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A study of the COVID-19 epidemic in India using the seird model

Quantitative Biology 2021年第3期9卷 317-328页

作者： Rudra Banerjee Srijit Bhattacharjee Pritish Kumar Varadwaj Department of Physics and Nanotechnology SRM Institute of Science and TechnologyKattankulathur 603203Tamil NaduIndia Department of Bioinformatics&Applied Sciences Indian Institute of Information TechnologyAllahabad 211015Uttar PradeshIndia

Background:The coronavirus pandemic(COVID-19)is causing a havoc globally,exacerbated by the newly discovered SARS-CoV-2 *** to its high population density,India is one of the most badly effected countries from the first wave of ***,it is extremely necessary to accurately predict the state-wise and overall dynamics of COVID-19 to get the effective and efficient organization of resources across ***:In this study,the dynamics of COVID-19 in India and several of its selected states with different demographic structures were analyzed using the seird epidemiological *** basic reproductive ratio Ra was systemically estimated to predict the dynamics of the temporal progression of COVID-19 in India and eight of its states,Andhra Pradesh,Chhattisgarh,Delhi,Gujarat,Madhya Pradesh,Maharashtra,Tamil Nadu,and Uttar ***:For India,the seird model calculations show that the peak of infection is expected to appear around the middle of October,***,we compared the model scenario to a Gaussian fit of the daily infected cases and obtained similar *** early imposition of a nation-wide lockdown has reduced the number of infected cases but delayed the appearance of the infection peak ***:After comparing our calculations using India’s data to the real life dynamics observed in Italy and Russia,we can conclude that the seird model can predict the dynamics of COVID-19 with sufficient accuracy.

关键词： COVID-19 SARS-CoV-2 epidemic statistical analysis seird model

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Estimation of Mortality Rate of COVID-19 in India using seird model

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OPSEARCH 2023年第1期60卷 539-553页

作者： Selvamuthu, Dharmaraja Khichar, Deepak Kalita, Priyanka Jain, Vidyottama Indian Inst Technol Delhi Delhi India Cent Univ Rajasthan Dept Math Ajmer 305801 India

In India, the number of infections is rapidly increased with a mounting death toll during the second wave of Coronavirus disease (COVID-19). To measure the severity of the said disease, the mortality rate plays an important role. In this research work, the mortality rate of COVID-19 is estimated by using the Susceptible-Exposed-Infected-Recovered-Dead (seird) epidemiological model. As the disease contains a significant amount of uncertainty, a fundamental seird model with minimal assumptions is employed. Further, a basic method is proposed to obtain time-dependent estimations of the parameters of the seird model by using historical data. From our proposed model and with the predictive analysis, it is expected that the infection may go rise in the month of May-2021 and the mortality rate could go as high as 1.8%. Such high rates of mortality may be used as a measure to understand the severity of the situation.

关键词： COVID-19 seird model Mortality rate Parameter estimation

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Coronavirus Epidemic Spreading Prediction combining random walk with susceptible–exposed–infected–recovered–deceased(seird) model

Coronavirus Epidemic Spreading Prediction combining random w...

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作者： Zhengxi Wang Yujian Yuan School of Advanced Technology Xi'an Jiaotong-Liverpool University Department of Mathematics Boston University

Since the breakout of COVID-19 pandemic in December 2019,effective modelling of the spreading of the virus has become an essential reference for the epidemic *** a bid to solve the problem of Epidemic prediction,susceptible-exposed-infected-recovered(SEIR) model are widely ***,this model seems lack the ability to handle random events which may occur during the spreading of the pandemic and the ability to simulate the pandemic spreading between different subdivided ***,we propose an early version of susceptible–exposed–infected–recovered–deceased(seird) model that combines the classic compartmental concepts of seird and the random walk methodology to forecast COVID-19 in real ***,this study will focus on improvement of the exposed–infected part of seird ***,the exposed–infected section of seird model will be applied to each subdivided regions ***,instead of entering infected–recovered part directly,the infected of each district will be selected and sent to linked districts by random walk system to mimic the commuting and irregular trips between ***,after the re-distribution of infected patients,the model will enter the infected–recovered *** argued model adopt the seird model to forecasting of virus spreading between small regions and taking irregular moving of citizens into consideration via random walk system,thus provide an effective reference for countries which aim to respond to the post-epidemic era.

