Occurrence and Epidemic Dynamics and Accurate Prevention and Control of Important Sugarcane Pests and Diseases in the Sugarcane Areas of Yunnan

Based on investigation and research, according to the current actual production of sugarcane, the occurrence dynamics and outbreak causes of important pests and diseases that seriously affect sugarcane production were summarized, and accurate and efficient green prevention and control technology was put forward according to the occurrence and damage characteristics of important pests and diseases, such as strengthening sugarcane introduction and quarantine, breeding and selecting varieties resistant to diseases and pests, promoting the use of detoxified healthy seedlings vigorously, applying lamp trapping technology on a large scale, scientifically guiding and promoting biological prevention and control technology, practically promoting the precise and efficient application of slow-release long- acting and low toxic pesticides, strengthening field management, spraying pesticides in time at the early stage of a disease, and doing a good job of monitoring and emergency prevention and control of sudden pests.

Matrix expression and vaccination control for epidemic dynamics over dynamic networks

This paper investigates epidemic dynamics over dynamic networks via the approach of semi-tensor product of matrices. First, a formal susceptible-infected-susceptible epidemic dynamic model over dynamic networks （SISED-DN） is given. Second, based on a class of determinate co-evolutionary rule, the matrix expressions are established for the dynamics of individual states and network topologies, respectively. Then, all possible final spreading equilibria are obtained for any given initial epidemic state and network topology by the matrix expression. Third, a sufficient and necessary condition of the existence of state feedback vaccination control is presented to make every individual susceptible. The study of illustrative examples shows the effectiveness of our new results.

Dynamic Modeling and Analysis of Occult Transmission of Omicron SARS-CoV-2 Epidemic

At present, the Omicron variant is still the dominant strain in the global novel coronavirus pneumonia pandemic, and has the characteristics of concealed transmission, which brings heavy pressure to the health systems of different countries. Omicron infections were first found in Chinese Mainland in Tianjin in December 2021, and Omicron epidemic broke out in many parts of China in 2022. In order to enable the country and government to make scientific and accurate decisions in the face of the epidemic, it is particularly important to predict and analyze the relevant factors of Omicron’s covert transmission. In this paper, based on the official data of Jilin City and the improved SEIR dynamic model, through parameter estimation, the contact infection probability of symptomatic infected persons in Omicron infected patients is 0.4265, and the attenuation factor is 0.1440. Secondly, the influence of infectious duration in different incubation periods, asymptomatic infected persons and other factors on the epidemic situation in this area was compared. Finally, the scale of epidemic development was predicted and analyzed.

An Analysis of the Impact of Stay-at-Home Measures on the Occurrence of Vaccine Shortages

COVID-19, a contagious respiratory disease, presents immediate and unforeseen challenges to people worldwide. Moreover, its transmission rapidly extends globally due to its viral transmissibility, emergence of novel strains (variants), absence of immunity, and human unawareness. This framework introduces a revised epidemic model, drawing upon mathematical principles. This model incorporates a modified vaccination and lockdown approach to comprehensively depict an epidemics transmission, containment, and decision-making processes within a community. This study aims to provide policymakers with precise information on real-world situations to assist them in making informed decisions about the implementation of lockdown strategies, maintenance variables, and vaccine availability. The suggested model has conducted stability analysis, strength number analysis, and first and second-order derivative analysis of the Lyapunov function and has established the existence and uniqueness of solutions of the proposed models. We examine the combined effects of an effective vaccination campaign and non-pharmaceutical measures such as lockdowns and states of emergency. We rely on the results of this research to assist policymakers in various countries in eradicating the illness by developing more innovative measures to control the outbreak.

Epidemic propagation on adaptive coevolutionary networks with preferential local-world reconnecting strategy

In the propagation of an epidemic in a population, individuals adaptively adjust their behavior to avoid the risk of an epidemic. Differently from existing studies where new links are established randomly, a local link is established preferentially in this paper. We propose a new preferentially reconnecting edge strategy depending on spatial distance （PR- SD）. For the PR-SD strategy, the new link is established at random with probability p and in a shortest distance with the probability 1 p. We establish the epidemic model on an adaptive network using Cellular Automata, and demonstrate the effectiveness of the proposed model by numerical simulations. The results show that the smaller the value of parameter p, the more difficult the epidemic spread is. The PR-SD strategy breaks long-range links and establishes as many short-range links as possible, which causes the network efficiency to decrease quickly and the propagation of the epidemic is restrained effectively.

Epidemic thresholds in a heterogenous population with competing strains

Among many epidemic models, one epidemic disease may transmit with the existence of other pathogens or other strains from the same pathogen. In this paper, we consider the case where all of the strains obey the susceptible-infected- susceptible mechanism and compete with each other at the expense of common susceptible individuals. By using the heterogenous mean-field approach, we discuss the epidemic threshold for one of two strains. We confirm the existence of epidemic threshold in both finite and infinite populations subject to underlying epidemic transmission. Simulations in the Barabasi-Albert （BA） scale-free networks are in good agreement with the analytical results.

