All microsimulation models require the same core functionalities such as event scheduling, output logging, model calibration, and common random number techniques. Few frameworks provide a comprehensive package of ready-to-use core functionalities, and modelers often need to implement these functionalities themselves, resulting in duplicate work and non-standardized and unvalidated code.
We present the MISCore (MISCAN Core) Python package with ready-to-use core functionalities for health-economic modelling. Besides the aforementioned core functionalities, it provides built-in tools for cost-effectiveness analyses, probabilistic sensitivity analyses, output stratification, and more. MISCore was developed as the simulation framework for the MISCAN (Microsimulation Screening Analysis) family of disease models. These models have a 40‑year history of informing screening policy through microsimulation modelling; they have provided key evidence for USPSTF guidelines on colorectal, cervical, and lung cancer screening as well as various guidelines throughout Europe. Although MISCore was originally developed to support the MISCAN models, it can be used for models for a wide range of applications, and offers an accessible way to begin microsimulation modelling in Python using an established framework. We are currently in the process of making MISCore publicly available as a Python package for non-commercial purposes.
During this course, participants will be introduced to the MISCore Python package to get a head start with microsimulation modelling in Python. The course will follow the three phases of simulation modeling: model development, model application, and model analysis. Each phase will start with a short presentation, followed by hands-on exercises in Python. Starting with model application, participants will learn the general structure of MISCore models, perform simulations with the MISCAN model for endometrial cancer (MISCAN-Endometrium), and evaluate endometrial cancer screening strategies. In the model analysis phase, participants will analyze model outputs of MISCAN-Endometrium and do a cost-effectiveness analysis using MISCore functionalities. Third, we cover the model development phase where participants will adjust the underlying code of a simple disease model and learn the underlying code structure of MISCore models, enabling them to build their own models in the future. Finally, we will shortly discuss other features available in MISCore, and its license which prescribes the permitted uses of MISCore and any MISCAN model made publicly available in the future. This schedule anticipates 3.5-hour tutorial, but we are flexible to shorten if less time is available.
At the end of this tutorial, participants will be familiar with discrete-event microsimulation modelling in Python using the MISCore package. They can continue expanding their skills through the elaborate tutorials provided on our online documentation webpage.
Pre-Tutorial Preparation Participants should be familiar with a scientific programming language such as Python, R, or a comparable programming language. We will share instructions for installing Python, PyCharm and the MISCore package before the course.
Expertise The Department of Public Health at Erasmus University Medical Center has a long track record of informing screening policy with the MISCAN microsimulation models. The faculty of this tutorial are researchers that used or developed MISCAN models for gastric, colorectal, lung, cervical and prostate cancer screening, as well as dementia. They were all involved in the development of MISCore over the past 5 years. Moreover, they organize a course on health-economic modelling using MISCore for BSc students in econometrics, as well as an EU-funded course on decision modelling in Slovenia which aims to build capacity for screening evaluations in Slovenia. Our tutorial will be based on these courses.
The geo-referencing of buildings and their inhabitants at detailed geographical levels can be required for multiple applications, for example, transportation planning, evaluation of housing policies, provision of health services, or flooding information. Indeed, the precise location of residence as well as place of employment of the individuals and households is crucial information in these fields. Spatial dynamic microsimulations especially project these individual units, over time and spatial dimensions, instead of aggregates to better understand behaviour at the individual level, when histories and heterogeneity are crucial. The German model MikroSim, in particular, represents the over 80 million German inhabitants and their households as a fully synthetic statistical twin and directly benefits from a detailed representation of their location. Individuals and household data often comes from census datasets, which depict aggregate population information in a grid cell representation of various resolutions. On the other hand, micro-level buildings datasets, such as the Official Real Estate Cadastral Information System (ALKIS), represent individual buildings, their type, associated geospatial attributes and locations. The merging of these two sources of geographical information consists in a new task, the assignment of building objects to grid cells. The allocation of buildings to the census grid cells is neither fully transparent, nor conducted purely on geographic localization alone, meaning that buildings can in principle be attributed to a neighbouring cell rather than the one they are geographically located in. It is suggested, that assignment of buildings to grid-cells occurs by address, rather than building location. However, the exact location of an address is also ambiguous. Indeed, this information is currently not published by the German National Statistical Institute (DESTATIS). Therefore, this allocation of buildings has to be conducted otherwise. This problem can be represented as a bipartite graph, where each building must then be assigned to close grid cells, ensuring that the total estimated population of assigned buildings does not exceed the grid cells capacity. This problem is similar to the class of generalized assignment problems (GAP). In this study, we present different mathematical formulations of this problem that aim at minimising differences in estimated building inhabitants and grid cells population level; and minimising distances between buildings and assigned grid cells. This problem presents some challenges, such as allocation of building complexes, heterogeneous building types or uncertainty in buildings capacity estimates as well as potential register errors.
