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| 1 | An updated estimation of the risk of transmission of the novel coronavirus(2019-nCov)显示文摘The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period,in a naïve,uninfected population.It is well-known that the estimation of this number may vary due to several methodological issues,including different assumptions and choice of parameters,utilized models,used datasets and estimation period.With the spreading of the novel coronavirus(2019-nCoV)infection,the reproduction number has been found to vary,reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate.Due to significant variations in the control strategies,which have been changing over time,and thanks to the introduction of detection technologies that have been rapidly improved,enabling to shorten the time from infection/symptoms onset to diagnosis,leading to faster confirmation of the new coronavirus cases,our previous estimations on the transmission risk of the 2019-nCoV need to be revised.By using time-dependent contact and diagnose rates,we refit our previously proposed dynamics transmission model to the data available until January 29th,2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions.We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak.Our updated findings suggest that the best measure is persistent and strict self-isolation.The epidemics will continue to grow,and can peak soon with the peak time depending highly on the public health interventions practically implemented. | Biao Tang Nicola Luigi Bragazzi Qian Li Sanyi Tang Yanni Xiao Jianhong Wu | 2020 | Infectious Disease Modelling2020，5，1： | 54 |
| 2 | Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020显示文摘An outbreak of COVID-19 developed aboard the Princess Cruises Ship during January eFebruary 2020.Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers and crew members,we characterize how the transmission potential varied over the course of the outbreak.Our estimate of the mean reproduction number in the confined setting reached values as high as^11,which is higher than mean estimates reported from community-level transmission dynamics in China and Singapore(approximate range:1.1e7).Our findings suggest that Rt decreased substantially compared to values during the early phase after the Japanese government implemented an enhanced quarantine control.Most recent estimates of Rt reached values largely below the epidemic threshold,indicating that a secondary outbreak of the novel coronavirus was unlikely to occur aboard the Diamond Princess Ship. | Kenji Mizumoto Gerardo Chowell | 2020 | Infectious Disease Modelling2020，5，1： | 14 |
| 3 | Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th,2020显示文摘The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified inWuhan,China in December 2019.While early cases of the disease were linked to a wet market,human-to-human transmission has driven the rapid spread of the virus throughout China.The Chinese government has implemented containment strategies of city-wide lockdowns,screening at airports and train stations,and isolation of suspected patients;however,the cumulative case count keeps growing every day.The ongoing outbreak presents a challenge for modelers,as limited data are available on the early growth trajectory,and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated.We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province,the epicenter of the epidemic,and for the overall trajectory in China,excluding the province of Hubei.We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China.Here,we provide 5,10,and 15 day forecasts for five consecutive days,February 5th through February 9th,with quantified uncertainty based on a generalized logistic growth model,the Richards growth model,and a sub-epidemic wave model.Our most recent forecasts reported here,based on data up until February 9,2020,largely agree across the three models presented and suggest an average range of 7409e7496 additional confirmed cases in Hubei and 1128e1929 additional cases in other provinces within the next five days.Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,588e13,499 in other provinces by February 24,2020.Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates(February 7th e 9th).We also observe that each of the models predicts that the epidemic has reached saturation in both Hubei and other provinces.Our findings suggest that the containment strategies implemented in China are successfully reducing transmission and that the epidemic growth has slowed in recent days. | K.Roosa Y.Lee R.Luo A.Kirpich R.Rothenberg J.M.Hyman P.Yan G.Chowell | 2020 | Infectious Disease Modelling2020，5，1： | 12 |
