Modeling the spread of covid 19 infection using a sir model
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The objective of this study was to develop The SIR compartmental mathematical model for prediction of COVID-19 epidemic dynamics considering different intervention scenarios which might give insights on the best interventions to reduce the epidemic risk.
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Essential Steps to Write a Bibliometric Paper
Bibliometric paper can be written before writing a literature review article and at the introduction section of any research papers. Bibliometric is a powerful tool for quantitatively investigating scientific outputs. Bibliometric analysis helps to visualize the trends in existing literature and overview current state-of-the-art topics to help future research. The data for bibliometric paper retrieved from scientific databases such as Web of Science and Scopus which is freely available for institutions. In this webinar, you will get familiar with “Essential Steps to Write a Bibliometric Paper”.
Cancer prognosis prediction using balanced stratified sampling
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
MINING HEALTH EXAMINATION RECORDS A GRAPH-BASED APPROACH
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Application of statistics in cse
In this slide presentation i have shown the basic application of statistics in computer science and engineering
COVID-19 FUTURE FORECASTING USING SUPERVISED MACHINE LEARNING MODELS
https://www.irjet.net/archives/V9/i5/IRJET-V9I5192.pdf
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Ijetr042188
IJETR, ER Publication, Research Papers, Free Journals, High Impact Journals, M.Tech Research Articles, Free Publication, UGC Journals,
Essential Steps to Write a Bibliometric Paper
Bibliometric paper can be written before writing a literature review article and at the introduction section of any research papers. Bibliometric is a powerful tool for quantitatively investigating scientific outputs. Bibliometric analysis helps to visualize the trends in existing literature and overview current state-of-the-art topics to help future research. The data for bibliometric paper retrieved from scientific databases such as Web of Science and Scopus which is freely available for institutions. In this webinar, you will get familiar with “Essential Steps to Write a Bibliometric Paper”.
Cancer prognosis prediction using balanced stratified sampling
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
MINING HEALTH EXAMINATION RECORDS A GRAPH-BASED APPROACH
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Application of statistics in cse
In this slide presentation i have shown the basic application of statistics in computer science and engineering
COVID-19 FUTURE FORECASTING USING SUPERVISED MACHINE LEARNING MODELS
https://www.irjet.net/archives/V9/i5/IRJET-V9I5192.pdf
Modeling and Analysis of Influenza A H1N1 Outbreaks in India
Influenza A H1N1 incidence in India, which is a highly contagious acute respiratory disease in humans caused by type A influenza virus. This study employs retrospective comparative study of the data from National Centre for Disease Control CDC yearly reports from April 2010 to April 2019. The case fatality rates of Influenza A H1N1 incidence in India was forecasted using autoregressive integrated moving average ARIMA models in order to build a predictive tool for Influenza A H1N1 surveillance. Clearly from the study, lack of rainfall spread the virus more efficiently and Maharashtra stood first in total number of cases and deaths of Influenza A H1N1 whereas Lakshadweep had no signs of disease. Further, number of Cases were reported in the year 2015 i.e. 28 and 25 of cases have been reported in 2017 when compared to ten years data. ARIMA 2, 1, 3 model was selected for its minimum value of normalized BIC, MAPE and good R Square among all other models. The model forecasted the decrease in case fatality rate of Influenza A H1N1 for next 10years. Thus, results indicate that, ARIMA models provide a means to better understand Influenza A H1N1 incidence yielding forecasts that can be used for public health planning at the national level. Stavelin Abhinandithe K | Sathya Velu R | Dr. Madhu B | Sahana S | Sowmyavalli R |Bibin John | Dr. Balasubramanian S "Modeling and Analysis of Influenza A H1N1 Outbreaks in India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39862.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/applied-mathamatics/39862/modeling-and-analysis-of-influenza-a-h1n1-outbreaks-in-india/stavelin-abhinandithe-k
Covid 19 Prediction in India using Machine Learning