Dynamics and Control of Infectious Diseases in Stochastic Metapopulation Models

The research on spatial epidemic models is a topic of considerable recent interest. In another hand, the advances in computer technology have stimulated the development of stochastic models. Metapopulation models are spatial designs that involve movements of individuals between distinct subpopulations. The purpose of the present work has been to develop stochastic models in order to study the transmission dynamics and control of infectious diseases in metapopulations. The authors studied Susceptible-Infected-Susceptible （SIS） and Susceptible-lnfected-Recovered （SIR） epidemic schemes, using the Gillespie algorithm, Computational numerical simulations were carried in order to explore the models. The results obtained show how the dynamics of transmission and the application of control measures within each subpopulation may affect all subpopulations of the system. They also show how the distribution of control measures among subpopulations affects the efficacy of these strategies. The dynamics of the stochastic models developed in the current study follow the trends observed in the classic deterministic designs. Also, the present models exhibit fluctuating behavior. This work highlights the importance of the spatial distribution of the population in spread and control of infectious diseases. In addition, it shows how chance could play an important role in these scenarios.

Epidemic Spreading Model Based on Social Active Degree in Social Networks

In this paper,an improved Susceptible-Infected-Susceptible(SIS) epidemic spreading model is proposed in order to provide a theoretical method to analyze and predict the spreading of diseases.This model is based on the following ideas:in social networks,the contact probability between nodes is decided by their social distances and their active degrees.The contact probability of two indirectly connected nodes is decided by the shortest path between them.Theoretical analysis and simulation experiment were conducted to evaluate the performance of this improved model.Because the proposed model is independent of the network structure,simulation experiments were done in several kinds of networks,namely the ER network,the random regular network,the WS small world network,and the BA scale-free network,in order to study the influences of certain factors have on the epidemic spreading,such as the social contact active degree,the network structure,the average degree,etc.This improved model provides an idea for studying the spreading rule of computer virus,attitudes,fashion styles and public opinions in social networks.

Improvement of the software for modeling the dynamics of epidemics and developing a user-friendly interface

The challenges humanity is facing due to the Covid-19 pandemic require timely and accurate forecasting of the dynamics of various epidemics to minimize the negative consequences for public health and the economy.One can use a variety of well-known and new mathematical models,taking into account a huge number of factors.However,complex models contain a large number of unknown parameters,the values of which must be determined using a limited number of observations,e.g.,the daily datasets for the accumulated number of cases.Successful experience in modeling the COVID-19 pandemic has shown that it is possible to apply the simplest SIR model,which contains 4 unknown parameters.Application of the original algo-rithm of the model parameter identification for the first waves of the COVID-19 pandemic in China,South Korea,Austria,Italy,Germany,France,Spain has shown its high accuracy in pre-dicting their duration and number of diseases.To simulate different epidemic waves and take into account the incompleteness of statistical data,the generalized SIR model and algorithms for determining the values of its parameters were proposed.The interference of the previous waves,changes in testing levels,quarantine or social behavior require constant monitoring of the epidemic dynamics and performing SIR simulations as often as possible with the use of a user-friendly interface.Such tool will allow predicting the dynamics of any epidemic using the data on the number of diseases over a limited period(e.g.,14 days).It will be possible to predict the daily number of new cases for the country as a whole or for its separate region,to estimate the number of carriers of the infection and the probability of facing such a carrier,as well as to estimate the number of deaths.Results of three SIR simulations of the COVID-19 epidemic wave in Japan in the summer of 2022 are presented and discussed.The predicted accumulated and daily numbers of cases agree with the results of observations,especially for the simulation based on the datasets corresponding to the period from July 3 to July 16,2022.A user-friendly interface also has to ensure an opportunity to compare the epidemic dynamics in different countries/regions and in different years in order to estimate the impact of vaccination levels,quarantine restrictions,social behavior,etc.on the numbers of new infections,death,and mortality rates.As example,the comparison of the COVID-19 pandemic dynamics in Japan in the summer of 2020,2021 and 2022 is presented.The high level of vaccinations achieved in the summer of 2022 did not save Japan from a powerful pandemic wave.The daily numbers of cases were about ten times higher than in the corresponding period of 2021.Nevertheless,the death per case ratio in 2022 was much lower than in 2020.

