Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions

As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A main challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. Focusing on the COVID-19 spread in Germany, we detect change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we can quantify the effect of interventions, and we can incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.Comment: 23 pages, 11 figures. Our code is freely available and can be readily adapted to any country or region ( https://github.com/Priesemann-Group/covid19_inference_forecast/

Dynamic Adaptive Computation: Tuning network states to task requirements

Neural circuits are able to perform computations under very diverse conditions and requirements. The required computations impose clear constraints on their fine-tuning: a rapid and maximally informative response to stimuli in general requires decorrelated baseline neural activity. Such network dynamics is known as asynchronous-irregular. In contrast, spatio-temporal integration of information requires maintenance and transfer of stimulus information over extended time periods. This can be realized at criticality, a phase transition where correlations, sensitivity and integration time diverge. Being able to flexibly switch, or even combine the above properties in a task-dependent manner would present a clear functional advantage. We propose that cortex operates in a "reverberating regime" because it is particularly favorable for ready adaptation of computational properties to context and task. This reverberating regime enables cortical networks to interpolate between the asynchronous-irregular and the critical state by small changes in effective synaptic strength or excitation-inhibition ratio. These changes directly adapt computational properties, including sensitivity, amplification, integration time and correlation length within the local network. We review recent converging evidence that cortex in vivo operates in the reverberating regime, and that various cortical areas have adapted their integration times to processing requirements. In addition, we propose that neuromodulation enables a fine-tuning of the network, so that local circuits can either decorrelate or integrate, and quench or maintain their input depending on task. We argue that this task-dependent tuning, which we call "dynamic adaptive computation", presents a central organization principle of cortical networks and discuss first experimental evidence.Comment: 6 pages + references, 2 figure

How contact patterns destabilize and modulate epidemic outbreaks

The spread of a contagious disease clearly depends on when infected individuals come in contact with others. Yet, it remains unclear how the timing of contacts, in particular the time of day and day of week, interacts with the latent and infectious stages of the disease. Here, we use real-world physical proximity data to study this interaction, and find that, compared to randomized controls, human contact patterns destabilize epidemic outbreaks and non-trivially modulate the basic reproduction number. By exploring simple generative models constrained by those data, we are able to attribute both of these intriguing observations to distinct aspects of the temporal statistics of contact patterns. We find the destabilization of outbreaks to be caused by a high probability of extreme events, i.e., super-spreading and zero-spreading, which we are able to reproduce in our models by including temporal clustering of contacts. Furthermore, contact patterns can either increase or decrease the basic reproduction number depending on the latent period. We find this modulation to be caused by the alignment of the infectious period with reoccurring daily and weekly variations in the conditional rate of secondary contacts, which we reproduce by including a cyclostationary contact rate in our models. Thus, by identifying and reproducing relevant non-Markovian statistics of human contacts, our work opens the possibility to systematically study the non-equilibrium physics of realistic disease spread, as well as of non-Markovian spreading processes in general.Comment: 10 pages, 4 figures plus supplementary informatio

Low case numbers enable long-term stable pandemic control without lockdowns

The traditional long-term solutions for epidemic control involve eradication or population immunity. Here, we analytically derive the existence of a third viable solution: a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events, and only moderate restrictions remain necessary. In this equilibrium, daily cases stabilize around ten new infections per million people or less. However, stability is endangered if restrictions are relaxed or case numbers grow too high. The latter destabilization marks a tipping point beyond which the spread self-accelerates. We show that a lockdown can reestablish control and that recurring lockdowns are not necessary given sustained, moderate contact reduction. We illustrate how this strategy profits from vaccination and helps mitigate variants of concern. This strategy reduces cumulative cases (and fatalities) 4x more than strategies that only avoid hospital collapse. In the long term, immunization, large-scale testing, and international coordination will further facilitate control.Comment: Final versio

Relaxing restrictions at the pace of vaccination increases freedom and guards against further COVID-19 waves

