arXiv subjects

Peter Klimek

Publications and source records attributed to Peter Klimek.

At least 37 records · Page 2Linked to original sources

arXiv2023

Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks

We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients' hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyse how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p $<$ 0.001, relative risk $>$ 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient's career as points where initially overlapping trajectories start to diverge towards different states. We identified 1,260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2-6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.

physics.med-ph↗

arXiv2023

Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability

Dependencies in the global food production network can lead to shortages in numerous regions, as demonstrated by the impacts of the Russia-Ukraine conflict on global food supplies. Here, we reveal the losses of $125$ food products after a localized shock to agricultural production in $192$ countries and territories using a multilayer network model of trade (direct) and conversion of food products (indirect), thereby quantifying $10^8$ shock transmissions. We find that a complete agricultural production loss in Ukraine has heterogeneous impacts on other countries, causing relative losses of up to $89\%$ in sunflower oil and $85\%$ in maize via direct effects, and up to $25\%$ in poultry meat via indirect impacts. Whilst previous studies often treated products in isolation and did not account for product conversion during production, our model studies the global propagation of local supply shocks along both production and trade relations, allowing comparison of different response strategies.

econ.GN↗

arXiv2023

A parameter-free population-dynamical approach to health workforce supply forecasting of EU countries

Many countries faced challenges in their health workforce supply like impending retirement waves, negative population growth, or a suboptimal distribution of resources across medical sectors even before the pandemic struck. Current quantitative models are often of limited usability as they either require extensive individual-level data to be properly calibrated or (in the absence of such data) become too simplistic to capture key demographic changes or disruptive epidemiological shocks like the SARS-CoV-2 pandemic. We propose a novel population-dynamical and stock-flow-consistent approach to health workforce supply forecasting that is complex enough to address dynamically changing behaviors while requiring only publicly available timeseries data for complete calibration. We demonstrate the usefulness of this model by applying it to 21 European countries to forecast the supply of generalist and specialist physicians until 2040, as well as how Covid-related mortality and increased healthcare utilization might impact this supply. Compared to staffing levels required to keep the physician density constant at 2019 levels, we find that in many countries there is indeed a significant trend toward decreasing density for generalist physicians at the expense of increasing densities for specialists. The trends for specialists are exacerbated in many Southern and Eastern European countries by expectations of negative population growth. Compared to the expected demographic changes in the population and the health workforce, we expect a limited impact of Covid on these trends even under conservative modelling assumptions. It is of the utmost importance to devise tools for decision makers to influence the allocation and supply of physicians across fields and sectors to combat these imbalances.

stat.AP↗

arXiv2022

Evidence-based policy-making in sports funding using a data-driven optimization approach

Many European countries face rising obesity rates among children, compounded by decreased opportunities for sports activities during the COVID-19 pandemic. Access to sports facilities depends on multiple factors, such as geographic location, proximity to population centers, budgetary constraints, and other socio-economic covariates. Here we show how an optimal allocation of government funds towards sports facilitators (e.g. sports clubs) can be achieved in a data-driven simulation model that maximizes children's access to sports facilities. We compile a dataset for all 1,854 football clubs in Austria, including estimates for their budget, geolocation, tally, and the age profile of their members. We find a characteristic sub-linear relationship between the number of active club members and the budget, which depends on the socio-economic conditions of the clubs' municipality. In the model, where we assume this relationship to be causal, we evaluate different funding strategies. We show that an optimization strategy where funds are distributed based on regional socio-economic characteristics and club budgets outperforms a naive approach by up to 117\% in attracting children to sports clubs for 5 million Euros of additional funding. Our results suggest that the impact of public funding strategies can be substantially increased by tailoring them to regional socio-economic characteristics in an evidence-based and individualized way.

physics.soc-ph↗

arXiv2022

Small coverage effect in epidemic network models shows that masks can become more effective with less people wearing them

