Sudan virus disease super-spreading, Uganda, 2022.

BACKGROUND: On 20 September 2022, Uganda declared its fifth Sudan virus disease (SVD) outbreak, culminating in 142 confirmed and 22 probable cases. The reproductive rate (R) of this outbreak was 1.25. We described persons who were exposed to the virus, became infected, and they led to the infection of an unusually high number of cases during the outbreak. METHODS: In this descriptive cross-sectional study, we defined a super-spreader person (SSP) as any person with real-time polymerase chain reaction (RT-PCR) confirmed SVD linked to the infection of ≥ 13 other persons (10-fold the outbreak R). We reviewed illness narratives for SSPs collected through interviews. Whole-genome sequencing was used to support epidemiologic linkages between cases. RESULTS: Two SSPs (Patient A, a 33-year-old male, and Patient B, a 26-year-old male) were identified, and linked to the infection of one probable and 50 confirmed secondary cases. Both SSPs lived in the same parish and were likely infected by a single ill healthcare worker in early October while receiving healthcare. Both sought treatment at multiple health facilities, but neither was ever isolated at an Ebola Treatment Unit (ETU). In total, 18 secondary cases (17 confirmed, one probable), including three deaths (17%), were linked to Patient A; 33 secondary cases (all confirmed), including 14 (42%) deaths, were linked to Patient B. Secondary cases linked to Patient A included family members, neighbours, and contacts at health facilities, including healthcare workers. Those linked to Patient B included healthcare workers, friends, and family members who interacted with him throughout his illness, prayed over him while he was nearing death, or exhumed his body. Intensive community engagement and awareness-building were initiated based on narratives collected about patients A and B; 49 (96%) of the secondary cases were isolated in an ETU, a median of three days after onset. Only nine tertiary cases were linked to the 51 secondary cases. Sequencing suggested plausible direct transmission from the SSPs to 37 of 39 secondary cases with sequence data. CONCLUSION: Extended time in the community while ill, social interactions, cross-district travel for treatment, and religious practices contributed to SVD super-spreading. Intensive community engagement and awareness may have reduced the number of tertiary infections. Intensive follow-up of contacts of case-patients may help reduce the impact of super-spreading events.

Bacterial burden and molecular characterization of Coxiella burnetii in shedding pregnant and postpartum ewes from Saint Kitts.

This study aimed to evaluate the bacterial burden and perform molecular characterization of Coxiella burnetii during shedding in pregnant (vaginal, mucus and feces) and postpartum (vaginal mucus, feces and milk) ewes from Saint Kitts. Positive IS1111 DNA (n=250) for C. burnetii samples from pregnant (n=87) and postpartum (n=74) Barbados Blackbelly ewes in a previous investigation were used for this study. Vaginal mucus (n=118), feces (n=100), and milk (n=32) positive IS1111 C. burnetii-DNA were analysed by real time qPCR (icd gene). For molecular characterization of C. burnetii, selected (n=10) IS1111 qPCR positive samples were sequenced for fragments of the IS1111 element and the 16 S rRNA gene. nBLAST, phylogenetic and haplotype analyses were performed. Vaginal mucus, feces and milk had estimated equal amounts of bacterial DNA (icd copies), and super spreaders were detected within the fecal samples. C. burnetii haplotypes had moderate to high diversity, were ubiquitous worldwide and similar to previously described in ruminants and ticks and humans.

SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks.

Identifying influential spreaders in complex networks is critical for information spread and malware diffusion suppression. In this paper, we propose a novel influential spreader identification method, called SpreadRank, which considers the path reachability in information spreading and uses its quantitative index as a measure of node spread centrality to obtain the spread influence of a single node. To avoid the overlapping of the influence range of the node spread, this method establishes a dynamic influential node set selection mechanism based on the spread centrality value and the principle of minimizing the maximum connected branch after network segmentation, and it selects a group of nodes with the greatest overall spread influence. Experiments based on the SIR model demonstrate that, compared to other existing methods, the selected influential spreaders of SpreadRank can quickly diffuse or suppress information more effectively.

Identifying Potential Super-Spreaders and Disease Transmission Hotspots Using White-Tailed Deer Scraping Networks.

