A twelve-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 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.

Dynamic reciprocal relationships between cognitive and functional declines along the Alzheimer’s disease continuum in the prospective COGICARE study

Background Thoroughly understanding the temporal associations between cognitive and functional dimensions along the dementia process is fundamental to define preventive measures likely to delay the disease’s onset. This work aimed to finely describe the trajectories of cognitive and functional declines, and assess their dynamic bidirectional relationships among subjects at different stages of the dementia process. Methods We leveraged extensive repeated data of cognition and functional dependency from the French prospective COGICARE study, designed to better characterize the natural history of cognitive and functional declines around dementia diagnosis. Cognition was measured by the Mini-Mental State Examination, the Isaacs Set Test for verbal fluency, the Benton Visual Retention Test for visuo-spatial memory, and Trail Making Test Part B for executive functioning. Functional dependency was measured by basic and instrumental activities of daily living. The study included 102 cognitively normal, 123 mildly cognitively impaired, and 72 dementia cases with a median of 5 repeated visits over up to 57 months. We used a dynamic causal model which addresses the two essential issues in temporal associations assessment: focusing on intra-individual change and accounting for time. Results Better cognitive abilities were associated with lower subsequent decline of the functional level among the three clinical stages with an intensification over time but no reciprocity of the association whatever the clinical status. Conclusion This work confirms that the progressive functional dependency could be induced by cognitive impairment. Subjects identified as early as possible with clinically significant cognitive impairments could benefit from preventive measures before the deterioration of activities of daily living and the appearance of dementia clinical signs.

Dorsal and ventral striatal functional connectivity shifts play a potential role in internet gaming disorder

Animal models suggest transitions from non-addictive to addictive behavioral engagement are associated with ventral-to-dorsal striatal shifts. However, few studies have examined such features in humans, especially in internet gaming disorder (IGD), a proposed behavioral addiction. We recruited 418 subjects (174 with IGD; 244 with recreational game use (RGU)). Resting-state fMRI data were collected and functional connectivity analyses were performed based on ventral and dorsal striatal seeds. Correlations and follow-up spectrum dynamic causal model (spDCM) analyses were performed to examine relationships between the ventral/dorsal striatum and middle frontal gyrus (MFG). Longitudinal data were also analysed to investigate changes over time. IGD relative to RGU subjects showed lower ventral-striatum-to-MFG (mostly involving supplementary motor area (SMA)) and higher dorsal-striatum-to-MFG functional connectivity. spDCM revealed that left dorsal-striatum-to-MFG connectivity was correlated with IGD severity. Longitudinal data within IGD and RGU groups found greater dorsal striatal connectivity with the MFG in IGD versus RGU subjects. These findings suggest similar ventral-to-dorsal striatal shifts may operate in IGD and traditional addictions.

Modelling the effect of an improved trace and isolate system in the wake of a highly transmissible Covid-19 variant with potential vaccine escape

Objective: How helpful would a properly functioning find, test, trace, isolate and support (FTTIS) system be now in the UK with new Covid19 infections at a low level and half the adult population immunised but with a highly transmissible variant becoming predominant? Design: a dynamic causal model of Covid-19 supplied with the latest available empirical data is used to assess the impact of a new highly transmissible variant. Setting: the United Kingdom. Participants: a population based study. Interventions: scenarios are used to explore a Covid-19 transmission rate 50% more and twice the current rate with or without a more effective FTTIS system. Main outcome measures: incidence, death rate and reproductive ratio Results: a small short third wave of infections occurs which does not occur if FTTIS effectiveness is improved from 25% to 30%. Conclusion: a modest improvement in FTTIS would prevent a third wave caused by a highly transmissible virus.

PROFESSIONAL CONSULTING, PLANNING AND DESIGN OF REGIONAL DEVELOPMENT IN THE FACE OF INCREASING UNCERTAINTIES AND RISKS

The article reveals features of the ongoing transformations in the field of consulting, planning and design in the field of regional development in the face of increasing uncertainties and risks, primarily related to climate change and biodiversity loss. It shows that the very basis of such work is significantly changing, as the world-system becomes more complex, during the transition from the concept of an “empty” world to a “full” world. This change includes increasing emphasis on ensuring the resilience of human-dominated ecosystems, a change in decision-making, using behavioral a “responsible” human model that best meets the needs of inclusive sustainable development.The authors define features of consulting, planning and design of regional development in the face of increasing uncertainties and risks, as a special type of thinking activity, in a situation of approaching radical uncertainty. The article shows the necessity of supplementing natural science approaches with the tools and practices of post-normal science and the narrative theory of beliefs, as well as the ideas of K. Friston, implemented within his dynamic causal model.Such an approach is based on a systematic approach to decision-making in regional development and pays special attention to the adaptation of individuals and communities to high risks and uncertainties, primarily climatic and natural. It helps to better reflect the diversity of geographical conditions, to clarify the range of effective solutions for sustainable development of regions and increase the long-term resilience of business.

Second waves, social distancing, and the spread of COVID-19 across the USA

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.

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

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.

Second waves, social distancing, and the spread of COVID-19 across the USA

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.

How vaccination and contact isolation might interact to suppress transmission of Covid-19: a DCM study

This report describes a dynamic causal model that could be used to address questions about the rollout and efficacy of vaccines in the United Kingdom. For example, is suppression of community transmission a realistic aspiration? And, if not, what kind of endemic equilibrium might be achieved? What percentage of the population needs to be vaccinated? And over what timescale? It focuses on the synergies among (i) vaccination, (ii) the supported isolation of contacts of confirmed cases and (iii) restrictions on contact rates (i.e., lockdown and social distancing). To model these mitigations, we used a dynamic causal model that embeds an epidemiological model into agent-based behavioural model. The model structure and parameters were optimised to best explain responses—to the first and subsequent waves—enabling predictions over the forthcoming year under counterfactual scenarios. Illustrative analyses suggest that the full potential of vaccination is realised by increasing the efficacy of contact tracing: for example, under idealised (best case) assumptions—of an effective vaccine and efficient isolation of infected pre-symptomatic cases— suppression of community transmission would require 50% herd immunity by vaccinating 22% by the end of 2021; i.e., 15 million people or about 50,000 per day. With no change in the isolation of contacts, 36% would require vaccination, i.e., 25 million people. These figures should not be read as estimates of the actual number of people requiring vaccination; however, they illustrate the potential of this kind of model to quantify interactions among public health interventions. We anticipate using this model in a few months—to estimate the average effectiveness of vaccines when more data become available.

A dynamic causal model on self-regulation of aversive emotion

Cognitive regulation of emotion has been proven to be effective to take control the emotional responses. Some cognitive models have also been proposed to explain the neural mechanism that underlies this process. However, some characteristics of the models are still unclear, such as whether the cognitive regulation will be spontaneously employed by participants implicitly. The present study recruited the fMRI experiment to focus on the discomfort induced by viewing aversive pictures, and the emotional self-regulation during picture viewing. By using the dynamic causal modeling (DCM), 50 putative models of brain functional networks were constructed, one optimal model that fitted the real data best won the comparison from the candidates. As a result, the optimal model suggests that both the ventral striatum (VS)-centric bottom-up and the dorsolateral prefrontal cortex (DLPFC)-centric top-down regulations are recruited for self-regulation on negative emotions. The DLPFC will exert modulatory influence on the VS only when the VS fails to suppress the induced emotions by self-inhibition.