Inferring country-specific import risk of diseases from the world air transportation network

Disease propagation between countries strongly depends on their effective distance, a measure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country-essentially, the likelihood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the 'import risk' model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak's origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model's precision increases for countries with stronger connections within the WAN, and it reveals a geographic distance dependence that implies a pull- rather than a push-dynamic in the distribution process.

Rhythm of relationships in a social fish over the course of a full year in the wild

BACKGROUND

Animals are expected to adjust their social behaviour to cope with challenges in their environment. Therefore, for fish populations in temperate regions with seasonal and daily environmental oscillations, characteristic rhythms of social relationships should be pronounced. To date, most research concerning fish social networks and biorhythms has occurred in artificial laboratory environments or over confined temporal scales of days to weeks. Little is known about the social networks of wild, freely roaming fish, including how seasonal and diurnal rhythms modulate social networks over the course of a full year. The advent of high-resolution acoustic telemetry enables us to quantify detailed social interactions in the wild over time-scales sufficient to examine seasonal rhythms at whole-ecosystems scales. Our objective was to explore the rhythms of social interactions in a social fish population at various time-scales over one full year in the wild by examining high-resolution snapshots of a dynamic social network.

METHODS

To that end, we tracked the behaviour of 36 adult common carp, Cyprinus carpio, in a 25 ha lake and constructed temporal social networks among individuals across various time-scales, where social interactions were defined by proximity. We compared the network structure to a temporally shuffled null model to examine the importance of social attraction, and checked for persistent characteristic groups over time.

RESULTS

The clustering within the carp social network tended to be more pronounced during daytime than nighttime throughout the year. Social attraction, particularly during daytime, was a key driver for interactions. Shoaling behavior substantially increased during daytime in the wintertime, whereas in summer carp interacted less frequently, but the interaction duration increased. Therefore, smaller, characteristic groups were more common in the summer months and during nighttime, where the social memory of carp lasted up to two weeks.

CONCLUSIONS

We conclude that social relationships of carp change diurnally and seasonally. These patterns were likely driven by predator avoidance, seasonal shifts in lake temperature, visibility, forage availability and the presence of anoxic zones. The techniques we employed can be applied generally to high-resolution biotelemetry data to reveal social structures across other fish species at ecologically realistic scales.

Evidence for positive long- and short-term effects of vaccinations against COVID-19 in wearable sensor metrics

Vaccines are among the most powerful tools to combat the COVID-19 pandemic. They are highly effective against infection and substantially reduce the risk of severe disease, hospitalization, ICU admission, and death. However, their potential for attenuating long-term changes in personal health and health-related wellbeing after a SARS-CoV-2 infection remains a subject of debate. Such effects can be effectively monitored at the individual level by analyzing physiological data collected by consumer-grade wearable sensors. Here, we investigate changes in resting heart rate, daily physical activity, and sleep duration around a SARS-CoV-2 infection stratified by vaccination status. Data were collected over a period of 2 years in the context of the German Corona Data Donation Project with around 190,000 monthly active participants. Compared to their unvaccinated counterparts, we find that vaccinated individuals, on average, experience smaller changes in their vital data that also return to normal levels more quickly. Likewise, extreme changes in vitals during the acute phase of the disease occur less frequently in vaccinated individuals. Our results solidify evidence that vaccines can mitigate long-term detrimental effects of SARS-CoV-2 infections both in terms of duration and magnitude. Furthermore, they demonstrate the value of large-scale, high-resolution wearable sensor data in public health research.

Enhancing global preparedness during an ongoing pandemic from partial and noisy data

As the coronavirus disease 2019 spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75, and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Compared to a country-level epidemic intelligence, our scalable integrated approach, that is pandemic intelligence, permits to enhance global preparedness to contrast the pandemic of respiratory pathogens such as SARS-CoV-2.

Estimating the share of SARS-CoV-2-immunologically naïve individuals in Germany up to June 2022

After the winter of 2021/2022, the coronavirus disease 2019 (COVID-19) pandemic had reached a phase where a considerable number of people in Germany have been either infected with a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, vaccinated or both, the full extent of which was difficult to estimate, however, because infection counts suffer from under-reporting, and the overlap between the vaccinated and recovered subpopulations is unknown. Yet, reliable estimates regarding population-wide susceptibility were of considerable interest: Since both previous infection and vaccination reduce the risk of severe disease, a low share of immunologically naïve individuals lowers the probability of further severe outbreaks, given that emerging variants do not escape the acquired susceptibility reduction. Here, we estimate the share of immunologically naïve individuals by age group for each of the sixteen German federal states by integrating an infectious-disease model based on weekly incidences of SARS-CoV-2 infections in the national surveillance system and vaccine uptake, as well as assumptions regarding under-ascertainment. We estimate a median share of 5.6% of individuals in the German population have neither been in contact with vaccine nor any variant up to 31 May 2022 (quartile range [2.5%-8.5%]). For the adult population at higher risk of severe disease, this figure is reduced to 3.8% [1.6%-5.9%] for ages 18-59 and 2.1% [1.0%-3.4%] for ages 60 and above. However, estimates vary between German states mostly due to heterogeneous vaccine uptake. Excluding Omicron infections from the analysis, 16.3% [14.1%-17.9%] of the population in Germany, across all ages, are estimated to be immunologically naïve, highlighting the large impact the first two Omicron waves had until the beginning of summer in 2022. The method developed here might be useful for similar estimations in other countries or future outbreaks of other infectious diseases.

