Cam Bowie
Biographic Data
| ID | 9996028 |
|---|---|
| NAME | Cam Bowie |
| GIVEN NAMES | Cam |
| FAMILY NAME | Bowie |
| SIGNATURE | BOWIE C |
| AFFILIATIONS | Retired |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
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
This technical report addresses the predictive validity of long-term epidemiological forecasting based upon dynamic causal modeling. It uses complementary prospective and retrospective analyses. The prospective analysis completes a series of (annual) reports comparing predictions with subsequent outcomes (i.e., cases, deaths, hospital admissions and Long COVID) reported a year later. Predictive validity is then addressed retrospectively by examin…
A follow up report validating long term predictions of the Covid-19 epidemic in the UK using a dynamic causal model
Background: This paper asks whether Dynamic Causal modelling (DCM) can predict the long-term clinical impact of the COVID-19 epidemic. DCMs are designed to continually assimilate data and modify model parameters, such as transmissibility of the virus, changes in social distancing and vaccine coverage-to accommodate changes in population dynamics and virus behavior. But as a novel way to model epidemics do they produce valid predictions? We presen…
Using a Dynamic Causal Model to validate previous predictions and offer a 12-month forecast of the long-term effects of the Covid-19 epidemic in the UK
Background: Predicting the future UK COVID-19 epidemic provides a baseline of a vaccine-only mitigation policy from which to judge the effects of additional public health interventions. A previous 12-month prediction of the size of the epidemic to October 2022 underestimated its sequelae by a fifth. This analysis seeks to explain the reasons for the underestimation before offering new long-term predictions. Methods: A Dynamic Causal Model was use…
A 12-month projection to September 2022 of the Covid-19 epidemic in the UK using a dynamic causal model
Objectives: Predicting the future UK COVID-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine program. Methods: A Dynamic Causal Model was used to estimate key model parameters of the UK epidemic, such as vaccine effectiveness and increased transmissibility of Alpha and Delta variants, the effectiveness of the vaccine program roll-out and changes in contact rates. The mo…
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A 12-month projection to September 2022 of the Covid-19 epidemic in the UK using a dynamic causal model
Objectives: Predicting the future UK COVID-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine program. Methods: A Dynamic Causal Model was used to estimate key model parameters of the UK epidemic, such as vaccine effectiveness and increased transmissibility of Alpha and Delta variants, the effectiveness of the vaccine program roll-out and changes in contact rates. The mo…
Using a Dynamic Causal Model to validate previous predictions and offer a 12-month forecast of the long-term effects of the Covid-19 epidemic in the UK
Background: Predicting the future UK COVID-19 epidemic provides a baseline of a vaccine-only mitigation policy from which to judge the effects of additional public health interventions. A previous 12-month prediction of the size of the epidemic to October 2022 underestimated its sequelae by a fifth. This analysis seeks to explain the reasons for the underestimation before offering new long-term predictions. Methods: A Dynamic Causal Model was use…
A follow up report validating long term predictions of the Covid-19 epidemic in the UK using a dynamic causal model
Background: This paper asks whether Dynamic Causal modelling (DCM) can predict the long-term clinical impact of the COVID-19 epidemic. DCMs are designed to continually assimilate data and modify model parameters, such as transmissibility of the virus, changes in social distancing and vaccine coverage-to accommodate changes in population dynamics and virus behavior. But as a novel way to model epidemics do they produce valid predictions? We presen…
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
This technical report addresses the predictive validity of long-term epidemiological forecasting based upon dynamic causal modeling. It uses complementary prospective and retrospective analyses. The prospective analysis completes a series of (annual) reports comparing predictions with subsequent outcomes (i.e., cases, deaths, hospital admissions and Long COVID) reported a year later. Predictive validity is then addressed retrospectively by examin…
COVID-19 epidemiological studies (4 works) · Disease (4 works) · Medicine (4 works) · Environmental health (3 works) · Pandemic (3 works) · Computer Science (2 works) · Demography (2 works) · Econometrics (2 works) · Epidemic model (2 works) · Mathematics (2 works)