ArticleInternational statistical review = Revue internationale de statistique2020
Statistical Implementations of Agent-Based Demographic Models.
Article in International statistical review = Revue internationale de statistique, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Article
- Using simulation modelling and systems science to help contain COVID-19: A systematic review.Systems research and behavioral science · 2022Article
- Individual heterogeneity influences the effects of translocation on urban dispersal of an invasive reptile.Movement ecology · 2022Article
- Inference in epidemiological agent-based models using ensemble-based data assimilation.PloS one · 2022Article
- A review of bioenergetic modelling for marine mammal populations.Conservation physiology · 2022Article
- Bridging implementation gaps to connect large ecological datasets and complex models.Ecology and evolution · 2021Article
- What processes must we understand to forecast regional-scale population dynamics?Proceedings. Biological sciences · 2020Article
- Statistical Implementations of Agent-Based Demographic Models.International statistical review = Revue internationale de statistique · 2020Article
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Authors and funding
3 authors.
Funding
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Abstract
A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing due to imperfect detection of individuals, the associated measurement error can be accommodated under certain study designs (e.g. those that involve multiple surveys or replication). However, the interaction of the measurement error and the underlying dynamic process can complicate the implementation of statistical agent-based models (ABMs) for population demography. In a Bayesian setting, traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of approaches for fitting statistical ABMs to data and demonstrate how to use multi-stage recursive Bayesian computing and statistical emulators to fit models in such a way that alleviates the need to have analytical knowledge of the ABM likelihood. Using two examples, a demographic model for survival and a compartment model for COVID-19, we illustrate statistical procedures for implementing ABMs. The approaches we describe are intuitive and accessible for practitioners and can be parallelised easily for additional computational efficiency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.