Joint taxation of married couples remains a central feature of many income tax systems, with significant implications for labor supply and household welfare. By pooling partners’ incomes into a single tax base, joint filing can create disincentives for secondary earners and generate marriage-related penalties, raising concerns about efficiency and equity. This paper studies the impact of joint taxation on the labor supply of couples in Belgium, where the personal income tax is formally individual but substantially adjusted at the household level. We estimate a Random Utility Random Opportunity (RURO) model of labor supply using rich administrative data linking tax records and demographic information. Using the FANTASI microsimulation model of the Belgian personal income tax, we perform a counterfactual analysis of a shift from joint to individual taxation.
European long-term care (LTC) systems face mounting sustainability pressures as population ageing increases the number of older adults living with functional and cognitive limitations. Yet projections of future LTC needs often rely on simplified “need” definitions (e.g., at least one ADL/iADL limitation), overlooking a critical institutional determinant of public expenditure: eligibility rules. Access to publicly funded LTC is not automatic; it is regulated by national (and in some cases regional) legislation that combines multiple vulnerability dimensions—limitations in activities of daily living (ADL), instrumental ADL (iADL), cognition, behavioural issues, and medical needs—into non-linear thresholds for benefit entitlement. Because these rules differ markedly across Europe, they can generate substantially different trajectories of potential demand for publicly financed LTC even under identical demographic and epidemiological trends. This study investigates how the heterogeneity of LTC eligibility criteria shapes the evolution of potential demand for formal domiciliary LTC across European countries in the coming decades. We use longitudinal microdata from the Survey of Health, Ageing and Retirement in Europe (SHARE), Waves 1–9 to operationalize country-specific eligibility rules. Using SHARE’s information on ADL/iADL limitations, mobility constraints, depression symptoms, and cognitive impairment, we construct harmonized indicators of “objective vulnerability” consistent with the assessment-of-need frameworks embedded in legislation. These eligibility indicators are then embedded in a dynamic microsimulation model, the EU-FEM (the SHARE-based version of the Future Elderly Model), which simulates individual life courses through a first-order Markov Monte Carlo process. Transition models generate probabilities of changes in health and functional status over time, allowing heterogeneous trajectories by age, risk factors, and baseline conditions, while the simulated population evolves by ageing and cohort replacement. We produce projections of the population-level prevalence of eligibility for publicly funded domiciliary LTC—interpreted as potential demand—under a baseline “no policy change” scenario. We then conduct counterfactual exercises to disentangle the role of rules versus epidemiology: (i) applying alternative countries’ eligibility rules to the same underlying population trajectories; (ii) simulating a synthetic “single-country” setting to isolate institutional effects; and (iii) evaluating stylized healthy-ageing interventions that reduce the incidence of selected physical and mental limitations by 25% among individuals aged 65–75. The simulations highlight that eligibility-rule heterogeneity translates into markedly different projected pathways of potential LTC demand across Europe. Moreover, the same healthy-ageing intervention can yield very different reductions in projected eligibility depending on which functional domains are targeted and how each country’s rules weight physical, cognitive, and mental limitations. These findings imply that cross-country comparisons of future LTC burdens—and evaluations of prevention-oriented strategies—must explicitly account for institutional eligibility design. Ongoing work extends the framework by incorporating newer SHARE waves, improving harmonization with broader international platforms, and translating projected eligibility into cost trajectories of potential demand.
