ArticleJournal of the American Statistical Association2019
Panel Data Analysis via Mechanistic Models.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Causal inference concepts can guide research into the effects of climate on infectious diseases.Nature ecology & evolution · 2025Article
- Article
- Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control.Nature communications · 2024Article
- Immunological feedback loops generate parasite persistence thresholds that explain variation in infection duration.Proceedings. Biological sciences · 2024Article
- The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021.BMC public health · 2024Article
- Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.Journal of the Royal Society, Interface · 2024Review
- Potential Drivers for the Spatiotemporal Patterns of the Global Burden of Chronic Obstructive Pulmonary Disease Attributable to Ambient Ozone, 1990-2019.International journal of public health · 2024Article
- Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models.PLoS computational biology · 2023Article
- Mechanisms for the circulation of influenza A(H3N2) in China: A spatiotemporal modelling study.PLoS pathogens · 2022Article
- Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics.Journal of mathematical biology · 2022Article
- Synchronized nonpharmaceutical interventions for the control of COVID-19.Nonlinear dynamics · 2021Article
- The contribution of host cell-directed vs. parasite-directed immunity to the disease and dynamics of malaria infections.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Monte Carlo profile confidence intervals for dynamic systems.Journal of the Royal Society, Interface · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising due to the combination of model complexity and dataset size. Supplementary materials for this article are available online.
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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.