Correction: Dynamic causal models in infectious disease epidemiology—an assessment of their predictive validity based on the COVID-19 epidemic in the UK 2020 to 2024

Authors

Institutions

Publication Details

Journal
Frontiers in Public Health
Published
2026-01-30
DOI
https://doi.org/10.3389/fpubh.2025.1766577
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Correction: Dynamic causal models in infectious disease epidemiology—an assessment of their predictive validity based on the COVID-19 epidemic in the UK 2020 to 2024

Cam Bowie
Frontiers in Public Health
Advanced Causal Inference Techniques
article

Correction: Dynamic causal models in infectious disease epidemiology—an assessment of their predictive validity based on the COVID-19 epidemic in the UK 2020 to 2024

Cam Bowie
article en

Abstract

No abstract available for this paper.
Frontiers in Public HealthVol. 13
National Hospital for Neurology and Neurosurgery (GB), University College London (GB)
Good health and well-being
Openalex Percentile: Top 75%
Advanced Causal Inference Techniques
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Correction: Dynamic causal models in infectious disease epidemiology—an assessment of their predictive validity based on the COVID-19 epidemic in the UK 2020 to 2024 — Cam Bowie · Frontiers in Public Health (2026) | TGRS Research Map | TGRS