LIAM2: a New Open Source Development Tool for Discrete-Time Dynamic Microsimulation Models

Menten, Gaëtan de; Dekkers, Gijs; Bryon, Geert; Liégeois, Philippe; O’Donoghue, Cathal

doi:10.18564/jasss.2574

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…FMF software is mature SMS software (30) as well as the most user-friendly of the available options. Other specialist software for implementing SMS include LIAM2 (31), the SMS package of R (32) and JAS-mine (33).…”

Section: Methodsmentioning

confidence: 99%

Small-Area Estimation and Analysis of HIV/AIDS Indicators for Precise Geographical Targeting of Health Interventions in Nigeria. A Spatial Microsimulation Approach

ABUBAKAR

Cunningham

2022

Preprint

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Background: Precise geographical targeting is well recognised as an indispensable intervention strategy for achieving many Sustainable Development Goals (SDGs). This is more cogent for health-related goals such as the reduction of the HIV/AIDS pandemic, which exhibits substantial spatial heterogeneity at various spatial scales (including at microscale levels). Despite the dire data limitations in Low and Middle Income Countries (LMICs), it is essential to produce ne-scale estimates of health-related indicators such as HIV/AIDS. Existing small-area estimates (SAEs) incorporate limited synthesis of the spatial and socio-behavioural aspects of the HIV/AIDS pandemic and/or are not adequately grounded in international indicator frameworks for sustainable development initiatives. They are, therefore, of limited policy-relevance, not least because of their inability to provide necessary ne-scale socio-spatial disaggregation of relevant indicators.Methods: The current study attempts to overcome these challenges through innovative utilisation of gridded demographic datasets for SAEs as well as the mapping of standard HIV/AIDS indicators in LMICs using spatial microsimulation (SMS).Results: The result is a spatially enriched synthetic individual-level population of the study area as well as microscale estimates of four standard HIV/AIDS and sexual behaviour indicators. The analysis of these indicators follows similar studies with the added advantage of mapping ne-grained spatial patterns to facilitate precise geographical targeting of relevant interventions. In doing so, the need to explicate socio-spatial variations through proper socioeconomic disaggregation of data is reiterated.Conclusions: In addition to creating SAEs of standard health-related indicators from disparate multivariate data, the outputs make it possible to establish more robust links (even at individual levels) with other mesoscale models, thereby enabling spatial analytics to be more responsive to evidence-based policymaking in LMICs. It is hoped that international organisations concerned with producing SDG-related indicators for LMICs move towards SAEs of such metrics using methods like SMS.

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Section: Methodsmentioning

confidence: 99%

Small-Area Estimation and Analysis of HIV/AIDS Indicators for Precise Geographical Targeting of Health Interventions in Nigeria. A Spatial Microsimulation Approach

ABUBAKAR

Cunningham

2022

Preprint

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“…These checks verify whether the status changes have occurred even in the predefined subpopulation and whether implausible combinations of characteristics occur. Naturally, a modular structure is implemented as standard in many existing microsimulation tools such as LIAM2 (O'Donoghue et al, 2009;de Menten et al, 2014).…”

Section: Step 4: Design Of the Microsimulation Modelmentioning

confidence: 99%

A generic business process model for conducting microsimulation studies

Burgard

Dieckmann

Krause

et al. 2020

Statistics in Transition New Series

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Microsimulations make use of quantitative methods to analyze complex phenomena in populations. They allow modeling socioeconomic systems based on micro-level units such as individuals, households, or institutional entities. However, conducting a microsimulation study can be challenging. It often requires the choice of appropriate data sources, micro-level modeling of multivariate processes, and the sound analysis of their outcomes. These work stages have to be conducted carefully to obtain reliable results. We present a generic business process model for conducting microsimulation studies based on an international statistics process model. This simplifies the comprehensive understanding of dynamic microsimulation models. A nine-step procedure that covers all relevant work stages from data selection to output analysis is presented. Further, we address technical problems that typically occur in the process and provide sketches as well as references of solutions.

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“…Simulation Model/ Objective Link / Open-source Availability De Menten et al 11 LIAM2 is an open-source development tool for discrete-time dynamic microsimulation models. The framework is general (not specifically health related).…”

Section: Authorsmentioning

confidence: 99%

Simulation Framework for Realistic Large-scale Individual-level Data Generation with an Application in the Health Domain

Tikka¹,

Hakanen²,

Saarela³

et al. 2020

Preprint

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We propose a general framework for realistic data generation and simulation of complex systems in the health domain. The main use cases of the framework are predicting the development of risk factors and disease occurrence, evaluating the impact of interventions and policy decisions, and statistical method development. We present the fundamentals of the framework using rigorous mathematical definitions. The framework supports calibration to a real population as well as various manipulations and data collection processes. The freely available open-source implementation in R embraces efficient data structures, parallel computing and fast random number generation which ensure reproducibility and scalability. With the framework it is possible to run daily-level simulations for populations of millions individuals for decades of simulated time. An example on the occurrence of stroke, type 2 diabetes and mortality illustrates the usage of the framework in the Finnish context. In the example, we demonstrate the data-collection functionality by studying the impact of non-participation on the estimated risk models.

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LIAM2: a New Open Source Development Tool for Discrete-Time Dynamic Microsimulation Models

Cited by 12 publications

References 6 publications

Small-Area Estimation and Analysis of HIV/AIDS Indicators for Precise Geographical Targeting of Health Interventions in Nigeria. A Spatial Microsimulation Approach

Small-Area Estimation and Analysis of HIV/AIDS Indicators for Precise Geographical Targeting of Health Interventions in Nigeria. A Spatial Microsimulation Approach

A generic business process model for conducting microsimulation studies

Simulation Framework for Realistic Large-scale Individual-level Data Generation with an Application in the Health Domain

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