Bounding pandemic spread by heat spread.

The beginning of a pandemic is a crucial stage for policymakers. Proper management at this stage can reduce overall health and economical damage. However, knowledge about the pandemic is insufficient. Thus, the use of complex and sophisticated models is challenging. In this study, we propose analytical and stochastic heat spread-based boundaries for the pandemic spread as indicated by the Susceptible-Infected-Recovered (SIR) model. We study the spread of a pandemic on an interaction (social) graph as a diffusion and compared it with the stochastic SIR model. The proposed boundaries are not requiring accurate biological knowledge such as the SIR model does.

Contextualizing Emerging Trends in Financial News Articles

Identifying and exploring emerging trends in news is becoming more essential than ever with many changes occurring around the world due to the global health crises. However, most of the recent research has focused mainly on detecting trends in social media, thus, benefiting from social features (e.g. likes and retweets on Twitter) which helped the task as they can be used to measure the engagement and diffusion rate of content. Yet, formal text data, unlike short social media posts, comes with a longer, less restricted writing format, and thus, more challenging. In this paper, we focus our study on emerging trends detection in financial news articles about Microsoft, collected before and during the start of the COVID-19 pandemic (July 2019 to July 2020). We make the dataset accessible and we also propose a strong baseline (Contextual Leap2Trend) for exploring the dynamics of similarities between pairs of keywords based on topic modeling and term frequency. Finally, we evaluate against a gold standard (Google Trends) and present noteworthy real-world scenarios regarding the influence of the pandemic on Microsoft. ©2022 Association for Computational Linguistics.

Understanding the E-Banking Channel Selection Behavior of Elderly Customers: A Small-World Network Perspective

To reveal the influence mechanism of e-banking channel selection of elderly customers, according to the analysis of elderly customers'decision-making process, a threshold model is proposed by using small world customer relationship network and variable setting in this study. The multi-agent simulation of e-banking channel selection behavior of elderly customers is carried out from the perspectives of channel diffusion speed and customer channel selection proportion in the context of Covid-19 pandemic. The research shows that channel performance and individual differences of customers affect the adoption of e-banking by elderly customers. This study also has found that network size and network density can regulate the impact of channel performance on the selection behavior of elderly groups. However, they could play a regulatory role under certain conditions. Finally, this study puts forward some suggestions to improve the channel diffusion efficiency, such as building an elderly friendly e-financial service channel and construction of elderly business market culture.

A general urban spreading pattern of COVID-19 and its underlying mechanism

Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

A general urban spreading pattern of COVID-19 and its underlying mechanism

Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

Infection spreading in cell culture as a reaction-diffusion wave: RAIRO: Numerical Analysis

Infection spreading in cell culture occurs due to virus replication in infected cells and its random motion in the extracellular space. Multiplicity of infection experiments in cell cultures are conventionally used for the characterization of viral infection by the number of viral plaques and the rate of their growth. We describe this process with a delay reaction-diffusion system of equations for the concentrations of uninfected cells, infected cells, virus, and interferon. Time delay corresponds to the duration of viral replication inside infected cells. We show that infection propagates in cell culture as a reaction-diffusion wave, we determine the wave speed and prove its existence. Next, we carry out numerical simulations and identify three stages of infection progression: infection decay during time delay due to virus replication, explosive growth of viral load when infected cells begin to reproduce it, and finally, wave-like infection progression in cell culture characterized by a constant or slowly growing total viral load. The modelling results are in agreement with the experimental data for the coronavirus infection in a culture of epithelial cells and for some other experiments. The presence of interferon produced by infected cells decreases the viral load but does not change the speed of infection progression in cell culture. In the 2D modelling, the total viral load grows faster than in the 1D case due to the increase of plaque perimeter.

The Diffusion Behavior of CO2 Adsorption from a CO2/N2 Gas Mixture on Zeolite 5A in a Fixed-Bed Column

The objective of this research was to investigate the behavior and conditions for CO2 adsorption using a mixture of CO2/N2 over a fixed-bed column of zeolite 5A. The study was performed with a variation in gas composition of CO2/N2 as a 20/80, 50/50, and 80/20 volume %, the adsorption temperatures as 298, 333, and 373 K and the total feed flow rates as 1, 2, and 4 L/h under 100 kPa pressure. The Bohart–Adams, Yoon–Nelson, and Thomas models were used to predict the breakthrough behavior of CO2 adsorption in a fixed column. Furthermore, the adsorption mechanism has been investigated using the kinetics adsorption of pseudo-first-order, pseudo-second-order, Boyd model, and intraparticle model. Increasing the CO2 composition of a gas mixture resulted in a high CO2 adsorption capacity because of the high partial pressure of CO2. The capacity of CO2 adsorption was decreased with increasing temperature because of physical adsorption with an exothermic reaction. The CO2 adsorption capacity was also decreased with increasing feed flow rates with inadequate time for CO2 adsorbates diffusion into the pores of the adsorbent before exiting the packed bed. The CO2 adsorption by zeolite 5A confirmed that the physical adsorption with intraparticle diffusion was the rate-controlling step of the whole process.