Modeling the impact of the Omicron infection wave in Germany

In November 2021, the first infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VOC) B.1.1.529 ('Omicron') was reported in Germany, alongside global reports of reduced vaccine efficacy (VE) against infections with this variant. The potential threat posed by its rapid spread in Germany was, at the time, difficult to predict. We developed a variant-dependent population-averaged susceptible-exposed-infected-recovered infectious-disease model that included information about variant-specific and waning VEs based on empirical data available at the time. Compared to other approaches, our method aimed for minimal structural and computational complexity and therefore enabled us to respond to changes in the situation in a more agile manner while still being able to analyze the potential influence of (non-)pharmaceutical interventions (NPIs) on the emerging crisis. Thus, the model allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs), the efficacy of contact reduction strategies, and the success of the booster vaccine rollout campaign. We expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity, nevertheless, even without additional NPIs. The projected figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid-February and mid-March. Most surprisingly, our model showed that early, strict, and short contact reductions could have led to a strong 'rebound' effect with high incidences after the end of the respective NPIs, despite a potentially successful booster campaign. The results presented here informed legislation in Germany. The methodology developed in this study might be used to estimate the impact of future waves of COVID-19 or other infectious diseases.

Germany's fourth COVID-19 wave was mainly driven by the unvaccinated

Background

While the majority of the German population was fully vaccinated at the time (about 65%), COVID-19 incidence started growing exponentially in October 2021 with about 41% of recorded new cases aged twelve or above being symptomatic breakthrough infections, presumably also contributing to the dynamics. So far, it remained elusive how significant this contribution was and whether targeted non-pharmaceutical interventions (NPIs) may have stopped the amplification of the crisis.

Methods

We develop and introduce a contribution matrix approach based on the next-generation matrix of a population-structured compartmental infectious disease model to derive contributions of respective inter- and intragroup infection pathways of unvaccinated and vaccinated subpopulations to the effective reproduction number and new infections, considering empirical data of vaccine efficacies against infection and transmission.

Results

Here we show that about 61%-76% of all new infections were caused by unvaccinated individuals and only 24%-39% were caused by the vaccinated. Furthermore, 32%-51% of new infections were likely caused by unvaccinated infecting other unvaccinated. Decreasing the transmissibility of the unvaccinated by, e. g. targeted NPIs, causes a steeper decrease in the effective reproduction number R than decreasing the transmissibility of vaccinated individuals, potentially leading to temporary epidemic control. Reducing contacts between vaccinated and unvaccinated individuals serves to decrease R in a similar manner as increasing vaccine uptake.

Conclusions

A minority of the German population-the unvaccinated-is assumed to have caused the majority of new infections in the fall of 2021 in Germany. Our results highlight the importance of combined measures, such as vaccination campaigns and targeted contact reductions to achieve temporary epidemic control.

Social networks predict the life and death of honey bees

In complex societies, individuals' roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individual's social network that can be obtained without interfering with the colony. This 'network age' accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function.

Finding disease outbreak locations from human mobility data

Finding the origin location of an infectious disease outbreak quickly is crucial in mitigating its further dissemination. Current methods to identify outbreak locations early on rely on interviewing affected individuals and correlating their movements, which is a manual, time-consuming, and error-prone process. Other methods such as contact tracing, genomic sequencing or theoretical models of epidemic spread offer help, but they are not applicable at the onset of an outbreak as they require highly processed information or established transmission chains. Digital data sources such as mobile phones offer new ways to find outbreak sources in an automated way. Here, we propose a novel method to determine outbreak origins from geolocated movement data of individuals affected by the outbreak. Our algorithm scans movement trajectories for shared locations and identifies the outbreak origin as the most dominant among them. We test the method using various empirical and synthetic datasets, and demonstrate that it is able to single out the true outbreak location with high accuracy, requiring only data of N = 4 individuals. The method can be applied to scenarios with multiple outbreak locations, and is even able to estimate the number of outbreak sources if unknown, while being robust to noise. Our method is the first to offer a reliable, accurate out-of-the-box approach to identify outbreak locations in the initial phase of an outbreak. It can be easily and quickly applied in a crisis situation, improving on previous manual approaches. The method is not only applicable in the context of disease outbreaks, but can be used to find shared locations in movement data in other contexts as well.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjds/s13688-021-00306-6.

COVID-19 lockdown induces disease-mitigating structural changes in mobility networks

In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the "small-world" effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by "flattening" the epidemic curve and delaying the spread to geographically distant regions.

[Cost unit accounting of strabismus surgery at a university eye hospital]

BACKGROUND

Strabismus surgery is frequently carried out in university centers. The aim of this work was to calculate the costs of strabismus surgery at a university hospital and to assess the remuneration of costs for outpatient procedures.

MATERIAL AND METHODS

Of all strabismus surgeries at the Hanover Medical School in the years 2018 and 2019, relevant surgical data, such as patient age, number of muscles operated on, incision to suture time, attendance time of the surgeons and anesthetists as well as the nursing staff, were evaluated based on the clinics own information system. During this process, the costs for personnel, material, room rental charges and overheads were computed applying cost unit accounting.

