Agent-Based Model: A Surging Tool to Simulate Infectious Diseases in the Immune System

Agent-based models (ABMs) are capable of constructing individual system components at different levels of representation to describe non-linear relationships between those components. Compared to a traditional mathematical modeling approach, agent-based models have an inherent spatial component with which they can easily describe local interactions and environmental heterogeneity. Furthermore, agent-based model maps interactions among agents inherently to the biological phenomenon by embedding the stochastic nature and dynamics transitions, thereby demonstrating suitability for the development of complex biological processes. Recently, an abundance of literature has presented application of agent-based modeling in the biological system. This review focuses on application of agent-based modeling to progression in simulation of infectious disease in the human immune system and discusses advantages and disadvantages of agent-based modeling application. Finally, potential implementation of agent-based modeling in relation to infectious disease modeling in future research is explored.

Choosing the right animal model for infectious disease research

A complex biological system is often required to study the myriad of host-pathogen interactions associated with infectious diseases, especially since the current basis of biology has reached the molecular level. The use of animal models is important for understanding the very complex temporal relationships that occur in infectious disease involving the body, its neuroendocrine and immune systems and the infectious organism. Because of these complex interactions, the choice of animal model must be a thoughtful and clearly defined process in order to provide relevant, translatable scientific data and to ensure the most beneficial use of the animals. While many animals respond similarly to humans from physiological, pathological, and therapeutic perspectives, there are also significant species-by-species differences. A welldesigned animal model requires a thorough understanding of similarities and differences in the responses between humans and animals and incorporates that knowledge into the goals of the study. Determining the intrinsic and extrinsic factors associated with the disease and creating a biological information matrix to compare the animal model and human disease courses is a useful tool to help choose the appropriate animal model. Confidence in the correlation of results from a model to the human disease can be achieved only if the relationship of the model to the human disease is well understood.

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.

Call for papers of the special issue on Animal Models and Infectious Diseases

Primates and animal models are major areas of coverage for Zoological Research （ZR）. Over the past few years, ZR has released a series of special issues/topics addressing various aspects of these areas, e.g., ge- netics, immunology, and physiology neuroscience. A special issue for 2017 focusing on ＂Animal Models of Infectious Diseases＂ is under preparation and, so far, includes original research articles and reviews on filo- viruses and coxsackievirus involving guinea pigs, mice, and other species. Further to this, ZR would like to extend a very warm invitation to all peer researchers in the field to submit outstanding work to the journal on this special issue.

Meaningful Contact Estimates among Children in a Childcare Centre with Applications to Contact Matrices in Infectious Disease Modelling

We present a mathematical model of a day care center in a developed country (such as Canada), in order to use it for the estimation of individual-to-individual contact rates in young age groups and in an educational group setting. In our model, individuals in the population are children (ages 1.5 to 4 years) and staff, and their interactions are modelled explicitly: person-to-person and person-to-environment, with a very high time resolution. Their movement and meaningful contact patterns are simulated and then calibrated with collected data from a child care facility as a case study. We present these calibration results as a first part in the further development of our model for testing and estimating the spread of infectious diseases within child care centers.

Application of Structural Equation Model to Evaluate the Perception of Service Quality of Medical Staffs of infectious Disease Department in Chinese Hospitals

Objective To enhance the quality of medical service for Chinese patients through research of service quality from Chinese medical personnel. Methods Serv Qual scale was used for infection medical staffs randomly by sampling questionnaire in Beijing, Shanghai, Chengdu, Chongqing, Guangzhou and Nanning. The data collected were entered and analyzed using SPSS 20.0. Statistical methods included frequency, factor analysis, reliability analysis, correlation analysis, independent samples t test, one-way analyses of variance, simultaneous regression analysis and structural equation model analysis. Results The Kaiser-Meyer-Olkin value for the factor analysis of the scale was 0.970. The Cronbach's α for the reliability analysis was 0.975. The Pearson correlation coefficients were 0.624-0.874 and statistically significant. Undergraduates felt good, Ph D students felt bad; the doctors felt bad; managers felt good. Standard 5 dimensions of the regression coefficients were positive, including empathy(β = 0.288), reliability(β = 0.241) impacting on perceived service quality mostly. The control ability and stability of the standard error of perceived service quality directly effected value were 0.646 and 0.382, respectively. Conclusions Medical staffs of infectious disease department have poor perception of service quality. Hospitals should improve awareness and of clinicians and deepen the reform of the medical care system.