White-tailed deer (Odocoileus virginianus, WTD) spread communicable diseases such the zoonotic coronavirus SARS-CoV-2, which is a major public health concern, and chronic wasting disease (CWD), a fatal, highly contagious prion disease occurring in cervids. Currently, it is not well understood how WTD are spreading these diseases. In this paper, we speculate that "super-spreaders" mediate disease transmission via direct social interactions and indirectly via body fluids exchanged at scrape sites. Super-spreaders are infected individuals that infect more contacts than other infectious individuals within a population. In this study, we used network analysis from scrape visitation data to identify potential super-spreaders among multiple communities of a rural WTD herd. We combined local network communities to form a large region-wide social network consisting of 96 male WTD. Analysis of WTD bachelor groups and random network modeling demonstrated that scraping networks depict real social networks, allowing detection of direct and indirect contacts, which could spread diseases. Using this regional network, we model three major types of potential super-spreaders of communicable disease: in-degree, out-degree, and betweenness potential super-spreaders. We found out-degree and betweenness potential super-spreaders to be critical for disease transmission across multiple communities. Analysis of age structure revealed that potential super-spreaders were mostly young males, less than 2.5 years of age. We also used social network analysis to measure the outbreak potential across the landscape using a new technique to locate disease transmission hotspots. To model indirect transmission risk, we developed the first scrape-to-scrape network model demonstrating connectivity of scrape sites. Comparing scrape betweenness scores allowed us to locate high-risk transmission crossroads between communities. We also monitored predator activity, hunting activity, and hunter harvests to better understand how predation influences social networks and potential disease transmission. We found that predator activity significantly influenced the age structure of scraping communities. We assessed disease-management strategies by social-network modeling using hunter harvests or removal of potential super-spreaders, which fragmented WTD social networks reducing the potential spread of disease. Overall, this study demonstrates a model capable of predicting potential super-spreaders of diseases, outlines methods to locate transmission hotspots and community crossroads, and provides new insight for disease management and outbreak prevention strategies.

A mechanics model based on information entropy for identifying influencers in complex networks.

The network, with some or all characteristics of scale-free, self-similarity, self-organization, attractor and small world, is defined as a complex network. The identification of significant spreaders is an indispensable research direction in complex networks, which aims to discover nodes that play a crucial role in the structure and function of the network. Since influencers are essential for studying the security of the network and controlling the propagation process of the network, their assessment methods are of great significance and practical value to solve many problems. However, how to effectively combine global information with local information is still an open problem. To solve this problem, the generalized mechanics model is further improved in this paper. A generalized mechanics model based on information entropy is proposed to discover crucial spreaders in complex networks. The influence of each neighbor node on local information is quantified by information entropy, and the interaction between each node on global information is considered by calculating the shortest distance. Extensive tests on eleven real networks indicate the proposed approach is much faster and more precise than traditional ways and state-of-the-art benchmarks. At the same time, it is effective to use our approach to identify influencers in complex networks.

Comparative evaluation of implant stability using bone expanders and conventional osteotomy.

Aim: The purpose of this study was to compare the implant stability and bone implant contact obtained using bone expanders to conventional osteotomy. Materials and methods: In this multiphasic study, the first phase was conducted on ex vivo porcine models to standardize the procedure and to check its feasibility. The second phase was conducted as human clinical trial. Phase I: A total of 10 implants were placed in the premolar region on five exvivo porcine models in randomized sequence using conventional osteotomy drills and bone expanders/screw spreaders. Implant stability was measured using resonance frequency analyser on the day of implant placement. Radiological analysis was done using micro-CT in two sectional block specimens randomly selected from each study groups. Phase II: Implants were placed on ten patients fulfilling the inclusion criteria. Implants were placed after randomizing the osteotomy sites. Bone expanders were used in 5 sites and conventional osteotomy technique was used in 5 other sites. Implant stability was measured on the day of implant placement and after three months in pre-loaded state using resonance frequency analyser. Results: Phase I: Average implant stability quotient for bone expanders were 71.2% ± 3.8% and 66.4% ± 1.3% for conventional osteotomy respectively. Bone to implant contact ratio values for bone expanders were 84.7% ± 7.9% and conventional osteotomy drills were 66.3% ± 13.6%. Phase II: Average primary stability at the day of surgery was 71.4 ± 1.3 for bone expanders and 65.6 ± 2.4 for conventional osteotomy drills. After three months (per-loaded state), average primary stability of bone expanders were 74.8 ± 1.1 and conventional osteotomy drills were 71.8 ± 2.5. Conclusion: The bone expanders used when indicated can enhance implant stability and bone to implant contact. Thus osteotomy by bone expanders may be suggested as a promising method especially in compromised bone.

