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Quantifying demographic and socioeconomic transitions for computational epidemiology: an open-source modeling approach applied to India
Journal Article

Background

Demographic and socioeconomic changes such as increasing urbanization, migration, and female education shape population health in many low- and middle-income countries. These changes are rarely reflected in computational epidemiological models, which are commonly used to understand population health trends and evaluate policy interventions. Our goal was to create a “backbone” simulation modeling approach to allow computational epidemiologists to explicitly reflect changing demographic and socioeconomic conditions in population health models.

Methods

We developed, evaluated, and “open-sourced” a generalized approach to incorporate longitudinal, commonly available demographic and socioeconomic data into epidemiological simulations, illustrating the feasibility and utility of our approach with data from India. We constructed a series of nested microsimulations of increasing complexity, calibrating each model to longitudinal sociodemographic and vital registration data. We then selected the model that was most consistent with the data (i.e., greater accuracy) while containing the fewest parameters (i.e., greater parsimony). We validated the selected model against additional data sources not used for calibration.

Results

We found that standard computational epidemiology models that do not incorporate demographic and socioeconomic trends quickly diverged from past mortality and population size estimates, while our approach remained consistent with observed data over decadal time courses. Our approach additionally enabled the examination of complex relations between demographic, socioeconomic and health parameters, such as the relationship between changes in educational attainment or urbanization and changes in fertility, mortality, and migration rates.

Conclusions

Incorporating demographic and socioeconomic trends in computational epidemiology is feasible through the “open source” approach, and could critically alter population health projections and model-based evaluations of health policy interventions in unintuitive ways.

 

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