Understanding the E-Banking Channel Selection Behavior of Elderly Customers: A Small-World Network Perspective

To reveal the influence mechanism of e-banking channel selection of elderly customers, according to the analysis of elderly customers’decision-making process, a threshold model is proposed by using small world customer relationship network and variable setting in this study. The multi-agent simulation of e-banking channel selection behavior of elderly customers is carried out from the perspectives of channel diffusion speed and customer channel selection proportion in the context of Covid-19 pandemic. The research shows that channel performance and individual differences of customers affect the adoption of e-banking by elderly customers. This study also has found that network size and network density can regulate the impact of channel performance on the selection behavior of elderly groups. However, they could play a regulatory role under certain conditions. Finally, this study puts forward some suggestions to improve the channel diffusion efficiency, such as building an elderly friendly e-financial service channel and construction of elderly business market culture.

SARS-CoV-2 Dynamics in the Mucus Layer of the Human Upper Respiratory Tract Based on Host–Cell Dynamics

A thorough understanding of the inhalation dynamics of infectious aerosols indoors and infection dynamics within the host by inhaled viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in the assessment and control of infection risks indoors. Here, by combining computational fluid–particle dynamics (CFPD) and host–cell dynamics (HCD), SARS-CoV-2 infection dynamics in the mucus layer of the human upper airway were studied. To reproduce the diffusive and convective transport of the virus in the nasal cavity–nasopharynx by mucociliary motion, a three-dimensional (3D)-shell model with a mucus layer was developed. The initial virus concentrations for HCD calculation were estimated based on the deposition distribution of droplets with representative sizes analyzed by CFPD. To develop a new HCD model, the target-cell-limited model was integrated with the convection–diffusion equation. Additionally, the sensitivity of the infection rate β to the infection dynamics was systematically investigated. The results showed that the time series of SARS-CoV-2 concentration in the mucus layer strongly depended on diffusion, convection, and β. Although the SARS-CoV-2 dynamics obtained here have not been verified by corresponding clinical data, they can preliminarily reveal its transmission mode in the upper airway, which will contribute to the prevention and treatment of coronavirus disease 2019.

Spatial Distribution of Exhalation Droplets in the Bus in Different Seasons

In closed buses, the spread of droplets with viruses/bacteria may cause the spread of respiratory infectious diseases. Discrete phase modeling is used to simulate the diffusion characteristics and concentration distribution of droplets at different temperatures and different exhalation positions by ANSYS FLUENT software. The integral concentration of droplets at different locations can be quantified, which leads to identification of low-risk areas and high-risk areas in the bus. Results show that a higher outdoor temperature leads to lower droplets’ diffusion speed and longer time until the droplets reach the driver. In addition, based on the integral concentration of droplets at the seats, regardless of whether a passenger exhales droplets in the front row of the bus, the position of the rear door or the last row of the bus, the seats in the last row of the bus away from the door belong to the low-risk area. In contrast, the seats near the door and the middle seat in the bus are higher risk areas. Consequently, this study proposed sitting on a seat in the low-risk area as a means to reduce the risk of passengers. Moreover, safety protection facilities around the driver should be modified to improve the isolation of the upper area of the driver’s location, so as to effectively prevent the droplet diffusion towards the driver, thereby effectively reducing the driver’s risk of infection.

Front Propagation of Exponentially Truncated Fractional-Order Epidemics

The existence of landscape constraints in the home range of living organisms that adopt Lévy-flight movement patterns, prevents them from making arbitrarily large displacements. Their random movements indeed occur in a finite space with an upper bound. In order to make realistic models, by introducing exponentially truncated Lévy flights, such an upper bound can thus be taken into account in the reaction-diffusion models. In this work, we have investigated the influence of the λ-truncated fractional-order diffusion operator on the spatial propagation of the epidemics caused by infectious diseases, where λ is the truncation parameter. Analytical and numerical simulations show that depending on the value of λ, different asymptotic behaviours of the travelling-wave solutions can be identified. For small values of λ (λ≳0), the tails of the infective waves can decay algebraically leading to an exponential growth of the epidemic speed. In that case, the truncation has no impact on the superdiffusive epidemics. By increasing the value of λ, the algebraic decaying tails can be tamed leading to either an upper bound on the epidemic speed representing the maximum speed value or the generation of the infective waves of a constant shape propagating at a minimum constant speed as observed in the classical models (second-order diffusion epidemic models). Our findings suggest that the truncated fractional-order diffusion equations have the potential to model the epidemics of animals performing Lévy flights, as the animal diseases can spread more smoothly than the exponential acceleration of the human disease epidemics.