INSIGHT ABOUT DETECTION, PREDICTION AND WEATHER IMPACT OF CORONAVIRUS (COVID-...ijaia
The world is facing a tough situation due to the catastrophic pandemic caused by novel coronavirus (COVID-19). The number people affected by this virus are increasing exponentially day by day and the number has already crossed 6.4 million. As no vaccine has been discovered yet, the early detection of patients and isolation is the only and most effective way to reduce the spread of the virus. Detecting infected persons from chest X-Ray by using Deep Neural Networks, can be applied as a time and laborsaving solution. In this study, we tried to detect Covid-19 by classification of Covid-19, pneumonia and normal chest X-Rays. We used five different Convolutional Pre-Trained Neural Network models (VGG16,
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%.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Forecasting the Drought in Bali using the Multilayer Perceptron Methodijtsrd
Disasters have a huge impact on a country and a region. Bali is one of the provinces in Indonesia which has some disaster, one of the disasters that occurred in Bali was drought. Forecasting of droughtinn Bali is necessary so that the government can prevent and manage this kind disastersand can make wise decisions based on information regarding the number of drought. This study aims to predict the number of drought in the next five years. The method used is the Multilayer Perceptron because it is able to predict time series events. The data used are drought disaster events from 2011 to 2019. The results of the analysis in this study indicate that the best Learning Rate and Hidden Layer for forecasting the number of disaster events are Learning Rate 0.7 and Hidden Layer 3.2 respectively with MAPE accuracy is 19.91 . Forecasting results in the coming years show that there is an increase and decrease in the number of drought in 2020 to 2024. Ni Putu Ratih Andini Putri "Forecasting the Drought in Bali using the Multilayer Perceptron Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38460.pdf Paper Url: https://www.ijtsrd.com/computer-science/data-miining/38460/forecasting-the-drought-in-bali-using-the-multilayer-perceptron-method/ni-putu-ratih-andini-putri
A huge revolution has taken place in the area of Genomic science. Sequencing of millions of DNA strands in parallel and also getting a higher throughput reduces the need to implement fragment cloning methods, where extra copies of genes are produced. The methodology of sequencing a large number of DNA strands in parallel is known as Next Generation Sequencing technique. An overview of how different sequencing methods work is described. Selection of two sequencing methods, Sanger Sequencing method and Next generation sequencing method and analysis of the parameters used in both these techniques. A Comparative study of these two methods is carried out accordingly. An overview of when to use Sanger sequencing and when to use Next generation sequencing is described. Increase in the amount of genomic data has given rise to challenges like sharing, integrating and analyzing the genetic data. Therefore, application of one of the big data techniques known as Map Reduce model is used to sequence the genetic data. A flow chart of how genetic is processed using MapReduce model is also present. Next Generation Sequencing for analysis of huge amount of genetic data is very useful but it has few limitations such as scaling and efficiency. Fortunately recent researches have proven that these demerits of Next Generation Sequencing can be easily overcome by implementing big data methodologies. Chinmayee C | Amrita Nischal | C R Manjunath | Soumya K N"Next Generation Sequencing in Big Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12975.pdf http://www.ijtsrd.com/computer-science/bioinformatics/12975/next-generation-sequencing-in-big-data/chinmayee-c
Classifying lymphoma and tuberculosis case reports using machine learning alg...journalBEEI
Available literature reports several lymphoma cases misdiagnosed as tuberculosis, especially in countries with a heavy TB burden. This frequent misdiagnosis is due to the fact that the two diseases can present with similar symptoms. The present study therefore aims to analyse and explore TB as well as lymphoma case reports using Natural Language Processing tools and evaluate the use of machine learning to differentiate between the two diseases. As a starting point in the study, case reports were collected for each disease using web scraping. Natural language processing tools and text clustering were then used to explore the created dataset. Finally, six machine learning algorithms were trained and tested on the collected data, which contained 765 lymphoma and 546 tuberculosis case reports. Each method was evaluated using various performance metrics. The results indicated that the multi-layer perceptron model achieved the best accuracy (93.1%), recall (91.9%) and precision score (93.7%), thus outperforming other algorithms in terms of correctly classifying the different case reports.
A genetic algorithm approach for predicting ribonucleic acid sequencing data ...TELKOMNIKA JOURNAL
Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively.