RESULTS

A total of 302 operations (inpatient proportion 92.1%) were carried out in most cases with the patient under general anesthesia. The mean patient age was 31 years (median 26 years), with 33 patients being children under 6 years of age. On average 1.84 muscles were treated per intervention. The mean incision to suture time was 51.5 min, mean anesthesia time was 85 min, the attendance time of surgical as well as anesthesia nursing staff each accounted for 104 min, the additional time in the postanesthesia care unit added 66 min. Average personnel costs originating from the overall process amounted to 642.14 €, with the addition of 109.23 € for material and medication (surgery and anesthesia) and costs for cleaning and room rental (including overheads) of 178.71 €. Therefore, the overall costs of an average strabismus surgery in our collective added up to 930.08 € (minimum 491.01 €, maximum 1729.29 €). Cost accounting of subgroups yielded substantially higher costs for anesthesia in children as well as for higher numbers of muscles operated on due to different treatment duration (37 min for 1 muscle to 72 min for 3 muscles) and anesthesia time, especially in children <6 years of age (on average 22 min longer than adults and children >5 years; the differences being 11 min for 1 muscle, 25 min for 2 muscles and 30 min for 3 or more muscles). The pure costs of a strabismus surgery at this clinic seem on average to exceed the revenues for strabismus surgery in the outpatient sector calculated by the German uniform evaluation benchmark (EBM) by about a factor of 2.

CONCLUSION

It could be shown that the purely economically calculated costs for strabismus surgery at a university clinic are significantly higher than the revenues achieved in the outpatient sector according to paragraph 115b, section 1, of the Social Security Act V (SGB V). Under these circumstances, such operations cannot be performed in a cost-effective manner.

Experiencing the risk of overutilising opioids among patients with chronic non-cancer pain in ambulatory care (ERONA): the protocol of an exploratory, randomised controlled trial

INTRODUCTION

The US opioid crisis and increasing prescription rates in Europe suggest inappropriate risk perceptions and behaviours of people who prescribe, take or advise on opioids: physicians, patients and pharmacists. Findings from cognitive and decision science in areas other than drug safety suggest that people's risk perception and behaviour can differ depending on whether they learnt about a risk through personal experience or description. Experiencing the risk of overutilising opioids among patients with chronic non-cancer pain in ambulatory care (ERONA) is the first-ever conducted trial that aims at investigating the effects of these two modes of learning on individuals' risk perception and behaviour in the long-term administration of WHO-III opioids in chronic non-cancer pain.

METHODS AND ANALYSIS

ERONA-an exploratory, randomised controlled online survey intervention trial with two parallel arms-will examine the opioid-associated risk perception and behaviour of four groups involved in the long-term administration of WHO-III opioids: (1) family physicians, (2) physicians specialised in pain therapy, (3) patients with chronic (≥3 months) non-cancer pain and (4) pharmacists who regularly dispense narcotic substances. Participants will be randomly assigned to one of two online risk education interventions, description based or experiencebased. Both interventions will present the best medical evidence available. Participants will be queried at baseline and after intervention on their risk perception of opioids' benefit-harm ratio, their medical risk literacy and their current/intended risk behaviour (in terms of prescribing, taking or counselling, depending on study group). A follow-up will occur after 9 months, when participants will be queried on their actual risk behaviour. The study was developed by the authors and will be conducted by the market research institution IPSOS Health.

ETHICS AND DISSEMINATION

The study was approved by the Institutional Review Board of the Max Planck Institute for Human Development. Results will be disseminated through peer-reviewed journals, conference presentations and social media.

TRIAL REGISTRATION NUMBER

DRKS00020358.

Comprehensive integrated NGS-based surveillance and contact-network modeling unravels transmission dynamics of vancomycin-resistant enterococci in a high-risk population within a tertiary care hospital

Vancomycin-resistant E. faecium (VRE) are an important cause of nosocomial infections, which are rapidly transmitted in hospitals. To identify possible transmission routes, we applied combined genomics and contact-network modeling to retrospectively evaluate routine VRE screening data generated by the infection control program of a hemato-oncology unit. Over 1 year, a total of 111 VRE isolates from 111 patients were collected by anal swabs in a tertiary care hospital in Southern Germany. All isolated VRE were whole-genome sequenced, followed by different in-depth bioinformatics analyses including genotyping and determination of phylogenetic relations, aiming to evaluate a standardized workflow. Patient movement data were used to overlay sequencing data to infer transmission events and strain dynamics over time. A predominant clone harboring vanB and exhibiting genotype ST117/CT469 (n = 67) was identified. Our comprehensive combined analyses suggested intra-hospital spread, especially of clone ST117/CT469, despite of extensive screening, single room placement, and contact isolation. A new interactive tool to visualize these complex data was designed. Furthermore, a patient-contact network-modeling approach was developed, which indicates both the periodic import of the clone into the hospital and its spread within the hospital due to patient movements. The analyzed spread of VRE was most likely due to placement of patients in the same room prior to positivity of screening. We successfully demonstrated the added value for this combined strategy to extract well-founded knowledge from interdisciplinary data sources. The combination of patient-contact modeling and high-resolution typing unraveled the transmission dynamics within the hospital department and, additionally, a constant VRE influx over time.

Massive parallelization boosts big Bayesian multidimensional scaling

Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian models for the analysis of complex phenomena in scientific inference and statistical learning. Standing as an example, Bayesian multidimensional scaling (MDS) can help scientists learn viral trajectories through space-time, but its computational burden prevents its wider use. Crucial MDS model calculations scale quadratically in the number of observations. We partially mitigate this limitation through massive parallelization using multi-core central processing units, instruction-level vectorization and graphics processing units (GPUs). Fitting the MDS model using Hamiltonian Monte Carlo, GPUs can deliver more than 100-fold speedups over serial calculations and thus extend Bayesian MDS to a big data setting. To illustrate, we employ Bayesian MDS to infer the rate at which different seasonal influenza virus subtypes use worldwide air traffic to spread around the globe. We examine 5392 viral sequences and their associated 14 million pairwise distances arising from the number of commercial airline seats per year between viral sampling locations. To adjust for shared evolutionary history of the viruses, we implement a phylogenetic extension to the MDS model and learn that subtype H3N2 spreads most effectively, consistent with its epidemic success relative to other seasonal influenza subtypes. Finally, we provide MassiveMDS, an open-source, stand-alone C++ library and rudimentary R package, and discuss program design and high-level implementation with an emphasis on important aspects of computing architecture that become relevant at scale.

Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

The recent outbreak of coronavirus disease 2019 (COVID-19) in mainland China was characterized by a distinctive subexponential increase of confirmed cases during the early phase of the epidemic, contrasting with an initial exponential growth expected for an unconstrained outbreak. We show that this effect can be explained as a direct consequence of containment policies that effectively deplete the susceptible population. To this end, we introduce a parsimonious model that captures both quarantine of symptomatic infected individuals, as well as population-wide isolation practices in response to containment policies or behavioral changes, and show that the model captures the observed growth behavior accurately. The insights provided here may aid the careful implementation of containment strategies for ongoing secondary outbreaks of COVID-19 or similar future outbreaks of other emergent infectious diseases.

Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

The recent outbreak of coronavirus disease 2019 (COVID-19) in mainland China was characterized by a distinctive subexponential increase of confirmed cases during the early phase of the epidemic, contrasting with an initial exponential growth expected for an unconstrained outbreak. We show that this effect can be explained as a direct consequence of containment policies that effectively deplete the susceptible population. To this end, we introduce a parsimonious model that captures both quarantine of symptomatic infected individuals, as well as population-wide isolation practices in response to containment policies or behavioral changes, and show that the model captures the observed growth behavior accurately. The insights provided here may aid the careful implementation of containment strategies for ongoing secondary outbreaks of COVID-19 or similar future outbreaks of other emergent infectious diseases.

[Digital epidemiology]

Digital epidemiology is a new and rapidly growing field. The technological revolution we have been witnessing during the last decade, the global rise of the Internet, the emergence of social media and social networks that connect individuals worldwide for information exchange and social interactions, and the almost complete social penetration of mobile devices such as smartphones provide access to data on individual behavior with unprecedented resolution and precision. In digital epidemiology, this type of high-resolution behavioral data is analyzed to advance our understanding of, for example, infectious disease dynamics and improve our abilities to forecast epidemic outbreaks and related phenomena.This article provides an overview on the topic. Different aspects of digital epidemiology are alluded to. Based on examples, I will explain how epidemiological data is integrated on new comprehensive and interactive websites, how the analysis of interactions and activities on social media platforms can yield answers to epidemiological questions, and finally how individual-based data collected by smartphones or wearable sensors in natural experiments can be used to reconstruct contact and physical proximity networks the knowledge of which substantially improves the predictive power of computational models for transmissible infectious diseases.The challenges posed in terms of privacy protection and data security will be discussed. Concepts and solutions will be explained that may help to improve public health by leveraging the new data while at the same time protecting the individual's data sovereignty and personal dignity.

A multiprotein complex consisting of the cellular coactivator p300, AP-1/ATF, as well as NF-kappaB is responsible for the activation of the mouse major histocompatibility class I (H-2K(b)) enhancer A

Major histocompatibility complex (MHC) class I genes encode highly polymorphic antigens that play an essential role in a number of immunological processes. Their expression is activated in response to a variety of signals and is mediated through several promoter elements among which the enhancer A is one of the key control regions. It contains binding sites for several transcription factors, for example: (i) a well-characterized binding site for rel/NF-kappaB transcription factors in its 3'-end (the H2TF1 or kappaB1 element), (ii) a second kappaB site (the kappaB2 element), which is located immediately adjacent 5' to the H2TF1 element and which is recognized by p65/relA in the human HLA system, and (iii) an AP-1/ATF recognition sequence in the 5' end (EnA-TRE). Here we demonstrate that latter element is bound by at least two distinct heterodimers of the AP-1/ATF transcription factor family, namely c-Jun/ATF-2 and c-Jun/Fra2. Moreover, our data reveal that the enhancer A is simultaneously bound by AP-1/ATF and rel/NF-kappaB transcription factors and that the cellular coactivator p300, which enhances enhancer A-driven reporter gene expression if cotransfected, is recruited to the enhancer A through this multiprotein complex. In contrast to the complete enhancer A, neither the EnA-TRE nor the H2TF1 element on their own are able to confer activation on a heterologous promoter in response to the phorbol ester tumor promoter TPA or the cytokine TNFalpha. Moreover, deletion of any one of the enhancer A control elements results in a dramatic loss of its inducibility by TNFalpha, and point mutations in either the EnA-TRE or the H2TF1 element lead to the loss of AP-1/ATF or NF-kappaB binding, respectively, and to the loss of enhancer A inducibility. Therefore, we conclude that the enhancer A is synergistically activated through a multiprotein complex containing AP-1/ATF, NF-kappaB transcription factors as well as the cellular coactivator p300.

