Influence of Human Behavior on COVID-19 Dynamics Based on a Reaction-Diffusion Model.

To investigate the influence of human behavior on the spread of COVID-19, we propose a reaction-diffusion model that incorporates contact rate functions related to human behavior. The basic reproduction number R0 is derived and a threshold-type result on its global dynamics in terms of R0 is established. More precisely, we show that the disease-free equilibrium is globally asymptotically stable if R0≤1; while there exists a positive stationary solution and the disease is uniformly persistent if R0>1. By the numerical simulations of the analytic results, we find that human behavior changes may lower infection levels and reduce the number of exposed and infected humans.

De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein.

The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process. The role of Machine Learning (ML) is critical in the search for novel drug compounds. In the current work, using the equivariant diffusion model, we built novel compounds targeting the spike protein of SARS-CoV-2. Using the ML models, 196 de novo compounds were generated which had no hits on any major chemical databases. These novel compounds fulfilled all the criteria of ADMET properties to be lead-like and drug-like compounds. Of the 196 compounds, 15 were docked with high confidence in the target. These compounds were further subjected to molecular docking, the best compound having an IUPAC name of (4aS,4bR,8aS,8bS)-4a,8a-dimethylbiphenylene-1,4,5,8(4aH,4bH,8aH,8bH)-tetraone and a binding score of -6.930 kcal/mol. The principal compound is labeled as CoECG-M1. Density Function Theory (DFT) and Quantum optimization was carried out along with the study of ADMET properties. This suggests that the compound has potential drug-like properties. The docked complex was further subjected to MD simulations, GBSA, and metadynamics simulations to gain insights into the stability of binding. The model can be in the future modified to improve the positive docking rate.

Efficacy of convective-diffusion models to study the transient behaviour of a sewage-sludge-filled packed column for aqueous phase adsorption of fluoroquinolones: Consideration of pseudo-kinetics driven depletion of species

Ciprofloxacin and ofloxacin belong to a class of antibiotics called Fluoroquinolones (FQs), which have a wide anti-bacterial activity against Gram-positive and Gram-negative bacteria. Since the recent Covid-19 pandemic witnessed a magnanimous rise in the use of antibiotics to prevent secondary bacterial infections, it led to vast production and use of such antibiotics. Ultimately the antibiotics get discharged into the municipal sewer pipes, thereby killing the useful microbial colony. In order to prevent environmental degradation a commercial scale-up of the adsorption of these antibiotics using raw sewage sludge is an absolute necessity. In this study, a continuous adsorption operation is conducted in a packed bed of semi-dried raw sewage sludge to remove the FQs from wastewater. Two transient convective-diffusion models are developed including pseudo-first and second-order kinetics driven depletion terms. The models are optimised using the data collected under various dynamic conditions in order to analyse the performance of the packed bed in terms of bed height, flow rate and initial concentration of the FQs. Damköhler numbers of the FQs are estimated to predict the breakthrough times of both the FQs. The ratios of Damköhler numbers of ciprofloxacin and ofloxacin do not change much with flow rate. In all the experiments, Das << 1 for both the FQs, indicating a faster diffusion process with respect to the rate of pseudo-reaction. Diffusion reaches an ‘equilibrium' well before the reaction achieves pseudo-chemical equilibrium. Ratios of the Damköhler numbers, meant to represent the first-order and second-order convective-diffusion models for ciprofloxacin to ofloxacin is < 1. © 2023 Elsevier Ltd

A New Chapter for Medical Image Generation: The Stable Diffusion Method

Data collecting and sharing have been widely accepted and adopted to improve the performance of deep learning models in almost every field. Nevertheless, in the medical field, sharing the data of patients can raise several critical issues, such as privacy and security or even legal issues. Synthetic medical images have been proposed to overcome such challenges;these synthetic images are generated by learning the distribution of realistic medical images but completely different from them so that they can be shared and used across different medical institutions. Currently, the diffusion model (DM) has gained lots of attention due to its potential to generate realistic and high-resolution images, particularly outperforming generative adversarial networks (GANs) in many applications. The DM defines state of the art for various computer vision tasks such as image inpainting, class-conditional image synthesis, and others. However, the diffusion model is time and power consumption due to its large size. Therefore, this paper proposes a lightweight DM to synthesize the medical image;we use computer tomography (CT) scans for SARS-CoV-2 (Covid-19) as the training dataset. Then we do extensive simulations to show the performance of the proposed diffusion model in medical image generation, and then we explain the key component of the model. © 2023 IEEE.

