A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model

despite an expressive (generative) model, with time-dependent epidemiological and sociobehavioral parameters, the model overestimated morbidity and mortality. Effectively, the model failed to accommodate the "law of declining virulence" over a timescale of years. This speaks to a fundamental issue in long-term forecasting: how to model decreases in virulence over a timescale of years? A potential answer may be available in a year when the predictions for 2024-under a model with slowly accumulating T-cell like immunity-can be assessed against actual outcomes.

Authors

Institutions

Publication Details

Journal
Frontiers in Public Health
Published
2024-09-09
DOI
https://doi.org/10.3389/fpubh.2024.1398297
Citations
2
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.79
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model

Cameron Bowie, Karl Friston
2 citations
Frontiers in Public Health
COVID-19 epidemiological studies
0.79
article

A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model

Cameron Bowie, Karl Friston
article en
2 citations

Abstract

despite an expressive (generative) model, with time-dependent epidemiological and sociobehavioral parameters, the model overestimated morbidity and mortality. Effectively, the model failed to accommodate the "law of declining virulence" over a timescale of years. This speaks to a fundamental issue in long-term forecasting: how to model decreases in virulence over a timescale of years? A potential answer may be available in a year when the predictions for 2024-under a model with slowly accumulating T-cell like immunity-can be assessed against actual outcomes.

Frontiers in Public HealthVol. 12
Somerset County Council (GB), Somerset College (GB), National Hospital for Neurology and Neurosurgery (GB), University College London (GB)
Openalex Percentile: Top 30%
COVID-19 epidemiological studies
0.79
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.

A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model — Cameron Bowie, Karl Friston · Frontiers in Public Health (2024) | TGRS Research Map | TGRS