| 4 | A primer on model selection using the Akaike Information Criterion显示文摘A powerful investigative tool in biology is to consider not a single mathematical model but a collection of models designed to explore different working hypotheses and select the best model in that collection.In these lecture notes,the usual workflow of the use of mathematical models to investigate a biological problem is described and the use of a collection of model is motivated.Models depend on parameters that must be estimated using observations;and when a collection of models is considered,the best model has then to be identified based on available observations.Hence,model calibration and selection,which are intrinsically linked,are essential steps of the workflow.Here,some procedures for model calibration and a criterion,the Akaike Information Criterion,of model selection based on experimental data are described.Rough derivation,practical technique of computation and use of this criterion are detailed. | Stéphanie Portet | 2020 | Infectious Disease Modelling2020，5，1： | 9 |
| 5 | Fitting dynamic models to epidemic outbreaks with quantified uncertainty:A primer for parameter uncertainty,identifiability,and forecasts显示文摘Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets. | Gerardo Chowell | 2017 | Infectious Disease Modelling2017，2，3： | 8 |
| 6 | To mask or not to mask:Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic显示文摘Face mask use by the general public for limiting the spread of the COVID-19 pandemic is controversial,though increasingly recommended,and the potential of this intervention is not well understood.We develop a compartmental model for assessing the communitywide impact of mask use by the general,asymptomatic public,a portion of which may be asymptomatically infectious.Model simulations,using data relevant to COVID-19 dynamics in the US states of New York and Washington,suggest that broad adoption of even relatively ineffective face masks may meaningfully reduce community transmission of COVID-19 and decrease peak hospitalizations and deaths.Moreover,mask use decreases the effective transmission rate in nearly linear proportion to the product of mask effectiveness(as a fraction of potentially infectious contacts blocked)and coverage rate(as a fraction of the general population),while the impact on epidemiologic outcomes(death,hospitalizations)is highly nonlinear,indicating masks could synergize with other nonpharmaceutical measures.Notably,masks are found to be useful with respect to both preventing illness in healthy persons and preventing asymptomatic transmission.Hypothetical mask adoption scenarios,for Washington and New York state,suggest that immediate near universal(80%)adoption of moderately(50%)effective masks could prevent on the order of 17e45%of projected deaths over two months in New York,while decreasing the peak daily death rate by 34e58%,absent other changes in epidemic dynamics.Even very weak masks(20%effective)can still be useful if the underlying transmission rate is relatively low or decreasing:InWashington,where baseline transmission is much less intense,80%adoption of such masks could reduce mortality by 24e65%(and peak deaths 15e69%),compared to 2e9%mortality reduction in New York(peak death reduction 9e18%).Our results suggest use of face masks by the general public is potentially of high value in curtailing community transmission and the burden of the pandemic.The community-wide benefits are likely to be greatest when face masks are used in conjunction with other non-pharmaceutical practices(such as social-distancing),and when adoption is nearly universal(nation-wide)and compliance is high. | Steffen E.Eikenberry Marina Mancuso Enahoro Iboi Tin Phan Keenan Eikenberry Yang Kuang Eric Kostelich Abba B.Gumel | 2020 | Infectious Disease Modelling2020，5，1： | 8 |
| 7 | Estimating epidemic exponential growth rate and basic reproduction number显示文摘The initial exponential growth rate of an epidemic is an important measure of the severeness of the epidemic,and is also closely related to the basic reproduction number.Estimating the growth rate from the epidemic curve can be a challenge,because of its decays with time.For fast epidemics,the estimation is subject to over-fitting due to the limited number of data points available,which also limits our choice of models for the epidemic curve.We discuss the estimation of the growth rate using maximum likelihood method and simple models. | Junling Ma | 2020 | Infectious Disease Modelling2020，5，1： | 7 |
