Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany

Background: Animal trade plays an important role for the spread of infectious diseases in livestock populations. As a case study, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. The central question is how infectious diseases can potentially spread within the system of trade contacts. We address this question by analyzing the underlying network of animal movements. Methodology/Findings: The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings: Our approach provides a general framework for a topological-temporal characterization of livestock trade networks. We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume does barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size.Comment: main text 33 pages, 17 figures, supporting information 7 pages, 7 figure

IMPLEMENTASI PERATURAN DAERAH NOMOR 3 TAHUN 2002 TENTANG PERIZINAN PENYELENGGARAAN HIBURAN (Studi Kasus Karoke Keluarga Kecamatan Lima Puluh Pekanbaru)

Penelitian ini dilatarbelakangi dengan adanya kebijakan PerdaNo.3 tahun 2002 tentang penyelenggaraan hiburan. Namun, dalam pelaksanaannya diantara berbagai jenis hiburan yang disebutkan, masih adanya kelemahan pada Perda nomor 3 tahun 2002 tentang perizinan penyelenggaraan hiburan tersebut yaitu pada jenis hiburan karaoke. Adanya kelemahan tersebut akan menimbulkan permasalahan yang terjadi di lapangan pada penyelenggaraan hiburan. Pada Perda Nomor 3 Tahun 2002 tentang perizinan penyelenggaraan hiburan dikota Pekanbaru tentang hiburan umum dijelaskan tentang waktu buka dan tutup tempat-tempat hiburan seperti karaoke dan tempat lokasi dan hal ini tidak sesuai dengan ketentuan PERDA tersebut. Peneliitan ini merupahkan kajian yang berbentuk study lapangan ( Field Research) yang dilakukan dikota Pekanbaru denganbatasan Kec. Lima Puluh. Alasan peneliti untuk memilih lokasi ini sebagai lokasi penelitian adalah karena lokasi tersebut terjangkau dengan waktu yang terbatas, dan peneliti lebih mengenal objek penelitian diwilayah tersebut.Subjek dalam penelitan ini adalah pengelola karoke keluarga, pengunjung karoke keluarga, serta masyarakat disekitar karoke keluarga, Sedangkan yang menjadi Objek dalam penelitian ini adalah implementasi Pelaksanaan Perda N0.3 tahun 2002 atas izin penyelenggaraan hiburan khususnya Karaoke Keluarga. Dengan demikian Populasi dalam penelitan ini adalah pengelola, pengunjung dan masyarakat yang tinggal di sekitar KTV yang beroperasi di Kecamatan Lima Puluh, yaitu KTV Furaya, KTV XP 88,KTV Holywood, KTV MP Entertaimen, dan Garden KTV. Karena keterbatasan penulis maka penulis mengambil dua lokasi yaitu KTV Furaya dan KTV XP 88 dengan jumlah sample keseluruhan adalah 46 Orang.Tehknik Pengumpulan Data yang digunakan adalah wawancara , observasi dan angket. Metode yang dipakai dalam analisis masalah ini adalah methode analisa data kualitatif. Di akhr penelitian penulis menemukan bahwa dalam implementasi PERDA no 32 tahun 2002 ditemukan adanya penyalahgunaan izin dalam empat hal. Penyalahgunaan izin yang pertama adalah dalam hal mengoperasikan tempat hiburan hingga pukul 02.00 WIB, padahal dalamPerda tersebut dinyatakan bahwa pengelola hiburan maksimal beroperasi hingga pukul 22..00 WIB. Penyalahgunaan izin yang kedua, sebagian besar tempat hiburan KotaPekanbaru masih menyimpang dari peruntukannya, sebagian besar dari mereka mengantongi izin sebagai Hotel dan Rumah makan. Penyalahgunaan izin yang ketiga adanyasejumlah tempat hiburan malam sudah beroperasi namun tidak menyesuaikan jarak lokasi sesuai peraturan daerah. Penyalahgunaan izin yang keempat adanya tempat hiburan karaoke yang baru dibuat dengan jarak kurang dari 1000 meter dari tempat ibadah, sekolah atau pendidikan dan perumahan.Mengingat banyaknya dampak negatif dari pelaksanaan dan penyalahgunaan perda ini maka berdasarkan metode syad zari`ah perda ini harus ditinjau ulang kembali pelaksanaannya

Monitoring Public Behavior During a Pandemic Using Surveys: Proof-of-Concept Via Epidemic Modelling

Implementing a lockdown for disease mitigation is a balancing act: Non-pharmaceutical interventions can reduce disease transmission significantly, but interventions also have considerable societal costs. Therefore, decision-makers need near real-time information to calibrate the level of restrictions. We fielded daily surveys in Denmark during the second wave of the COVID-19 pandemic to monitor public response to the announced lockdown. A key question asked respondents to state their number of close contacts within the past 24 hours. Here, we establish a link between survey data, mobility data, and, hospitalizations via epidemic modeling. Using Bayesian analysis, we then evaluate the usefulness of survey responses as a tool to monitor the effects of lockdown and then compare the predictive performance to that of mobility data. We find that, unlike mobility, self-reported contacts track the immediate behavioral response after the lockdown's announcement, weeks before the lockdown's national implementation. The survey data agree with the inferred effective reproduction number and their addition to the model results in greater improvement of predictive performance than mobility data. A detailed analysis of contact types indicates that disease transmission is driven by friends and strangers, whereas contacts to colleagues and family members (outside the household) only played a minor role despite Christmas holidays. Our work shows that an announcement of non-pharmaceutical interventions can lead to immediate behavioral responses, weeks before the actual implementation. Specifically, we find that self-reported contacts capture this early signal and thus qualify as a reliable, non-privacy invasive monitoring tool to track the implementation of non-pharmaceutical interventions