关键词： COVID-19 Spread of pandemic Random walk seird model

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modeling and forecasting of COVID-19 using a hybrid dynamic model based on seird with ARIMA corrections

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Infectious Disease modelling 2021年第1期6卷 98-111页

作者： Maher Ala’raj Munir Majdalawieh Nishara Nizamuddin Department of Information Systems College of Technological InnovationZayed UniversityDubai19282United Arab Emirates

The outbreak of novel coronavirus(COVID-19)attracted worldwide *** has posed a significant challenge for the global economies,especially the healthcare *** with a robust healthcare system,countries were not prepared for the ramifications of *** statistical,dynamic,and mathematical models of the COVID-19 outbreak including the SEIR model have been developed to analyze the infection its transmission *** objective of this research is to use public data to study the properties associated with the COVID-19 pandemic to develop a dynamic hybrid model based on seird and ascertainment rate with automatically selected *** proposed model consists of two parts:the modified seird dynamic model and ARIMA *** fit seird model parameters against historical values of infected,recovered and deceased population divided by ascertainment rate,which,in turn,is also a parameter of the *** of the first model for infected,recovered,and deceased populations are then corrected using ARIMA *** model can analyze the input data in real-time and provide long-and short-term forecasts with confidence *** model was tested and validated on the US COVID statistics dataset from the COVID Tracking *** validation,we use unseen recent statistical *** use five common measures to estimate model prediction ability:MAE,MSE,MLSE,Normalized MAE,and Normalized *** proved a great model ability to make accurate predictions of infected,recovered,and deceased *** output of the model can be used by the government,private sectors,and policymakers to reduce health and economic risks significantly improved consumer credit scoring.

关键词： COVID 19 Coronavirus seird model ARIMA Hybrid model

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Coronavirus Outburst Prediction in India using seird, Logistic Regression and ARIMA model 11

Coronavirus Outburst Prediction in India using SEIRD, Logist...

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11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Darapaneni, Narayana Nikam, Deepali Lomate, Anagha Kherde, Vaibhav Katdare, Swanand Paduri, Anwesh Reddy Rao, Kameswara Shukla, Anima Great Learning AIML Pune Maharashtra India Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9781728196565

The COVID-19 (SARS-CoV-2) pandemic is a major global health threat. According to the World Health Organization (WHO) COVID-19 situation report as on June 13, 2020, a total of 7,553,182 confirmed cases and 423,349 deaths have been reported across the world. Total confirmed cases in India as on 30th March'20 is 1071 of which 942 are active COVID-19 cases. There have been 29 death cases. Methods: We had used the mathematical model which monitors the five compartments namely, Susceptible, Exposed, Infected, Recovered and Deaths, collectively expressed as seird to derive the epidemic curve on India and top two most affected states (Maharashtra and Delhi). We also used ARIMA and Logistic Regression model on India data set and two states to p confirmed cases and calculated R-Squared value. Results: As per the model, the growth rate is 4.25, India is likely to reach a peak by August, showing a gradual decrease by end of October or Mid November. Conclusion: Our seird model was good in foreseeing the number of confirmed cases of COVID-19 for the upcoming days, we additionally reproduced the spread of disease in India for next 100 days by utilizing seird model and anticipating the quantity of affirmed cases for next 14 days through ARIMA and Logistic Regression.

