About

The DAEDALUS Explore dashboard

DAEDALUS Explore is an interactive dashboard tool that allows users to project the health, education and economic impacts of epidemic scenarios and their mitigation measures. DAEDALUS Explore allows users to simulate what would happen in specific countries if one of 7 hypothetical pandemics of varying severity were to occur. The 7 diseases are hypothetical but informed by the characteristics of pathogens that have caused pandemics in the past. Daedalus Explore supports projections for 67 countries, and pulls in data on demography, social mixing patterns, and economic sector productivity and workforces from publicly available sources.

Projections in DAEDALUS Explore are generated by the integrated economic-epidemiological Daedalus model, allowing users to explore the model with a simplified interface and a few top-level parameter options. The Daedalus model is based on Doohan et al (2025)¹ and Haw et al (2022)². For full details on the underlying Daedalus model visit the Daedalus model website. Please use the Daedalus R package for advanced use cases that are not yet supported by the dashboard.

Hypothetical diseases

Users choose among 7 diseases, to set model parameters for transmissibility, delays and severity based on known characteristics of the historical pathogens (see Table 1). We assume there is no prior immunity to the disease amongst the countries’ populations.

Policy responses

The user then chooses a policy response amongst four options: No closures, school closures, business closures, and elimination (see Table 2). These policy responses simulate non-pharmaceutical economic restrictions that governments can mandate once the epidemic reaches pre-defined thresholds, as detailed below.

Under ‘Elimination’, restrictions are implemented when total hospitalisations (hospital occupancy) breach a country’s hospital surge capacity or 30 days after the start of the epidemic, whichever is sooner. We model economic restrictions as the reduction in workplace contacts due to the partial closure of economic sectors, which affects both transmissions and economic output. Economic restrictions are lifted again once virus transmission is suppressed, i.e., the effective reproduction number is below 1. Under school and business closures, the same thresholds for restrictions imposition and lifting apply, but only education or non-essential economic activity, respectively, are targeted for closures. In school closure scenarios, we assume that the lost present value of future earnings due to lost education is reduced due to remote education.

Global vaccine investment

Users can explore the impact of pandemic preparedness on estimated losses, specifically global investments into vaccines and vaccine acceptance that are funded by the international community in advance of a pandemic’s arrival. A higher level of investment into vaccines allows vaccination to begin earlier, be delivered at a faster rate, and to be accepted by a higher proportion of the population. This reduces infections and the need for costly closures. There are four levels of investments, ranging from none to high (see Table 3).

Change in public behaviour

It is likely that many individuals change their behaviour and adopt precautions as cases increase and they perceive a high risk of infection. The user can allow for such effects and specify behaviour change ranging from ‘None’ which assumes no protective behaviour to ‘High’ which assumes the population perceives a high risk of infection, resulting in high adoption of behaviour that protects against infection. Note that behaviour change reduces transmissions but has not direct impact on economic output.

Hospital surge capacity

The number of hospital beds that are available nationally for the treatment of epidemic patients is prepopulated with the actual value from the chosen country. Note that available beds are lower than total beds, which allows for the treatment of patients with other urgent conditions. Users can adjust bed capacity up or down within reasonable limits.

Results

Estimates of losses

The dashboard provides estimates of losses after 600 days in the chosen country. Estimated losses are:

• Gross Domestic Product (GDP)
• Education
• Life years

Losses in GDP are the short-term losses in economic output due to business closures, which occur unless the ‘No closure’ response is chosen. Sickness and deaths amongst workers also result in economic losses. The magnitude of losses is based on economic data collected during the COVID-19 pandemic for countries that followed one of the three closure strategies. Lost education results in reduced life-time income for affected student. We use existing estimates of income reductions and the days of interrupted in-person schooling to estimate lost education. To measure deaths in monetary terms, we use the value-of-a-statistical-life (VSL) approach, which considers remaining life expectancy at death and GDP of the country to value the loss of life years (see Value of life-years lost for further details). Losses are provided in both % of pre-pandemic GDP and in US dollars. We also output life-years lost and number of deaths in natural units.

Time series

Time series display prevalence, hospital demand or occupancy, cumulative deaths and numbers vaccinated. Toggles allow the user to switch the four outcomes to numbers ‘New per day’, i.e. to infection incidence, hospitalizations, deaths and numbers vaccinated per day. Hospital surge capacity is indicated by the dashed red line. The blue block ‘Pandemic response’ shows the time period over which closures are implemented. User can download all figures and the underlying data in various formats and see the R code snippet for running the model with the current parameter set.

DAEDALUS Explore allows users to directly compare scenarios against each other, by choosing parameters of specific interest. Scenarios can be compared across countries, diseases, response strategies, levels of global vaccine investment, change in public behaviour or hospital surge capacity. Results are displayed in stacked bars and tables, in total losses or as difference from the baseline. For fine-grained control over model parameters and functionality, R users can run the model directly through the Daedalus R package.

