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1.
Supplementary material for paper Early spread of COVID-19 in Romania: imported cases from Italy and human-to-human transmission networks
Marian-Gabriel Hâncean, Matjaž Perc, Jürgen Lerner, 2020, original scientific article

Abstract: This individual-level dataset describes (a) the early spread of the novel coronavirus (COVID-19) and (b) the first human-to-human transmission networks, in Romania. Specifically, in the first set of data (a), we profile the first 147 cases referring to: whether an individual is an index case, place of residence, sex, age, probable citizenship, probable country and place of infection, arrival date to a Romanian county, COVID-19 confirmation date as well as the sources of information. Also, the second set of data (b) contains the first observed human-to-human COVID-19 transmission networks (attributes of the nodes and the direction of COVID-19 transmission, i.e. who infects whom). Networks embed 159 nodes and 203 transmission ties. Indirect identifiers are masked / de-identified.
Keywords: coronavirus, COVID-19, transmission networks, human mobility, migration, nosocomial infections
Published: 16.12.2020; Views: 152; Downloads: 39
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2.
Early spread of COVID-19 in Romania: imported cases from Italy and human-to-human transmission networks
Marian-Gabriel Hâncean, Matjaž Perc, Jürgen Lerner, 2020, original scientific article

Abstract: We describe the early spread of the novel coronavirus (COVID-19) and the first human-to-human transmission networks, in Romania. We profiled the first 147 cases referring to sex, age, place of residence, probable country of infection, return day to Romania, COVID-19 confirmation date and the probable modes of COVID-19 transmissions. Also, we analysed human-to-human transmission networks and explored their structural features and time dynamics. In Romania, local cycles of transmission were preceded by imported cases, predominantly from Italy. We observed an average of 4.8 days (s.d. = 4.0) between the arrival to a Romanian county and COVID-19 confirmation. Furthermore, among the first 147 COVID-19 patients, 88 were imported cases (64 carriers from Italy), 54 were domestic cases, while for five cases the source of infection was unknown. The early human-to-human transmission networks illustrated a limited geographical dispersion, the presence of super-spreaders and the risk of COVID-19 nosocomial infections. COVID-19 occurred in Romania through case importation from Italy. The largest share of the Romanian diaspora is concentrated especially in the northern parts of Italy, heavily affected by COVID-19. Human mobility (including migration) accounts for the COVID-19 transmission and it should be given consideration while tailoring prevention measures.
Keywords: coronavirus, COVID-19, transmission networks, human mobility, migration, nosocomial infections
Published: 16.12.2020; Views: 154; Downloads: 57
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3.
Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling
Qingyun Wang, Guanrong Chen, Matjaž Perc, 2011, original scientific article

Abstract: This paper investigates the dependence of synchronization transitions of bursting oscillations on the information transmission delay over scale-free neuronal networks with attractive and repulsive coupling. It is shown that for both types of coupling, the delay always plays a subtle role in either promoting or impairing synchronization. In particular, depending on the inherent oscillation period of individual neurons, regions of irregular and regular propagating excitatory fronts appear intermittently as the delay increases. These delay-induced synchronization transitions are manifested as well-expressed minima in the measure for spatiotemporal synchrony. For attractive coupling, the minima appear at every integer multiple of the average oscillation period, while for the repulsive coupling, they appear at every odd multiple of the half of the average oscillation period. The obtained results are robust to the variations of the dynamics of individual neurons, the system size, and the neuronal firing type. Hence, they can be used to characterize attractively or repulsively coupled scale-free neuronal networks with delays.
Keywords: synchronization, neuronal networks, noise, stochastic processes, scale-free networks, information transmission delay
Published: 19.06.2017; Views: 599; Downloads: 307
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4.
ERROR PROBABILITY MODEL FOR IEEE 802.15.4 WIRELESS TRANSMISSION WITH CO-CHANNEL INTERFERENCE AND BACKGROUND NOISE
Uroš Pešović, 2016, doctoral dissertation

Abstract: Data transmission sent through wireless channel is usually affected by background noise, multipath fading and interference which cause data errors. Influence of such disturbances is the most commonly expressed in a form of error probability statistics. Effects of these disturbances on IEEE 802.15.4 wireless transmissions are previously studied, except influence of co-channel interference (CCI) which originates from collision between IEEE 802.15.4 devices which perform simultaneous radio transmission. Our thesis puts forward the assumption that it is possible to derive more accurate analytical error probability model for higher data level error probability parameters without the idealization of PN spreading sequences. Additionally, thesis is that is possible to derive an accurate analytical error probability model in the case of CCI influenced by background noise by consideration of constellation diagram. IEEE 802.15.4 standard uses CSMA/CA (Carrier Sense Multiple Access with Collision Detection) channel access mechanism to prevent collisions between devices, but this mechanism doesn't provide protection from hidden node problem which is primary source of co-channel interference. Using Monte Carlo simulations we determined frequency of hidden node collisions, which shown that co-channel interference frequently occur in parts of the network with high traffic load. Some prior works in this field tend to idealize these non-ideal spreading sequences in order to simplify calculations for error probability parameters. Our doctor thesis presents analytical model of data level error probability parameters (symbol, bit and packet) for IEEE 802.15.4, which uses original non-ideal spreading sequences without their idealization. Proposed error probability model consists of mutually dependent chip, symbol, bit and packet error probability models. Derived error probability models are linked together, so each of error probability parameters can be determined using error probability parameter from the previous stage. Error probability model for IEEE 802.15.4 wireless communication could be used in network simulation tools in order to accurately simulate energy efficient medium access protocols in realistic scenarios. Presented theoretical results are tested by independent numerical simulation of IEEE 802.15.4 transmission according to Monte Carlo method. Simulation results shows that derived models for error probability parameters were matched by two simulation scenarios in background noise, for multipath fading and co-channel interface, respectively Furthermore, the accuracy of derived mathematical models was tested in real-world experiment using IEEE 802.15.4 compliant wireless transceivers for creating co-channel interference. Packets were received by software defined radio platform, which enabled realization of coherent receiver in which all error probability statistics could be collected. The results of the experiment show consistency with proposed analytical error probability models, but some deviations are caused by poor preamble synchronization under low SNR (Signal to Noise Ratio) value. The thesis was proved with Monte Carlo simulations of the physical level of the IEEE 802.15.4 communication and experimental measurements on a real physical communication system.
Keywords: IEEE 802.15.4 standard; error probability model; co-channel interference; Rician fading channel; additive white Gaussian noise; wireless transmission, wireless sensor networks, numerical simulations, software defined radio
Published: 14.10.2016; Views: 1421; Downloads: 79
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