SIPHER Microsimulation for Interrogation of Social and Health Systems - MINOS
This is a framework for testing the effects that policy interventions might have on health outcomes (including mental health, physical health, quality adjusted life years and equivalent income).
Taking as input Understanding Society longitudinal data and the SIPHER Synthetic population, SIPHER MINOS applies policy interventions at the individual level, for example raising the hourly wage of an individual worker. Through a series of pathway models (for example improvement to housing quality resulting from an increase in income), individuals transition from one state to another (e.g. from poor quality to good quality housing).
The cumulative effect of these pathways has an impact on that individual’s health outcomes. Improvements can be compared against a baseline (no intervention) run, so providing a view of the effectiveness and cost efficiency of a given policy.
SIPHER Glossary For clarification of our terminology and use of acronyms.
Contact: sipher@glasgow.ac.uk
Related Resources
- MINOS Documentation hosted on GitHub
- MINOS Software code and detailed user instructions hosted on GitHub
- SHOWCASE MINOS Model Showcase Display Demo hosted on GitHub
- SIPHER Glossary For clarification of our terminology and use of acronyms.
Publications:
An example of MINOS applied to an energy price rise and the mediation of government energy price support policies. Published by (and presented at) the 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 21:1-21:6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.GIScience.2023.21
Outlining MINOS applied to three income scenarios (Scottish Child Payment, Living Wage increase, energy price increase)
News
- At 12th International Conference on Geographic Information Science (GIScience 2023), Leeds in September, Rob Clay, Researcher in our Policy Microsimulation (Workstrand 5), presented a short paper Using the Dynamic Microsimulation MINOS to Evidence the Effect of Energy Crisis Income Support Policy.
Technical Information
Provides technical details of the characteristics including strengths and limitations for this dynamic microsimulation.
| Characteristic | Details |
|---|---|
| Main Perspective | Individual Level (Micro) |
| Purpose | This Microsimulation For Interrogation Of Social And Health Systems (MINOS) dynamic microsimulation, using longitudinal survey data such as the SIPHER Synthetic Population, provides a very granular picture of the impact of policy interventions on different population groups. This model uses individual-level data and simulates the transitions of individuals across different states (such as health states) over time, based on a specific set of models describing these transitions. |
| Strengths | Designated longitudinal approach for the individual-level while outcomes can also be aggregated to reflect changes for population subgroups and geographical areas. |
| Limitations | Interventions can be applied to specific variables, and outcomes applied to specific health variables. |
| Geography | DZ/LSOA Level for Scotland, England, and Wales |
| Time Period | The ‘jump off’ point for the scenarios is the latest period in the underlying Understanding Society input data (currently wave k (2019-2021). The ‘time horizon’ for the scenario is set at 2037. |
| Adjustments / Extensions | Features of each respective intervention, including the amount of uplift or characteristics of recipients receiving the uplift. |
| Data Requirements | Understanding Society (waves a-k). If spatial results are required, the latest version of the Synthetic Population (see data for details). |
| Applications | Shocks and policy interventions which can be expressed as changes at the individual level. For example: changes to disposable income. Transition models need to be constructed for new problems. |
| Modelling Assumptions | The model relies on the assumption that transitions between states over time - representing the characteristics of an individual - can be modelled using a set of specified and measured characteristics of this individual. In addition, the Markov assumption needs to hold meanings that the time spent in a particular state (i.e. unemployed) does not have an impact on the probability of transitioning into other states (i.e. employed). |
| User Options | Character, target group, and magnitude of particular interventions. In addition, users can assess the impacts for LSOAs/DZs within a given area. |
| User Type(s) | Modellers, decision makers |
| Examples / Link with Other Models and Data | This model uses SIPHER Synthetic Population. |
| Software Requirement(s) | Python |
| Options for Extension | Building different models for different interventions. Factors impacting transitions can be adjusted based on different contexts and assumptions. |