Contact-based model for epidemic spreading on temporal networks: [Preprint]

We present a contact-based model to study the spreading of epidemics by means of extending the dynamic message passing approach to temporal networks. The shift in perspective from nodeto edge-centric quantities allows to accurately model Markovian susceptible-infected-recovered outbreaks on time-varying trees, i.e., temporal networks with a loop-free underlying topology. On arbitrary graphs, the proposed contact-based model incorporates potential structural and temporal heterogeneity of the underlying contact network and improves analytic estimations with respect to the individual-based (node-centric) approach at a low computational and conceptual cost. Within this new framework, we derive an analytical expression for the epidemic threshold on temporal networks and demonstrate the feasibility on empirical data

Linking social network structure and function to social preferences

Social network structures play an important role in the lives of humans and non-human animals by affecting wellbeing, the spread of disease and information, and evolutionary processes. Nevertheless, we still lack a good understanding of how these structures emerge from individual behaviour. Here we present a general model for the emergence of social structures, which is based on a key aspect of real social systems observed across species, namely social preferences for traits (individual characteristics such as age, sex, etc.). We first show that the model can generate diverse artificial social structures, and consider its potential for being combined with real network data. We then use the model to gain fundamental insights into how two main categories of social preferences (similarity and popularity) affect social structure and function. The results show that the types of social preference, in combination with the types of trait they are used with, can have important consequences for the spread of information and disease, and the robustness of social structures against fragmentation. The results also suggest that symmetric degree distributions could be expected to be common in social networks. More generally, the study implies that trait-based social preferences can have consequences for social systems that go far beyond their effect on direct benefits from social partners. We discuss the implications of the results for social evolution.Comment: 19 pages, + 16 pages supplementary material. 4 figures, + 11 supplementary figure

Early warning of infectious disease outbreaks on cattle-transport networks.

Surveillance of infectious diseases in livestock is traditionally carried out at the farms, which are the typical units of epidemiological investigations and interventions. In Central and Western Europe, high-quality, long-term time series of animal transports have become available and this opens the possibility to new approaches like sentinel surveillance. By comparing a sentinel surveillance scheme based on markets to one based on farms, the primary aim of this paper is to identify the smallest set of sentinel holdings that would reliably and timely detect emergent disease outbreaks in Swiss cattle. Using a data-driven approach, we simulate the spread of infectious diseases according to the reported or available daily cattle transport data in Switzerland over a four year period. Investigating the efficiency of surveillance at either market or farm level, we find that the most efficient early warning surveillance system [the smallest set of sentinels that timely and reliably detect outbreaks (small outbreaks at detection, short detection delays)] would be based on the former, rather than the latter. We show that a detection probability of 86% can be achieved by monitoring all 137 markets in the network. Additional 250 farm sentinels-selected according to their risk-need to be placed under surveillance so that the probability of first hitting one of these farm sentinels is at least as high as the probability of first hitting a market. Combining all markets and 1000 farms with highest risk of infection, these two levels together will lead to a detection probability of 99%. We conclude that the design of animal surveillance systems greatly benefits from the use of the existing abundant and detailed animal transport data especially in the case of highly dynamic cattle transport networks. Sentinel surveillance approaches can be tailored to complement existing farm risk-based and syndromic surveillance approaches

Ranking in evolving complex networks

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google’s PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes

Carbon Nanodots Inhibit Tumor Necrosis Factor-α-Induced Endothelial Inflammation through Scavenging Hydrogen Peroxide and Upregulating Antioxidant Gene Expression in EA.hy926 Endothelial Cells

Carbon nanodots (CNDs) are a new type of nanomaterial with a size of less than 10 nanometers and excellent biocompatibility, widely used in fields such as biological imaging, transmission, diagnosis, and drug delivery. However, its potential and mechanism to mediate endothelial inflammation have yet to be explored. Here, we report that the uptake of CNDs by EA.hy926 endothelial cells is both time and dose dependent. The concentration of CNDs used in this experiment was found to not affect cell viability. TNF-α is a known biomarker of vascular inflammation. Cells treated with CNDs for 24 h significantly inhibited TNF-α (0.5 ng/mL)-induced expression of intracellular adhesion molecule 1 (ICAM-1) and interleukin 8 (IL-8). ICAM-1 and IL-8 are two key molecules responsible for the activation and the firm adhesion of monocytes to activated endothelial cells for the initiation of atherosclerosis. ROS, such as hydrogen peroxide, play an important role in TNF-α-induced inflammation. Interestingly, we found that CNDs effectively scavenged H2O2 in a dose-dependent manner. CNDs treatment also increased the activity of the antioxidant enzyme NQO1 in EA.hy926 endothelial cells indicating the antioxidant properties of CNDs. These results suggest that the anti-inflammatory effects of CNDs may be due to the direct H2O2 scavenging properties of CNDs and the indirect upregulation of antioxidant enzyme NQO1 activity in endothelial cells. In conclusion, CND can inhibit TNF-α-induced endothelial inflammation, possibly due to its direct scavenging of H2O2 and the indirect upregulation of antioxidant enzyme NQO1 activity in endothelial cells

Spreading of infection on temporal networks: an edge-centered perspective

We discuss a continuous-time extension of the contact-based (CB) model, as proposed in [Koher et al. Phys. Rev. X 9, 031017 (2019)], for infections with permanent immunity on temporal networks. At the core of our methodology is a fundamental change to an edge-centered perspective, which allows for an accurate model on temporal networks, where the underlying time-aggregated graph has a tree structure. From the continuous-time CB model, we derive the infection propagator for the low prevalence limit and propose a novel spectral criterion to estimate the epidemic threshold. In addition, we explore the relation between the continuous-time CB model and the previously proposed edge-based compartmental model, as well as the message-passing framework