Parameter Adaptive SEIRD model for Epidemic Prediction

The classical infectious disease diffusion model has a deficiency of static parameters, which will lead to server prediction error. Therefore, this article used three different parameter fitting methods to construct a dynamic update mechanism of outbreak spread parameters and reversed fitting through the actual data of the epidemic. The best epidemic transmission parameters can effectively predict the growth of the outbreak in the next cycle. Then, we take the second wave of the outbreak in India as an example, the dynamic update mechanism of the epidemic spread parameters can effectively improve the accuracy of the prediction of the evolution of the novel coronavirus epidemic. According to the test results,we believe it can help the government make correct decisions, implement effective control and realize the reasonable allocation of emergency resources. © 2022 IEEE.

Policy Driven Epidemiological (PDE) Model for Prediction of COVID-19 in India

The fast spread of COVID-19 has made it a global issue. Despite various efforts, proper forecasting of COVID-19 spread is still in question. Government lockdown policies play a critical role in controlling the spread of coronavirus. However, existing prediction models have ignored lockdown policies and only focused on other features such as age, sex ratio, travel history, daily cases etc. This work proposes a Policy Driven Epidemiological (PDE) Model with Temporal, Structural, Profile, Policy and Interaction Features to forecast COVID-19 in India and its 6 states. PDE model integrates two models: Susceptible-Infected-Recovered-Deceased (SIRD) and Topical affinity propagation (TAP) model to predict the infection spread within a network for a given set of infected users. The performance of PDE model is assessed with respect to linear regression model, three epidemiological models (Susceptible-Infectious-Recovered-Model (SIR), Susceptible-Exposed-Infectious-Recovered-Model (SEIR) and SIRD) and two diffusion models (Time Constant Cascade Model and Time Decay Feature Cascade Model). Experimental evaluation for India and six Indian states with respect to different government policies from 15th June to 30th June, i.e., Maharashtra, Gujarat, Tamil Nadu, Delhi, Rajasthan and Uttar Pradesh divulge that prediction accuracy of PDE model is in close proximity with the real time for the considered time frame. Results illustrate that PDE model predicted the COVID-19 cases up to 94% accuracy and reduced the Normalize Mean Squared Error (NMSE) up to 50%, 35% and 42% with respect to linear regression, epidemiological models and diffusion models, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Factors influencing electronic human resource management implementation in public organisations in an emerging economy: An empirical study

Purpose: Governments in developing countries are riddled with operational inefficiencies. Many have turned to electronic service delivery to address these operational problems. With coronavirus disease 2019 (COVID-19) pandemic, the push for digitalisation has only got stronger. We use the technology acceptance model (TAM) and innovation diffusion model (IDM) to investigate the factors that influence the implementation of electronic human resource management (e-HRM) in selected public organisations in an emerging economy. Design/methodology/approach: Data were collected from key informants composed of human resource (HR) officers, supervisors, line managers and sections of employees in selected public sector organisations. The data were analysed using hierarchical regression techniques. Findings/results: The various dimensions of TAM and IDM were found to contribute to the implementation of e-HRM in public organisations significantly. Specifically, perceived simplicity of usage, perceived usefulness, self-efficacy, compatibility and facilitating conditions showed significant positive effects on e-HRM implementation intentions. Furthermore, compatibility and perceived ease of use significantly predicted perceived usefulness of e-HRM. Practical implications: The influence of the dimensions of TAM and IDM in e-HRM implementation intentions in public institutions in this study dictates that governments in developing nations need to pay attention to both technology features and employee's technology capabilities to ensure smooth digitalisation of government business. Originality/value: The integration of TAM and IDM in assessing e-HRM implementation in a developing nation enriches e-government and HR management literature. Copyright: © 2023. The Authors. Licensee: AOSIS. This work is licensed under the Creative Commons Attribution License.

Pricing extreme mortality risk in the wake of the COVID-19 pandemic

In pricing extreme mortality risk, it is commonly assumed that interest rate and mortality rate are independent. However, the COVID-19 pandemic calls this assumption into question. In this paper, we employ a bivariate affine jump-diffusion model to describe the joint dynamics of interest rate and excess mortality, allowing for both correlated diffusions and joint jumps. Utilizing the latest U.S. mortality and interest rate data, we find a significant negative correlation between interest rate and excess mortality, and a much higher jump intensity when the pandemic experience is considered. Moreover, we construct a risk-neutral pricing measure that accounts for both diffusion and jump risk premia, and we solve for the market prices of risk based on mortality bond prices. Our results show that the pandemic experience can drastically change investors' perception of the mortality risk market in the post-pandemic era. © 2022 Elsevier B.V.