Connectivity of diagnostic technologies: improving surveillance and accelerating tuberculosis elimination

In regard to tuberculosis (TB) and other major global epidemics, the use of new diagnostic tests is increasing dramatically, including in resource-limited countries. Although there has never been as much digital information generated, this data source has not been exploited to its full potential. In this opinion paper, we discuss lessons learned from the global scale-up of these laboratory devices and the pathway to tapping the potential of laboratory-generated information in the field of TB by using connectivity. Responding to the demand for connectivity, innovative third-party players have proposed solutions that have been widely adopted by field users of the Xpert(®) MTB/RIF assay. The experience associated with the utilisation of these systems, which facilitate the monitoring of wide laboratory networks, stressed the need for a more global and comprehensive approach to diagnostic connectivity. In addition to facilitating the reporting of test results, the mobility of digital information allows the sharing of information generated in programme settings. When they become easily accessible, these data can be used to improve patient care, disease surveillance and drug discovery. They should therefore be considered as a public health good. We list several examples of concrete initiatives that should allow data sources to be combined to improve the understanding of the epidemic, support the operational response and, finally, accelerate TB elimination. With the many opportunities that the pooling of data associated with the TB epidemic can provide, pooling of this information at an international level has become an absolute priority.

Human Mobility, Networks and Disease Dynamics on a Global Scale

Disease dynamics is a complex phenomenon and in order to address these questions expertises from many disciplines need to be integrated. One method that has become particularly important during the past few years is the development of computational models and computer simulations that help addressing these questions. In the focus of this chapter are emergent infectious diseases that bear the potential of spreading across the globe, exemplifying how connectivity in a globalized world has changed the way human-mediated processes evolve in the 21st century. The examples of most successful predictions of disease dynamics given in the chapter illustrate that just feeding better and faster computers with more and more data may not necessarily help understanding the relevant phenomena. It might rather be much more useful to change the conventional way of looking at the patterns and to assume a correspondingly modified viewpoint—as most impressively shown with the examples given in this chapter.

Cover time for random walks on arbitrary complex networks

We present an analytical method for computing the mean cover time of a discrete-time random walk process on arbitrary, complex networks. The cover time is defined as the time a random walker requires to visit every node in the network at least once. This quantity is particularly important for random search processes and target localization on network structures. Based on the global mean first-passage time of target nodes, we derive a method for computing the cumulative distribution function of the cover time based on first-passage time statistics. Our method is viable for networks on which random walks equilibrate quickly. We show that it can be applied successfully to various model and real-world networks. Our results reveal an intimate link between first-passage and cover time statistics and offer a computationally efficient way for estimating cover times in network-related applications.

Molecular surveillance of measles and rubella in the WHO European Region: new challenges in the elimination phase

BACKGROUND

The WHO European Region (EUR) has adopted the goal of eliminating measles and rubella but individual countries perform differently in achieving this goal. Measles virus spread across the EUR by mobile groups has recently led to large outbreaks in the insufficiently vaccinated resident population. As an instrument for monitoring the elimination process and verifying the interruption of endemic virus transmission, molecular surveillance has to provide valid and representative data. Irrespective of the country's specific situation, it is required to ensure the functionality of the laboratory surveillance that is supported by the WHO Global Measles and Rubella Laboratory Network.

AIMS

To investigate whether the molecular surveillance in the EUR is adequate for the challenges in the elimination phase, we addressed the quality assurance of molecular data, the continuity and intensity of molecular monitoring, and the analysis of transmission chains.

SOURCES

Published articles, the molecular External Quality Assessment Programme of the WHO, the Centralized Information System for Infectious Diseases of the WHO EUR and the WHO Measles and Rubella Nucleotide Surveillance databases served as information sources.

CONTENT

Molecular proficiency testing conducted by the WHO in 2016 has shown that the expertise for measles and rubella virus genotyping exists in all parts of the EUR. The analysis of surveillance data reported nationally to the WHO in 2013-2016 has revealed some countries with outbreaks but not sufficiently representative molecular data. Long-lasting supranational MV transmission chains were identified.

IMPLICATIONS

A more systematic molecular monitoring and recording of the transmission pattern for the whole EUR could help to create a meaningful picture of the elimination process.

Effective distances for epidemics spreading on complex networks

We show that the recently introduced logarithmic metrics used to predict disease arrival times on complex networks are approximations of more general network-based measures derived from random walks theory. Using the daily air-traffic transportation data we perform numerical experiments to compare the infection arrival time with this alternative metric that is obtained by accounting for multiple walks instead of only the most probable path. The comparison with direct simulations reveals a higher correlation compared to the shortest-path approach used previously. In addition our method allows to connect fundamental observables in epidemic spreading with the cumulant-generating function of the hitting time for a Markov chain. Our results provides a general and computationally efficient approach using only algebraic methods.

Spatial and functional heterogeneities shape collective behavior of tumor-immune networks

Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions. The transition from tumor suppression to tumor promotion is mediated by a tumor-induced shift in the local immune state, and despite the clinical challenge this shift poses, little is known about how such dysfunctional immune states are initiated. Clinical and experimental observations have indicated that differences in both the composition and spatial distribution of different cell types and/or signaling molecules within the tumor microenvironment can strongly impact tumor pathogenesis and ultimately patient prognosis. How such “functional” and “spatial” heterogeneities confer such effects, however, is not known. To investigate these phenomena at a level currently inaccessible by direct observation, we developed a computational model of a nascent metastatic tumor capturing salient features of known tumor-immune interactions that faithfully recapitulates key features of existing experimental observations. Surprisingly, over a wide range of model formulations, we observed that heterogeneity in both spatial organization and cell phenotype drove the emergence of immunosuppressive network states. We determined that this observation is general and robust to parameter choice by developing a systems-level sensitivity analysis technique, and we extended this analysis to generate other parameter-independent, experimentally testable hypotheses. Lastly, we leveraged this model as an in silico test bed to evaluate potential strategies for engineering cell-based therapies to overcome tumor associated immune dysfunction and thereby identified modes of immune modulation predicted to be most effective. Collectively, this work establishes a new integrated framework for investigating and modulating tumor-immune networks and provides insights into how such interactions may shape early stages of tumor formation.

