One epidemic, many estimates (1EME)
Bridging methods to measure excess mortality
Organised by Hampton Gaddy and Eric Schneider (LSE)
Excess mortality methods are essential for quantifying the demographic and social impacts of contemporary and historical mortality crises. The excess mortality methods in use across demography, epidemiology, and quantitative history involve a diversity of techniques, model assumptions, and means of quantifying uncertainty. A growing body of theoretical and applied work shows how excess mortality estimates and their interpretations are often highly sensitive to the construction of the underlying models. This work includes Andreasen and Simonsen (2011), Schöley (2021), Nepomuceno et al. (2022), Duerst and Schöley (2024), and Wakefield and Knutson (2025). Wider awareness of these issues and a collaborative approach towards developing the best practices for particular use cases of excess mortality methods would be a welcome step forwards for academic and governmental stakeholders.
To that end, the Historical Economic Demography Group and Pop@LSE, the two demography research groups at the London School of Economics, are convening a many analyst collaboration (data available to teams on 15 September 2025; submissions due by 15 March 2026). Then, the LSE will convene a two-day workshop on excess mortality methodology on 21–22 May 2026.
The results of the many analyst collaboration and the set of best practices that emerge from the workshop will be written up by the steering committee for submission to an international demography or general science journal. All participants in the many analyst collaboration will be eligible for authorship.
"Many analyst" projects give teams of researchers the same data and the same research questions, then see how they use the methods of their choice to arrive at different answers to those questions. Prior to the workshop, there will be a “many analyst” collaboration (see a previous example here). Teams will be asked to analyse the mortality of the 1918–20 influenza pandemic in the US populations of Kentucky and rural Maryland. They will be asked to estimate between 3 and 6 quantities. These quantities will relate to 1) the absolute level of pandemic mortality across space, 2) the relative level of pandemic mortality between social groups (defined by gender, race, and geography), and 3) the timing of the pandemic.
Cleaned, highly granular death count and denominator data will be provided to teams. Death count data will be available by date of death, county of death, (imputed) cause of death, and single year of age for both states. In Kentucky, death counts will also be available by the deceased’s registered gender, and in Maryland, they will be available by the registered race. 20 years of death count data will be available from Kentucky, and 10 years of data will be available from Maryland. For those who want to incorporate it into their model, granular weather data and shapefiles will also be available.
Teams can comprise up to 5 researchers. Participants will be asked to submit their estimates, reproducible code that generates those estimates (in any programming language), and a written description of their estimation technique (between 250 and 1,000 words).
Data will be available to teams starting on 15 September 2025 and all submissions are due by 15 March 2026. You can already sign up to participate in the collaboration using the link below!
Day 1 (21 May 2026)
10.30am: Coffee
11.00am: Hampton Gaddy (LSE)
Announcing the results of the “One Epidemic, Many Estimates” (1EME) project
11.45am: Victoria Knutson (University of Washington)
Keynote: What do we estimate when we estimate excess mortality?
12.30pm: Lunch
1.30pm: Erica Charters (University of Oxford)
Excess death: Counting the costs of eighteenth-century war, disease, and empire
2.00pm: David S Jones (Harvard University)
Excess deaths: History of a contested concept
2.30pm: Coffee
3.00pm: Eugenio Paglino (University of Helsinki)
Excess mortality as causal inference
3.30pm: Maria Gargiulo (LSHTM)
What data do we need to estimate excess mortality well? (Part 1: Completeness)
4.00pm: Hampton Gaddy (LSE) with input from Maarten van Wijhe (University of Roskilde) and Nathaniel Darling (University of Cambridge)
What data do we need to estimate excess mortality well? (Part 2: Granularity)
4.30pm: Wen Su (University of Oxford) and Hampton Gaddy (LSE)
Unpacking the hidden assumptions of excess mortality models
5.00pm: Jouni Kuha (LSE)
Quantifying uncertainty in excess mortality
5.45pm: Drinks (2-minute walk: Ye Old White Horse, LSE)
7.00pm: Dinner (2–minute walk: Cooper’s, 49 Lincoln's Inn Fields, London WC2A 3PF)
Day 2 (22 May 2026)
9.00 am: Coffee
9.30am: Peter Tammes (Office for National Statistics)
The ONS approach to excess mortality
10.00am: Katarina Matthes (University of Zurich)
Frontiers of methodology in excess mortality (1): Spatial approaches
10.20am: Charles Rahal (University of Oxford)
Frontiers of methodology in excess mortality (2): The limits of out-of-sample prediction
10.40am Julio Romero Prieto (LSHTM)
Frontiers of methodology in excess mortality (3): Survey-based approaches
11.00am: Hampton Gaddy (LSE) with input from Maysoon Dahab (LSHTM) and Maria Gargiulo (LSHTM)
The other tools of mortality estimation
11.30am: Discussion sessions
- What is excess mortality estimation good for?
- What are the major conceptual challenges for estimating excess mortality?
- What are the major data problems for estimating excess mortality?
12.30pm: Lunch
1.30pm: Discussion sessions
- What are the potential solutions to the major conceptual challenges?
- What are the potential solutions to the major data problems?
- What other best practices are there for estimating excess mortality?
- What other problems related to estimating excess mortality are there that this workshop did not discuss?
- Hampton Gaddy (LSE)
- Monica Alexander (University of Toronto)
- Jennifer Dowd (University of Oxford)
- Eric Schneider (LSE)
- Maarten van Wijhe (Roskilde University)
Contact Hampton (h.g.gaddy@lse.ac.uk) if you have any questions.
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