The effectiveness of non-pharmaceutical interventions to curb the spread of SARS-CoV-2 is determined by numerous contextual factors, including adherence. Conventional wisdom holds that the effectiveness of protective behaviour such as wearing masks always increases with the number of people adopting it. Here we show in a simulation study that this is not true in general. We employ a parsimonious network model based on the well-established empirical facts that (i) adherence to such interventions wanes over time and (ii) individuals tend to align their adoption strategies with their close social ties (homophily). When combining these assumptions, a broad dynamical regime emerges where the individual-level infection risk reduction for those adopting protective behaviour increases as the adherence to protective behavior decreases. For instance, for a protective coverage of 10% we find the infection risk for adopting individuals can be reduced by close to 30% compared to situations where the coverage is 60%. Using estimates for the effectiveness of surgical masks, we find that reductions in relative risk of masking versus non-masking individuals range between 5% and 15%, i.e., vary by a factor of three. This small coverage effect originates from system-dynamical network properties that conspire to increase the chance that an outbreak will be over before the pathogen is able to invade small but tightly connected groups of individuals that protect themselves. Our results contradict the popular belief that masking becomes ineffectual as more people drop their masks and might have far-reaching implications for the protection of vulnerable population groups under resurgent infection waves.

physics.soc-ph↗

arXiv2022

Stress-testing the Resilience of the Austrian Healthcare System Using Agent-Based Simulation

Patients do not access physicians at random but rather via naturally emerging networks of patient flows between them. As retirements, mass quarantines and absence due to sickness during pandemics, or other shocks thin out these networks, the system might be pushed closer to a tipping point where it loses its ability to deliver care to the population. Here we propose a data-driven framework to quantify the regional resilience to such shocks of primary and secondary care in Austria via an agent-based model. For each region and medical specialty we construct detailed patient-sharing networks from administrative data and stress-test these networks by removing increasing numbers of physicians from the system. This allows us to measure regional resilience indicators describing how many physicians can be removed from a certain area before individual patients won't be treated anymore. We find that such tipping points do indeed exist and that regions and medical specialties differ substantially in their resilience. These systemic differences can be related to indicators for individual physicians by quantifying how much their hypothetical removal would stress the system (risk score) or how much of the stress from the removal of other physicians they would be able to absorb (benefit score). Our stress-testing framework could enable health authorities to rapidly identify bottlenecks in access to care as well as to inspect these naturally emerging physician networks and how potential absences would impact them.

physics.soc-ph↗

arXiv2021

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

physics.soc-ph↗

arXiv2021

A look into the future of the COVID-19 pandemic in Europe: an expert consultation

How will the coronavirus disease 2019 (COVID-19) pandemic develop in the coming months and years? Based on an expert survey, we examine key aspects that are likely to influence COVID-19 in Europe. The future challenges and developments will strongly depend on the progress of national and global vaccination programs, the emergence and spread of variants of concern, and public responses to nonpharmaceutical interventions (NPIs). In the short term, many people are still unvaccinated, VOCs continue to emerge and spread, and mobility and population mixing is expected to increase over the summer. Therefore, policies that lift restrictions too much and too early risk another damaging wave. This challenge remains despite the reduced opportunities for transmission due to vaccination progress and reduced indoor mixing in the summer. In autumn 2021, increased indoor activity might accelerate the spread again, but a necessary reintroduction of NPIs might be too slow. The incidence may strongly rise again, possibly filling intensive care units, if vaccination levels are not high enough. A moderate, adaptive level of NPIs will thus remain necessary. These epidemiological aspects are put into perspective with the economic, social, and health-related consequences and thereby provide a holistic perspective on the future of COVID-19.

q-bio.OT↗

arXiv2021

Agent-based simulations for protecting nursing homes with prevention and vaccination strategies