Over the course of tumor growth, cancer cells interact with normal cells via processes that are difficult to understand by experiment alone. This challenge is particularly pronounced at early stages of tumor formation, when experimental observation is most limited. Elucidating such interactions could inform both understanding of cancer and clinical practice. To address this need we developed a computational model capturing the current understanding of how individual metastatic tumor cells and immune cells sense and contribute to the tumor environment, which in turn enabled us to investigate the complex, collective behavior of these systems. Surprisingly, we discovered that tumor escape from immune control was enhanced by the existence of small differences (or heterogeneities) in the responses of individual immune cells to their environment, as well as by heterogeneities in the way that cells and the molecules they secrete are arranged in space. These conclusions held true over a range of model formulations, suggesting that this is a general feature of these tumor-immune networks. Finally, we used this model as a test bed to evaluate potential strategies for enhancing immunological control of early tumors, ultimately predicting that specifically modulating tumor-associated immune dysfunction may be more effective than simply enhanced tumor killing.

Saving Human Lives: What Complexity Science and Information Systems can Contribute

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.

Understanding and predicting the global spread of emergent infectious diseases

The emergence and global spread of human infectious diseases has become one of the most serious public health threats of the 21st century. Sophisticated computer simulations have become a key tool for understanding and predicting disease spread on a global scale. Combining theoretical insights from nonlinear dynamics, stochastic processes and complex network theory these computational models are becoming increasingly important in the design of efficient mitigation and control strategies and for public health in general.

Origin Detection During Food-borne Disease Outbreaks - A Case Study of the 2011 EHEC/HUS Outbreak in Germany

The key challenge during food-borne disease outbreaks, e.g. the 2011 EHEC/HUS outbreak in Germany, is the design of efficient mitigation strategies based on a timely identification of the outbreak's spatial origin. Standard public health procedures typically use case-control studies and tracings along food shipping chains. These methods are time-consuming and suffer from biased data collected slowly in patient interviews. Here we apply a recently developed, network-theoretical method to identify the spatial origin of food-borne disease outbreaks. Thereby, the network captures the transportation routes of contaminated foods. The technique only requires spatial information on case reports regularly collected by public health institutions and a model for the underlying food distribution network. The approach is based on the idea of replacing the conventional geographic distance with an effective distance that is derived from the topological structure of the underlying food distribution network. We show that this approach can efficiently identify most probable epicenters of food-borne disease outbreaks. We assess and discuss the method in the context of the 2011 EHEC epidemic. Based on plausible assumptions on the structure of the national food distribution network, the approach can correctly localize the origin of the 2011 German EHEC/HUS outbreak.

Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2

Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.

What explains the geographic dispersal of emerging pathogens? Reconstructions of evolutionary history from pathogen gene sequences offer qualitative descriptions of spatial spread, but current approaches are poorly equipped to formally test and quantify the contribution of different potential explanatory factors, such as human mobility and demography. Here, we use a novel phylogeographic method to evaluate multiple potential predictors of viral spread in human influenza dynamics. We identify air travel as the predominant driver of global influenza migration, whilst also revealing the contribution of other mobility processes at more local scales. We demonstrate the power of our inter-disciplinary approach by using it to predict the global pandemic expansion of H1N1 influenza in 2009. Our study highlights the importance of integrating evolutionary and ecological information when studying the dynamics of infectious disease.

The hidden geometry of complex, network-driven contagion phenomena

The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic-mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1 influenza pandemic and 2003 SARS epidemic.

Perturbative solution to susceptible-infected-susceptible epidemics on networks

Herein we provide a closed form perturbative solution to a general M-node network susceptible-infected-susceptible (SIS) model using the transport rates between nodes as a perturbation parameter. We separate the dynamics into a short-time regime and a medium-to-long-time regime. We solve the short-time dynamics of the system and provide a limit before which our explicit, analytical result of the first-order perturbation for the medium-to-long-time regime is to be employed. These stitched calculations provide an approximation to the full temporal dynamics for rather general initial conditions. To further corroborate our results, we solve the mean-field equations numerically for an infectious SIS outbreak in New Zealand (NZ, Aotearoa) recomposed into 23 subpopulations where the virus is spread to different subpopulations via (documented) air traffic data, and the country is internationally quarantined. We demonstrate that our analytical predictions compare well to the numerical solution.

Eyjafjallajökull and 9/11: the impact of large-scale disasters on worldwide mobility

Large-scale disasters that interfere with globalized socio-technical infrastructure, such as mobility and transportation networks, trigger high socio-economic costs. Although the origin of such events is often geographically confined, their impact reverberates through entire networks in ways that are poorly understood, difficult to assess, and even more difficult to predict. We investigate how the eruption of volcano Eyjafjallajökull, the September 11th terrorist attacks, and geographical disruptions in general interfere with worldwide mobility. To do this we track changes in effective distance in the worldwide air transportation network from the perspective of individual airports. We find that universal features exist across these events: airport susceptibilities to regional disruptions follow similar, strongly heterogeneous distributions that lack a scale. On the other hand, airports are more uniformly susceptible to attacks that target the most important hubs in the network, exhibiting a well-defined scale. The statistical behavior of susceptibility can be characterized by a single scaling exponent. Using scaling arguments that capture the interplay between individual airport characteristics and the structural properties of routes we can recover the exponent for all types of disruption. We find that the same mechanisms responsible for efficient passenger flow may also keep the system in a vulnerable state. Our approach can be applied to understand the impact of large, correlated disruptions in financial systems, ecosystems and other systems with a complex interaction structure between heterogeneous components.