关键词： COVID-19 Epidemic modelling Trend Simulation Forecasting seird model ARIMA Logistic Regression

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Kalman Filter-Based Epidemiological model for Post-COVID-19 Era Surveillance and Prediction

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SENSORS 2025年第8期25卷

作者： Shi, Yuanyou Zhu, Xinhang Zhu, Xinhe Cheng, Baiqi Zhong, Yongmin RMIT Univ Sch Engn Melbourne Vic 3000 Australia North China Univ Technol Sch Mech & Mat Engn Beijing 100041 Peoples R China

In the post-COVID-19 era, the dynamic spread of COVID-19 poses new challenges to epidemiological modelling, particularly due to the absence of large-scale screening and the growing complexity introduced by immune failure and reinfections. This paper proposes an AEIHD (antibody-acquired, exposed, infected, hospitalised, and deceased) model to analyse and predict COVID-19 transmission dynamics in the post-COVID-19 era. This model removes the susceptible compartment and combines the recovered and vaccinated compartments into an "antibody-acquired" compartment. It also introduces a new hospitalised compartment to monitor severe cases. The model incorporates an antibody-acquired infection rate to account for immune failure. The Extended Kalman Filter based on the AEIHD model is proposed for real-time state and parameter estimation, overcoming the limitations of fixed-parameter approaches and enhancing adaptability to nonlinear dynamics. Simulation studies based on reported data from Australia validate the AEIHD model, demonstrating its capability to accurately capture COVID-19 transmission dynamics with limited statistical information. The proposed approach addresses the key limitations of traditional SIR and SEIR models by integrating hospitalisation data and time-varying parameters, offering a robust framework for monitoring and predicting epidemic behaviours in the post-COVID-19 era. It also provides a valuable tool for public health decision-making and resource allocation to handle rapidly evolving epidemiology.

关键词： COVID-19 antibody-acquired viral variants seird model extended Kalman filter

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Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting

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NONLINEAR DYNAMICS 2023年第1期111卷 549-558页

作者： Valeriano, Joao Pedro Cintra, Pedro Henrique Libotte, Gustavo Reis, Igor Fontinele, Felipe Silva, Renato Malta, Sandra Univ Estadual Paulista Inst Fis Teor R Dr Bento Teobaldo Ferraz 271Bloco 2 BR-01140070 Sao Paulo SP Brazil Univ Estadual Campinas Inst Fis Gleb Wataghin Rua Sergio Buarque Holanda 777 BR-13083859 Campinas SP Brazil Lab Nacl Computcao Cient Av Getulio Vargas 333 BR-25651076 Petropolis RJ Brazil Univ Estado Rio De Janeiro Polytech Inst Dept Computat Modeling Nova Friburgo Brazil Univ Sao Paulo Inst Fis Sao Carlos Av Trab Sao Carlense 400 BR-13566590 Sao Carlos SP Brazil Univ Alberta Dept Phys 116 St & 85 Ave Edmonton AB T6G 2E1 Canada

The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an approximate Bayesian computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting.

关键词： Covid-19 Epidemic forecasting Approximate Bayesian computation seird model

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A Branch and Bound algorithm for multidimensional Holder optimization: Estimation of the age-dependent viral hepatitis A infection force

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 217卷 311-326页

作者： Naffeti, Bechir Ammar, Hamadi Ben Aribi, Walid Univ Tunis El Manar Pasteur Inst Tunis Lab BioInformat Biomath & Biostat Tunis Tunisia Carthage Univ Natl Sch Adv Sci & Technol BorjCedria LR16ES08 LR16ES08 Tunis Tunisia Carthage Univ Fac Econ & Management Nabeul Nabeul Tunisia Esprit Sch Business Sch Business Tunis 2083 Tunisia

In the present framework, we develop a branch and bound multidimensional Holder optimiza-tion method. This method is composed of two subroutines. The first one allows converting the multivariate objective function into a single variable one using the alpha-dense space fitting curves. In the second subroutine, we minimize the single variable function resulting from the first subroutine. To achieve this task, we develop a novel iterative optimization method reducing the feasible region in each iteration taking into account the property of Holder of the objective function. We apply this method to solve a parameters identification problem resulting from the modelling of the spread of viral hepatitis A in the central west of Tunisia following the increase in the number of infections. The Tunisian health authorities wanted to know the prevalence and the infection force of the virus in order to identify the situation and take the necessary measures. The modelling leads to a minimization problem of the least square multivariate error between the observed values and the theoretical ones. Besides, we implement the proposed method in some numerical experiments to evaluate its efficiency.