Pricing Extreme Mortality Risk in the Wake of the COVID-19 Pandemic

In pricing extreme mortality risk, it is commonly assumed that interest rate and mortality rate are independent. However, the COVID-19 pandemic calls this assumption into question. In this paper, we employ a bivariate affine jump-diffusion model to describe the joint dynamics of interest rate and excess mortality, allowing for both correlated diffusions and joint jumps. Utilizing the latest U.S. mortality and interest rate data, we find a significant negative correlation between interest rate and excess mortality, and a much higher jump intensity when the pandemic experience is considered. Moreover, we construct a risk-neutral pricing measure that accounts for both diffusion and jump risk premia, and we solve for the market prices of risk based on mortality bond prices. Our results show that the pandemic experience can drastically change investors' perception of the mortality risk market in the post-pandemic era.

Modeling the External, Internal, and Multi-Center Transmission of Infectious Diseases: The COVID-19 Case

We used the Bass model to investigate the transmission dynamics of COVID-19 taking the United States and China as examples. The Bass model was originated from business literature and initially modeled the process of new products getting adopted by the population with an external and internal influence term. First, we fit the cumulative number of confirmed COVID-19 cases in 8 major cities in the United States with the Bass model. The external and internal parameters of Bass were calculated and correlation analyses were performed between the parameters and the volume of traveling across different cities and within a city. The results show that the Bass model fits the epidemics data better than the logistic distribution which only has an internal influence term and the SIR model which is a classical infectious disease model. Besides, there is a significant positive correlation between the external parameter of Bass and the number of passengers at the airport as well as between the internal parameter of Bass and the number of short-distance trips in a city. Therefore, it is closer to true circumstances considering both external and internal transmission rather than assuming a region to be isolated. The external infection rate rises as the number of enplanements rises and the internal infection rate rises as the number of short-distance trips in a city rises. Second, we put forward an adapted multi-center Bass model for the multi-chain COVID-19 transmission in China and compared it with the original Bass model. The results indicated that the accuracy of the multicenter Bass model was higher than that of the original Bass model. In conclusion, the Bass model distinguishes the external and internal effects and is suitable for simulating the spread of COVID-19 and analyzing the infection rate caused by social interactions among different regions and inside a region. The adapted multi-center Bass model commendably described disease transmission when there is more than one transmission center. Our research proves the Bass model to be a useful tool for fine-level analyses on the transmission mechanism of COVID-19. © 2020 Tsinghua University Press.

Structural Effects of Agent Heterogeneity in Agent-Based Models: Lessons from the Social Spread of COVID-19

Modeling human behavior in the context of social systems in which we are embedded realistically requires capturing the underlying heterogeneity in human populations. However, trade-offs associated with different approaches to introducing heterogeneity could either enhance or obfuscate our understanding of outcomes and the processes by which they are generated. Thus, the question arises: how to incorporate heterogeneity when modeling human behavior as part of population-scale phenomena such that greater understanding is obtained? We use an agent-based model to compare techniques of introducing heterogeneity at initialization or generated during the model’s runtime. We show that initializations with unstructured heterogeneity can interfere with a structural understanding of emergent processes, especially when structural heterogeneity might be a key part of driving how behavioral responses dynamically shape emergence in the system. We find that incorporating empirical population heterogeneity – even in a limited sense – can substantially contribute to improved understanding of how the system under study works. © 2022, University of Surrey. All rights reserved.

Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users

In this paper, a homogeneous continuous time Markov chain (CTMC) is used to model information diffusion or dissemination, also to determine influencers on Twitter dynamically. The tweeting process can be modeled with a homogeneous CTMC since the properties of Markov chains are fulfilled. In this case, the tweets that are received by followers only depend on the tweets from the previous followers. Knowledge Discovery in Database (KDD) in Data Mining is used to be research methodology including pre-processing, data mining process using homogeneous CTMC, and post-pro-cessing to get the influencers using visualization that predicts the number of affected users. We assume the number of affected users follows a logarithmic function. Our study examines the Indonesian Twitter data users with tweets about covid19 vaccination resulted in dynamic influencer rankings over time. From these results, it can also be seen that the users with the highest number of followers are not necessarily the top influencer. © 2022, Growing Science. All rights reserved.

