Identifying Multiple Influential Spreaders in Complex Networks by Considering the Dispersion of Nodes

Identifying multiple influential spreaders, which relates to finding k (k > 1) nodes with the most significant influence, is of great importance both in theoretical and practical applications. It is usually formulated as a node-ranking problem and addressed by sorting spreaders’ influence as measured based on the topological structure of interactions or propagation process of spreaders. However, ranking-based algorithms may not guarantee that the selected spreaders have the maximum influence, as these nodes may be adjacent, and thus play redundant roles in the propagation process. We propose three new algorithms to select multiple spreaders by taking into account the dispersion of nodes in the following ways: (1) improving a well-performed local index rank (LIR) algorithm by extending its key concept of the local index (an index measures how many of a node’s neighbors have a higher degree) from first-to second-order neighbors; (2) combining the LIR and independent set (IS) methods, which is a generalization of the coloring problem for complex networks and can ensure the selected nodes are non-adjacent if they have the same color; (3) combining the improved second-order LIR method and IS method so as to make the selected spreaders more disperse. We evaluate the proposed methods against six baseline methods on 10 synthetic networks and five real networks based on the classic susceptible-infected-recovered (SIR) model. The experimental results show that our proposed methods can identify nodes that are more influential. This suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.

A molecular surveillance-guided vector control response to concurrent dengue and West Nile virus outbreaks in a COVID-19 hotspot of Florida

Simultaneous dengue virus (DENV) and West Nile virus (WNV) outbreaks in Florida, USA, in 2020 resulted in 71 dengue virus serotype 1 and 86 WNV human cases. Our outbreak response leveraged a molecular diagnostic screen of mosquito populations for DENV and WNV in Miami-Dade County to quickly employ targeted mosquito abatement efforts. We detected DENV serotypes 2 and 4 in mosquito pools, highlighting the silent circulation of diverse dengue serotypes in mosquitoes. Additionally, we found WNV-positive mosquito pools in areas with no historical reports of WNV transmission. These findings demonstrate the importance of proactive, strategic arbovirus surveillance in mosquito populations to prevent and control outbreaks, particularly when other illnesses (e.g., COVID-19), which present with similar symptoms are circulating concurrently. Growing evidence for substantial infection prevalence of dengue in competent mosquito vectors in the absence of local index cases suggests a higher level of dengue endemicity in Florida than previously thought.

A blockchain-assisted framework for secure and reliable data sharing in distributed systems

The explosive growth of big data is pushing forward the paradigm of cloud-based data store today. Among other, distributed storage systems are widely adopted due to their superior performance and continuous availability. However, due to the potentially wide attacking surfaces of the public cloud, outsourcing data store inevitably raises new concerns on user privacy exposure and unauthorized data access. Besides, directly introducing a centralized third-party authority for query authorization management does not work because it still can be compromised. In this paper, we propose a blockchain-assisted framework that can support trustworthy data sharing services. In particular, data owners allow to outsource their sensitive data to distributed systems in encrypted form. By leveraging smart contracts of blockchain, a data owner can distribute secret keys for authorized users without extra round interaction to generate the permitted search tokens. Meanwhile, such blockchain-assisted framework naturally solves the trust issues of query authorization. Besides, we devise a secure local index framework to support encrypted keyword search with forward privacy and mitigate blockchain overhead. To validate our design, we implement the prototype and deploy it at Amazon Cloud. Extensive experiments demonstrate the security, efficiency, and effectiveness of the blockchain-assisted design.

EXPLORATORY SPATIAL ANALYSIS OF HOUSING PRICES OBTAINED FROM WEB SCRAPING TECHNIQUE

The exploratory spatial analysis allows to describe patterns of spatial distribution, to identify clusters and outliers through specific techniques of spatial association and data model. The objective of the study is to verify the spatial autocorrelation between the mean prices of the housing obtained from web scraping technique in online platforms in the city of Salvador, on the coast of northeast Brazil. For this purpose, the Global Moran’s Index (which provides a general measure of association) and the Local Index of Spatial Association (LISA) were calculated. The results of Global Moran’s Index indicate positive autocorrelation between the mean prices of housing prices in the 163 districts of the municipally that are statistically significant, such as identification of clusters through LISA. Thus, the analysis allows to conclude the existence of a heterogeneous pattern in the distribution of these mean prices in the urban space of Salvador.

