scholarly journals A twelve-month projection to September 2022 of the Covid-19 epidemic in the UK using a Dynamic Causal Model

2021 ◽  
Author(s):  
Cam Bowie ◽  
Karl Friston
Keyword(s):  
Public Health ◽  
Causal Model ◽  
Hospital Admissions ◽  
Economic Cost ◽  
Model Parameters ◽  
Health Measures ◽  
Contact Rates ◽  
The Future ◽  
The Uk ◽  
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 programme. Methods A Dynamic Causal Model (DCM) is used to estimate the model parameters of the epidemic such as vaccine effectiveness and increased transmissibility of alpha and delta variants, the vaccine programme roll-out and changes in contact rates. The model predicts the future trends in infections, long-Covid, hospital admissions and deaths. Results Two dose vaccination given to 66% of the UK population prevents transmission following infection by 44%, serious illness by 86% and death by 93%. Despite this, with no other public health measures used, cases will increase from 37 million to 61 million, hospital admission from 536,000 to 684,000 and deaths from 136,000 to 142,000 over twelve months. Discussion Vaccination alone will not control the epidemic. Relaxation of mitigating public health measures carries several risks including overwhelming the health services, the creation of vaccine resistant variants and the economic cost of huge numbers of acute and chronic cases.

scholarly journals Emotional and Mental Wellbeing Following COVID-19 Public Health Measures on People Living With Dementia and Carers

2021 ◽  
pp. 089198872199681
Author(s):  
Kerry Hanna ◽  
Clarissa Giebel ◽  
Hilary Tetlow ◽  
Kym Ward ◽  
Justine Shenton ◽  
...  
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Social Support ◽  
Support Services ◽  
Mental Wellbeing ◽  
Daily Lives ◽  
Health Measures ◽  
Social Support Services ◽  
The Uk ◽  
The Impact

Background: To date, there appears to be no evidence on the longer-term impacts caused by COVID-19 and its related public health restrictions on some of the most vulnerable in our societies. The aim of this research was to explore the change in impact of COVID-19 public health measures on the mental wellbeing of people living with dementia (PLWD) and unpaid carers. Method: Semi-structured, follow-up telephone interviews were conducted with PLWD and unpaid carers between June and July 2020. Participants were asked about their experiences of accessing social support services during the pandemic, and the impact of restrictions on their daily lives. Results: 20 interviews were conducted and thematically analyzed, which produced 3 primary themes concerning emotional responses and impact to mental health and wellbeing during the course of the pandemic: 1) Impact on mental health during lockdown, 2) Changes to mental health following easing of public health, and 3) The long-term effect of public health measures. Conclusions: The findings from this research shed light on the longer-term psychological impacts of the UK Government’s public health measures on PLWD and their carers. The loss of social support services was key in impacting this cohort mentally and emotionally, displaying a need for better psychological support, for both carers and PLWD.

scholarly journals The potential dangers of not understanding COVID-19 public health restrictions in dementia: “It’s a groundhog day – every single day she does not understand why she can’t go out for a walk”

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Clarissa Giebel ◽  
Kerry Hanna ◽  
Manoj Rajagopal ◽  
Aravind Komuravelli ◽  
Jacqueline Cannon ◽  
...  
Keyword(s):  
Public Health ◽  
Cognitive Deficits ◽  
Third Sector ◽  
Cognitive Capacity ◽  
The Public ◽  
Health Measures ◽  
Third Sector Organisations ◽  
The Uk ◽  
Second Wave

Abstract Background Sudden public health restrictions can be difficult to comprehend for people with cognitive deficits. However, these are even more important for them to adhere to due to their increased levels of vulnerability, particularly to COVID-19. With a lack of previous evidence, we explored the understanding and changes in adherence to COVID-19 public health restrictions over time in people living with dementia (PLWD). Methods Unpaid carers and PLWD were interviewed over the phone in April 2020, shortly after the nationwide UK lockdown, with a proportion followed up from 24th June to 10th July. Participants were recruited via social care and third sector organisations across the UK, and via social media. Findings A total of 70 interviews (50 baseline, 20 follow-up) were completed with unpaid carers and PLWD. Five themes emerged: Confusion and limited comprehension; Frustration and burden; Putting oneself in danger; Adherence to restrictions in wider society; (Un) changed perceptions. Most carers reported limited to no understanding of the public health measures in PLWD, causing distress and frustration for both the carer and the PLWD. Due to the lack of understanding, some PLWD put themselves in dangerous situations without adhering to the restrictions. PLWD with cognitive capacity who participated understood the measures and adhered to these. Discussion In light of the new second wave of the pandemic, public health measures need to be simpler for PLWD to avoid unwilful non-adherence. Society also needs to be more adaptive to the needs of people with cognitive disabilities more widely, as blanket rules cause distress to the lives of those affected by dementia.

