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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-310697

ABSTRACT

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several instantiations of this (epidemic) model to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity—and the exchange of people between regions—and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

2.
Wellcome Open Res ; 5: 103, 2020.
Article in English | MEDLINE | ID: covidwho-1218720

ABSTRACT

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity-and the exchange of people between regions-and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

3.
J Alzheimers Dis ; 79(1): 31-36, 2021.
Article in English | MEDLINE | ID: covidwho-949040

ABSTRACT

Patients admitted with COVID-19 can develop delirium due to predisposing factors, isolation, and the illness itself. Standard delirium prevention methods focus on interaction and stimulation. It can be challenging to deliver these methods of care in COVID settings where it is necessary to increase patient isolation. This paper presents a typical clinical vignette of representative patients in a tertiary care hospital and how a medical team modified an evidence-based delirium prevention model to deliver high-quality care to COVID-19 patients. The implemented model focuses on four areas of delirium-prevention: Mobility, Sleep, Cognitive Stimulation, and Nutrition. Future studies will be needed to track quantitative outcome measures.


Subject(s)
Alzheimer Disease/diagnosis , Alzheimer Disease/therapy , COVID-19/prevention & control , Delirium/prevention & control , Aged , Alzheimer Disease/psychology , COVID-19/epidemiology , COVID-19/psychology , Delirium/epidemiology , Delirium/psychology , Humans , Male
4.
ProQuest Central; 2020.
Preprint in English | ProQuest Central | ID: ppcovidwho-2097

ABSTRACT

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity—and the exchange of people between regions—and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

5.
Postdigital Science and Education ; 2020.
Article | WHO COVID | ID: covidwho-716473
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