An Analytic Approximate Solution of the SIR Model

The SIR(D) epidemiological model is defined through a system of transcendental equations, not solvable by elementary functions. In the present paper those equations are successfully replaced by approximate ones, whose solutions are given explicitly in terms of elementary functions, originating, piece-wisely, from generalized logistic functions: they ensure exact (in the numerical sense) asymptotic values, besides to be quite practical to use, for example with fit to data algorithms;moreover they unveil a useful feature, that in fact, at least with very strict approximation, is also owned by the (numerical) solutions of the exact equations. The novelties in the work are: the way the approximate equations are obtained, using simple, analytic geometry considerations;the easy and practical formulation of the final approximate solutions;the mentioned useful feature, never disclosed before. The work’s method and result prove to be robust over a range of values of the well known non-dimensional parameter called basic reproduction ratio, that covers at least all the known epidemic cases, from influenza to measles: this is a point which doesn’t appear much discussed in analogous works.

A Comparison of Deterministic and Stochastic Susceptible-Infected-Susceptible (SIS) and Susceptible-Infected-Recovered (SIR) Models

In this paper we build and analyze two stochastic epidemic models with death. The model assumes that only susceptible individuals (S) can get infected (I) and may die from this disease or a recovered individual becomes susceptible again (SIS model) or completely immune (SIR Model) for the remainder of the study period. Moreover, it is assumed there are no births, deaths, immigration or emigration during the study period;the community is said to be closed. In these infection disease models, there are two central questions: first it is the disease extinction or not and the second studies the time elapsed for such extinction, this paper will deal with this second question because the first answer corresponds to the basic reproduction number defined in the bibliography. More concretely, we study the mean-extinction of the diseases and the technique used here first builds the backward Kolmogorov differential equation and then solves it numerically using finite element method with FreeFem++. Our contribution and novelty are the following: however the reproduction number effectively concludes the extinction or not of the disease, it does not help to know its extinction times because example with the same reproduction numbers has very different time. Moreover, the SIS model is slower, a result that is not surprising, but this difference seems to increase in the stochastic models with respect to the deterministic ones, it is reasonable to assume some uncertainly.

Enigmatic origin of hepatitis B virus:An ancient travelling companion or a recent encounter?

Hepatitis B virus(HBV)is the leading cause of liver disease and infects an estimated 240 million people worldwide.It is characterised by a high degree of genetic heterogeneity because of the use of a reverse transcriptase during viral replication.The ten genotypes(A-J)that have been described so far further segregate into a number of subgenotypes which have distinct ethno-geographic distribution.Genotypes A and D are ubiquitous and the most prevalent genotypes in Europe(mainly represented by subgenotypes D1-3 and A2);genotypes B and C are restricted to eastern Asia and Oceania;genotype E to central and western Africa;and genotypes H and F(classified into 4 subgenotypes)to Latin America and Alaska.This review summarises the data obtained by studying the global phylodynamics and phylogeography of HBV genotypes,particularly those concerning the origin and dispersion histories of genotypes A,D,E and F and their subgenotypes.The lack of any consensus concerning the HBV substitution rate and the conflicting data obtained using different calibration approaches make the time of origin and divergence of the various genotypes and subgenotypes largely uncertain.It is hypothesised that HBV evolutionary rates are time dependent,and that the changes depend on the main transmission routes of the genotypes and the dynamics of the infected populations.

GLOBAL ATTRACTIVENESS OF DISCRETE-TIME EPIDEMIC OUTBREAK IN NETWORKS

Epidemic dynamics in networks have attracted a great deal of attention from researchers of many fields. In this paper, we mainly study the global behaviors of discrete-time epidemic model in heterogenous networks. By theoretical analysis, we show that the model can be characterized by the basic reproduction number R0. When R0 is smaller than unit, the disease-free equilibrium is globally stable, while R0 is larger than unit, the unique positive equilibrium is globally attractive.

Threshold analysis for epidemic models with high-risk immunization on networks

In this paper, an SIRS epidemic model with high-risk immunization was investigated, where a susceptible neighbor of an infected node is immunized with rate h. Through analyzing the discrete-time model, we found that the epidemic threshold above which an epidemic can prevail and persist in a population is inversely proportional to 1 - h value. We also studied the continuous-time epidemic model and obtained a different result： the epidemic threshold does not depend on the immunization parameter h. Our results suggest that the difference between the discrete-time epidemic model and the continuous-time epidemic model exists in the high-risk immunization.

Modeling and analysis of epidemic spreading on community network with node's birth and death

In this paper, a modified susceptible infected susceptible （SIS） epidemic model is proposed on community structure networks considering birth and death of node. For the existence of node＇s death would change the topology of global network, the characteristic of network with death rate is discussed. Then we study the epidemiology behavior based on the mean-field theory and derive the relationships between epidemic threshold and other parameters, such as modularity coefficient, birth rate and death rates （caused by disease or other reasons）. In addition, the stability of endemic equilibrium is analyzed. Theoretical analysis and simulations show that the epidemic threshold increases with the increase of two kinds of death rates, while it decreases with the increase of the modularity coefficient and network size.

