A Geospatial Dynamic Microsimulation Model for Household Population Projections

Abstract

Forecasting Populations (FPOP) is a microsimulation model (MSM) that is the demographic core of an extensible modeling framework. The framework, with FPOP at its core, enables the geospatial projection of a population under purely demographic processes or under the additional influence of exogenous factors such as disease, policy changes and prevention programs, or environmental stressors. Empirically-derived transition probabilities of life events such as birth, death, marriage, divorce and migration, captured in lookup table format, drive the simulation. These transition probabilities can be modified dynamically by external user-defined functions or other external MSMs. The use of MSM structures and methodologies enables FPOP to portray the impact of heterogeneity in the geospatial dimension (e.g., distribution of environmental factors or distribution of intervention programs), as well as the social dimension (e.g., household or social network correlates), on the projections. POP is designed and structured

Forecasting the Impact of Maternal Undernutrition on Child Health Outcomes in Indonesia

“…FPOP is a geospatial ABM that ages a synthetic population by linking key life events over time. 11,12 The life events that affect households and populations include mortality, birth, marriage/union formation and dissolution, and migration. The occurrence of life events for each individual at each time step is stochastically determined using transition probabilities.…”

A Demographic Microsimulation Model for the Long-Term Evolutionof Synthetic Populations in Saint-Petersburg

“…As a result, one can get an "evolved" synthetic population without detailed demographic data (in this case the justification of the obtained populations is possible using the aggregated data) and even project it into the future. In the current study, we develop such a model using the idea from RTI International team [12]. We use the same standard of the synthetic populations [13].…”

Global Sensitivity Analysis of a Model Simulating an Individual’s Health State through Their Lifetime

“…Microsimulation models are becoming increasing popular in public health (Atella, Belotti, Carrino, & Piano Mortari, 2017;Goldman et al, 2009;Gonzalez-Gonzalez, Tysinger, Goldman, & Wong, 2017;Hennessy et al, 2015;Hunt et al, 2017;McPherson, Marsh, & Brown, 2007;Rogers et al, 2014). These models have been used to predict the future epidemiological and economic impacts of risk factors (e.g.…”