Online information disorder: fake news, bots and trolls.

Recent years have seen a tremendous increase in the propagation of different types of misinformation and disinformation, including among others fake news, rumours, clickbait and conspiracy theories. Misinformation involved in satire and clickbait, among others, has a different intention from disinformation. Despite many attempts by the research community, the development of technology to assist experts in detecting disinformation remains an open problem due to a number of challenges. For example, there are various biases such as confirmation bias and peer pressure that hinder users from recognizing non-credible information. In addition, fake news is intentionally written to confuse the readers, often containing a mixture of false and real information. To this end, in this editorial we present current challenges in the area of fake news identification and discuss contributions published in our special issue.

Controlling COVID-19 transmission with isolation of influential nodes.

To understand the transmission dynamics of any infectious disease outbreak, identification of influential nodes plays a crucial role in a complex network. In most infectious disease outbreaks, activities of some key nodes can trigger rapid disease transmission in the population. Identification and immediate isolation of those influential nodes can impede the disease transmission effectively. In this paper, the technique for order of preference by similarity to ideal solution (TOPSIS) method with a novel formula has been proposed to detect the influential and top ranked nodes in a complex social network, which involves analyzing and studying of structural organization of a network. In the proposed TOPSIS method, several centrality measures have been used as multi-attributes of a complex social network. A new formula has been designed for calculating the transmission probability of an epidemic disease to identify the impact of isolating influential nodes. To verify the robustness of the proposed method, we present a comprehensive comparison with five node-ranking methods, which are being used currently for assessing the importance of nodes. The key nodes can be considered as a person, community, cluster or a particular area. The Susceptible-infected-recovered (SIR) epidemic model is exploited in two real networks to examine the spreading ability of the nodes, and the results illustrate the effectiveness of the proposed method. Our findings have unearthed that quarantine or isolation of influential nodes following proper health protocols can play a pivotal role in curbing the transmission rate of COVID-19.

Modeling the dynamics of coronavirus with super-spreader class: A fractal-fractional approach.

Super-spreaders of the novel coronavirus disease (or COVID-19) are those with greater potential for disease transmission to infect other people. Understanding and isolating the super-spreaders are important for controlling the COVID-19 incidence as well as future infectious disease outbreaks. Many scientific evidences can be found in the literature on reporting and impact of super-spreaders and super-spreading events on the COVID-19 dynamics. This paper deals with the formulation and simulation of a new epidemic model addressing the dynamics of COVID-19 with the presence of super-spreader individuals. In the first step, we formulate the model using classical integer order nonlinear differential system composed of six equations. The individuals responsible for the disease transmission are further categorized into three sub-classes, i.e., the symptomatic, super-spreader and asymptomatic. The model is parameterized using the actual infected cases reported in the kingdom of Saudi Arabia in order to enhance the biological suitability of the study. Moreover, to analyze the impact of memory index, we extend the model to fractional case using the well-known Caputo-Fabrizio derivative. By making use of the Picard-Lindelöf theorem and fixed point approach, we establish the existence and uniqueness criteria for the fractional-order model. Furthermore, we applied the novel fractal-fractional operator in Caputo-Fabrizio sense to obtain a more generalized model. Finally, to simulate the models in both fractional and fractal-fractional cases, efficient iterative schemes are utilized in order to present the impact of the fractional and fractal orders coupled with the key parameters (including transmission rate due to super-spreaders) on the pandemic peaks.

An Efficient Partition-Based Approach to Identify and Scatter Multiple Relevant Spreaders in Complex Networks.