Various computational models are used around the world to predict the number of infected individuals and the death rate of the COVID 19 outbreak 3 . Machine learning based models are important to take proper actions. Due to the ample of uncertainty and crucial data, the aerodynamic models have been challenged regarding higher accuracy for long term prediction of this disease 1 . By researching the COVID19 problem, it is observed that lockdown and isolation are important techniques for preventing the spread of COVID 19 2 . In India, public health and the economical condition are impacted by COVID 19, our goal is to visualize the spread of this disease 5 . Machine Learning Algorithms are used in various applications for detecting adverse risk factors. Three ML algorithms we are using that is Logistic Regression LR , Support Vector Machine SVM , and Random Forest Classifier RFC . These machine learning models are predicting the total number of recovered patients as per the date of each state in India 8 . Sarfraj Alam | Vipul Kumar | Sweta Singh | Sweta Joshi | Madhu Kirola "Covid-19 Prediction in India using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42458.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42458/covid19-prediction-in-india-using-machine-learning/sarfraj-alam
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In this study, a comprehensive analysis of classical linear regression forecasting models and deep learning techniques for predicting coronavirus disease of 2019 (COVID-19) pandemic data was presented. Among the deep learning models, the long short-term memory (LSTM) neural network demonstrated superior performance, delivering accurate predictions with minimal errors. The neural network effectively addressed overfitting and underfitting issues through rigorous tuning. However, the diversity of countries and dataset attributes posed challenges in achieving universally optimal predictions. The current study explored the application of the LSTM in predicting healthcare resource demand and optimizing hospital management to provide potential solutions for overcrowding and cost reduction. The results showed the importance of leveraging advanced deep learning techniques for improved COVID-19 forecasting and extending the application of the models to address broader healthcare challenges beyond the pandemic. To further enhance the model performance, future work needed to incorporate additional attributes, such as vaccination rates and immune percentages.
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The coronavirus (COVID-19) epidemic has spread massively to almost all countries including Indonesia, in just a few months. An important step to overcoming the spread of the COVID-19 is understanding its epidemiology through mathematical modeling intervention. Knowledge of epidemic dynamics patterns is an important part of making timely decisions and preparing hospitals for the outbreak peak. In this study, we developed the susceptible-infected-recovered-dead (SIRD) model, which incorporates the key epidemiological parameters to model and estimate the long-term spread of the COVID-19. The proposed model formulation is data-based analysis using public COVID-19 data from March 2, 2020 to May 15, 2021. Based on numerical analysis, the spread of the pandemic will begin to fade out after November 5, 2021. As a consequence of this virus attack, the cumulative number of infected, recovered, and dead people were estimated at ≈ 3,200,000, ≈ 3,437,000 and ≈ 63,000 people, respectively. Besides, the key epidemiological parameter indicates that the average reproduction number value of COVID-19 in Indonesia is 7.32. The long-term prediction of COVID-19 in Indonesia and its epidemiology can be well described using the SIRD model. The model can be applied in specific regions or cities in understanding the epidemic pattern of COVID-19.
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https://www.irjet.net/archives/V9/i4/IRJET-V9I4526.pdf
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Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.
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Coronavirus disease 2019 (COVID-19) has made a huge pandemic situation in many countries of the world including Bangladesh. If the increase rate of this threat can be forecasted, immediate measures can be taken. This study is an effort to forecast the threat of present pandemic situation using machine learning (ML) forecasting models. Forecasting was done in three categories in the next 30 days range. In our study, multiple linear regression performed best among the other algorithms in all categories with R2 score of 99% for first two categories and 94% for the third category. Ridge regression performed great for the first two categories with R2 scores of 99% each but performed poorly for the third category with R2 score of 43%. Lasso regression performed reasonably well with R2 scores of 97%, 99% and 75% for the three categories. We also used Facebook Prophet to predict 30 days beyond our train data which gave us healthy R2 scores of 92% and 83% for the first two categories but performed poorly for the third category with R2 score of 34%. Also, all the models’ performances were evaluated with a 40- day prediction interval in which multiple linear regression outperformed other algorithms.
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The oral presentation of the research has been presented at the International Research Conference 2020 of Sri Lanka Technological Campus on 17th June, 2020.