Add Control: plant virtualization for control solutions in WWTP

This paper summarizes part of the research work carried out in the Add Control project, which proposes an extension of the wastewater treatment plant (WWTP) models and modelling architectures used in traditional WWTP simulation tools, addressing, in addition to the classical mass transformations (transport, physico-chemical phenomena, biological reactions), all the instrumentation, actuation and automation & control components (sensors, actuators, controllers), considering their real behaviour (signal delays, noise, failures and power consumption of actuators). Its ultimate objective is to allow a rapid transition from the simulation of the control strategy to its implementation at full-scale plants. Thus, this paper presents the application of the Add Control simulation platform for the design and implementation of new control strategies at the WWTP of Mekolalde.

Systematic evaluation of biofilm models for engineering practice: components and critical assumptions

Biofilm models are valuable tools for the design and evaluation of biofilm-based processes despite several uncertainties including the dynamics and rate of biofilm detachment, concentration gradients external to the biofilm surface, and undefined biofilm reactor model calibration protocol. The present investigation serves to (1) systematically evaluate critical biofilm model assumptions and components and (2) conduct a sensitivity analysis with the aim of identifying parameter subsets for biofilm reactor model calibration. AQUASIM was used to describe submerged-completely mixed combined carbon oxidation and nitrification IFAS and MBBR systems, and tertiary nitrification and denitrification MBBRs. The influence of uncertainties in model parameters on relevant model outputs was determined for simulated scenarios by means of a local sensitivity analysis. To obtain reasonable simulation results for partially penetrated biofilms that accumulated a substantial thickness in the modelled biofilm reactor (e.g. 1,000 microm), an appropriate biofilm discretization was applied to properly model soluble substrate concentration gradients and, consistent with the assumed mechanism for describing biofilm biomass distribution, biofilm biomass spatial variability. The MTBL thickness had a significant impact on model results for each of the modelled reactor configurations. Further research is needed to develop a mathematical description (empirical or otherwise) of the MTBL thickness that is relevant to modern biofilm reactors. No simple recommendations for a generally applicable calibration protocol are provided, but sensitivity analysis has been proven to be a powerful tool for the identification of highly sensitive parameter subsets for biofilm (reactor) model calibration.

The structure of borders in a small world

Territorial subdivisions and geographic borders are essential for understanding phenomena in sociology, political science, history, and economics. They influence the interregional flow of information and cross-border trade and affect the diffusion of innovation and technology. However, it is unclear if existing administrative subdivisions that typically evolved decades ago still reflect the most plausible organizational structure of today. The complexity of modern human communication, the ease of long-distance movement, and increased interaction across political borders complicate the operational definition and assessment of geographic borders that optimally reflect the multi-scale nature of today's human connectivity patterns. What border structures emerge directly from the interplay of scales in human interactions is an open question. Based on a massive proxy dataset, we analyze a multi-scale human mobility network and compute effective geographic borders inherent to human mobility patterns in the United States. We propose two computational techniques for extracting these borders and for quantifying their strength. We find that effective borders only partially overlap with existing administrative borders, and show that some of the strongest mobility borders exist in unexpected regions. We show that the observed structures cannot be generated by gravity models for human traffic. Finally, we introduce the concept of link significance that clarifies the observed structure of effective borders. Our approach represents a novel type of quantitative, comparative analysis framework for spatially embedded multi-scale interaction networks in general and may yield important insight into a multitude of spatiotemporal phenomena generated by human activity.

Evaluating operating conditions for outcompeting nitrite oxidizers and maintaining partial nitrification in biofilm systems using biofilm modeling and Monte Carlo filtering

In practice, partial nitrification to nitrite in biofilms has been achieved with a range of different operating conditions, but mechanisms resulting in reliable partial nitrification in biofilms are not well understood. In this study, mathematical biofilm modeling combined with Monte Carlo filtering was used to evaluate operating conditions that (1) lead to outcompetition of nitrite oxidizers from the biofilm, and (2) allow to maintain partial nitrification during long-term operation. Competition for oxygen was found to be the main mechanism for displacing nitrite oxidizers from the biofilm, and preventing re-growth of nitrite oxidizers in the long-term. To maintain partial nitrification in the model, a larger oxygen affinity (i.e., smaller half saturation constant) for ammonium oxidizers compared to nitrite oxidizers was required, while the difference in maximum growth rate was not important for competition under steady state conditions. Thus, mechanisms for washout of nitrite oxidizing bacteria from biofilms are different from suspended cultures where the difference in maximum growth rate is a key mechanism. Inhibition of nitrite oxidizers by free ammonia was not required to outcompete nitrite oxidizers from the biofilm, and to maintain partial nitrification to nitrite. But inhibition by free ammonia resulted in faster washout of nitrite oxidizers.

Comparing global sensitivity analysis for a biofilm model for two-step nitrification using the qualitative screening method of Morris or the quantitative variance-based Fourier Amplitude Sensitivity Test (FAST)

Two different methods for global sensitivity analysis were compared exemplarily for a biofilm model for two-step nitrification. Especially for biofilm models, local sensitivity analysis is not very useful as parameters can vary over a large range. Parameters that were evaluated included kinetic and stoichiometric parameters, and also biofilm parameters, such as internal and external mass transfer, the biofilm thickness, and the biomass density. Global sensitivity analyses were performed for a range of operating conditions of a biofilm reactor. The results of the qualitative screening method of Morris were compared with the results of the quantitative variance-based method FAST regarding the input parameters indicated as unimportant. Both methods resulted in similar sets of parameters with a small influence on the model output, but the screening method of Morris required a much smaller number of model evaluations to compute the sensitivity measures than the FAST method.

