19 research outputs found
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 UniversitÀtsmedizin zu Covid-19EC/H2020/101003480/EU/COVID-19-Outbreak Response combining E-health, Serolomics, Modelling, Artificial Intelligence and Implementation Research/CORESM
Evaluating vaccine allocation strategies using simulation-assisted causal modeling
We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the coronavirus disease 2019 (COVID-19) pandemic. To estimate the effect of allocation on the expected severe-case incidence, we employ a simulation-assisted causal modeling approach that combines a compartmental infection-dynamics simulation, a coarse-grained causal model, and literature estimates for immunity waning. We compare Israelâs strategy, implemented in 2021, with counterfactual strategies such as no prioritization, prioritization of younger age groups, or a strict risk-ranked approach; we find that Israelâs implemented strategy was indeed highly effective. We also study the impact of increasing vaccine uptake for given age groups. Because of its modular structure, our model can easily be adapted to study future pandemics. We demonstrate this by simulating a pandemic with characteristics of the Spanish flu. Our approach helps evaluate vaccination strategies under the complex interplay of core epidemic factors, including age-dependent risk profiles, immunity waning, vaccine availability, and spreading rates
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
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 menâs 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