Abstract Link: http://repo.sltc.ac.lk/handle/1/82 Symptom-Based Prediction of COVID-19 using Machine Learning Models with SMOTE...
https://www.irjet.net/archives/V10/i9/IRJET-V10I921.pdf
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In this time of a global urgency where people are losing lives each day in a large number, people are trying to develop ways/technology to solve the challenges of COVID-19. Machine learning (ML) and artificial intelligence (AI) tools have been employed previously as well to the times of pandemic where they have proven their worth by providing reliable results in varied fields this is why ML tools are being used extensively to fight this pandemic as well. This review describes the applications of ML in the post and pre COVID-19 conditions for contact tracing, vaccine development, prediction and diagnosis, risk management, and outbreak predictions to help the healthcare system to work efficiently. This review discusses the ongoing research on the pandemic virus where various ML models have been employed to a certain data set to produce outputs that can be used for risk or outbreak prediction of virus in the population, vaccine development, and contact tracing. Thus, the significance and the contribution of ML against COVID-19 are self-explanatory but what should not be compromised is the quality and accuracy based on which solutions/methods/policies adopted or produced from this analysis which will be implied in the real world to real people.
Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimizati...
The opinion of disease is important for Covid 19 as the antigen kit and RTPCR are unperfect and should be better for diagnosing such disease. Real Time Return Transcription real time converse transcription - polymerase chain . Healthcare practices include the collection of various sorts of patient data to help the physician diagnose the patients health. These data could be simple symptoms, first diagnosis by a doctor, or an in depth laboratory test. These data are therefore used for analyses only by a doctor, who subsequently uses his particular medical skills to found the ailment. In order to classify Covid 19 disease datasets such mild, middle and severe diseases, the proposed model utilizes the notion of controlled machine education and GWO optimization to regulate if the patient is affecting or not. An efficiency analysis is calculated and compared of disease data for both algorithms. The results of the simulations illustrate the effective nature and complexity of the data set for the grading techniques. Compared to SVM, the suggested model provides 7.8 percent improved prediction accuracy. The prediction accuracy is 8 better than the SVM. This results in an F1 score of 2 percent better than an SVM forecast. Swati Shilpi | Dr. Damodar Prasad Tiwari "Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid-19 Pandemic Crisis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46400.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/46400/health-risk-prediction-using-support-vector-machine-with-gray-wolf-optimization-in-covid19-pandemic-crisis/swati-shilpi
Covid 19 Health Prediction using Supervised Learning with Optimization
The assessment of infection is significant for Covid 19 as the antigen pack and RTPCR are imperfect and ought to be better for diagnosing such sickness. Continuous Return Transcription constant talk record polymerase chain . Medical services rehearse incorporate the assortment of different kinds of patient information to assist the doctor with diagnosing the patients wellbeing. This information could be basic side effects, first analysis by a specialist, or an inside and out research facility test. This information is in this manner utilized for examinations simply by a specialist, who thusly utilizes his specific clinical abilities to track down the illness. To group Covid 19 sickness datasets like gentle, center and serious infections, the proposed model uses the idea of controlled machine training and GWO advancement to manage in the event that the patient is impacted or not. Effectiveness investigation is determined and thought about of infection information for the two calculations. The consequences of the reenactments outline the compelling nature and intricacy of the informational index for the reviewing strategies. Contrasted with SVM, the proposed model gives 7.8 percent further developed forecast exactness. The forecast exactness is 8 better than the SVM. This outcome F1 score of 2 is better than an SVM conjecture. Akash Malvi | Nikesh Gupta "Covid-19 Health Prediction using Supervised Learning with Optimization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61266.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/61266/covid19-health-prediction-using-supervised-learning-with-optimization/akash-malvi
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Abstract The modeling and analysis of the epidemic disease outbreaks in huge realistic populations is a data intensive task that requires immense computational resources. Such effective computational support becomes useful to study disease outbreak and to facilitate decision making. Epidemiology is one of the traditional approaches which are being used for studying and analyzing the outbreaks of epidemic diseases. Although useful for obtaining numbers of sick, infected and recovered individuals in a population, this traditional approach does not capture the complexity of human interactions. The model is only limited to the person to person interaction in order to track the surveillance of disease and it also having performance issues with large realistic data. In this paper we propose, the combination of computational epidemiology and modern data mining techniques with their comparative analysis for the Swine Flu prediction. The clustering algorithm K mean is used to make a group or cluster of Swine Flu suspects in a particular area. The Decision tree algorithm and Naive Bayes classifier are applied on the same inputs to find out the actual count of suspects and predict the possible surveillance of a Swine Flu in a nearby area from suspected area. The performances of these techniques are compared, based on accuracy. In our case the Naive Bayes classifier performs better than decision tree algorithm while finding the accurate count of suspects. Keywords: Computational Epidemiology, Aerosol-borne Disease, Clustering, Predictive analysis.