Mass vaccination offers a promising exit strategy for the COVID-19 pandemic. However, as vaccination progresses, demands to lift restrictions increase, despite most of the population remaining susceptible. Using our age-stratified SEIRD-ICU compartmental model and curated epidemiological and vaccination data, we quantified the rate (relative to vaccination progress) at which countries can lift non-pharmaceutical interventions without overwhelming their healthcare systems. We analyzed scenarios ranging from immediately lifting restrictions (accepting high mortality and morbidity) to reducing case numbers to a level where test-trace-and-isolate (TTI) programs efficiently compensate for local spreading events. In general, the age-dependent vaccination roll-out implies a transient decrease of more than ten years in the average age of ICU patients and deceased. The pace of vaccination determines the speed of lifting restrictions; Taking the European Union (EU) as an example case, all considered scenarios allow for steadily increasing contacts starting in May 2021 and relaxing most restrictions by autumn 2021. Throughout summer 2021, only mild contact restrictions will remain necessary. However, only high vaccine uptake can prevent further severe waves. Across EU countries, seroprevalence impacts the long-term success of vaccination campaigns more strongly than age demographics. In addition, we highlight the need for preventive measures to reduce contagion in school settings throughout the year 2021, where children might be drivers of contagion because of them remaining susceptible..

The challenges of containing SARS-CoV-2 via test-trace-and-isolate

Without a cure, vaccine, or proven long-term immunity against SARS-CoV-2, test-trace-and-isolate (TTI) strategies present a promising tool to contain its spread. For any TTI strategy, however, mitigation is challenged by pre- and asymptomatic transmission, TTI-avoiders, and undetected spreaders, who strongly contribute to hidden infection chains. Here, we studied a semi-analytical model and identified two tipping points between controlled and uncontrolled spread: (1) the behavior-driven reproduction number of the hidden chains becomes too large to be compensated by the TTI capabilities, and (2) the number of new infections exceeds the tracing capacity. Both trigger a self-accelerating spread. We investigated how these tipping points depend on challenges like limited cooperation, missing contacts, and imperfect isolation. Our model results suggest that TTI alone is insufficient to contain an otherwise unhindered spread of SARS-CoV-2, implying that complementary measures like social distancing and improved hygiene remain necessary

Interplay Between Risk Perception, Behavior, and COVID-19 Spread

Pharmaceutical and non-pharmaceutical interventions (NPIs) have been crucial for controlling COVID-19. They are complemented by voluntary health-protective behavior, building a complex interplay between risk perception, behavior, and disease spread. We studied how voluntary health-protective behavior and vaccination willingness impact the long-term dynamics. We analyzed how different levels of mandatory NPIs determine how individuals use their leeway for voluntary actions. If mandatory NPIs are too weak, COVID-19 incidence will surge, implying high morbidity and mortality before individuals react; if they are too strong, one expects a rebound wave once restrictions are lifted, challenging the transition to endemicity. Conversely, moderate mandatory NPIs give individuals time and room to adapt their level of caution, mitigating disease spread effectively. When complemented with high vaccination rates, this also offers a robust way to limit the impacts of the Omicron variant of concern. Altogether, our work highlights the importance of appropriate mandatory NPIs to maximise the impact of individual voluntary actions in pandemic control.BMBF, 01KX2021, Nationales Forschungsnetzwerk der Universitätsmedizin zu Covid-19EC/H2020/101003480/EU/COVID-19-Outbreak Response combining E-health, Serolomics, Modelling, Artificial Intelligence and Implementation Research/CORESM

MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity

Here we present our Python toolbox 'MR. Estimator' to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling - the difficulty to observe the whole system in full detail - limits our capability to record. Applications range from epidemic spreading to any system that can be represented by an autoregressive process. In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system's dynamic working point

Case Numbers Beyond Contact Tracing Capacity Are Endangering the Containment of COVID-19.

[No abstract available

Impact of the Euro 2020 championship on the spread of COVID-19

In this Bayesian inference study, the authors aim to quantify the impact of the men’s 2020 UEFA Euro Football Championship on COVID-19 spread in twelve participating countries. They estimate that 0.84 million cases and 1,700 deaths were attributable to the championship, with most impacts in England and Scotland