One of the main problems in graph analysis is the correct identification of relevant nodes for spreading processes. Spreaders are crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases, rumors, and more. Correct identification of spreaders in graph analysis is a relevant task to optimally use the network structure and ensure a more efficient flow of information. Additionally, network topology has proven to play a relevant role in the spreading processes. In this sense, more of the existing methods based on local, global, or hybrid centrality measures only select relevant nodes based on their ranking values, but they do not intentionally focus on their distribution on the graph. In this paper, we propose a simple yet effective method that takes advantage of the underlying graph topology to guarantee that the selected nodes are not only relevant but also well-scattered. Our proposal also suggests how to define the number of spreaders to select. The approach is composed of two phases: first, graph partitioning; and second, identification and distribution of relevant nodes. We have tested our approach by applying the SIR spreading model over nine real complex networks. The experimental results showed more influential and scattered values for the set of relevant nodes identified by our approach than several reference algorithms, including degree, closeness, Betweenness, VoteRank, HybridRank, and IKS. The results further showed an improvement in the propagation influence value when combining our distribution strategy with classical metrics, such as degree, outperforming computationally more complex strategies. Moreover, our proposal shows a good computational complexity and can be applied to large-scale networks.

Patterns and descriptors of COVID-19 testing and lab-confirmed COVID-19 incidence in Manitoba, Canada, March 2020-May 2021: A population-based study.

BACKGROUND: We studied lab-confirmed COVID-19 infection (LCCI) testing, incidence, and severity. METHODS: We included all Manitoba residents and limited our severity analysis to LCCI patients. We calculated testing, incidence and vaccination rates between March 8, 2020 and June 1, 2021. We estimated the association between patient characteristics and testing (rate ratio [RR]; Poisson regression), including the reason for testing (screening, symptomatic, contact/outbreak asymptomatic), incidence (hazard ratio [HR]; Cox regression), and severity (prevalence ratio [PR], Cox regression). FINDINGS: The overall testing rate during the second/third wave was 570/1,000 person-years, with an LCCI rate of 50/1,000 person-years. The secondary attack rate during the second/third wave was 16%. Across regions, young children (<10) had the lowest positivity for symptomatic testing, the highest positivity for asymptomatic testing, and the highest risk of LCCI as asymptomatic contact. People in the lowest income quintile had the highest risk of LCCI, 1.3-6x the hazard of those in the highest income quintile. Long-term care (LTC) residents were particularly affected in the second wave with HRs>10 for asymptomatic residents. INTERPRETATION: Although the severity of LCCI in children was low, they have a high risk of asymptomatic positivity. The groups most vulnerable to LCCI, who should remain a focus of public health, were residents of Manitoba's North, LTC facilities, and low-income neighbourhoods. FUNDING: Canada Research Chair Program.

Seed Dibbling Method for the Growth of High-Quality Diamond on GaN.

The integration of diamond and GaN has been highly pursued for thermal management purposes as well as combining their exceptional complementary properties for power electronics applications and novel semiconductor heterostructures. However, the growth of diamond-on-GaN is challenging due to the high lattice and thermal expansion mismatches. The weak adhesion of diamond to GaN and high residual stresses after the deposition often result in the diamond film delamination or development of cracks, which hinder the subsequent device fabrication. Here, we present a new seed dibbling method for seeding and growing high-quality diamond films on foreign substrates, in particular on cost-effective GaN-on-Si, with significantly improved adhesion. Diamond films grown conformally on patterned GaN-on-Si presented high quality with significantly larger grains and a 95% sp3/sp2 ratio, excellent interface between diamond and GaN, and lower residual stresses (as low as 0.2 GPa) compared to conventional methods. In addition, the method provided excellent adhesion, enabling a reliable polishing of the as-grown diamond films on GaN on Si without any delamination, resulting in smooth diamond-on-GaN substrates with subnanometer root-mean-square roughness. Diamond layers deposited via seed dibbling resulted in a 2-fold improvement in the effective thermal conductivity for GaN-on-Si with only a 20 µm thick diamond layer. This method opens many new possibilities for the development of high-performance power electronic devices and integrated devices with excellent thermal management based on a diamond-on-GaN platform. In addition, this technique could be extended to other substrates to combine the outstanding properties of diamond with other kinds of devices.

Using Soccer Games as an Instrument to Forecast the Spread of COVID-19 in Europe.