Direct observation of chaotic resonances in optical microcavities

Optical microcavities play a significant role in the study of classical and quantum chaos. To date, most experimental explorations of their internal wave dynamics have focused on the properties of their inputs and outputs, without directly interrogating the dynamics and the associated mode patterns inside. As a result, this key information is rarely retrieved with certainty, which significantly restricts the verification and understanding of the actual chaotic motion. Here we demonstrate a simple and robust approach to directly and rapidly map the internal mode patterns in chaotic microcavities. By introducing a local index perturbation through a pump laser, we report a spectral response of optical microcavities that is proportional to the internal field distribution. With this technique, chaotic modes with staggered mode spacings can be distinguished. Consequently, a complete chaos assisted tunneling (CAT) and its time-reversed process are experimentally verified in the optical domain with unprecedented certainty.

Multiscale Laser Written Photonic Structures in Bulk Chalcogenide Glasses for Infrared Light Transport and Extraction

Direct ultrafast laser processing is nowadays considered the most flexible technique allowing to generate complex 3D optical functions in bulk glasses. The fact that the built-in optical element is embedded in the material brings several advantages in terms of prototype stability and lifetime, but equally in terms of complexity and number of possible applications, due to the 3D design. The generated optical functions, and in particular the single mode character of the light guiding element alongside the accessibility toward different spectral windows, depend on the refractive index contrast that can be achieved within the material transparency window and on the characteristic dimensions of the optical modification. In particular, the accessibility to the infrared and mid-infrared spectral domains, and to the relevant applications in sensing and imaging, requires increasing the cross-section of the guiding element in order to obtain the desired normalized frequency. Moreover, efficient signal extraction from the transported light requires nanometer size void-like index structures. All this demands a thorough knowledge and an optimal control of the material response within the interaction with the ultrafast laser pulse. We present here an overview of some recent results concerning large-mode-area light transport and extraction in sulfur-based chalcogenide mid-infrared glasses, putting emphasis on the study of the glass response to ultrafast lasers. We then demonstrate the utilization of the achieved optimized local index modifications for building efficient and compact embedded spectrometers (linear optical functions) and saturable absorbers (nonlinear optical functions) for integrated photonic applications in the infrared and mid-infrared spectral ranges.

Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic

Background Unprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic to control the spread of SARS-CoV-2 virus. It is a challenge to implement timely and appropriate public health interventions. Methods and findings Population and COVID-19 epidemiological data between 21st January 2020 to 15th November 2020 from 216 countries and territories were included with the implemented public health interventions. We used deep reinforcement learning, and the algorithm was trained to enable agents to try to find optimal public health strategies that maximized total reward on controlling the spread of COVID-19. The results suggested by the algorithm were analyzed against the actual timing and intensity of lockdown and travel restrictions. Early implementations of the actual lockdown and travel restriction policies, usually at the time of local index case were associated with less burden of COVID-19. In contrast, our agent suggested to initiate at least minimal intensity of lockdown or travel restriction even before or on the day of the index case in each country and territory. In addition, the agent mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the policies implemented by governments, but did not always encourage rapid full lockdown and full border closures. The limitation of this study was that it was done with incomplete data due to the emerging COVID-19 epidemic, inconsistent testing and reporting. In addition, our research focuses only on population health benefits by controlling the spread of COVID-19 without balancing the negative impacts of economic and social consequences. Interpretation Compared to actual government implementation, our algorithm mostly recommended earlier intensity of lockdown and travel restrictions. Reinforcement learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.

Spatial Patterns in Hospital-Acquired Infections in Portugal (2014–2017)

Background: Hospital-Acquired Infections (HAIs) represent the most frequent adverse event associated with healthcare delivery and result in prolonged hospital stays and deaths worldwide. Aim: To analyze the spatial patterns of HAI incidence from 2014 to 2017 in Portugal. Methods: Data from the Portuguese Discharge Hospital Register were used. We selected episodes of patients with no infection on admission and with any of the following HAI diagnoses: catheter-related bloodstream infections, intestinal infections by Clostridium difficile, nosocomial pneumonia, surgical site infections, and urinary tract infections. We calculated age-standardized hospitalization rates (ASHR) by place of patient residence. We used empirical Bayes estimators to smooth the ASHR. The Moran Index and Local Index of Spatial Autocorrelation (LISA) were calculated to identify spatial clusters. Results: A total of 318,218 HAIs were registered, with men accounting for 49.8% cases. The median length of stay (LOS) was 9.0 days, and 15.7% of patients died during the hospitalization. The peak of HAIs (n = 81,690) occurred in 2015, representing 9.4% of the total hospital admissions. Substantial spatial inequalities were observed, with the center region presenting three times the ASHR of the north. A slight decrease in ASHR was observed after 2015. Pneumonia was the most frequent HAI in all age groups. Conclusion: The incidence of HAI is not randomly distributed in the space; clusters of high risk in the central region were seen over the entire study period. These findings may be useful to support healthcare policymakers and to promote a revision of infection control policies, providing insights for improved implementation.