A Feasibility Study of Developing eLCV Shared Architecture in Taiwan

Vehicle electrification has become an important strategy adopted worldwide, including in Taiwan, as a means to achieving net zero emissions. Taiwan is capable of building a whole light commercial vehicle and has technological strength in producing critical EV parts. This study applies the Bass diffusion model to assess the feasibility of developing eLCV shared architecture in Taiwan and estimates that the annual replacement demand for eLCVs could reach 20,221 units. This exceeds the threshold number of 5000 units, which could motivate the automakers to develop eLCV shared architecture. The simulation result shows that achieving full market penetration would take at least 13 years and would be highly correlated with policy support, the vehicle selling price and the battery pack price. The B2B model is a suitable way of introducing eLCVs into the logistics fleets. In the initial promotion phase, policy support and complementary measures would be needed, e.g., public sectors’ purchases, financial incentives and constructing a user-friendly environment. The simulation results further indicate that Taiwan’s eLCV market has a high price elasticity, and in the future, a tendency for the battery pack price to decline may speed up the replacement process.

A Jump Diffusion Analysis for American and European Equity Markets in the beginning of the Corona Pandemic

This paper deals with the analysis of jumps in equity markets in conjunction with the Corona Pandemic in the beginning of 2020. The aim is to identify, analyse and compare jumps in both American and European stock markets using SX5E Index and SPX Index, with jumps measured by Kou's Double Exponential Jump Diffusion Model. To the best of our knowledge this is the first paper which applies a pure time series model to analyse stock market behavior in terms of jumps using intraday data. The result is that jumps in both markets have similiarities and differences in terms of model behavior before, during and after the V-shaped market movement early 2020. © 2021 Better Advances Press. All rights reserved.

A Black–Scholes user's guide to the Bachelier model

To cope with the negative oil futures price caused by the COVID–19 recession, global commodity futures exchanges temporarily switched the option model from Black–Scholes to Bachelier in 2020. This study reviews the literature on Bachelier's pioneering option pricing model and summarizes the practical results on volatility conversion, risk management, stochastic volatility, and barrier options pricing to facilitate the model transition. In particular, using the displaced Black–Scholes model as a model family with the Black–Scholes and Bachelier models as special cases, we not only connect the two models but also present a continuous spectrum of model choices. © 2022 Wiley Periodicals LLC

Vaccination Diffusion and Incentive: Empirical Analysis of the US State of Michigan.

Vaccination is the only way to reach herd immunity and help people return to normal life. However, vaccination rollouts may not be as fast as expected in some regions due to individuals' vaccination hesitation. For this reason, in Detroit, Michigan, the city government has offered a $50 prepaid card to people who entice city residents to visit vaccination sites. This study examined vaccination rates in the US using Detroit, Michigan, as the setting. It sought to address two issues. First, we analyzed the vaccination diffusion process to predict whether any region would reach a vaccination completion level that ensures herd immunity. Second, we examined a natural experiment involving a vaccination incentive scheme in Detroit and discovered its causal inference. We collected weekly vaccination data and demographic Census data from the state of Michigan and employed the Bass model to study vaccination diffusion. Also, we used a synthetic control method to evaluate the causal inference of a vaccination incentive scheme utilized in Detroit. The results showed that many Michigan counties-as well as the city of Detroit-would not reach herd immunity given the progress of vaccination efforts. Also, we found that Detroit's incentive scheme indeed increased the weekly vaccination rate by 44.19% for the first dose (from 0.86 to 1.25%) but was ineffective in augmenting the rate of the second dose. The implications are valuable for policy makers to implement vaccination incentive schemes to boost vaccination rates in geographical areas where such rates remain inadequate for achieving herd immunity.

On the necessity of proper quarantine without lock down for 2019-nCoV in the absence of vaccine.

Presently the world is passing through a critical phase due to the prevalence of the Novel Corona virus, 2019-nCoV or COVID-19, which has been declared a pandemic by WHO. The virus transmits via droplets of saliva or discharge from the nose when an infected person coughs or sneezes. Due to the absence of vaccine, to prevent the disease, social distancing and proper quarantine of infected populations are needed. Non-resident citizens coming from several countries need to be quarantined for 14 days prior to their entrance. The same is to be applied for inter-state movements within a country. The purpose of this article is to propose mathematical models, based on quarantine with no lock down, that describe the dynamics of transmission and spread of the disease thereby proposing an effective preventive measure in the absence of vaccine.