ArticleAmerican journal of epidemiology2022
Dynamical Modeling as a Tool for Inferring Causation.
Article in American journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Article
- Invited Commentary: Combining Information to Answer Epidemiologic Questions About a Target Population.American journal of epidemiology · 2024Article
- Selective deforestation and exposure of African wildlife to bat-borne viruses.Communications biology · 2024Article
- Structural racism theory, measurement, and methods: A scoping review.Frontiers in public health · 2023Article
- Research gaps and opportunities in precision nutrition: an NIH workshop report.The American journal of clinical nutrition · 2022Article
- Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk.PLoS computational biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Dynamical models, commonly used in infectious disease epidemiology, are formal mathematical representations of time-changing systems or processes. For many chronic disease epidemiologists, the link between dynamical models and predominant causal inference paradigms is unclear. In this commentary, we explain the use of dynamical models for representing causal systems and the relevance of dynamical models for causal inference. In certain simple settings, dynamical modeling and conventional statistical methods (e.g., regression-based methods) are equivalent, but dynamical modeling has advantages over conventional statistical methods for many causal inference problems. Dynamical models can be used to transparently encode complex biological knowledge, interference and spillover, effect modification, and variables that influence each other in continuous time. As our knowledge of biological and social systems and access to computational resources increases, there will be growing utility for a variety of mathematical modeling tools in epidemiology.
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What OpenQuestion holds
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.