| 8 | Simulation of four respiratory viruses and inference of epidemiological parameters显示文摘While influenza has been simulated extensively to better understand its behavior and predict future outbreaks,most other respiratory viruses have seldom been simulated.In this study,we provide an overview of four common respiratory viral infections:respiratory syncytial virus(RSV),respiratory adenovirus,rhinovirus and parainfluenza,present specimen data collected 2004e2014,and simulate outbreaks in 19 overlapping regions in the United States.Pairing a compartmental model and data assimilation methods,we infer key epidemiological parameters governing transmission:the basic reproductive number R0 and length of infection D.RSV had been previously simulated,and our mean estimate of D and R0 of 5.2 days and 2.8,respectively,are within published clinical and modeling estimates.Among the four virus groupings,mean estimates of R0 range from 2.3 to 3.0,with a lower and upper quartile range of 2.0e2.8 and 2.6e3.2,respectively.As rapid PCR testing becomes more common,estimates of the observed virulence and duration of infection for these viruses could inform decision making by clinicians and officials for managing patient treatment and response. | Julia Reis Jeffrey Shaman | 2018 | Infectious Disease Modelling2018，3，1： | 6 |
| 9 | Reproduction numbers of infectious disease models显示文摘This primer article focuses on the basic reproduction number,ℛ0,for infectious diseases,and other reproduction numbers related toℛ0 that are useful in guiding control strategies.Beginning with a simple population model,the concept is developed for a threshold value ofℛ0 determining whether or not the disease dies out.The next generation matrix method of calculatingℛ0 in a compartmental model is described and illustrated.To address control strategies,type and target reproduction numbers are defined,as well as sensitivity and elasticity indices.These theoretical ideas are then applied to models that are formulated for West Nile virus in birds(a vector-borne disease),cholera in humans(a disease with two transmission pathways),anthrax in animals(a disease that can be spread by dead carcasses and spores),and Zika in humans(spread by mosquitoes and sexual contacts).Some parameter values from literature data are used to illustrate the results.Finally,references for other ways to calculateℛ0 are given.These are useful for more complicated models that,for example,take account of variations in environmental fluctuation or stochasticity. | Pauline van den Driessche | 2017 | Infectious Disease Modelling2017，2，3： | 5 |
| 10 | Why is it difficult to accurately predict the COVID-19 epidemic?显示文摘Since the COVID-19 outbreak in Wuhan City in December of 2019,numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported.These model predictions have shown a wide range of variations.In our study,we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations.Using the Akaike Information Criterion(AIC)for model selection,we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data.This indicates that predictions using more complex models may not be more reliable compared to using a simpler model.We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23,2020.We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic,and modeling the potential of a second outbreak after the return-to-work in the city. | Weston C.Roda Marie B.Varughese Donglin Han Michael Y.Li | 2020 | Infectious Disease Modelling2020，5，1： | 5 |
| 11 | A primer on stochastic epidemic models:Formulation,numerical simulation,and analysis显示文摘Some mathematical methods for formulation and numerical simulation of stochastic epidemic models are presented.Specifically,models are formulated for continuous-time Markov chains and stochastic differential equations.Some well-known examples are used for illustration such as an SIR epidemic model and a host-vector malaria model.Analytical methods for approximating the probability of a disease outbreak are also discussed. | Linda J.S.Allen | 2017 | Infectious Disease Modelling2017，2，2： | 4 |
| 12 | Is it growing exponentially fast? -Impact of assuming exponential growth for characterizing and forecasting epidemics with initial near-exponential growth dynamics显示文摘The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic forecasts.Improved models for epidemic forecasting could be achieved by identifying signature features of epidemic growth,which could inform the design of models of disease spread and reveal important characteristics of the transmission process.In particular,it is often taken for granted that the early growth phase of different growth processes in nature follow early exponential growth dynamics.In the context of infectious disease spread,this assumption is often convenient to describe a transmission process with mass action kinetics using differential equations and generate analytic expressions and estimates of the reproduction number.In this article,we carry out a simulation study to illustrate the impact of incorrectly assuming an exponential-growth model to characterize the early phase(e.g.,3e5 disease generation intervals)of an infectious disease outbreak that follows near-exponential growth dynamics.Specifically,we assess the impact on:1)goodness of fit,2)bias on the growth parameter,and 3)the impact on short-term epidemic forecasts.Our findings indicate that devising transmission models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit,improved estimates of key transmission parameters,and more realistic epidemic forecasts. | Gerardo Chowell Cecile Viboud | 2016 | Infectious Disease Modelling2016，1，1： | 4 |