In the arid and semi-arid lands of northwest Kenya, climate change threatens to compound existing disparities in agricultural activities and health infrastructure and exacerbate food insecurity and malnutrition. To estimate the distribution of nutrition-associated health outcomes and assess projected disease burdens under climate change scenarios, a microsimulation model was combined with health impact assessment methods. A synthetic population covering Turkana, Samburu, and Laikipia counties was generated from census data and a survey conducted in the study region in 2025 provided socioeconomic attributes and baseline dietary intake data. Population growth estimates were obtained from the national statistics authority and forecasts of future edible food availability were estimated up to 2050 from a validated open-source agricultural land-use model. Relative risks of nutrition-associated health outcomes were simulated at the individual-level using standard exposure-response functions at baseline and then at five-year intervals under four representative concentration pathways. The attributable burden in disability-adjusted life years was then estimated to generate the projected morbidity burden in demographic groups. This work expands on an earlier microsimulation model framework developed to assess policies to promote the use of clean cooking fuels on individual exposure to ambient and household air pollution emissions and associated gender-specific mortality in three densely populated Kenyan municipalities. With this additional iteration, we demonstrate how geographic- and hazard-specific assessments of disease burdens in rural and urban populations may contribute toward an improved understanding of differential climate vulnerability in Kenya.
We simulate the distributional effects of a €45/tCO2 carbon price on Belgian households’ heating and transport fuels using microdata from the 2016 Household Budget Survey. Without compensation, the policy is regressive and increases energy poverty, with especially large burdens for singles, seniors, and households heating with oil. We compare three revenue-recycling designs: equal transfers per household, equal transfers per capita, and a fuel-type-differentiated scheme that provides larger supplements to fossil-heated households. Per-household recycling protects vulnerable households better than per-capita recycling, which tends to undercompensate small households. Differentiating transfers by heating fuel further reduces large losses and within-income-group dispersion, and it prevents an increase in energy poverty while preserving overall progressivity of the reform.
This paper examines the interplay between child contingent income support and out of pocket (OOP) childcare costs in four European countries—Belgium, Poland, Spain, and Sweden. While existing research has extensively analysed cash benefits and early childhood education and care (ECEC) services separately, considerably less is known about how these policies jointly shape families’ income adequacy and labour market participation. Using EUROMOD, enriched with detailed information with regard to legislation on childcare fees, we introduce a novel indicator—the compensation ratio—which captures the degree to which child contingent benefits offset OOP childcare expenses.
Across countries, the compensation ratio reveals distinct income related patterns. In Poland and Sweden, benefits generally exceed OOP childcare costs across most of the income distribution, reflecting strong low income targeting. In Belgium, the compensation ratio is above one only for lower income families, declining sharply with income as childcare fees increase more steeply than benefits. Spain shows a similar but more moderate pattern, with low income families roughly compensated and higher income families receiving insufficient support relative to childcare costs.
Overall, our findings demonstrate that the interaction between childcare fees and child related income support substantially shapes the affordability of childrearing and, by extension, families’ capacity to undergo employment transitions. As the compensation ratio declines with income in several countries, our results suggest that these policy designs may inadvertently create labour market disincentives. The analysis underscores the need for conjoint, rather than isolated, assessment of family policy measures in European welfare states.
As the population ages and the sustainability gap in public finances in Finland widens, new solutions are needed to ensure sufficient funding for public services. One potential solution is to place greater emphasis on private wealth in the financing of care services. At present, client fees for long-term social and health care services in Finland are determined based on clients’ income. This study examines the potential effects of also taking clients’ assets into account. We focus on the fiscal and distributional implications of such a reform. The analysis is based on the SOTE-SISU static microsimulation model and unique administrative register including wealth.
Due to the limitations of wealth data, the analysis concerns clients’ financial wealth in the form of investment funds, shares, and investment properties. Together, these account for slightly less than one-third of the total wealth of people aged 65 and over. The main component of wealth—owner-occupied housing—is excluded from the analysis. In the simulation, wealth was taken into account by adding 15 per cent of assets exceeding €15,000 to annual income, following the formula used in the housing allowance for pensioners.
According to the results, the total amount of client fees collected for long-term residential care would increase by approximately 12 per cent under the reform. The increase is substantial considering that the change would affect only about 14 per cent of clients and that the types of assets included in the analysis represent only a small share of older people’s total wealth. Among clients whose wealth would be considered, the fees could rise considerably (average increase €830 per month, +88 %). The largest number of affected clients would be found in the third income decile, which also has the highest prevalence of residential care users, while the probability of being affected increases with income level. Following the reform, the out-of-pocket financing of residential care would rise from about 16 per cent to 18 per cent.
