total incidence during an epidemic (Lasse Leskelä, Tom Britton)
- Updates from the different countries and general discussion with topic: Situation after relaxation of containment measures
31 March 2022
The effect of behavior and seasonality on the spread of SARS-CoV-2 in Norway and Sweden
- Felix Günter, Stockholm University
28 April 2022
Monitoring COVID-19 surveillance data time series
- Michael Höhle, Stockholm University
In order to achieve situational awareness from the reported coronavirus disease (COVID-19) data across individual countries, territories and areas, the WHO uses a quantitative-qualitative process to assess the
epidemiological COVID-19 trends. The quantitative stage consists of a dynamics algorithm, which jointly uses the time series of reported cases and reported COVID-19 associated deaths to calculate a risk
classification. This quantitative classification is then further substantiated in a subsequent qualitative stage using additional information.
Focus of this talk is on the statistical algorithm used to perform the above described quantitative classification. In consists of four components: A real-time estimation of the case fatality fraction and the
delay distribution between cases and deaths, a short-term projection of reported cases and, subsequently, a short-term projection for reported deaths. By adjusting these projections for potential under-reporting a
final risk class can be derived.
The presented work is joint work with Finlay Campbell, Martina McMenamin, Henry Laurenson-Schafer and Olivier Le Polain from the COVID-19 Analytics Team at WHO HQ, Geneva.
October 6 2022
Sequential Monte Carlo approach for estimation of timevarying reproduction number for Covid-19
- Geir Storvik and Arnoldo Frigessi
During the first months, the Covid-19 pandemic has required most countries to implement complex sequences of non-pharmaceutical interventions, with the aim of controlling the transmission of the virus in the population. To be able to take rapid decisions, a detailed understanding of the current situation is necessary. Estimates of time-varying, instantaneous reproduction numbers represent a way to quantify the viral transmission in real time. They are often defined through a mathematical compartmental model of the epidemic, like a stochastic SEIR model, whose parameters must be estimated from multiple time series of epidemiological data. Because of very high dimensional parameter spaces (partly due to the stochasticity in the spread models) and incomplete and delayed data, inference is very challenging. We propose a state space formalisation of the model and a sequential Monte Carlo approach which allow to estimate a daily-varying reproduction number for the Covid-19 epidemic in Norway with sufficient precision, on the basis of daily hospitalisation and positive test incidences. The method is in regular use in Norway and is a powerful instrument for epidemic monitoring and management.
November 17 2022
Assessing the effects of outbreak interventions in an age-structured population: what can we learn from COVID-19?
- Francesca Lovell-Read
Age-related factors can play a critical role in shaping the transmission dynamics of infectious pathogens. Many infectious diseases are characterised by significant age-related variations in pathophysiology: for SARS-CoV-2, for example, children are thought to be less susceptible to infection than adults. Younger individuals are also more likely to experience asymptomatic or subclinical courses of infection, making them less infectious on average than older individuals who are at increased risk of developing clinical symptoms. Additionally, individuals in different age groups exhibit markedly different patterns of social contacts: younger individuals tend to have far more contacts each day than older people, giving them more opportunities to transmit a pathogen to others.
Understanding the contribution to transmission from individuals of different ages is crucial if we are to assess the effectiveness of control interventions that target specific age groups within the population. For example, determining whether school closures are effective at interrupting chains of transmission is only possible if we understand the infection risk posed specifically by school-aged individuals. Incorporating age structure into transmission models is therefore critical for evaluating and comparing potential intervention strategies.
In this talk, we will construct a branching process model to estimate the risk that an infectious case arriving in a new location will initiate a local outbreak, showing how age-related heterogeneities in social contact patterns and pathophysiology can be accounted for. Using SARS-CoV-2 as a case study, we will demonstrate that the risk of a local outbreak occurring depends strongly on the age of the index case and may vary substantially from the whole population average. Using these age-stratified risk estimates, we will explore the effects of interventions that target individuals of different ages, including school closures, workplace closures and population wide social distancing policies, and consider how the success of these measures may vary between countries with differing age demographics and social structures.
December 1, 2022
Overview of the activities of mathematical modelling at ECDC.
- Bastian Prasse, European Centre for Disease Prevention and Control Dear NordicMathCovid researchers
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Webinars 2021
12 January 2021
The official NordicMathCovid kick-off meeting
- Tom Britton: Short introduction to project
What urgent questions do the three Public Health Agencies want us to address?