| 13 | Mathematical modeling and the transmission dynamics in predicting the Covid-19-What next in combating the pandemic显示文摘Mathematical predictions in combating the epidemics are yet to reach its perfection.The rapid spread,the ways,and the procedures involved in containment of a pandemic demand the earliest understanding in finding solutions in line with the habitual,physiological,biological,and environmental aspects of life with better computerised mathematical modeling and predictions.Epidemiology models are key tools in public health management programs despite having a high level of uncertainty in each one of these models.This paper describes the outcome and the challenges of SIR,SEIR,SEIRU,SIRD,SLIAR,ARIMA,SIDARTHE,etc models used in prediction of spread,peak,and reduction of Covid-19 cases. | A.Anirudh | 2020 | Infectious Disease Modelling2020，5，1： | 4 |
| 14 | Adult female syphilis prevalence,congenital syphilis case incidence and adverse birth outcomes,Mongolia 2000e2016:Estimates using the Spectrum STI tool显示文摘Introduction:Mongolia's health ministry prioritizes control of Sexually Transmitted Infections,including syphilis screening and treatment in antenatal care(ANC).Methods:Adult syphilis prevalence trends were fitted using the Spectrum-STI estimation tool,using data from ANC surveys and routine screening over 1997e2016.Estimates were combined with programmatic data to estimate numbers of treated and untreated pregnant women with syphilis and associated incidence congenital syphilis(CS)and CS-attributable adverse birth outcomes(ABO),which we compared with CS case reports.Results:Syphilis prevalence in pregnant women was estimated at 1.7%in 2000 and 3.0%in 2016.We estimated 652 CS cases,of which 410 ABO,in 2016.Far larger,annually increasing numbers of CS cases and ABO were estimated to have been prevented:1654 cases,of which 789 ABO in 2016thanks to increasing coverages of ANC(99%in 2016),ANC-based screening(97%in 2016)and treatment of women diagnosed(81%in 2016).The 42 CS cases reported nationally over 2016(liveborn infants only)represented 27%of liveborn infants with clinical CS,but only 7%of estimated CS cases among women found syphilis-infected in ANC,and 6%of all estimated CS cases including those born to women with undiagnosed syphilis.Discussion/Conclusion:Mongolia's ANC-based syphilis screening program is reducing CS,but maternal prevalence remains high.To eliminate CS(target:<50 cases per 100,000 live births),Mongolia should strengthen ANC services,limiting losses during referral for treatment,and under-diagnosis of CS including still-births and neonatal deaths,and expand syphilis screening and prevention programs. | Erdenetungalag Enkhbat Eline L.Korenromp Jugderjav Badrakh Setsen Zayasaikhan Purevsuren Baya Enkhjargal Orgiokhuu Narantuya Jadambaa Sergelen Munkhbaatar Delgermaa Khishigjargal Narantuya Khad Guy Mahiane Naoko Ishikawa Davaalkham Jagdagsuren Melanie M.Taylor | 2018 | Infectious Disease Modelling2018，3，1： | 4 |
| 15 | COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models显示文摘The coronavirus disease outbreak of 2019(COVID-19)has been spreading rapidly to all corners of the word,in a very complex manner.A key research focus is in predicting the development trend of COVID-19 scientifically through mathematical modelling.We conducted a systematic review of epidemic prediction models of COVID-19 and the public health intervention strategies by searching the Web of Science database.55 studies of the COVID-19 epidemic model were reviewed systematically.It was found that the COVID-19 epidemic models were different in the model type,acquisition method,hypothesis and distribution of key input parameters.Most studies used the gamma distribution to describe the key time period of COVID-19 infection,and some studies used the lognormal distribution,the Erlang distribution,and theWeibull distribution.The setting ranges of the incubation period,serial interval,infectious period and generation time were 4.9-7 days,4.41-8.4 days,2.3-10 days and 4.4-7.5 days,respectively,and more than half of the incubation periods were set to 5.1 or 5.2 days.Most models assumed that the latent period was consistent with the incubation period.Some models assumed that asymptomatic infections were infectious or pre-symptomatic transmission was possible,which overestimated the