INSIGHT ABOUT DETECTION, PREDICTION AND WEATHER IMPACT OF CORONAVIRUS (COVID-...ijaia
The world is facing a tough situation due to the catastrophic pandemic caused by novel coronavirus (COVID-19). The number people affected by this virus are increasing exponentially day by day and the number has already crossed 6.4 million. As no vaccine has been discovered yet, the early detection of patients and isolation is the only and most effective way to reduce the spread of the virus. Detecting infected persons from chest X-Ray by using Deep Neural Networks, can be applied as a time and laborsaving solution. In this study, we tried to detect Covid-19 by classification of Covid-19, pneumonia and normal chest X-Rays. We used five different Convolutional Pre-Trained Neural Network models (VGG16,
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%.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Forecasting the Drought in Bali using the Multilayer Perceptron Methodijtsrd
Disasters have a huge impact on a country and a region. Bali is one of the provinces in Indonesia which has some disaster, one of the disasters that occurred in Bali was drought. Forecasting of droughtinn Bali is necessary so that the government can prevent and manage this kind disastersand can make wise decisions based on information regarding the number of drought. This study aims to predict the number of drought in the next five years. The method used is the Multilayer Perceptron because it is able to predict time series events. The data used are drought disaster events from 2011 to 2019. The results of the analysis in this study indicate that the best Learning Rate and Hidden Layer for forecasting the number of disaster events are Learning Rate 0.7 and Hidden Layer 3.2 respectively with MAPE accuracy is 19.91 . Forecasting results in the coming years show that there is an increase and decrease in the number of drought in 2020 to 2024. Ni Putu Ratih Andini Putri "Forecasting the Drought in Bali using the Multilayer Perceptron Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38460.pdf Paper Url: https://www.ijtsrd.com/computer-science/data-miining/38460/forecasting-the-drought-in-bali-using-the-multilayer-perceptron-method/ni-putu-ratih-andini-putri
A huge revolution has taken place in the area of Genomic science. Sequencing of millions of DNA strands in parallel and also getting a higher throughput reduces the need to implement fragment cloning methods, where extra copies of genes are produced. The methodology of sequencing a large number of DNA strands in parallel is known as Next Generation Sequencing technique. An overview of how different sequencing methods work is described. Selection of two sequencing methods, Sanger Sequencing method and Next generation sequencing method and analysis of the parameters used in both these techniques. A Comparative study of these two methods is carried out accordingly. An overview of when to use Sanger sequencing and when to use Next generation sequencing is described. Increase in the amount of genomic data has given rise to challenges like sharing, integrating and analyzing the genetic data. Therefore, application of one of the big data techniques known as Map Reduce model is used to sequence the genetic data. A flow chart of how genetic is processed using MapReduce model is also present. Next Generation Sequencing for analysis of huge amount of genetic data is very useful but it has few limitations such as scaling and efficiency. Fortunately recent researches have proven that these demerits of Next Generation Sequencing can be easily overcome by implementing big data methodologies. Chinmayee C | Amrita Nischal | C R Manjunath | Soumya K N"Next Generation Sequencing in Big Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12975.pdf http://www.ijtsrd.com/computer-science/bioinformatics/12975/next-generation-sequencing-in-big-data/chinmayee-c
Classifying lymphoma and tuberculosis case reports using machine learning alg...journalBEEI
Available literature reports several lymphoma cases misdiagnosed as tuberculosis, especially in countries with a heavy TB burden. This frequent misdiagnosis is due to the fact that the two diseases can present with similar symptoms. The present study therefore aims to analyse and explore TB as well as lymphoma case reports using Natural Language Processing tools and evaluate the use of machine learning to differentiate between the two diseases. As a starting point in the study, case reports were collected for each disease using web scraping. Natural language processing tools and text clustering were then used to explore the created dataset. Finally, six machine learning algorithms were trained and tested on the collected data, which contained 765 lymphoma and 546 tuberculosis case reports. Each method was evaluated using various performance metrics. The results indicated that the multi-layer perceptron model achieved the best accuracy (93.1%), recall (91.9%) and precision score (93.7%), thus outperforming other algorithms in terms of correctly classifying the different case reports.