- Camilla Stoltenberg, Norway (5 minutes)
- Anders Tegnell, Sweden (5 minutes)
- Markku Tervahauta, Finland (5 minutes)
28 January 2021
Current activities of the THL modelling group
- Kari Auranen and Mikhail Shubin (TBA)
25 February 2021
Modelling and mitigating the risks of airborne virus transmission in a restaurant setting
- Nina Atanasova (University of Helsinki) & Antti Hellsten (Finnish Meteorological Institute)
11 March 2021
Statistical inference
- Introduction to the theme (opening: Arnoldo)
- Inverse correlation between serial time and transmission risk, and inference from disease data (opening: Kaare)
- Experience with running seq ABC with various statistics (opening: Solveig)
- Structural identifiability of compartmental epidemic models (opening: Lasse)
- Experiences of fitting SIR models to the previous waves of covid-19 (opening: Michael)
8 April 2021
Vaccination strategies
Three short talks related to three countries by Tom Britton (SWE), Louis Chan (NOR), and Jeta Molla (FIN).
6 May 2021
How to optimize dosing for anti-infectives
- Pia Abel-Zur Wiesch Genannt Hülshoff
26 August 2021
Predicting regional COVID-19 hospital admissions in Sweden using mobility data
- Philip Gerlee, Chalmers
21 October 2021
Immunization strategies for minimizing the number of deaths from emerging diseases
- Hiraoka Takayuki, Aalto University
Network epidemiology has advocated that immunization targeting high-centrality nodes, or “hubs”, generally outperforms random vaccination in preventing epidemics in heterogeneous networks. In the case of COVID-19, however, vaccine priority has been given to those who are at a higher infection fatality risk (IFR) from the disease in many countries because the objective of the vaccination was set primarily to reduce the number of severe cases and deaths, not to reduce the epidemic size. The differences in the strategies optimized for the different epidemic outcomes have been shown for the COVID-19 specific parameters, but a systematic understanding of such an optimality divide is still lacking. Here, we highlight these differences by analyzing an epidemiological process in a heterogeneous population described by the configuration network model. Both for immunization taking place before the epidemic and in the middle of it,
18 November 2021
Immune responses induced by SARS-CoV-2 infection and vaccination.
- Gunilla Karlsson Hedestam, Professor at Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Sweden
Waning immunity: modelling challenges and impact on disease persistence.
- Francesca Scarabel, Research associate at Department of Mathematics, University of Manchester, UK.
Using some toy epidemic models including waning of immunity, I will discuss the impact of modelling choices on the dynamics of the system. In particular I will discuss the role of different mechanisms and model parameters on the persistence of the disease and the appearance of recurrent epidemic waves.
After the talks, we will have updates from the different countries.
16 December 2021
The topic of the meeting is (collaborative) short-term forecasting and nowcasting of the current pandemic and lessons learned in the previous months. We will start with an invited talk of Johannes Bracher (Karlsruher Institut für Technologie, KIT, Heidelberg Institute for Theoretical Studies):
Collaborative Nowcasting and Short-Term Forecasting of COVID-19
- Johannes Bracher, Karlsruher Institut für Technologie, KIT, Heidelberg Institute for Theoretical Studies
There has been a surge in research activity on epidemic short-term forecasting and nowcasting during the COVID-19 pandemic, with a large number of approaches published and run in real time. This raises the need for systematic comparison and evaluation of methods, both as a prerequisite for model improvement and to assess how reliably forecasts can inform public health decision making. Moreover, the large number of available models opens new avenues for ensemble forecasting, as commonly used in fields like meteorology and economics. I will speak about challenges encountered and lessons learned during several collaborative projects I have been involved in, including the European COVID-19 Forecast Hub (https://covid19forecasthub.eu/) and the recent German COVID-19 Nowcast Hub (https://covid19nowcasthub.de/).
Bayesian hierarchical model for Nowcasting
- Felix Günter, Stockholm University
I (Felix) will present a Bayesian hierarchical model for Nowcasting that we use for nowcasting hospitalization counts in Germany and use to contribute to the German COVID-19 Nowcast Hub on a daily basis. I will talk about some recent work in the area with an outlook on current ideas and future plans. There will also be some time to discuss potential collaborations and application scenarios of these models for the Nordic countries and updates/current news from the different countries.
Webinars 2020
5 November 2020
First Seminar;
- Tom Britton -- talk about recent work on effects of immunity.
- Lasse Leskelä -- the project plan
19 November 2020
Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data and models
Mikko Kivelä, Aalto University
3 December 2020
Spatial modelling of covid-19 in Norway
- Solveig Engebretsen
Discussion about mobility data relevant for covid modelling, with 5-7 min summary about Finnish mobility data streams
- Sara Heydari, Aalto University
17 December 2020
Nowcasting the epidemic curve of the COVID-19 pandemic
- Michael Höhle