'Dark matter', second waves and epidemiological modelling

Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling

Introduction

Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data.

Methods

A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation.

Results

Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values.

Conclusion

DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment.

SEIR models in the light of Critical Realism - A critique of exaggerated claims about the effectiveness of Covid 19 vaccinations

In a recent modeling study Watson et al. (Lancet Infect Dis 2022;3099:1-10) claim that Covid-19 vaccinations have helped to prevent roughly 14-20 million deaths in 2021. This conclusion is based on an epidemiological susceptible-exposed-infectious-recovered (SEIR) model trained on partially simulated data and yielding a reproduction number distribution which was then applied to a counterfactual scenario in which the efficacy of vaccinations was removed. Drawing on the meta-theory of Critical Realism, we point out several caveats of this model and caution against believing in its predictions. We argue that the absence of vaccinations would have significantly changed the causal tendencies of the system being modelled, yielding a different reproduction number than obtained from training the model on actually observed data. Furthermore, the model omits many important causal factors. Therefore this model, similar to many previous SEIR models, has oversimplified the complex interplay between biomedical, social and cultural dimensions of health and should not be used to guide public health policy. In order to predict the future in epidemic situations more accurately, continuously optimized dynamic causal models which can include the not directly tangible, yet real causal mechanisms affecting public health appear to be a promising alternative to SEIR-type models.

Computational psychiatry: from synapses to sentience

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.

Precision Public Health and Structural Racism in the United States: promoting health equity in the COVID-19 pandemic response

UNSTRUCTURED

The COVID-19 pandemic has revealed deeply entrenched structural inequalities that resulted in an excess of mortality and morbidity in certain racial and ethnic groups in the US. Therefore, this paper examines from the US perspective how structural racism and defective data collections on racial and ethnic minorities can negatively influence the development of precision public health approaches to tackle the ongoing COVID-19 pandemic. Importantly, the effects of structural and data racism on the elaboration of fair and inclusive data-driven components of precision public health interventions are discussed, such as with the use of machine learning algorithms to predict public health risks. The objective of this viewpoint is thus to inform public health policy-making with regard to the development of ethically sound precision public health interventions against COVID-19. Particular attention is given to components of structural racism (e.g. hospital segregation, implicit and organizational bias, digital divide and sociopolitical influences) that are likely to hinder such approaches from achieving their social justice and health equity goals.

The gut microbiome as a biomarker of differential susceptibility to SARS-CoV-2

Coronavirus disease 2019 (COVID-19) continues to exact a devastating global toll. Ascertaining the factors underlying differential susceptibility and prognosis following viral exposure is critical to improving public health responses. We propose that gut microbes may contribute to variation in COVID-19 outcomes. We synthesise evidence for gut microbial contributions to immunity and inflammation, and associations with demographic factors affecting disease severity. We suggest mechanisms potentially underlying microbially mediated differential susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These include gut microbiome-mediated priming of host inflammatory responses and regulation of endocrine signalling, with consequences for the cellular features exploited by SARS-CoV-2 virions. We argue that considering gut microbiome-mediated mechanisms may offer a lens for appreciating differential susceptibility to SARS-CoV-2, potentially contributing to clinical and epidemiological approaches to understanding and managing COVID-19.

Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent

Objective

To compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models.

Study design and setting

We explored two models developed by Imperial College that considered only NPIs without accounting for mobility (model 1) or only mobility (model 2), and a model accounting for the combination of mobility and NPIs (model 3). Imperial College applied models 1 and 2 to 11 European countries and to the USA, respectively. We applied these models to 14 European countries (original 11 plus another 3), over two different time horizons.

Results

While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproduction number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons.

Conclusion

Inferences on effects of NPIs are non-robust and highly sensitive to model specification. In the SIR modeling framework, the impacts of lockdown are uncertain and highly model-dependent.