A genetic algorithm approach for predicting ribonucleic acid sequencing data ...TELKOMNIKA JOURNAL
Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
Covid 19 Prediction in India using Machine LearningYogeshIJTSRD
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
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Artificial intelligence to fight against covid19saritamathania
Artificial intelligence (AI) and machine learning are playing a significant role in understanding and addressing the crisis caused by COVID-19. The technology mimic human intelligence and ingest great volumes of data to quickly chart patterns and identify insights.
One example is when BenevolentAI, a global leader in the development and application of artificial intelligence for drug discovery, took just few days to find that Baricitinib (a drug currently approved for rheumatoid arthritis, owned by Eli Lilly) is a strongest candidate and can be a potential treatment for COVID-19 patients.
This accelerated the clinical trials of #Baricitinib and Eli Lilly (a giant American Pharmaceutical company) has already commenced phase III clinical trials of Baricitinib to treat COVID-19.
Few more names include Deepmind, ImmunoPrecise, Insilico, healx, Imperial College, Tech Mahindra, and Deargen. Some Indian companies include NIRAMAI, Staqu, Qure.AI, Tech Mahindra, and DiyCam.
WEBINAR: The Yosemite Project PART 6 -- Data-Driven Biomedical Research with ...DATAVERSITY
In this presentation, our speaker, Dr. Michel Dumontier, will explore the use of Semantic Web technologies to reduce the overwhelming burden of integrating clinical data with public biomedical data, and enabling a new generation of translational research and their clinical application.
The susceptible-infected-recovered-dead model for long-term identification o...IJECEIAES
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.
Modified SEIR and machine learning prediction of the trend of the epidemic o...IJECEIAES
Susceptible exposed infectious recovered (SEIR) is a fitting model for coronavirus disease (COVID-19) spread prediction. Hence, to examine the effect of different levels of social distancing on the spreading of the disease, a variable was introduced in the SEIR equations system used in this work. We also used an artificial intelligence approach using a machine learning (ML) method known as deep neural network. This modified SEIR model was applied on the available initial spread data until June 25th, 2021 for the Hashemite Kingdom of Jordan. Without lockdown in Jordan, the analysis demonstrates potential infection to roughly 3.1 million people during the peak of spread approximately 3 months, starting from the date of lockdown (March 21st). Conversely, the present partial lockdowns strategy by the Kingdom was expected to reduce the predicted number of infections to 0.5 million in 9 months period. The analysis also demonstrates the ability of stricter lockdowns to effectively flatten the graph curve of COVID-19 in Jordan. Our modified SEIR and deep neural network (DNN) model were efficient in the prediction of COVID-19 epidemic sizes and peaks. The measures taken to control the epidemic by the government decreased the size of the COVID-19 epidemic.
Prediction analysis on the pre and post COVID outbreak assessment using machi...IJICTJOURNAL
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.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
Covid 19 Prediction in India using Machine LearningYogeshIJTSRD
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
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Artificial intelligence to fight against covid19saritamathania
Artificial intelligence (AI) and machine learning are playing a significant role in understanding and addressing the crisis caused by COVID-19. The technology mimic human intelligence and ingest great volumes of data to quickly chart patterns and identify insights.
One example is when BenevolentAI, a global leader in the development and application of artificial intelligence for drug discovery, took just few days to find that Baricitinib (a drug currently approved for rheumatoid arthritis, owned by Eli Lilly) is a strongest candidate and can be a potential treatment for COVID-19 patients.
This accelerated the clinical trials of #Baricitinib and Eli Lilly (a giant American Pharmaceutical company) has already commenced phase III clinical trials of Baricitinib to treat COVID-19.
Few more names include Deepmind, ImmunoPrecise, Insilico, healx, Imperial College, Tech Mahindra, and Deargen. Some Indian companies include NIRAMAI, Staqu, Qure.AI, Tech Mahindra, and DiyCam.
WEBINAR: The Yosemite Project PART 6 -- Data-Driven Biomedical Research with ...DATAVERSITY
In this presentation, our speaker, Dr. Michel Dumontier, will explore the use of Semantic Web technologies to reduce the overwhelming burden of integrating clinical data with public biomedical data, and enabling a new generation of translational research and their clinical application.
