A5102022850- COVID-19 epidemiological studies
- SARS-CoV-2 and COVID-19 Research
- Energy, Environment, Economic Growth
- Banking stability, regulation, efficiency
- Housing Market and Economics
- Market Dynamics and Volatility
- COVID-19 Pandemic Impacts
- Influenza Virus Research Studies
- Data-Driven Disease Surveillance
- Energy, Environment, and Transportation Policies
- Sustainable Supply Chain Management
- Energy Load and Power Forecasting
- COVID-19 and Mental Health
- COVID-19 diagnosis using AI
- Complex Network Analysis Techniques
- Housing, Finance, and Neoliberalism
- Diverse Music Education Insights
- COVID-19 Clinical Research Studies
- Sustainable Industrial Ecology
- Social Acceptance of Renewable Energy
- Environmental Impact and Sustainability
- Fractional Differential Equations Solutions
- Biomedical and Engineering Education
- Complex Systems and Time Series Analysis
- Evaluation and Optimization Models
Nanjing Tech University
2017-2025
Tianjin University
2023-2024
Lincoln University College
2024
Ocean University of China
2024
Xinjiang Medical University
2020-2023
Beijing University of Chemical Technology
2023
Augusta University
2023
Sun Yat-sen University
2023
Nanjing University of Information Science and Technology
2023
Jiangsu Provincial Academy of Environmental Science
2023
Abstract Estimating the key epidemiological features of novel coronavirus (2019-nCoV) epidemic proves to be challenging, given incompleteness and delays in early data reporting, particular, severe under-reporting bias epicenter, Wuhan, Hubei Province, China. As a result, current literature reports widely varying estimates. We developed an alternative geo-stratified debiasing estimation framework by incorporating human mobility with case reporting three stratified zones, i.e., Province...
<abstract> <p>The novel coronavirus disease 2019 (COVID-19) infection broke out in December Wuhan, and rapidly overspread 31 provinces mainland China on January 2020. In the face of increasing number daily confirmed infected cases, it has become a common concern worthy pondering when will appear turning points, what is final size would be ultimately controlled. Based current control measures, we proposed dynamical transmission model with contact trace quarantine predicted peak...
The novel coronavirus disease (COVID-19) poses a serious threat to global public health and economics. Serial interval (SI), time between the onset of symptoms primary case secondary case, is key epidemiological parameter. We estimated SI COVID-19 in Shenzhen, China based on 27 records transmission chains. adopted three parametric models: Weibull, lognormal gamma distributions, an interval-censored likelihood framework. models were compared using corrected Akaike information criterion...
COVID-19 may have a demonstrable influence on disease patterns. However, it remained unknown how tuberculosis (TB) epidemics are impacted by the outbreak. The purposes of this study to evaluate impacts outbreak decreases in TB case notifications and forecast epidemiological trends China.The monthly incidents from January 2005 December 2020 were taken. Then, we investigated causal pandemic reductions using intervention analysis under Bayesian structural time series (BSTS) method. Next, split...
Given the highly nonlinear and unstable characteristics of lithium carbonate prices in China’s battery industry chain, accuracy a single prediction model is limited. This study introduces related materials macro-environmental indicators as key influencing factors. utilizes Variational Mode Decomposition (VMD) Sparrow Search Algorithm (SSA) to further develop Long Short-Term Memory (LSTM) network, resulting VMD–SSA–LSTM combination for predicting pricing. The research results indicate that...
This study constructed a DGC-t-MSV model by integrating dynamic correlation and Granger causality into the MSV framework. Using daily closing price data from 4 January 2022 to 21 November 2024, it empirically analyzed volatility spillover effects between China’s carbon market traditional manufacturing an industrial heterogeneity perspective. The findings are as follows: (1) exhibits significant unidirectional on carbon-intensive industries, such steel, chemicals, shipbuilding, automobile...
While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects reliability forecasts. To address this issue, research employs a genetic-particle swarm optimization (GA-PSO) algorithm and develops GA-PSO-BP neural network model through integration BP network. Building upon foundation, study considers several pivotal factors affecting housing prices dataset comprising 1,824 transactions second-hand homes...