scholarly journals COVID-19 public health measures: a reduction in hospital admissions for COPD exacerbations

Thorax ◽  
2020 ◽  
pp. thoraxjnl-2020-216083
Author(s):  
Jing Yuan Tan ◽  
Edwin Philip Conceicao ◽  
Liang En Wee ◽  
Xiang Ying Jean Sim ◽  
Indumathi Venkatachalam
Keyword(s):  
Public Health ◽  
Clinical Outcomes ◽  
Hospital Admissions ◽  
Viral Infections ◽  
Significant Morbidity ◽  
Common Cause ◽  
Copd Exacerbations ◽  
Health Measures ◽  
Acute Exacerbations ◽  
Respiratory Viral Infections

Hospitalisations for acute exacerbations of COPD (AECOPD) carry significant morbidity and mortality. Respiratory viral infections (RVIs) are the most common cause of AECOPD and are associated with worse clinical outcomes. During the COVID-19 pandemic, public health measures, such as social distancing and universal masking, were originally implemented to reduce transmission of SARS-CoV-2; these public health measures were subsequently also observed to reduce transmission of other common circulating RVIs. In this study, we report a significant and sustained decrease in hospital admissions for all AECOPD as well as RVI-associated AECOPD, which coincided with the introduction of public health measures during the COVID-19 pandemic.

scholarly journals Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

2022 ◽  
Author(s):  
Christopher M Pooley ◽  
Andrea B Doeschl-Wilson ◽  
Glenn Marion
Keyword(s):  
Hospital Admissions ◽  
Epidemiological Data ◽  
Joint Analysis ◽  
Model Parameters ◽  
Inference Algorithm ◽  
Public Response ◽  
Contact Rates ◽  
Reproduction Numbers ◽  
Public Data ◽  
Approximate Bayesian

Well parameterised epidemiological models including accurate representation of contacts, are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here we fit age-stratified models, including re-estimation of relative contact rates between age-classes, to public data describing the 2020-21 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions, and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing Approximate Bayesian Computation (ABC) methodology with model-based proposals (MBP) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalisation rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrisation of dynamic transmission models that can inform data-driven public health policy and interventions.

scholarly journals Global public health training in the UK: preparing for the future

2011 ◽  
Vol 33 (2) ◽  
pp. 310-316 ◽  
Author(s):  
A. C. K. Lee ◽  
J. A. Hall ◽  
K. L. Mandeville
Keyword(s):  
Public Health ◽  
Global Public Health ◽  
Health Training ◽  
Public Health Training ◽  
The Future ◽  
The Uk

scholarly journals Testing and tracking in the UK: A dynamic causal modelling study

2021 ◽  
Vol 5 ◽  
pp. 144
Author(s):  
Karl J. Friston ◽  
Thomas Parr ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Model Parameters ◽  
Clear Cut ◽  
Causal Modelling ◽  
The United Kingdom ◽  
Serological Studies ◽  
The Uk ◽  
Dynamic Causal Model ◽  
Second Wave ◽  
Face Validation ◽  
Dynamic Causal Modelling

By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.

scholarly journals The impact of COVID-19 on hospital admissions and emergency department visits: A population-based study

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252441
Author(s):  
Elissa Rennert-May ◽  
Jenine Leal ◽  
Nguyen Xuan Thanh ◽  
Eddy Lang ◽  
Shawn Dowling ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Hospital Admissions ◽  
Population Based ◽  
Emergency Department Visits ◽  
System Capacity ◽  
Population Based Study ◽  
Health Measures ◽  
Ed Visits ◽  
The Impact