value of R0.For the prediction differences under different public health strategies,the most significant effect was in travel restrictions.There were different studies on the impact of contact tracking and social isolation,but it was considered that improving the quarantine rate and reporting rate,and the use of protective face mask were essential for epidemic prevention and control.The input epidemiological parameters of the prediction models had significant differences in the prediction of the severity of the epidemic spread.Therefore,prevention and control institutions should be cautious when formulating public health strategies by based on the prediction results of mathematical models. | Yue Xiang Yonghong Jia Linlin Chen Lei Guo Bizhen Shu Enshen Long | 2021 | Infectious Disease Modelling2021，6，1： | 3 |
| 16 | Capacity-need gap in hospital resources for varying mitigation and containment strategies in India in the face of COVID-19 pandemic显示文摘Background:Due to uncertainties encompassing the transmission dynamics of COVID-19,mathematical models informing the trajectory of disease are being proposed throughout the world.Current pandemic is also characterized by surge in hospitalizations which has overwhelmed even the most resilient health systems.Therefore,it is imperative to assess health system preparedness in tandem with need projections for comprehensive outlook.Objective:We attempted this study to forecast the need for hospital resources for one year period and correspondingly assessed capacity and tipping points of Indian health system to absorb surges in need due to COVID-19.Methods:We employed age-structured deterministic SEIR model and modified it to allow for testing and isolation capacity to forecast the need under varying scenarios.Projections for documented cases were made for varying degree of containment and mitigation strategies.Correspondingly,data on health resources was collated from various government records.Further,we computed daily turnover of each of these resources which was then adjusted for proportion of cases requiring mild,severe and critical care to arrive at maximum number of COVID-19 cases manageable by health care system of India.Findings:Our results revealed pervasive deficits in the capacity of public health system to absorb surge in need during peak of epidemic.Also,model suggests that continuing strict lockdown measures in India after mid-May 2020 would have been ineffective in suppressing total infections significantly.Augmenting testing to 1,500,000 tests per day during projected peak(mid-September)under social-distancing measures and current test to positive rate of 9.7%would lead to more documented cases(60,000,000 to 90,000,000)culminating to surge in demand for hospital resources.A minimum allocation of 13x,70x and 37x times more beds for mild cases,ICU beds and mechanical ventilators respectively would be required to commensurate with need under that scenario.However,if testing capacity is limited to 9,000,000 tests per day(current situation as of 19th August 2020)under continued social-distancing measures,documented cases would plummet significantly,still requiring 5x,31x and 16x times the current allocated resources(beds for mild cases,ICU beds and mechanical ventilators respectively)to meet unmet need for COVID-19 treatment in India. | Veenapani Rajeev Verma Anuraag Saini Sumirtha Gandhi Umakant Dash Shaffi Fazaludeen Koya | 2020 | Infectious Disease Modelling2020，5，1： | 3 |
| 17 | Mathematical modeling of COVID-19 epidemic with effect of awareness programs显示文摘Severe acute respiratory syndrome coronavirus 2(SARS-COV-2)is a novel virus that emerged in China in late 2019 and caused a pandemic of coronavirus disease 2019(COVID-19).The epidemic has largely been controlled in China since March 2020,but continues to inflict severe public health and socioeconomic burden in other parts of the world.One of the major reasons for China’s success for the fight against the epidemic is the effectiveness of its health care system and enlightenment(awareness)programs which play a vital role in the control of the COVID-19 pandemic.Nigeria is currently witnessing a rapid increase of the epidemic likely due to its unsatisfactory health care system and inadequate awareness programs.In this paper,we propose a mathematical model to study the transmission dynamics of COVID-19 in Nigeria.Our model incorporates awareness programs and different hospitalization strategies for mild and severe cases,to assess the effect of public awareness on the dynamics of COVID-19 infection.We fit the model to the cumulative number of confirmed COVID-19 cases in Nigeria from 29 March to 12 June 2020.We find that the epidemic could increase if awareness programs are not properly adopted.We presumed that the effect of awareness programs could be estimated.Further,our results suggest that the awareness programs and timely hospitalization of active cases are essential tools for effective control and mitigation of COVID-19 pandemic in Nigeria and beyond.Finally,we perform sensitive analysis to point out the key parameters that should be considered to effectively control the epidemic. | Salihu Sabiu Musa Sania Qureshi Shi Zhao Abdullahi Yusuf Umar Tasiu Mustapha Daihai He | 2021 | Infectious Disease Modelling2021，6，1： | 3 |