In late 2019, the coronavirus disease 2019 (COVID-19) pandemic soundlessly slinked in and swept world, exerting a tremendous impact on lifestyles. This study investigated changes infection rates of COVID-19 urban built environment 45 areas Manhattan, New York, relationship between factors COVID-19. was used as outcome variable, which represents situation under normal conditions vs. non-pharmacological intervention (NPI), to analyze macroscopic (macro) microscopic (micro) environment....
The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework forecast prevalence and mortality series COVID-19 in USA, UK, Russia, India, predictive performance ETS was compared with most frequently used autoregressive integrated moving average (ARIMA) model.The data India between 20 February 2020 15 May were extracted from WHO website. Then, subsamples 3 treated as training horizon, others testing horizon construct ARIMA models models.Based on model evaluation...
In this paper, a stochastic COVID-19 model with large-scale nucleic acid detection and isolation measures is proposed. Firstly, the existence uniqueness of global positive solution obtained. Secondly, threshold criteria for extinction persistence in mean probability one are established. Moreover, sufficient condition unique ergodic stationary distribution any also Finally, numerical simulations carried out combination real data from Urumqi, China theoretical results verified.
Improving transparency of food safety supervision information can reduce the occurrence asymmetry, decrease incidents, and promote socially joint regulation for safety. In this study, an index system (FSSIT) is constructed using fuzzy-ANP comprehensive evaluation model. Using system, FSSIT in China evaluated. A total 1651 questionnaires containing 139525 data are collected from drug administration (FDA), consumer association (CA), media at central, provincial, prefectural, county levels....
Abstract In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), autoregressive integrated moving average (ARIMA), with nonlinear artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perform time series modeling and forecasting based on COVID-19 prevalence mortality data from 28 February 2020 27 June in South Africa Nigeria. By comparing accuracy level of measurements basic ARIMA NARANN models, it was...
<abstract> <p>An immunogenic and safe vaccine against COVID-19 for use in the healthy population will become available near future. In this paper, we aim to determine optimal administration strategy refugee camps considering maximum daily limited total supply. For purpose, extended SEAIRD compartmental models are established describe epidemic dynamics with both single-dose double-dose administration. Taking vaccination rates different susceptible compartments as control...
This study constructs the network diffusion model of food safety scare behavior under effect information transparency and examines topology evolution characteristics in a numerical simulation. The main conclusions this are as follows. (1) Under transparency, degree distribution demonstrates decreasing diminishing margins. (2) Food increases with dissemination rate consumer concern about incidents shows monotone increasing. And increasing government supervision media whole is declining...
Abstract Routine immunizations and supplementary immunization activities (SIAs) have significantly improved measles control over the past two decades in China. Progress towards eliminating currently faces multiple challenges as infection age increases, adult-targeted SIA strategies are being considered. This study developed an age-stratified susceptible-exposed-infectious-recovered model using a recently published contact matrix to depict transmissions between individuals seven groups....
In order to study the impact of risk aversion characteristics enterprises on supply chain transmission, utility function is introduced, and elasticity coefficient used construct a supplier-dominated low-carbon transmission model. Simulation analysis conducted investigate emission reduction revenue risks caused by internal external contingent factors. The reveals that under conditions market demand uncertainty, effect unaffected members in chain. While risk-aversion suppliers can decrease...
COVID-19 has profoundly impacted global daily life, emphasizing the need for effective virus suppression strategies. In response, China established numerous nucleic acid testing sites to facilitate rapid and curb outbreaks. However, these often experience congestion, increasing transmission risks reducing efficiency. This study focuses on spatial–temporal analysis of site distribution associated infection in Shenzhen, China. Data from all Shenzhen were analyzed week October 24–30, 2022,...
This study employed the dynamic conditional correlation algorithm and incorporated temporal dynamics of spillover effect to enhance Multivariate Stochastic Volatility (MSV) model. Consequently, a DGC-t-MSV model (multiple stochastic volatility coefficient with Granger causality test) was constructed simulate examine effects between China’s carbon market traditional energy market. The findings reveal following: (1) A significant in price exists markets, notably fluctuating index. China exerts...
Traditional BP neural networks frequently encounter local optima during carbon price forecasts. This study adopts a hybrid approach, combining genetic algorithm and particle swarm optimization (GA-PSO) to improve the network, resulting in creation of GA-PSO-BP network model. Seven critical factors were identified affecting prices, we utilized data on emission trading prices from China for analysis. Compared traditional models, including GA-BP models optimized solely with algorithms PSO-BP...