Controlling disease spread on networks with feedback mechanism

Many real-world networks have the ability to adapt themselves in response to the state of their nodes. This paper studies controlling disease spread on network with feedback mechanism, where the susceptible nodes are able to avoid contact with the infected ones by cutting their connections with probability when the density of infected nodes reaches a certain value in the network. Such feedback mechanism considers the networks＇ own adaptivity and the cost of immunization. The dynamical equations about immunization with feedback mechanism ave solved and theoretical predictions are in agreement with the results of large scale simulations. It shows that when the lethality a increases, the prevalence decreases more greatly with the same immunization g. That is, with the same cost, a better controlling result can be obtained. This approach offers an effective and practical policy to control disease spread, and also may be relevant to other similar networks.

Simulation of Spread of Infectious Diseases and Population Mobility in a Deterministic Epidemic Patch Model

Computer simulation models are widely applied in various areas of the health care sector, including the spread of infectious diseases. Patch models involve explicit movements of people between distinct locations. The aim of the present work has been designed and explored a patch model with population mobility between different patches and between each patch and an external population. The authors considered a SIR （susceptible-infected-recovered） scheme. The model was explored by computer simulations. The results show how endemic levels are reached in all patches of the system. Furthermore, the performed explorations suggest that the people mobility between patches, the immigration from outside the system and the infection rate in each patch, are factors that may influence the dynamics of epidemics and should be considered in health policy planning.

Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

Epidemic spreading on networks with vaccination

In this paper, a new susceptible-infected-susceptible （SIS） model on complex networks with imperfect vaccination is proposed. Two types of epidemic spreading patterns （the recovered individuals have or have not immunity） on scale-free networks are discussed. Both theoretical and numerical analyses are presented. The epidemic thresholds related to the vaccination rate, the vaccination-invalid rate and the vaccination success rate on scale-free networks are demonstrated, showing different results from the reported observations. This reveals that whether or not the epidemic can spread over a network under vaccination control is determined not only by the network structure but also by the medicine＇s effective duration. Moreover, for a given infective rate, the proportion of individuals to vaccinate can be calculated theoretically for the case that the recovered nodes have immunity. Finally, simulated results are presented to show how to control the disease prevalence.

Criticality of Parasitic Disease Transmission in a Diffusive Population

Through using the methods of finite-size effect and short time dynamic scaling,we study the criticalbehavior of parasitic disease spreading process in a diffusive population mediated by a static vector environment.Throughcomprehensive analysis of parasitic disease spreading we find that this model presents a dynamical phase transition fromdisease-free state to endemic state with a finite population density.We determine the critical population density,abovewhich the system reaches an epidemic spreading stationary state.We also perform a scaling analysis to determine theorder parameter and critical relaxation exponents.The results show that the model does not belong to the usual directedpercolation universality class and is compatible with the class of directed percolation with diffusive and conserved fields.

Dynamics and control strategies of infectious disease under different scenarios on hierarchical geographical networks

Human settlements are embedded in traffic networks with hierarchical structures. In order to understand the spreading mechanism of infectious diseases and deploy control measures, the susceptible-infected-removed spreading process is studied with agents moving globally on the hierarchical geographic network, taking into account agents’ preference for node layers and memory of initial nodes. We investigate the spreading behavior in the case of global infection under different scenarios, including different directions of human flow, different locations of infection source, and different moving behaviors of agents between layers. Based on the above-mentioned analysis, we propose screening strategies based on layer rank and moving distance, and compare their effects on delaying epidemic spreading. We find that in the case of global infection,infection spreads faster in high layers than in low layers, and early infection in high layers and moving to high layers both accelerate epidemic spreading. Travels of high-layer and low-layer residents have different effects on accelerating epidemic spreading, and moving between high and low layers increases the peak value of new infected cases more than moving in the same layer or between adjacent layers. Infection in intermediate nodes enhances the effects of moving of low-layer residents more than the moving of high-layer residents on accelerating epidemic spreading. For screening measures, improving the success rate is more effective on delaying epidemic spreading than expanding the screening range. With the same number of moves screened, screening moves into or out of high-layer nodes combined with screening moves between subnetworks has better results than only screening moves into or out of high-layer nodes, and screening long-distance moves has the worst results when the screening range is small, but it achieves the best results in reducing the peak value of new infected cases when the screening range is large enough. This study probes into the spreading process and control measures under different scenarios on the hierarchical geographical network, and is of great significance for epidemic control in the real world.

