Traffic-driven SIR epidemic spread dynamics on scale-free networks

Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible-Infected-Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks.

Adversarial Training for Predicting the Trend of the COVID-19 Pandemic

It is significant to accurately predict the epidemic trend of COVID-19 due to its detrimental impact on the global health and economy. Although machine learning-based approaches have been applied to predict epidemic trend, standard models have shown low accuracy for long-term prediction due to a high level of uncertainty and lack of essential training data. This paper proposes an improved machine learning framework employing generative adversarial network (GAN) and long short-term memory (LSTM) for adversarial training to forecast the potential threat of COVID-19 in countries where COVID-19 is rapidly spreading. It also investigates the most updated COVID-19 epidemiological data before October 18, 2020 and models the epidemic trend as time series that can be fed into the proposed model for data augmentation and trend prediction of the epidemic. The model is trained to predict daily numbers of cumulative confirmed cases of COVID-19 in Italy, USA, China, Germany, UK, and across the world. The paper further analyzes and suggests which populations are at risk of contracting COVID-19.

Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks

The Coronavirus disease 2019 (COVID-19) is a respiratory illness that can spread from person to person. Since the COVID-19 pandemic is spreading rapidly over the world and its outbreak has affected different people in different ways, it is significant to study or predict the evolution of its epidemic trend. However, most of the studies focused solely on either classical epidemiological models or machine learning models for COVID-19 pandemic forecasting, which either suffer from the limitation of the generalization ability and scalability or the lack of surveillance data. In this work, we propose T-SIRGAN that integrates the strengths of the epidemiological theories and deep learning models to be able to represent complex epidemic processes and model the non-linear relationship for more accurate prediction of the growth of COVID-19. T-SIRGAN first adopts the Susceptible-Infectious-Recovered (SIR) model to generate epidemiological-based simulation data, which are then fed into a generative adversarial network (GAN) as adversarial examples for data augmentation. Then, Transformers are used to predict the future trends of COVID-19 based on the generated synthetic data. Extensive experiments on real-world datasets demonstrate the superiority of our method. We also discuss the effectiveness of vaccine based on the difference between the predicted and the reported number of COVID-19 cases.

CAN E-COMMERCE IMPROVE THE PROFITABILITY OF AGRICULTURAL CO-OPERATIVES?

In 2020, COVID-19 has exerted a significant impact on the sale of agricultural products in China. Against this background, the e-commerce has played an important role in solving this problem. Therefore, it has once again become the focus of attention of the Chinese government and researchers. The co-operative is an important channel for Chinese farmers to sell agricultural products, and thus, it is a vital research issue to discuss whether the application of e-commerce in the co-operative is conducive to the improvement of its profitability. This study therefore uses Logit model to analyze the factors affecting the use of e-commerce in co-operatives and PSM model to calculate the impact of e-commerce on the profit margin of co-operatives. The following conclusions can thus be drawn: (1) The gender and education level of decisionmakers and the degree of processing and relevant certification of products are important influencing factors, (2) e-commerce can significantly improve the profit margin of co-operatives, and (3) e-commerce plays a more significant role in improving the profit margin of co- operatives selling primary processed products. These study conclusions are based on national-level survey, and thus, the industrial policies should be formulated according to these influencing factors to support the development of e-commerce in co-operatives, especially those selling primary processed products.