Modellings of Infectious Diseases and Cancers under Wars and Pollution Impacts in Iraq with Reference to a Novel Mathematical Model and Literature Review

Microbial pathogens include bacteria, viruses, fungi, and parasites and together account for a significant percentage of acute and chronic human diseases. In addition to understanding the mechanisms by which various pathogens cause human disease, research in microbial pathogenesis also addresses mechanisms of antimicrobial resistance and the development of new antimicrobial agents and vaccines. Answering fundamental questions regarding host-microbe interactions requires an interdisciplinary approach, including microbiology, genomics, informatics, molecular and cellular biology, biochemistry, immunology, epidemiology, environment and interaction between host and microbe. Studies investigating the direct effects of pollutants on respiratory tract infections are very vast, but those interested in the role of a pre-existing disease and effects of the exposure on the response to secondary stresses are few. In an experimental study at concentrations of air pollutants found in urban environments, frank toxicological responses are rarely observed, however, exposure to secondary stress like the respiratory challenge with infectious bacteria can exacerbate the response of the experimental host. The models like experimental, mechanical, and mathematical are the most abstract, but they allow analysis and logical proofs in a way that other approaches do not permit. The present review is mostly concerned with these model representations particularly with a novel mathematical model explaining the interaction between pathogen and immunity including the equivalence point.

Mucosal-associated invariant T cells from induced pluripotent stem cells:A novel approach for modeling human diseases

Mice have frequently been used to model human diseases involving immune dysregulation such as autoimmune and inflammatory diseases.These models help elucidatethe mechanisms underlying the disease and in the development of novel therapies.However,if mice are deficient in certain cells and/or effectors associated with human diseases,how can their functions be investigated in this species?Mucosal-associated invariant T(MAIT)cells,a novel innate-like T cell family member,are a good example.MAIT cells are abundant in humans but scarce in laboratory mice.MAIT cells harbor an invariant T cell receptor and recognize nonpeptidic antigens vitamin B2metabolites from bacteria and yeasts.Recent studies have shown that MAIT cells play a pivotal role in human diseases such as bacterial infections and autoimmune and inflammatory diseases.MAIT cells possess granulysin,a human-specific effector molecule,but granulysin and its homologue are absent in mice.Furthermore,MAIT cells show poor proliferation in vitro.To overcome these problems and further our knowledge of MAIT cells,we have established a method to expand MAIT cells via induced pluripotent stem cells(iP SCs).In this review,we describe recent advances in the field of MAIT cell research and our approach for human disease modeling with iP SCderived MAIT cells.

Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic.This report summarizes the rich discussions that occurred during the workshop.The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater monitoring.Significant advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends.The role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic preparedness.Overall,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

Bifurcation and optimal control for an infectious disease model with the impact of information

A nonlinear infectious disease model with information-influenced vaccination behavior and contact patterns is proposed in this paper,and the impact of information related to disease prevalence on increasing vaccination coverage and reducing disease incidence during the outbreak is considered.First,we perform the analysis for the existence of equilibria and the stability properties of the proposed model.In particular,the geometric approach is used to obtain the sufficient condition which guarantees the global asymptotic stability of the unique endemic equilibrium Ee when the basic reproduction number Ro>1.Second,mathematical derivation combined with numerical simulation shows the existence of the double Hopf bifurcation around Ee.Third,based on the numerical results,it is shown that the information coverage and the average information delay may lead to more complex dynamical behaviors.Finally,the optimal control problem is established with information-infuenced vaccination and treatment as control variables.The corresponding optimal paths are obtained analytically by using Pontryagin's maximum principle,and the applicability and validity of virous intervention strategies for the proposed controls are presented by numerical experiments.

The Mathematical Model for Streptococcus suis Infection in Pig-Human Population with Humidity Effect

In this paper,we developed a mathematical model for Streptococcus suis,which is an epidemic by considering the moisture that affects the infection.The disease is caused by Streptococcus suis infection found in pigs which can be transmitted to humans.The patients of Streptococcus suis were generally found in adults males and the elderly who contacted pigs or who ate uncooked pork.In human cases,the infection can cause a severe illness and death.This disease has an impact to the financial losses in the swine industry.In the development of models for this disease,we have divided the population into 7 related groups which are susceptible pig compartment,infected pig compartment,quarantined pig compartment,recovered pig compartment,susceptible human compartment,infected human compartment,and recovered human compartment.After that,we use this model and a quarantine strategy to analyze the spread of the infection.In addition,the basic reproduction number R0 is determined by using the next-generation matrix which can analyze the stability of the model.The numerical simulations of the proposed model are illustrated to confirm the results from theorems.The results showed that there is an effect from moisture to the disease transmission.When the moisture increases the disease infection also increases.