关键词： Multidimensional optimization Holder functions Branch and bound methods Alpha dense space fitting curves modelling of the viral hepatitis A seird model Seroprevalence Infection force Linear predictor Logit link

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Mathematical modeling Based Study and Prediction of COVID-19 Epidemic Dissemination Under the Impact of Lockdown in India

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FRONTIERS IN PHYSICS 2020年 8卷

作者： Tiwari, Vipin Deyal, Namrata Bisht, Nandan S. Kumaun Univ Dept Phys Naini Tal Uttarakhand India

COVID-19 (SARS-CoV-2) is rapidly spreading in South Asian countries, especially in India. India is the fourth most COVID-19 affected country at present i.e., until July 10, 2020. With limited medical facilities and high transmission rate, the study of COVID-19 progression and its subsequent trajectory needs to be analyzed in India. Epidemiologic mathematical models have the potential to predict the epidemic peak of COVID-19 under different scenarios. Lockdown is one of the most effective mitigation policies adopted worldwide to control the transmission rate of COVID-19 cases. In this study, we use an improvised five compartment mathematical model, i.e., Susceptible (S)-Exposed (E)-Infected (I)-Recovered (R)-Death (D) (seird) to investigate the progression of COVID-19 and predict the epidemic peak under the impact of lockdown in India. The aim of this study is to provide a more precise prediction of epidemic peak and to evaluate the impact of lockdown on epidemic peak shift in India. For this purpose, we examine the most recent data (from January 30, 2020 to July 10, 2020 i.e., 160 days) to enhance the accuracy of outcomes obtained from the proposed model. The model predicts that the total number of COVID-19 active cases would be around 5.8 x 10(5) on August 15, 2020 under current circumstances. In addition, our study indicates the existence of under-reported cases i.e., 10(5) during the post-lockdown period in India. Consequently, this study suggests that a nationwide public lockdown would lead to epidemic peak suppression in India. It is expected that the obtained results would be beneficial for determining further COVID-19 mitigation policies not only in India but globally as well.

关键词： epidemic peak seird model COVID-19 lockdown India under reporting

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A fractional-order model for the novel coronavirus (COVID-19) outbreak

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NONLINEAR DYNAMICS 2020年第1期101卷 711-718页

作者： Rajagopal, Karthikeyan Hasanzadeh, Navid Parastesh, Fatemeh Hamarash, Ibrahim Ismael Jafari, Sajad Hussain, Iqtadar Amirkabir Univ Technol Dept Biomed Engn 424 Hafez Ave Tehran 158754413 Iran Ton Duc Thang Univ Fac Elect & Elect Engn Nonlinear Syst & Applicat Ho Chi Minh City Vietnam Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran Univ Kurdistan Hewler Dept Comp Sci & Engn Erbil Iraq Salahaddin Univ Dept Elect Engn Erbil Iraq Amirkabir Univ Technol Hlth Technol Res Inst 424 Hafez Ave Tehran 158754413 Iran Qatar Univ Dept Math Stat & Phys Doha 2713 Qatar

The outbreak of the novel coronavirus (COVID-19), which was firstly reported in China, has affected many countries worldwide. To understand and predict the transmission dynamics of this disease, mathematical models can be very effective. It has been shown that the fractional order is related to the memory effects, which seems to be more effective for modeling the epidemic diseases. Motivated by this, in this paper, we propose fractional-order susceptible individuals, asymptomatic infected, symptomatic infected, recovered, and deceased (seird) model for the spread of COVID-19. We consider both classical and fractional-order models and estimate the parameters by using the real data of Italy, reported by the World Health Organization. The results show that the fractional-order model has less root-mean-square error than the classical one. Finally, the prediction ability of both of the integer- and fractional-order models is evaluated by using a test data set. The results show that the fractional model provides a closer forecast to the real data.

关键词： Fractional-order derivative COVID-19 seird model

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