Susceptible-Infected-Recovered-Susceptible Virus Spreading Model Based on Two Dimensional Sparse Lattice

In order to clarify the virus＇ spreading rules, a SIRS （Susceptible-Infected-Recovered-Susceptible） disease spread model based on sparsely distributed crowd is proposed. In this model, the effects of crowd-density, spread efficiency and the moving of individuals on the spreading of viruses are researched. The theoretical analysis and analog simulation shows that there exist a critical value, only when the product of spread efficiency and crowd density goes beyond the critical value, can viruses spread in crowd continuously and steadily. Besides, the moving of individuals can promote the spreading of viruses. These results are helpful guiding people to defense and control virus＇ spreading process.

The Control Research Progress of Liberobacter asiaticum in Taizhou City

After decades of researches and discussions on occurrence regularity and control technology of citrus psyl id （Diaphorina citri Kuwayana） and Liberobacter asi-aticum, the occurrence regularity and sampling technique of citrus psyl id in orange forests in Taizhou was defined and the connections between the infection rate and carrying rate of citrus psyl id and Liberobacter asiaticum as wel as the correlations between the Liberobacter asiaticum and citrus yield loss were discussed. This paper discussed the warnings of Liberobacter asiaticum and citrus psylid control index, the economic life span of orange forests was then predicted by building the diseases spreading models with different management styles. At last, the paper put forward the comprehensive prevention and control technology and the concept that Liber-obacter asiaticum was preventable and control able.

Stochastic Lattice gas Cellular Automata Model for Epidemics

The aim of this study was to develop and explore a stochastic lattice gas cellular automata （LGCA） model for epidemics. A computer program was development in order to implement the model. An irregular grid of cells was used. A susceptible-infected-recovered （SIR） scheme was represented. Stochasticity was generated by Monte Carlo method. Dynamics of model was explored by numerical simulations. Model achieves to represent the typical SIR prevalence curve. Performed simulations also show how infection, mobility and distribution of infected individuals may influence the dynamics of propagation. This simple theoretical model might be a basis for developing more realistic designs.

Quantifying the impacts of human mobility restriction on the spread of coronavirus disease 2019:an empirical analysis from 344 cities of China

Background:Since the outbreak of coronavirus disease 2019(COVID-19),human mobility restriction measures have raised controversies,partly because of the inconsistent findings.An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction.The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.Methods:Our study applied the difference-in-difference(DID)model to assess the declines of population mobility at the city level,and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.Results:The DID model showed that a continual expansion of the relative declines over time in 2020.After 4 weeks,population mobility declined by-54.81%(interquartile range,-65.50%to-43.56%).The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks(ie,1%decline of population mobility was associated with 0.72%[95%CI:0.50%-0.93%]reduction of cumulative cases for 1 week,1.42%2 weeks,1.69%3 weeks,1.72%4 weeks,1.64%5 weeks,and 1.52%6 weeks).The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter.The effects on cumulative cases differed by cities of different population sizes,with greater effects seen in larger cities.Conclusions:Persistent population mobility restrictions are well deserved.Implementation of mobility restrictions in major cities with large population sizes may be even more important.

Different Epidemic Models on Complex Networks

Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals＇ relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemic models on complex networks, and obtain the epidemic threshold for each ease. Finally, we present numerical simulations for each case to verify our results.

Interactive effects between travel behaviour and COVID-19:a questionnaire study

This study has two main objectives:(i)to analyse the effect of travel characteristics on the spreading of disease,and(ii)to determine the effect of COVID-19 on travel behaviour at the individual level.First,the study analyses the effect of passenger volume and the proportions of different modes of travel on the spread of COVID-19 in the early stage.The developed spatial autoregressive model shows that total passenger volume and proportions of air and railway passenger volumes are positively associated with the cumulative confirmed cases.Second,a questionnaire is analysed to determine changes in travel behaviour after COVID-19.The results indicate that the number of total trips considerably decreased.Public transport usage decreased by 20.5%,while private car usage increased by 6.4%.Then the factors affecting the changes in travel behaviour are analysed by logit models.The findings reveal significant factors,including gender,occupation and travel restriction.It is expected that the findings from this study would be helpful for management and control of traffic during a pandemic.

Are Runners More Prone to Become Infected with COVID-19?An Approach from the Raindrop Collisional Model

It is known that COVID-19 spread mainly from person-to-person through respiratory droplets produced when an infected person coughs or sneezes,and as a result certain ideas about contagious of COVID-19 have been spread.One of them is the widespread belief that close runners,owing to the stronger exhalation,can be more prone to be infected with COVID-19 because the collision with the suspended respiratory droplets should the runner in front be infected.However,because of the low Stokes number this idea cannot be generalized without carefully thought and in fact can be put into question.Utilizing the raindrop collisional model and with the help of computational fluid dynamics (CFD),it is shown that the probability of collision with respiratory droplets is not always increasing with the approaching velocity of the runner but rather there is a maximum velocity threshold at which the efficiency of collision drops.