Study of tree shrew biology and models: A booming and prosperous field for biomedical research

The tree shrew(Tupaia belangeri)has long been proposed as a suitable alternative to non-human primates(NHPs)in biomedical and laboratory research due to its close evolutionary relationship with primates.In recent years,significant advances have facilitated tree shrew studies,including the determination of the tree shrew genome,genetic manipulation using spermatogonial stem cells,viral vector-mediated gene delivery,and mapping of the tree shrew brain atlas.However,the limited availability of tree shrews globally remains a substantial challenge in the field.Additionally,determining the key questions best answered using tree shrews constitutes another difficulty.Tree shrew models have historically been used to study hepatitis B virus(HBV)and hepatitis C virus(HCV)infection,myopia,and psychosocial stress-induced depression,with more recent studies focusing on developing animal models for infectious and neurodegenerative diseases.Despite these efforts,the impact of tree shrew models has not yet matched that of rodent or NHP models in biomedical research.This review summarizes the prominent advancements in tree shrew research and reflects on the key biological questions addressed using this model.We emphasize that intensive dedication and robust international collaboration are essential for achieving breakthroughs in tree shrew studies.The use of tree shrews as a unique resource is expected to gain considerable attention with the application of advanced techniques and the development of viable animal models,meeting the increasing demands of life science and biomedical research.

An application of small-world network on predicting the behavior of infectious disease on campus

Networks haven been widely used to understand the spread of infectious disease.This study examines the properties of small-world networks in modeling infectious disease on campus.Two different small-world models are developed and the behaviors of infectious disease in the models are observed through numerical simulations.The results show that the behavior pattern of infectious disease in a small-world network is different from those in a regular network or a random network.The spread of the infectious disease increases as the proportion of long-distance connections p increasing,which indicates that reducing the contact among people is an effective measure to control the spread of infectious disease.The probability of node position exchange in a network(p2)had no significant effect on the spreading speed,which suggests that reducing human mobility in closed environments does not help control infectious disease.However,the spreading speed is proportional to the number of shared nodes(s),which means reducing connections between different groups and dividing students into separate sections will help to control infectious disease.In the end,the simulating speed of the small-world network is tested and the quadratic relationship between simulation time and the number of nodes may limit the application of the SW network in areas with large populations.

AI for science: Predicting infectious diseases

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.

Numerical Simulation and Parameter Estimation of Fractional-Order Dynamic Epidemic Model for COVID-19

The outbreak of COVID-19 in 2019 resulted in numerous infections and deaths. In order to better study the transmission of COVID-19, this article adopts an improved fractional-order SIR model. Firstly, the properties of the model are studied, including the feasible domain and bounded solutions of the system. Secondly, the stability of the system is discussed, among other things. Then, the GMMP method is introduced to obtain numerical solutions for the COVID-19 system and combined with the improved MH-NMSS-PSO parameter estimation method to fit the real data of Delhi, India from April 1, 2020 to June 30, 2020. The results show that the fitting effect is quite ideal. Finally, long-term predictions were made on the number of infections. We accurately estimate that the peak number of infections in Delhi, India, can reach around 2.1 million. This paper also compares the fitting performance of the integer-order COVID-19 model and the fractional-order COVID-19 model using the real data from Delhi. The results indicate that the fractional-order model with different orders, as we proposed, performs the best.

Establishment of a Fractional Order COVID-19 Model and Its Feasibility Analysis

This paper investigates an improved SIR model for COVID-19 based on the Caputo fractional derivative. Firstly, the properties of the model are studied, including the feasible domain and bounded solutions of the system. Secondly, the stability of the system is discussed, among other things. Then, the GMMP method is introduced to obtain numerical solutions for the COVID-19 system. Numerical simulations were conducted using MATLAB, and the results indicate that our model is valuable for studying virus transmission.

Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases

Similar to species immigration or exotic species invasion,infectious disease transmis-sion is strengthened due to the globalization of human activities. Using schistosomiasis as an exam-ple,we propose a conceptual model simulating the spatio-temporal dynamics of infectious diseases. We base the model on the knowledge of the interrelationship among the source,media,and the hosts of the disease. With the endemics data of schistosomiasis in Xichang,China,we demonstrate that the conceptual model is feasible; we introduce how remote sensing and geographic information systems techniques can be used in support of spatio-temporal modeling; we compare the different effects caused to the entire population when selecting different groups of people for schistosomiasis control. Our work illustrates the importance of such a modeling tool in supporting spatial decisions. Our mod-eling method can be directly applied to such infectious diseases as the plague,lyme disease,and hemorrhagic fever with renal syndrome. The application of remote sensing and geographic informa-tion systems can shed light on the modeling of other infectious disease and invasive species studies.

First two months of the 2019 Coronavirus Disease(COVID-19)epidemic in China:realtime surveillance and evaluation with a second derivative model

Background:Similar to outbreaks of many other infectious diseases,success in controlling the novel 2019 coronavirus infection requires a timely and accurate monitoring of the epidemic,particularly during its early period with rather limited data while the need for information increases explosively.Methods:In this study,we used a second derivative model to characterize the coronavirus epidemic in China with cumulatively diagnosed cases during the first 2 months.The analysis was further enhanced by an exponential model with a close-population assumption.This model was built with the data and used to assess the detection rate during the study period,considering the differences between the true infections,detectable and detected cases.Results:Results from the second derivative modeling suggest the coronavirus epidemic as nonlinear and chaotic in nature.Although it emerged gradually,the epidemic was highly responsive to massive interventions initiated on January 21,2020,as indicated by results from both second derivative and exponential modeling analyses.The epidemic started to decelerate immediately after the massive actions.The results derived from our analysis signaled the decline of the epidemic 14 days before it eventually occurred on February 4,2020.Study findings further signaled an accelerated decline in the epidemic starting in 14 days on February 18,2020.Conclusions:The coronavirus epidemic appeared to be nonlinear and chaotic,and was responsive to effective interventions.The methods used in this study can be applied in surveillance to inform and encourage the general public,public health professionals,clinicians and decision-makers to take coordinative and collaborative efforts to control the epidemic.

A generalized differential equation compartmental model of infectious disease transmission

For decades,mathematical models of disease transmission have provided researchers and public health officials with critical insights into the progression,control,and prevention of disease spread.Of these models,one of the most fundamental is the SIR differential equation model.However,this ubiquitous model has one significant and rarely acknowledged shortcoming:it is unable to account for a disease's true infectious period distribution.As the misspecification of such a biological characteristic is known to significantly affect model behavior,there is a need to develop new modeling approaches that capture such information.Therefore,we illustrate an innovative take on compartmental models,derived from their general formulation as systems of nonlinear Volterra integral equations,to capture a broader range of infectious period distributions,yet maintain the desirable formulation as systems of differential equations.Our work illustrates a compartmental model that captures any Erlang distributed duration of infection with only 3 differential equations,instead of the typical inflated model sizes required by traditional differential equation compartmental models,and a compartmental model that captures any mean,standard deviation,skewness,and kurtosis of an infectious period distribution with 4 differential equations.The significance of our work is that it opens up a new class of easyto-use compartmental models to predict disease outbreaks that do not require a complete overhaul of existing theory,and thus provides a starting point for multiple research avenues of investigation under the contexts of mathematics,public health,and evolutionary biology.

The collaboration between infectious disease modeling and public health decision-making based on the COVID-19

Public health decision-making may have great uncertainty especially in dealing with emerging infectious diseases,so it is necessary to establish a collaborative mechanism among modelers,epidemiologists,and public health decision-makers to reduce the uncertainty as much as possible.We searched the relevant studies on transmission dynamics modeling of infectious diseases,SARS,MERS,and COVID-19 as of March 1,2021 based on PubMed.We compared the key health decision-making time points of SARS,MERS,and COVID-19 prevention and control,and the publication time points of modeling research,to reveal the collaboration between infectious disease modeling and public health decision-making in the context of the COVID-19 pandemic.Searching with infectious disease and mathematical model as keywords,there were 166,81 and 1289 studies on the modeling of infectious disease transmission dynamics of SARS,MERS,and COVID-19 were retrieved respectively.Based on the modeling application framework of public health practice proposed in the current study,the collaboration among modelers,epidemiologists and public health decision-makers should be strengthened in the future.