| 18 | Dynamic model of tuberculosis considering multi-drug resistance and their applications显示文摘Infectious diseases have always been a problem that threatens people's health and tuberculosis is one of the major.With the development of medical scientific research,drug-resistant infectious diseases have become a more intractable threat because various drugs and antibiotics are widely used in the process of fighting against infectious diseases.In this paper,an improved dynamic model of infectious diseases considering population dynamics and drug resistance is established.The feasible region,equilibrium points and stability of the model are analyzed.Based on the existing data,this model can predict the development of the epidemic situation through numerical simulation,and put forward some relevant measures and suggestions. | Yi Yu Yi Shi Wei Yao | 2018 | Infectious Disease Modelling2018，3，1： | 3 |
| 19 | Estimating effective reproduction number using generation time versus serial interval,with application to COVID-19 in the Greater Toronto Area,Canada显示文摘BACKGROUND.The effective reproduction number Re(t)is a critical measure of epidemic potential.Re(t)can be calculated in near real time using an incidence time series and the generation time distribution:the time between infection events in an infector-infectee pair.In calculating Re(t),the generation time distribution is often approximated by the serial interval distribution:the time between symptom onset in an infector-infectee pair.However,while generation time must be positive by definition,serial interval can be negative if transmission can occur before symptoms,such as in COVID-19,rendering such an approximation improper in some contexts.METHODS.We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions.We then compared estimates of Re(t)for COVID-19 in the Greater Toronto Area of Canada using:negative-permitting versus non-negative serial interval distributions,versus the inferred generation time distribution.RESULTS.We estimated the generation time of COVID-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days.Relative to the generation time distribution,non-negative serial interval distribution caused overestimation of Re(t)due to larger mean,while negative-permitting serial interval distribution caused underestimation of Re(t)due to larger variance.IMPLICATIONS.Approximation of the generation time distribution of COVID-19 with non-negative or negative-permitting serial interval distributions when calculating Re(t)may result in over or underestimation of transmission potential,respectively. | Jesse Knight Sharmistha Mishra | 2020 | Infectious Disease Modelling2020，5，1： | 3 |
| 20 | Study on the SEIQR model and applying the epidemiological rates of COVID-19 epidemic spread in Saudi Arabia显示文摘This article attempts to establish a mathematical epidemic model for the outbreak of the new COVID-19 coronavirus.A new consideration for evaluating and controlling the COVID-19 outbreak will be constructed based on the SEIQR Pandemic Model.In this paper,the real data of COVID-19 spread in Saudi Arabia has been used for the mathematical model and dynamic analyses.Including the new reproductive number and detailed stability analysis,the dynamics of the proposed SEIQR model have been applied.The local sensitivity of the reproduction number has been analyzed.The domain of solution and equilibrium based on the SEIQR model have been proved using a Jacobian linearization process.The state of equilibrium and its significance have been proved,and a study of the integrity of the disease-free equilibrium has been carried out.The Lyapunov stability theorem demonstrated the global stability of the current model equilibrium.The SEIQR model has been numerically validated and projected by contrasting the results from the SEIQR model with the actual COVID-19 spread data in Saudi Arabia.The result of this paper shows that the SEIQR model is a model that is effective in analyzing epidemic spread,such as COVID-19.At the end of the study,we have implemented the protocol which helped the Saudi population to stop the spread of COVID-19 rapidly. | Hamdy Youssef Najat Alghamdi Magdy A.Ezzat Alaa A.El-Bary Ahmed M.Shawky | 2021 | Infectious Disease Modelling2021，6，1： | 3 |
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