The static simulation is indicative as it does not account for potential behavioural or other dynamic changes—for instance, changes in how older people might use, transfer, or convert their wealth, or shift toward private services. On the other hand, the analysis covers only a limited subset of household wealth, and future cohorts of care users are likely to be both larger and wealthier than those in 2022 data.
Tax-benefit microsimulation models typically operate at the national level, using household survey weights calibrated to aggregate population targets. Subnational analysis—at the level of states, congressional districts, or local authorities—requires datasets that simultaneously satisfy geographic distributional constraints while preserving household-level detail. We present a method based on L0 regularisation that jointly optimises survey weight magnitudes and sparsity to produce calibrated subnational microsimulation datasets.
Our approach builds on the Hard Concrete distribution (Louizos, Welling, and Kingma, 2017), which induces exact sparsity by multiplying each households weight by a learned stochastic gate that collapses to a deterministic zero or one at inference time. We parameterise each gate with a log-alpha and temperature parameter, and jointly optimise these alongside log-transformed weight magnitudes using a single loss function combining scale-invariant relative calibration error, an L0 sparsity penalty on the expected count of active households, and a light L2 regulariser on weight magnitudes.
The pipeline begins with the US Current Population Survey. Each household record is cloned multiple times and assigned to random census blocks drawn from a population-weighted distribution. Program participation indicators (SNAP, Medicaid, SSI, TANF, etc.) are re-randomised per geographic assignment using local takeup rates. Each clone is then run through PolicyEngines tax-benefit microsimulation engine to generate geography-specific outputs. The L0 optimiser selects which household-geography combinations to retain, calibrating simultaneously against approximately 37,800 targets across three geographic levels. At the congressional district level, targets include IRS Statistics of Income data (AGI, income tax, EITC by number of children, capital gains, self-employment income, pension income, and other income and deduction categories), Census ACS age distributions (18 age bands), and SNAP household counts. At the state level, additional targets include USDA administrative SNAP spending, CMS Medicaid enrollment, and Census state income tax collections. National targets include CBO budget projections, JCT tax expenditure estimates, SSA benefit totals by program, and other administrative totals.
The sparsity penalty is configurable: a higher penalty produces a compact national dataset (approximately 50,000 records), while a lower penalty yields a larger dataset (approximately 3–4 million records) covering all 436 congressional districts and 50 states individually. Target groups are normalised so that each domain (national, state, congressional district) contributes equally to the loss, preventing large-count groups from dominating.
The method is implemented as the open-source l0-python PyTorch package. We discuss the approach in the context of PolicyEngines US microsimulation model (policyengine.org/us/model), which uses these calibrated datasets for subnational policy analysis, and compare it to alternative calibration approaches including iterative proportional fitting and generalised regression (GREG) estimators.
We examine the role of social transfers for self-employed in Belgium using the microsimulation model BELMOD. The self-employed represent a significant and increasing group in Belgium. The measurement of income from self-employment comes with particular methodological challenges and research shows that material deprivations tends to be lower for self-employed compared to employees. Despite the existing limitations we will focus on financial poverty for self-employed. The combination of microsimulation techniques and administrative data allows us to analyze which parts of the tax-benefit system contribute most to the poverty reduction for self-employed. We use administrative data and BELMOD to study the poverty reducing role of various household income sources for the self-employed in more detail. Other incomes in the household, besides the income from the individual self-employed person, reduce the poverty risk significantly. For example, the poverty risk of self-employed is reduced by more than half when taking into account labour incomes from other household members. Furthermore, it is reduced by about a third when taking into account all social benefits in the household. Our results indicate that poverty is reduced substantially by social transfers, but to a different degree for various family types. Child allowances clearly contribute to the reduction of the poverty risk for self-employed with children. For families without children, we see the largest reduction in the poverty risk for the household by pension related benefits, followed by contributory sickness/disability benefits. This work is a first step in trying to identify groups of vulnerable self-employed and assessing the role of the tax-benefit system.
