The goal of our proposal is to develop a statistical framework for probabilistic population projections and for assessing uncertainty in linked demographic-disease models. This will yield probability-based prediction intervals around all outputs of the models, allowing policy makers to assess and compare the risks and benefits of different decisions. Our framework will be Bayesian melding, which was first developed for whale population estimation and projection by the principal investigator, and applied successfully to policy-making in that context. The extensions that we propose to human populations involve overcoming technical challenges not previously overcome in the work on whales. The most common approach to communicating uncertainty in population projections is the scenario, or High-Medium-Low, approach, which has been convincingly criticized as having no probabilistic basis and leading to inconsistencies. We propose Bayesian melding as an alternative that can take account of all the available evidence and uncertainties about inputs and outputs from population projection models, to yield a predictive distribution of any quantity of policy interest. Uncertainty is even more important for linked demographic-disease models, when the goal is to forecast future population and disease prevalence in the presence of an epidemic. The United Nations Population Division does not currently issue probabilistic population projections, and has decided to assess Bayesian melding as a method for doing so, with a plan for possible implementation in the 2011 Revision of the official UN population projections. The UNAIDS Reference Group on Estimates, Models and Projections has decided to use Bayesian melding as the basis for assessing uncertainty in their demographic and prevalence projections. The development of Bayesian melding methods for these two cases raise new methodological issues that will guide our research.
The specific aims of the research will be: (1) Methodological development of Bayesian melding to assess probabilistic forecasts, to deal with measurement and systematic errors, to provide a framework for model improvement, model selection and model uncertainty, and to develop more computationally efficient methods. (2) Develop Bayesian melding methods for probabilistic population projections/including fertility, mortality and migration. (3) Develop Bayesian melding methods for linked demographic-disease models, including the incorporation of multiple data sources, and the assessment of behavior change. (4) Produce and distribute software implementing the new methods produced by our research.
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| Sharrow, David J; Godwin, Jessica; He, Yanjun et al. (2018) Probabilistic population projections for countries with generalized HIV/AIDS epidemics. Popul Stud (Camb) 72:1-15 |
| Scrucca, Luca; Raftery, Adrian E (2018) clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R. J Stat Softw 84: |
| Godwin, Jessica; Raftery, Adrian E (2017) Bayesian projection of life expectancy accounting for the HIV/AIDS epidemic. Demogr Res 37:1549-1610 |
| Raftery, Adrian E; Zimmer, Alec; Frierson, Dargan M W et al. (2017) Less Than 2 °C Warming by 2100 Unlikely. Nat Clim Chang 7:637-641 |
| Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao et al. (2017) fastBMA: scalable network inference and transitive reduction. Gigascience 6:1-10 |
| McCormick, Tyler H; Lee, Hedwig; Cesare, Nina et al. (2017) Using Twitter for Demographic and Social Science Research: Tools for Data Collection and Processing. Sociol Methods Res 46:390-421 |
| Onorante, Luca; Raftery, Adrian E (2016) Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window. Eur Econ Rev 81:2-14 |
| Šev?íková, Hana; Raftery, Adrian E (2016) bayesPop: Probabilistic Population Projections. J Stat Softw 75: |
| Wheldon, Mark C; Raftery, Adrian E; Clark, Samuel J et al. (2016) Bayesian population reconstruction of female populations for less developed and more developed countries. Popul Stud (Camb) 70:21-37 |
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