We provide strong empirical support for the contribution of soccer games held in Europe to the spread of the COVID-19 virus in March 2020. We analyze more than 1,000 games across 194 regions from 10 European countries. Daily cases of COVID-19 grow significantly faster in regions where at least one soccer game took place two weeks earlier, consistent with the existence of an incubation period. These results weaken as we include stadiums with smaller capacity. We discuss the relevance of these variables as instruments for the identification of the causal effect of COVID-19 on firms, the economy, and financial markets.

Real-time PCR on skin biopsies for super-spreaders' detection in bovine besnoitiosis.

BACKGROUND: Bovine besnoitiosis, an emerging disease in Europe that can be transmitted by vectors, is caused by the apicomplexan Besnoitia besnoiti. Bovine besnoitiosis is difficult to control due to the complexity of its diagnosis in the acute stage of the disease, poor treatment success and chronically asymptomatic cattle acting as parasite reservoirs. When serological prevalence is low, detection and specific culling of seropositive cattle is feasible; however, economic considerations preclude this approach when serological prevalence is high. The aims of this study were to evaluate the accuracy of detection of super-spreaders in highly infected herds and to test their selective elimination as a new control strategy for bovine besnoitiosis. METHODS: Previous real-time PCR analyses performed on skin tissues from 160 asymptomatic animals sampled at slaughterhouses showed that the tail base was the best location to evaluate the dermal parasite DNA load. All seropositive animals (n = 518) from eight dairy or beef cattle farms facing a high serological prevalence of besnoitiosis were sampled at the tail base and their skin sample analysed by real-time PCR. A recommendation of rapid and selective culling of super-spreaders was formulated and provided to the cattle breeders. Subsequent serological monitoring of naïve animals was used to evaluate the interest of this control strategy over time. RESULTS: Among the 518 seropositive animals, a low proportion of individuals (14.5%) showed Cq values below 36, 17.8% had doubtful results (36 < Cq ≤ 40) and 67.8% had negative PCR results. These proportions were grossly similar on the eight farms, regardless of their production type (beef or dairy cattle), size, geographical location or history of besnoitiosis. Within two weeks of the biopsy, the rapid culling of super-spreaders was implemented on only three farms. The numbers of newly infected animals were lower on these farms compared to those where super-spreaders were maintained in the herd. CONCLUSIONS: Real-time PCR analyses performed on skin biopsies of seropositive cattle showed huge individual variabilities in parasite DNA load. The rapid culling of individuals considered as super-spreaders seems to be a new and encouraging strategy for bovine besnoitiosis control.

COVID control strategy--is there any light at the end of the tunnel?

CONTEXT: India has been witnessing a huge surge of COVID-19 cases, with increasing number of new cases and deaths daily. There is yet no effective vaccine, drug or strategy to combat this disease. Various models of COVID-19 trend and management have been put forward by different researchers, yet no prediction has yet turned out to be close to the reality. AIMS: To find an effective public health strategy against COVID control. SETTINGS AND DESIGN: Ahmedabad district in Gujarat. METHODS AND MATERIAL: Ahmedabad Model for control of COVID-19 based on Ct threshold has been put forth which stresses upon the fact that higher viral load (super-spreaders) could be an important determinant in spreading infections in the community. RESULTS: The cycle threshold (Ct)-based segregation of laboratory-confirmed positive cases along with contact tracing of all of them of previous 5 days has been found to be effective strategy and needs to be adopted for further management. The Ahmedabad model of COVID-19 control was practiced during 3rd week of June 2020 onwards. Following implementation, cases started declining in Ahmedabad district whereas it showed an increasing trend in rest of Gujarat where it was not implemented. CONCLUSIONS: Cases with low viral load may be quarantined at home with standard precaution whereas cases with higher viral load need to be quarantined in institutions (hospital or separate premises away from family).

A graph exploration method for identifying influential spreaders in complex networks.