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VGG19, Xception, InceptionV3 and Resnet50) and compared their performance. VGG16 and VGG19 shows precise performance in classification. Both models can classify between three kinds of X-Rays with an accuracy over 92%. Another part of our study was to find the impact of weather factors (temperature, humidity, sun hour and wind speed) on this pandemic using Decision Tree Regressor. We found that temperature, humidity and sun-hour jointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death due to Covid-19 where humidity has 8.09% impact on death. We also tried to predict the death of an individual based on age, gender, country, and location due to COVID-19 using the Logistic Regression, which can predict death of an individual with a model accuracy of 94.40%.
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The prediction of coronavirus disease 2019 outbreak on Bangladesh perspectiv...
Coronavirus disease 2019 (COVID-19) has made a huge pandemic situation in many countries of the world including Bangladesh. If the increase rate of this threat can be forecasted, immediate measures can be taken. This study is an effort to forecast the threat of present pandemic situation using machine learning (ML) forecasting models. Forecasting was done in three categories in the next 30 days range. In our study, multiple linear regression performed best among the other algorithms in all categories with R2 score of 99% for first two categories and 94% for the third category. Ridge regression performed great for the first two categories with R2 scores of 99% each but performed poorly for the third category with R2 score of 43%. Lasso regression performed reasonably well with R2 scores of 97%, 99% and 75% for the three categories. We also used Facebook Prophet to predict 30 days beyond our train data which gave us healthy R2 scores of 92% and 83% for the first two categories but performed poorly for the third category with R2 score of 34%. Also, all the models’ performances were evaluated with a 40- day prediction interval in which multiple linear regression outperformed other algorithms.
Statistical Prediction for analyzing Epidemiological Characteristics of COVID...
Statistical Prediction for analyzing Epidemiological Characteristics of COVID...Nuwan Sriyantha Bandara
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The oral presentation of the research has been presented at the International Research Conference 2020 of Sri Lanka Technological Campus on 17th June, 2020.
Abstract Link: http://repo.sltc.ac.lk/handle/1/82 Symptom-Based Prediction of COVID-19 using Machine Learning Models with SMOTE...
https://www.irjet.net/archives/V10/i9/IRJET-V10I921.pdf
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In this time of a global urgency where people are losing lives each day in a large number, people are trying to develop ways/technology to solve the challenges of COVID-19. Machine learning (ML) and artificial intelligence (AI) tools have been employed previously as well to the times of pandemic where they have proven their worth by providing reliable results in varied fields this is why ML tools are being used extensively to fight this pandemic as well. This review describes the applications of ML in the post and pre COVID-19 conditions for contact tracing, vaccine development, prediction and diagnosis, risk management, and outbreak predictions to help the healthcare system to work efficiently. This review discusses the ongoing research on the pandemic virus where various ML models have been employed to a certain data set to produce outputs that can be used for risk or outbreak prediction of virus in the population, vaccine development, and contact tracing. Thus, the significance and the contribution of ML against COVID-19 are self-explanatory but what should not be compromised is the quality and accuracy based on which solutions/methods/policies adopted or produced from this analysis which will be implied in the real world to real people.
Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimizati...