Due to its high lethality amongst the elderly, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures becoming available at scale, such as antigen or RT-LAMP tests, and increasing availability of vaccinations, nursing homes might be able to safely relax prohibitory measures while controlling the spread of infections (meaning an average of one or less secondary infections per index case). Here, we develop a detailed agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal prevention strategies. The model is microscopically calibrated to high-resolution data from nursing homes in Austria, including detailed social contact networks and information on past outbreaks. We find that the effectiveness of mitigation testing depends critically on the timespan between test and test result, the detection threshold of the viral load for the test to give a positive result, and the screening frequencies of residents and employees. Under realistic conditions and in absence of an effective vaccine, we find that preventive screening of employees only might be sufficient to control outbreaks in nursing homes, provided that turnover times and detection thresholds of the tests are low enough. If vaccines that are moderately effective against infection and transmission are available, control is achieved if 80% or more of the inhabitants are vaccinated, even if no preventive testing is in place and residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular voluntary screening and sequencing of virus genomes is advised to enable early identification of new variants of concern.

physics.soc-ph↗

arXiv2020

Behavioral gender differences are reinforced during the COVID-19 crisis

Behavioral gender differences are known to exist for a wide range of human activities including the way people communicate, move, provision themselves, or organize leisure activities. Using mobile phone data from 1.2 million devices in Austria (15% of the population) across the first phase of the COVID-19 crisis, we quantify gender-specific patterns of communication intensity, mobility, and circadian rhythms. We show the resilience of behavioral patterns with respect to the shock imposed by a strict nation-wide lock-down that Austria experienced in the beginning of the crisis with severe implications on public and private life. We find drastic differences in gender-specific responses during the different phases of the pandemic. After the lock-down gender differences in mobility and communication patterns increased massively, while sleeping patterns and circadian rhythms tend to synchronize. In particular, women had fewer but longer phone calls than men during the lock-down. Mobility declined massively for both genders, however, women tend to restrict their movement stronger than men. Women showed a stronger tendency to avoid shopping centers and more men frequented recreational areas. After the lock-down, males returned back to normal quicker than women; young age-cohorts return much quicker. Differences are driven by the young and adolescent population. An age stratification highlights the role of retirement on behavioral differences. We find that the length of a day of men and women is reduced by one hour. We discuss the findings in the light of gender-specific coping strategies in response to stress and crisis.

physics.soc-ph↗

arXiv2020

Why are most COVID-19 infection curves linear?

Many countries have passed their first COVID-19 epidemic peak. Traditional epidemiological models describe this as a result of non-pharmaceutical interventions that pushed the growth rate below the recovery rate. In this new phase of the pandemic many countries show an almost linear growth of confirmed cases for extended time-periods. This new containment regime is hard to explain by traditional models where infection numbers either grow explosively until herd immunity is reached, or the epidemic is completely suppressed (zero new cases). Here we offer an explanation of this puzzling observation based on the structure of contact networks. We show that for any given transmission rate there exists a critical number of social contacts, $D_c$, below which linear growth and low infection prevalence must occur. Above $D_c$ traditional epidemiological dynamics takes place, as e.g. in SIR-type models. When calibrating our corresponding model to empirical estimates of the transmission rate and the number of days being contagious, we find $D_c\sim 7.2$. Assuming realistic contact networks with a degree of about 5, and assuming that lockdown measures would reduce that to household-size (about 2.5), we reproduce actual infection curves with a remarkable precision, without fitting or fine-tuning of parameters. In particular we compare the US and Austria, as examples for one country that initially did not impose measures and one that responded with a severe lockdown early on. Our findings question the applicability of standard compartmental models to describe the COVID-19 containment phase. The probability to observe linear growth in these is practically zero.

physics.soc-ph↗

arXiv2019

The role of mainstreamness and interdisciplinarity for the relevance of scientific papers