The problem of identifying the influential spreaders - the important nodes - in a real world network is of high importance due to its theoretical interest as well as its practical applications, such as the acceleration of information diffusion, the control of the spread of a disease and the improvement of the resilience of networks to external attacks. In this paper, we propose a graph exploration sampling method that accurately identifies the influential spreaders in a complex network, without any prior knowledge of the original graph, apart from the collected samples/subgraphs. The method explores the graph, following a deterministic selection rule and outputs a graph sample - the set of edges that have been crossed. The proposed method is based on a version of Rank Degree graph sampling algorithm. We conduct extensive experiments in eight real world networks by simulating the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) epidemic models which serve as ground truth identifiers of nodes spreading efficiency. Experimentally, we show that by exploring only the 20% of the network and using the degree centrality as well as the k-core measure, we are able to identify the influential spreaders with at least the same accuracy as in the full information case, namely, the case where we have access to the original graph and in that graph, we compute the centrality measures. Finally and more importantly, we present strong evidence that the degree centrality - the degree of nodes in the collected samples - is almost as accurate as the k-core values obtained from the original graph.

Estimating finite-population reproductive numbers in heterogeneous populations.

The basic reproductive number, R0, is one of the most important epidemiological quantities. R0 provides a threshold for elimination and determines when a disease can spread or when a disease will die out. Classically, R0 is calculated assuming an infinite population of identical hosts. Previous work has shown that heterogeneity in the host mixing rate increases R0 in an infinite population. However, it has been suggested that in a finite population, heterogeneity in the mixing rate may actually decrease the finite-population reproductive numbers. Here, we outline a framework for discussing different types of heterogeneity in disease parameters, and how these affect disease spread and control. We calculate "finite-population reproductive numbers" with different types of heterogeneity, and show that in a finite population, heterogeneity has complicated effects on the reproductive number. We find that simple heterogeneity decreases the finite-population reproductive number, whereas heterogeneity in the intrinsic mixing rate (which affects both infectiousness and susceptibility) increases the finite-population reproductive number when R0 is small relative to the size of the population and decreases the finite-population reproductive number when R0 is large relative to the size of the population. Although heterogeneity has complicated effects on the finite-population reproductive numbers, its implications for control are straightforward: when R0 is large relative to the size of the population, heterogeneity decreases the finite-population reproductive numbers, making disease control or elimination easier than predicted by R0.

3D Bridged Carbon Nanoring/Graphene Hybrid Paper as a High-Performance Lateral Heat Spreader.

Graphene paper (GP) has attracted great attention as a heat dissipation material due to its unique thermal transfer property exceeding the limit of graphite. However, the relatively poor thermal transfer properties in the normal direction of GP restricts its wider applications in thermal management. In this work, a 3D bridged carbon nanoring (CNR)/graphene hybrid paper is constructed by the intercalation of polymer carbon source and metal catalyst particles, and the subsequent in situ growth of CNRs in the confined intergallery spaces between graphene sheets through thermal annealing. Further investigation demonstrates that the CNRs are covalently bonded to the graphene sheets and highly improve the thermal transport in the normal direction of the CNR/graphene hybrid paper. This full-carbon architecture shows excellent heat dissipation ability and is much more efficient in removing hot spots than the reduced GP without CNR bridges. This highly thermally conductive CNR/graphene hybrid paper can be easily integrated into next generation commercial high-power electronics and stretchable/foldable devices as high-performance lateral heat spreader materials. This full-carbon architecture also has a great potential in acting as electrodes in supercapacitors or hydrogen storage devices due to the high surface area.

Generality of endemic prevalence formulae.

In simple infection models, the susceptible proportion s(*) in endemic equilibrium is related to the basic reproduction number R0 by s(*)=1/R0. We investigate the extent to which this relationship remains valid under more realistic modelling assumptions. In particular, we relax the biologically implausible assumptions that individuals' lifetimes and infectious periods follow exponential distributions; allow a general recruitment process; allow for multiple stages of infection; and consider extension to a multigroup model in which the groups may represent, for instance, spatial heterogeneity, or the existence of super-spreaders. For a homogeneous population, we find that: (i) the susceptible proportion is s(*)=1/R0(e), where R0(e) is a modified reproduction number, equal to R0 only in certain circumstances; (ii) the proportions of the population in each stage of infection are proportional to the expected time spent by an infected individual in that stage before recovery or death. We demonstrate robustness of the formula s(*)=1/R0 for many human infections by noting conditions under which R0(e) is approximately equal to R0, while pointing out other circumstances under which this approximation fails. For heterogeneous populations, the formula s(*)=1/R0 does not hold in general, but we are able to exhibit symmetry conditions under which it is valid.