Estimation of kinetic parameters of a model for deammonification in biofilms and evaluation of the model

A systematic approach to estimate and evaluate parameters for deammonification in biofilms from available experimental data was evaluated. Parameter estimation was based on a regional steady state sensitivity analysis to select relevant parameters and design of experiments based on a local identifiability analysis. The calibrated model was evaluated under different experimental conditions. Nine of the 16 kinetic and stoichiometric parameters had a significant influence on model predictions. Of these nine parameters only six kinetic parameters were identifiable from batch experiments regardless of the experimental design. More parameters were not identifiable due to high correlations between growth rates and the corresponding affinity constant for oxygen. Data from a batch experiment at 2 mg/L dissolved oxygen (DO) were used to estimate inactivation rates and affinity constants for oxygen for ammonium oxidisers (AO), nitrite oxidisers (NO) and anaerobic ammonium oxidisers (AN). In addition, it was found that not only kinetic and stoichiometric parameters but also the external mass transfer resistance significantly affected model predictions. The resulting model was able to reproduce batch test and continuous reactor operation where DO concentrations were similar to those in the batch experiment used for parameter estimation. However, the model overestimated deammonification for a batch experiment at a much higher DO concentration (5 mg/L). Thus, parameter values that are identifiable and are estimated for given environmental conditions may not necessarily be valid for significantly different experimental conditions.

The scaling laws of human travel

The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools. Human travel, for example, is responsible for the geographical spread of human infectious disease. In the light of increasing international trade, intensified human mobility and the imminent threat of an influenza A epidemic, the knowledge of dynamical and statistical properties of human travel is of fundamental importance. Despite its crucial role, a quantitative assessment of these properties on geographical scales remains elusive, and the assumption that humans disperse diffusively still prevails in models. Here we report on a solid and quantitative assessment of human travelling statistics by analysing the circulation of bank notes in the United States. Using a comprehensive data set of over a million individual displacements, we find that dispersal is anomalous in two ways. First, the distribution of travelling distances decays as a power law, indicating that trajectories of bank notes are reminiscent of scale-free random walks known as Lévy flights. Second, the probability of remaining in a small, spatially confined region for a time T is dominated by algebraically long tails that attenuate the superdiffusive spread. We show that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and conclude that human travel on geographical scales is an ambivalent and effectively superdiffusive process.

Forecast and control of epidemics in a globalized world

The rapid worldwide spread of severe acute respiratory syndrome demonstrated the potential threat an infectious disease poses in a closely interconnected and interdependent world. Here we introduce a probabilistic model that describes the worldwide spread of infectious diseases and demonstrate that a forecast of the geographical spread of epidemics is indeed possible. This model combines a stochastic local infection dynamics among individuals with stochastic transport in a worldwide network, taking into account national and international civil aviation traffic. Our simulations of the severe acute respiratory syndrome outbreak are in surprisingly good agreement with published case reports. We show that the high degree of predictability is caused by the strong heterogeneity of the network. Our model can be used to predict the worldwide spread of future infectious diseases and to identify endangered regions in advance. The performance of different control strategies is analyzed, and our simulations show that a quick and focused reaction is essential to inhibiting the global spread of epidemics.

Particle dispersion on rapidly folding random heteropolymers

We investigate the dynamics of a particle moving randomly along a disordered heteropolymer subjected to rapid conformational changes which induce superdiffusive motion in chemical coordinates. We study the antagonistic interplay between the enhanced diffusion and the quenched disorder. The dispersion speed exhibits universal behavior independent of the folding statistics. On the other hand it is strongly affected by the structure of the disordered potential. The results may serve as a reference point for a number of translocation phenomena observed in biological cells, such as protein dynamics on DNA strands.

The multifunctional role of E1A in the transcriptional regulation of CREB/CBP-dependent target genes

Oncoproteins encoded by the early region 1A (E1A) of adenoviruses (Ads) have been shown to be powerful tools to study gene regulatory mechanisms. As E1A proteins lack a sequence-specific DNA-binding activity, they modulate viral and cellular gene expression by interacting directly with a diverse array of cellular factors, among them sequence-specific transcription factors, proteins of the general transcription machinery, co-activators and chromatin-modifying enzymes. By making use of these factors, E1A affects major cellular events such as cell cycle control, differentiation, apoptosis, and oncogenic transformation. In this review we will focus on the interaction of E1A with cellular components involved in the cAMP/PKA signal transduction pathway and we will discuss the consequences of these interactions in respect to the activation of CREB/CBP-dependent target genes.

Lévy flights in inhomogeneous media

We investigate the impact of external periodic potentials on superdiffusive random walks known as Lévy flights and show that even strongly superdiffusive transport is substantially affected by the external field. Unlike ordinary random walks, Lévy flights are surprisingly sensitive to the shape of the potential while their asymptotic behavior ceases to depend on the Lévy index mu. Our analysis is based on a novel generalization of the Fokker-Planck equation suitable for systems in thermal equilibrium. Thus, the results presented are applicable to the large class of situations in which superdiffusion is caused by topological complexity, such as diffusion on folded polymers and scale-free networks.