There is demand from science funders, industry, and the public that science should become more risk-taking, more out-of-the-box, and more interdisciplinary. Is it possible to tell how interdisciplinary and out-of-the-box scientific papers are, or which papers are mainstream? Here we use the bibliographic coupling network, derived from all physics papers that were published in the Physical Review journals in the past century, to try to identify them as mainstream, out-of-the-box, or interdisciplinary. We show that the network clusters into scientific fields. The position of individual papers with respect to these clusters allows us to estimate their degree of mainstreamness or interdisciplinary. We show that over the past decades the fraction of mainstream papers increases, the fraction of out-of-the-box decreases, and the fraction of interdisciplinary papers remains constant. Studying the rewards of papers, we find that in terms of absolute citations, both, mainstream and interdisciplinary papers are rewarded. In the long run, mainstream papers perform less than interdisciplinary ones in terms of citation rates. We conclude that to avoid a trend towards mainstreamness a new incentive scheme is necessary.

cs.DL↗

arXiv2019

Identification of gatekeeper diseases on the way to cardiovascular mortality

Multimorbidity, the co-occurrence of two or more chronic diseases such as diabetes, obesity or cardiovascular diseases in one patient, is a frequent phenomenon. To make care more efficient, it is of relevance to understand how different diseases condition each other over the life time of a patient. However, most of our current knowledge on such patient careers is either confined to narrow time spans or specific (sets of) diseases. Here, we present a population-wide analysis of long-term patient trajectories by clustering them according to their disease history observed over 17 years. When patients acquire new diseases, their cluster assignment might change. A health trajectory can then be described by a temporal sequence of disease clusters. From the transitions between clusters we construct an age-dependent multilayer network of disease clusters. Random walks on this multilayer network provide a more precise model for the time evolution of multimorbid health states when compared to models that cluster patients based on single diseases. Our results can be used to identify decisive events that potentially determine the future disease trajectory of a patient. We find that for elderly patients the cluster network consists of regions of low, medium and high in-hospital mortality. Diagnoses of diabetes and hypertension are found to strongly increase the likelihood for patients to subsequently move into the high-mortality region later in life.

physics.med-ph↗

arXiv2019

Economic resilience from input-output susceptibility improves predictions of economic growth and recovery

Modern macroeconomic theories were unable to foresee the last Great Recession and could neither predict its prolonged duration nor the recovery rate. They are based on supply-demand equilibria that do not exist during recessionary shocks. Here we focus on resilience as a nonequilibrium property of networked production systems and develop a linear response theory for input-output economics. By calibrating the framework to data from 56 industrial sectors in 43 countries between 2000 and 2014, we find that the susceptibility of individual industrial sectors to economic shocks varies greatly across countries, sectors, and time. We show that susceptibility-based predictions that take sector- and country-specific recovery into account, outperform--by far--standard econometric growth-models. Our results are analytically rigorous, empirically testable, and flexible enough to address policy-relevant scenarios. We illustrate the latter by estimating the impact of recently imposed tariffs on US imports (steel and aluminum) on specific sectors across European countries.

econ.GN↗

arXiv2019

Fashion and art cycles are driven by counter-dominance signals of elite competition: quantitative evidence from music styles

Human symbol systems such as art and fashion styles emerge from complex social processes that govern the continuous re-organization of modern societies. They provide a signaling scheme that allows members of an elite to distinguish themselves from the rest of society. Efforts to understand the dynamics of art and fashion cycles have been based on 'bottom-up' and 'top down' theories. According to 'top down' theories, elite members signal their superior status by introducing new symbols (e.g., fashion styles), which are adopted by low-status groups. In response to this adoption, elite members would need to introduce new symbols to signal their status. According to many 'bottom-up' theories, style cycles evolve from lower classes and follow an essentially random pattern. We propose an alternative explanation based on counter-dominance signaling. There, elite members want others to imitate their symbols; changes only occur when outsider groups successfully challenge the elite by introducing signals that contrast those endorsed by the elite. We investigate these mechanisms using a dynamic network approach on data containing almost 8 million musical albums released between 1956 and 2015. The network systematically quantifies artistic similarities of competing musical styles and their changes over time. We formulate empirical tests for whether new symbols are introduced by current elite members (top-down), randomness (bottom-up) or by peripheral groups through counter-dominance signals. We find clear evidence that counter-dominance-signaling drives changes in musical styles. This provides a quantitative, completely data-driven answer to a century-old debate about the nature of the underlying social dynamics of fashion cycles.