Intended use

The DAEDALUS Explore dashboard tool and the Daedalus model are projection and decision-support tools and not forecasting tools. The Daedalus model projects outcomes for diseases similar to pathogens that have caused historical pandemics. DAEDALUS Explore is not re-creating historical disease outbreaks, and it is not fitted to historical epidemic data. Instead, by changing the dashboard’s parameters, DAEDALUS Explore illustrates the projected value of differing pandemic preparedness and response activities, and in doing so, endeavours to support effective future pandemic planning, preparedness and response. The Daedalus R package offers many more options for customising pathogen, country, and response parameters, allowing for modelling real-time policy responses and their projected outcomes.

Contact us

If you have any queries regarding DAEDALUS Explore, please contact: daedalus.explore@imperial.ac.uk

Contribution Acknowledgements

The DAEDALUS Explore dashboard and the underlying Daedalus model were created by ‘The Jameel Institute – Kenneth C Griffin Initiative for the Economics of Pandemic Preparedness’ (EPPI), led by Prof Katharina Hauck. It is a partnership between the Jameel Institute at Imperial, the Imperial Business School, the World Health Organization and Singapore’s Programme For Research In Epidemic Preparedness And Response (PREPARE), National Centre for Infectious Diseases (NCID) and Umeå University in Sweden. For more information, visit the website of the EPPI Initiative.

A large team of authors have contributed to the development of DAEDALUS Explore, and the underlying code, since spring 2020.

Software team

David Mears^a,b, Pratik Gupte^a,b, Emma Russell^a, Anmol Thapar^a, Mantra Kusumgar^a, Rich FitzJohn^a

Modelling team

Patrick Doohan^b*, Rob Johnson^b*, Pratik Gupte^a,b, David Haw^b,c, Christian Morgenstern^b, Haokun Peng^g, Pablo Perez Guzman^b, Kanchan Parchani^b, Guillaume Morel^b,h, Anh Pham^b,c, Paula Christen^b, Matteo Pianella^b, Wei Hao Kwok^e, Jeremy Chan^e, Kelvin Bryan Tan^e, Peter C Smith^f, Marisa Miraldo^g, Giovanni Forchini^b,h, Katharina Hauck^b

* contributed equally

Project management and communiciations team

Ellie Farrow^b, Sara Williams^b

Affiliations

1. Reside-IC, MRC Centre for Global Infectious Disease Analysis & Department of Infectious Disease Epidemiology, Imperial, London, United Kingdom
2. MRC Centre for Global Infectious Disease Analysis, WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute, Imperial, London, United Kingdom
3. Mathematical Sciences & Infection, Veterinary and Ecological Sciences, University of Liverpool, United Kingdom
4. Department of Economics, Stockholm University, Sweden
5. PREPARE, National Centre for Infectious Diseases, Singapore
6. Centre for Health Economics, University of York, United Kingdom
7. Imperial Business School, Imperial, London, United Kingdom
8. Umeå School of Business, Economics and Statistics, Umeå Universitet, Umeå, Sweden

Funding Acknowledgements

The EPPI Initiative gratefully acknowledges research funding from Community Jameel and Kenneth C Griffin which enabled the development of the DAEDALUS model and DAEDALUS Explore dashboard.

Disclaimer and Licensing

Model code license

The Daedalus model code is provided “as is”, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement.

In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.

Icon attributions

rotate-device.svg
Icon by Esri in MIT License via SVG Repo

carbonSEEarth.svg
Icon by Carbon Design in Apache License via SVG Repo

vectopusVaccine.svg
Vectors and icon by Vectopus in MIT License via SVG Repo

pictogrammersMaterialBacteria.svg
Icon by Carbon Design in Apache License via SVG Repo

axes-white.png, axes-black.png
Icon by LAFS in Flaticon License via Flaticon

info.png Icon by Digby Garrett in Flaticon License via Flaticon

social-distance-2-meters-mdi.svg Icon by Michael Irigoyen in Apache 2.0 License via Pictogrammers

World map disclaimer and attribution

The country borders shown on the world map in DAEDALUS Explore are reflective of boundaries as employed by the World Health Organization (WHO), including both national borders and disputed borders: https://gis-who.hub.arcgis.com/pages/detailedboundary. The code underlying the WHO-aligned world map can be accessed here.

The country borders depicted on the world map do not reflect any expression of opinions by the DAEDALUS Explore creators. The world map may be intermittently updated in line with code rewrites, however the timing of such updates will be at the discretion of the DAEDALUS Explore creators.

References

1. Doohan, Patrick, Rob Johnson, Alessandra Løchen, Christian Morgenstern, David Haw, Alice Sabino, Edith Patouillard, Giovanni Forchini, and Katharina Hauck. Mitigating Future Respiratory Pandemics in Low-, Middle- and High-Income Countries: A Modelling Study of Health, Economic and Educational Losses. . Available at SSRN, 2025.
2. Haw, David J., Giovanni Forchini, Patrick Doohan, Paula Christen, Matteo Pianella, Robert Johnson, Sumali Bajaj, Alexandra B. Hogan, Peter Winskill, Marisa Miraldo, Peter J. White, Azra C. Ghani, Neil M. Ferguson, Peter C. Smith, and Katharina Hauck. 2022. Optimizing Social and Economic Activity While Containing SARS-CoV-2 Transmission Using DAEDALUS. Nature Computational Science 2 (4): 223–33.