The opinion of disease is important for Covid 19 as the antigen kit and RTPCR are unperfect and should be better for diagnosing such disease. Real Time Return Transcription real time converse transcription - polymerase chain . Healthcare practices include the collection of various sorts of patient data to help the physician diagnose the patients health. These data could be simple symptoms, first diagnosis by a doctor, or an in depth laboratory test. These data are therefore used for analyses only by a doctor, who subsequently uses his particular medical skills to found the ailment. In order to classify Covid 19 disease datasets such mild, middle and severe diseases, the proposed model utilizes the notion of controlled machine education and GWO optimization to regulate if the patient is affecting or not. An efficiency analysis is calculated and compared of disease data for both algorithms. The results of the simulations illustrate the effective nature and complexity of the data set for the grading techniques. Compared to SVM, the suggested model provides 7.8 percent improved prediction accuracy. The prediction accuracy is 8 better than the SVM. This results in an F1 score of 2 percent better than an SVM forecast. Swati Shilpi | Dr. Damodar Prasad Tiwari "Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid-19 Pandemic Crisis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46400.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/46400/health-risk-prediction-using-support-vector-machine-with-gray-wolf-optimization-in-covid19-pandemic-crisis/swati-shilpi
Covid 19 Health Prediction using Supervised Learning with Optimization
The assessment of infection is significant for Covid 19 as the antigen pack and RTPCR are imperfect and ought to be better for diagnosing such sickness. Continuous Return Transcription constant talk record polymerase chain . Medical services rehearse incorporate the assortment of different kinds of patient information to assist the doctor with diagnosing the patients wellbeing. This information could be basic side effects, first analysis by a specialist, or an inside and out research facility test. This information is in this manner utilized for examinations simply by a specialist, who thusly utilizes his specific clinical abilities to track down the illness. To group Covid 19 sickness datasets like gentle, center and serious infections, the proposed model uses the idea of controlled machine training and GWO advancement to manage in the event that the patient is impacted or not. Effectiveness investigation is determined and thought about of infection information for the two calculations. The consequences of the reenactments outline the compelling nature and intricacy of the informational index for the reviewing strategies. Contrasted with SVM, the proposed model gives 7.8 percent further developed forecast exactness. The forecast exactness is 8 better than the SVM. This outcome F1 score of 2 is better than an SVM conjecture. Akash Malvi | Nikesh Gupta "Covid-19 Health Prediction using Supervised Learning with Optimization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61266.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/61266/covid19-health-prediction-using-supervised-learning-with-optimization/akash-malvi
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Modeling the spread of covid 19 infection using a sir model
- 1. Name-ManishSingh Guidanceby-Dr.BhavanaMam ID-MU19MCA(L)007 Sem-V DepartmentofMSIT,MatsUniversity,Raipur,Chhattisgarh,India Modeling the Spread of COVID-19 Infection Using an SIR Model Research Paper On
- 2. Contents • Objective. • Introduction. • Literature Review. • SIR Model. • Methodology • Result and Analysis. • Conclusion. • References.
- 3. Objective The objective of this study was to develop The SIR compartmental mathematical model for prediction of COVID-19 epidemic dynamics considering different intervention scenarios which might give insights on the best interventions to reduce the epidemic risk.
- 4. Introduction This module is designed to use Euler's Method for Systems to stimulate interest in the derivative as a tool for modeling the rate of change.
- 5. Literature Review The SIR epidemic model which is used in this work is one of the simplest compartmental models firstly used by Kermack and McKendrick (1927).Compartmental model denotes mathematical modeling of infectious diseases where the population is separated in various compartments for example, S, I, or R, (Susceptible, Infectious, or Recovered). Many works have been undergone during the coronavirus pandemics utilizing the compartmental models and Imperial College COVID-19 Response Team (2020) provides a useful overview of this classic model to show that these models can be applied to understand the current health.
- 6. SIRmodel Susceptible (S) Infectious (I) R ecovered (R) Originally developed by Kermackand McKendrik β γ β is the mixing rate of thepopulation γis the rate of recovery
- 8. Methodology The paper considers Euler`s method to solve differential system which describes SIR epidemic model.
- 9. Datasets The Johns Hopkins University (JHU) datasets are currently the most common COVID-19 data sources available. The data for about 180 countries were updated daily. These datasets can be found at https://github.com/CSSEGISandData/COVID-19.
- 10. Statistical summary of EDA
- 12. Code
- 13. Cont.
- 14. Conclusion The primary objective of this analysis was to test models for the provision of smart healthcare capable of forecasting the onset of pandemics such as COVID-19. This thesis proposes and applies the SIR model with ML algorithms and provides both statistical and numerical analyzes and simulation performance.
- 15. References [1] Data, Johns Hopkins University COVID-19 map, 2020. https://coro navirus.jhu.edu/map.html. accessed date: June, 4, 2020. [Accessed]. [2] Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Niu, P.: China novel coronavirus investigating and research team. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382(8), 727– 733 (2020) Google Scholar [3] Khatua, D., De, A., Kar, S., Samanta, E., and Mandal, S.M. (2020). A Dynamic Optimal Control Model for SARS-CoV-2 in India. Available at SSRN 3597498. [4] Bhattacharyya, A., Bhowmik, D., and Mukherjee, J. (2020). Forecast and interpretation of daily affected people during 21 days lockdown due to COVID 19 pandemic in India. medRxiv.
- 16. Thank you!