physics.soc-ph↗

arXiv2018

When does a disaster become a systemic event? Estimating indirect economic losses from natural disasters

Reliable estimates of indirect economic losses arising from natural disasters are currently out of scientific reach. To address this problem, we propose a novel approach that combines a probabilistic physical damage catastrophe model with a new generation of macroeconomic agent-based models (ABMs). The ABM moves beyond the state of the art by exploiting large data sets from detailed national accounts, census data, and business information, etc., to simulate interactions of millions of agents representing \emph{each} natural person or legal entity in a national economy. The catastrophe model introduces a copula approach to assess flood losses, considering spatial dependencies of the flood hazard. These loss estimates are used in a damage scenario generator that provides input for the ABM, which then estimates indirect economic losses due to the event. For the first time, we are able to link environmental and economic processes in a computer simulation at this level of detail. We show that moderate disasters induce comparably small but positive short- to medium-term, and negative long-term economic impacts. Large-scale events, however, trigger a pronounced negative economic response immediately after the event and in the long term, while exhibiting a temporary short- to medium-term economic boost. We identify winners and losers in different economic sectors, including the fiscal consequences for the government. We quantify the critical disaster size beyond which the resilience of an economy to rebuild reaches its limits. Our results might be relevant for the management of the consequences of systemic events due to climate change and other disasters.

econ.GN↗

arXiv2017

Election forensic analysis of the Turkish Constitutional Referendum 2017

With a majority of 'Yes' votes in the Constitutional Referendum of 2017, Turkey continues its transition from democracy to autocracy. By the will of the Turkish people, this referendum transferred practically all executive power to president Erdogan. However, the referendum was confronted with a substantial number of allegations of electoral misconducts and irregularities, ranging from state coercion of 'No' supporters to the controversial validity of unstamped ballots. In this note we report the results of an election forensic analysis of the 2017 referendum to clarify to what extent these voting irregularities were present and if they were able to influence the outcome of the referendum. We specifically apply novel statistical forensics tests to further identify the specific nature of electoral malpractices. In particular, we test whether the data contains fingerprints for ballot-stuffing (submission of multiple ballots per person during the vote) and voter rigging (coercion and intimidation of voters). Additionally, we perform tests to identify numerical anomalies in the election results. We find systematic and highly significant support for the presence of both, ballot-stuffing and voter rigging. In 6% of stations we find signs for ballot-stuffing with an error (probability of ballot-stuffing not happening) of 0.15% (3 sigma event). The influence of these vote distortions were large enough to tip the overall balance from 'No' to a majority of 'Yes' votes.

stat.AP↗

arXiv2016

Disentangling genetic and environmental risk factors for individual diseases from multiplex comorbidity networks

Most disorders are caused by a combination of multiple genetic and/or environmental factors. If two diseases are caused by the same molecular mechanism, they tend to co-occur in patients. Here we provide a quantitative method to disentangle how much genetic or environmental risk factors contribute to the pathogenesis of 358 individual diseases, respectively. We pool data on genetic, pathway-based, and toxicogenomic disease-causing mechanisms with disease co-occurrence data obtained from almost two million patients. From this data we construct a multilayer network where nodes represent disorders that are connected by links that either represent phenotypic comorbidity of the patients or the involvement of a certain molecular mechanism. From the similarity of phenotypic and mechanism-based networks for each disorder we derive measure that allows us to quantify the relative importance of various molecular mechanisms for a given disease. We find that most diseases are dominated by genetic risk factors, while environmental influences prevail for disorders such as depressions, cancers, or dermatitis. Almost never we find that more than one type of mechanisms is involved in the pathogenesis of diseases.

q-bio.MN↗