Projection of healthcare demand in Germany and Switzerland urged by Omicron wave (January–March 2022)

In January 2022, after the implementation of broad vaccination programs, the Omicron wave was propagating across Europe. There was an urgent need to understand how population immunity affects the dynamics of the COVID-19 pandemic when the loss of vaccine protection was concurrent with the emergence of a new variant of concern. In particular, assessing the risk of saturation of the healthcare systems was crucial to manage the pandemic and allow a transition towards the endemic course of SARS-CoV-2 by implementing more refined mitigation strategies that shield the most vulnerable groups and protect the healthcare systems. We investigated the epidemic dynamics by means of compartmental models that describe the age-stratified social-mixing and consider vaccination status, type, and waning of the efficacy. In response to the acute situation, our model aimed at (i) providing insight into the plausible scenarios that were likely to occur in Switzerland and Germany in the midst of the Omicron wave, (ii) informing public health authorities, and (iii) helping take informed decisions to minimize negative consequences of the pandemic. Despite the unprecedented numbers of new positive cases, our results suggested that, in all plausible scenarios, the wave was unlikely to create an overwhelming healthcare demand;due to the lower hospitalization rate and the effectiveness of the vaccines in preventing a severe course of the disease. This prediction came true and the healthcare systems in Switzerland and Germany were not pushed to the limit, despite the unprecedentedly large number of infections. By retrospective comparison of the model predictions with the official reported data of the epidemic dynamic, we demonstrate the ability of the model to capture the main features of the epidemic dynamic and the corresponding healthcare demand. In a broader context, our framework can be applied also to endemic scenarios, offering quantitative support for refined public health interventions in response to recurring waves of COVID-19 or other infectious diseases.

Projection of healthcare demand in Germany and Switzerland urged by Omicron wave (January-March 2022).

In January 2022, after the implementation of broad vaccination programs, the Omicron wave was propagating across Europe. There was an urgent need to understand how population immunity affects the dynamics of the COVID-19 pandemic when the loss of vaccine protection was concurrent with the emergence of a new variant of concern. In particular, assessing the risk of saturation of the healthcare systems was crucial to manage the pandemic and allow a transition towards the endemic course of SARS-CoV-2 by implementing more refined mitigation strategies that shield the most vulnerable groups and protect the healthcare systems. We investigated the epidemic dynamics by means of compartmental models that describe the age-stratified social-mixing and consider vaccination status, type, and waning of the efficacy. In response to the acute situation, our model aimed at (i) providing insight into the plausible scenarios that were likely to occur in Switzerland and Germany in the midst of the Omicron wave, (ii) informing public health authorities, and (iii) helping take informed decisions to minimize negative consequences of the pandemic. Despite the unprecedented numbers of new positive cases, our results suggested that, in all plausible scenarios, the wave was unlikely to create an overwhelming healthcare demand; due to the lower hospitalization rate and the effectiveness of the vaccines in preventing a severe course of the disease. This prediction came true and the healthcare systems in Switzerland and Germany were not pushed to the limit, despite the unprecedentedly large number of infections. By retrospective comparison of the model predictions with the official reported data of the epidemic dynamic, we demonstrate the ability of the model to capture the main features of the epidemic dynamic and the corresponding healthcare demand. In a broader context, our framework can be applied also to endemic scenarios, offering quantitative support for refined public health interventions in response to recurring waves of COVID-19 or other infectious diseases.

Model Selection in the World of Maximum Entropy

Science aims at identifying suitable models that best describe a population based on a set of features. Lacking information about the relationships among features there is no justification to a priori fix a certain model. Ideally, we want to incorporate only those relationships into the model which are supported by observed data. To achieve this goal the model that best balances goodness of fit with simplicity should be selected. However, parametric approaches to model selection encounter difficulties pertaining to the precise definition of the invariant content that enters the selection procedure and its interpretation. A naturally invariant formulation of any statistical model consists of the joint distribution of features, which provides all the information that is required to answer questions in classification tasks or identification of feature relationships. The principle of Maximum Entropy (maxent) offers a framework to directly estimate a model for this joint distribution based on phenomenological constraints. Reformulating the inverse problem to obtain a model distribution as an under-constrained linear system of equations, where the remaining degrees of freedom are fixed by entropy maximization, tremendously simplifies large-N expansions around the optimal distribution of Maximum Entropy. We have exploited this conceptual advancement to clarify the nature of prominent model-selection schemes providing an approach to systematically select significant constraints evidenced by the data. To facilitate the treatment of higher-dimensional problems, we propose hypermaxent - a clustering method to efficiently tackle the maxent selection procedure. We demonstrate the utility of our approach by applying the advocated methodology to analyze long-range interactions from spin glasses and uncover three-point effects in COVID-19 data.