Comparative Effects of Avoidance and Immunization on Epidemic Spreading in a Dynamic Small-World Network with Community Structure

Considering the actual behavior of people’s short-term travel,we propose a dynamic small-world community network model with tunable community strength which has constant local links and time varying long-range jumps.Then an epidemic model of susceptible-infected-recovered is established based on the mean-field method to evaluate the inhibitory effects of avoidance and immunization on epidemic spreading.And an approximate formula for the epidemic threshold is obtained by mathematical analysis.The simulation results show that the epidemic threshold decreases with the increase of inner-community motivation rate and inter-community long-range motivation rate,while it increases with the increase of immunization rate or avoidance rate.It indicates that the inhibitory effect on epidemic spreading of immunization works better than that of avoidance.

Will the large-scale vaccination succeed in containing the COVID-19 pandemic and how soon?

Background:The availability of vaccines provides a promising solution to contain the COVID-19 pandemic.However,it remains unclear whether the large-scale vaccination can succeed in containing the COVID-19 pandemic and how soon.We developed an epidemiological model named SUVQC(Suceptible-Unquarantined-Vaccined-Quarantined-Conflrmed)to quantitatively analyze and predict the epidemic dynamics of COVID-19 under vaccination.Methods:In addition to the impact of non-pharmaceutical interventions(NPIs),our model explicitly parameterizes key factors related to vaccination,including the duration of immunity,vaccine efficacy,and daily vaccination rate etc.The model was applied to the daily reported numbers of confirmed cases of Israel and the USA to explore and predict trends under vaccination based on their current epidemic statuses and intervention measures.We further provided a formula for designing a practical vaccination strategy,which simultaneously considers the effects of the basic reproductive number of COVID-19,intensity of NPIs,duration of immunological memory after vaccination,vaccine efficacy and daily vaccination rate.Results:In Israel,53.83%of the population is fully vaccinated,and under the current NPI intensity and vaccination scheme,the pandemic is predicted to end between May 14,2021,and May 16,2021,assuming immunity persists for 180 days to 365 days.If NPIs are not implemented after March 24,2021,the pandemic will end later,between July 4,2021,and August 26,2021.For the USA,if we assume the current vaccination rate(0.268%per day)and intensity of NPIs,the pandemic will end between January 20,2022,and October 19,2024,assuming immunity persists for 180 days to 365 days.However,assuming immunity persists for 180 days and no NPIs are implemented,the pandemic will not end and instead reach an equilibrium state,with a proportion of the population remaining actively infected.Conclusions:Overall,the daily vaccination rate should be decided according to vaccine efficacy and immunity duration to achieve herd immunity.In some situations,vaccination alone cannot stop the pandemic,and NPIs are necessary to supplement vaccination and accelerate the end of the pandemic.Considering that vaccine efficacy and duration of immunity may be reduced for new mutant strains,it is necessary to remain cautiously optimistic about the prospect of,ending the pandemic under vaccination.

Simulations of a logistic-type model with delays for Chagas disease with insecticide spraying

This work studies and numerically simulates a logistic-type model for the dynamics of Chagas disease, which is caused by the parasite T. cruzi and affects millions of humans and domestic mammals throughout rural areas in Central and South America. A basic model for the disease dynamics that includes insecticide spraying was developed in Spagnuolo et al. （2010） [271 and consists of a delay-differential equation for the vectors and three nonlinear ordinary differential equations for the populations of the infected vectors, infected humans and infected domestic mammals. In this work, the vector equation is modified by using a logistic term with zero, one or two delays or time lags. The aim of this study is three-fold： to numerically study the effects of using different numbers of delays on the model behavior; to find if twice yearly insecticide spraying schedules improve vector control; and to study the sensitivity of the system to the delays in the case of two delays, by introducing randomness in the delays. It is found that the vector equation with different number of delays has very different solutions. The ＂best＂ day of spraying is the middle of Spring and twice annual sprayings cause only minor improvements in disease control. Finally, the model is found to be insensitive to the values of the delays, when the delays are randomly distributed within rather narrow intervals or ranges centered on the parameter values used in Coffield et al. （2014） [8].

Dynamic analysis of an SEIRS model with nonlinear infectivity on complex networks

In this paper, we study the spreading of infections on complex heterogeneous networks based on an SEIRS epidemic model with nonlinear infectivity. By mathematical analysis, the basic reproduction number R0 is obtained. When R0 is less than one, the disease-free equilibrium is globally asymptotically stable and the disease dies out, while R0 is greater than one, the disease-free equilibrium becomes unstable and the disease is permanent, and in the meantime there exists a unique endemic equilibrium which is globally attrac- tive under certain conditions. Finally, the effects of various immunization schemes are studied. To verify our theoretical results, the corresponding numerical simulations are also included.