Background As a result of the novel coronavirus disease 2019 (COVID-19), there have been widespread changes in healthcare access. We conducted a retrospective population-based study in Alberta, Canada (population 4.4 million), where there have been approximately 1550 hospital admissions for COVID-19, to determine the impact of COVID-19 on hospital admissions and emergency department (ED visits), following initiation of a public health emergency act on March 15, 2020. Methods We used multivariable negative binomial regression models to compare daily numbers of medical/surgical hospital admissions via the ED between March 16-September 23, 2019 (pre COVID-19) and March 16-September 23, 2020 (post COVID-19 public health measures). We compared the most frequent diagnoses for hospital admissions pre/post COVID-19 public health measures. A similar analysis was completed for numbers of daily ED visits for any reason with a particular focus on ambulatory care sensitive conditions (ACSC). Findings There was a significant reduction in both daily medical (incident rate ratio (IRR) 0.86, p<0.001) and surgical (IRR 0.82, p<0.001) admissions through the ED in Alberta post COVID-19 public health measures. There was a significant decline in daily ED visits (IRR 0.65, p<0.001) including ACSC (IRR 0.75, p<0.001). The most common medical/surgical diagnoses for hospital admissions did not vary substantially pre and post COVID-19 public health measures, though there was a significant reduction in admissions for chronic obstructive pulmonary disease and a significant increase in admissions for mental and behavioral disorders due to use of alcohol. Conclusions Despite a relatively low volume of COVID-19 hospital admissions in Alberta, there was an extensive impact on our healthcare system with fewer admissions to hospital and ED visits. This work generates hypotheses around causes for reduced hospital admissions and ED visits which warrant further investigation. As most publicly funded health systems struggle with health-system capacity routinely, understanding how these reductions can be safely sustained will be critical.

scholarly journals The importance of timing of a population level intervention on COVID-19 mortality

2020 ◽  
Author(s):  
Jasper S. Johnston ◽  
Eloïse S. Johnston ◽  
Sebastian L. Johnston
Keyword(s):  
Public Health ◽  
Intensive Care Unit ◽  
Hospital Admissions ◽  
Population Level ◽  
Global Public Health ◽  
Mortality Trends ◽  
Specific Mortality ◽  
The Uk ◽  
Country Specific ◽  
Effect Magnitude

AbstractCOVID-19 poses an immense and immediate threat to global public health. Population level interventions (PLIs) impact this threat, with estimable large effects on reducing mortality. Many countries worldwide have currently zero/low mortality and many have yet to implement such PLIs. The importance of timing of PLI implementation on mortality outcomes is poorly understood. We extracted cumulative daily country-specific COVID-19 mortality for France, Germany, Italy, Spain and the UK to examine country-specific mortality trends and found that all five countries experienced COVID-19 mortality epidemics initially of exponential nature. We estimated the magnitude of effect on mortality of the nationwide PLI implemented on day 18 of Italy’s mortality epidemic and assessed the importance of timing of PLI implementation by computing the effect of implementation of a PLI of this magnitude at various times on subsequent mortality. The nationwide PLI in Italy saved an estimated 6,170 lives by day 30 of the Italian epidemic. Implementing a PLI with this effect magnitude in a country of 60 million people on the day of the first death, and on days 7, 10, 14 and 17 thereafter, compared to implementation on day 18, resulted in substantially greater numbers of lives saved. Implementation on day 1 resulted in an additional 3,477 lives saved, 6,955 intensive care unit admissions and 52,162 hospital admissions prevented, beyond that achieved by implementation on day 18. PLI implementation earlier than day 18 substantially enhances benefit. Intervention on the day of the first mortality event in a country achieves the greatest benefit.

scholarly journals A Social Network Model of the COVID-19 Pandemic

2020 ◽  
Author(s):  
Pei Jun Zhao
Keyword(s):  
Public Health ◽  
United States ◽  
Social Distance ◽  
The United States ◽  
Viral Transmission ◽  
Model Parameters ◽  
Weighted Graphs ◽  
The Public ◽  
Health Measures ◽  
The U.S

AbstractIn the COVID-19 coronavirus pandemic, currently vaccines and specific anti-viral treatment are not yet available. Thus, preventing viral transmission by case isolation, quarantine, and social distancing is essential to slowing its spread. Here we model social networks using weighted graphs, where vertices represent individuals and edges represent contact. As public health measures are implemented, connectivity in the graph decreases, resulting in lower effective reproductive numbers, and reduced viral transmission. For COVID-19, model parameters were derived from the coronavirus epidemic in China, validated by epidemic data in Italy, then applied to the United States. We calculate that, in the U.S., the public is able to contain viral transmission by limiting the average number of contacts per person to less than 7 unique individuals over each 5 day period. This increases the average social distance between individuals to 10 degrees of separation.

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