The susceptible-infected-recovered-dead model for long-term identification o...IJECEIAES
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.
Modified SEIR and machine learning prediction of the trend of the epidemic o...IJECEIAES
Susceptible exposed infectious recovered (SEIR) is a fitting model for coronavirus disease (COVID-19) spread prediction. Hence, to examine the effect of different levels of social distancing on the spreading of the disease, a variable was introduced in the SEIR equations system used in this work. We also used an artificial intelligence approach using a machine learning (ML) method known as deep neural network. This modified SEIR model was applied on the available initial spread data until June 25th, 2021 for the Hashemite Kingdom of Jordan. Without lockdown in Jordan, the analysis demonstrates potential infection to roughly 3.1 million people during the peak of spread approximately 3 months, starting from the date of lockdown (March 21st). Conversely, the present partial lockdowns strategy by the Kingdom was expected to reduce the predicted number of infections to 0.5 million in 9 months period. The analysis also demonstrates the ability of stricter lockdowns to effectively flatten the graph curve of COVID-19 in Jordan. Our modified SEIR and deep neural network (DNN) model were efficient in the prediction of COVID-19 epidemic sizes and peaks. The measures taken to control the epidemic by the government decreased the size of the COVID-19 epidemic.
Prediction analysis on the pre and post COVID outbreak assessment using machi...IJICTJOURNAL
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.
The first three months of the COVID-19 epidemic:
Epidemiological evidence for two separate strains of SARSCoV-2 viruses spreading and implications for prevention
strategies
ANALYSIS OF COVID-19 IN THE UNITED STATES USING MACHINE LEARNINGmlaij
The unprecedented outbreak of COVID-19 also known as the coronavirus has caused a pandemic like none
ever seen before this century. Its impact has been massive on a global level. The deadly virus has
commanded nations around the world to increase their efforts to fight against the spread of the virus after
the stress it has put on resources. With the number of new cases increasing day by day around the world,
the objective of this paper is to contribute towards the analysis of the virus by leveraging machine learning
models to understand its behavior and predict future patterns in the United States (US) based on data
obtained from the COVID-19 Tracking Project.
Analysis of Covid-19 in the United States using Machine Learningmlaij
The unprecedented outbreak of COVID-19 also known as the coronavirus has caused a pandemic like none ever seen before this century. Its impact has been massive on a global level. The deadly virus has commanded nations around the world to increase their efforts to fight against the spread of the virus after the stress it has put on resources. With the number of new cases increasing day by day around the world, the objective of this paper is to contribute towards the analysis of the virus by leveraging machine learning models to understand its behavior and predict future patterns in the United States (US) based on data obtained from the COVID-19 Tracking Project.
impact of metropolitanization on covid 19 cases in india using entropy weight...ijtsrd
The global pandemic COVID 19 which was started early this year spreading rapidly in developed countries as all well as developing countries of the world. The noticeable fact is that most of the metropolitan cities of the world are severely affected by Corona pandemic and toped in their respective country among the COVID cases. The impact of the metropolitan city on COVID 19 cases, example can be cited around every corner of the world, From Wuhan to New York, Mumbai to Sao Paulo and Moscow to Madrid the Metropolitan cities of the world’s come out as deep rooted hotspots of novel coronavirus pandemic. Mumbai, Delhi, Chennai, and Kolkata are the leading metropolis in India also leading in COVID 19 cases it can be explained by their connectivity to the rest of the world by the people and products. Therefore this article aims to summerise the impact of six metropolitan cities on the total COVID 19 cases of their states and try to find out which city have best suited in the concept of the Metropolitanization of COVID 19 cases. Entropy based TOPSIS methods are applied to compare the dataset of six metropolitan cities of India, and try to find out which city best fitted in the concept of metropolitanization of COVID 19 cases. Seven factors are chosen to analyze the impact of metropolitan cities on COVID 19 cases such as city population, percentage of slum population, number of COVID cases, airport traffic movement, relative humidity, and temperature. Entropy methods applied to weights the criterion for finding which criteria have maximum influence on COVID 19 cases. After that on the basis of Entropy weights, the TOPSIS method has been used to evaluate the dataset of six cities to track down the relative position of cities on the concentration of COVID 19 cases. After comparing the alternatives in TOPSIS method i.e those six cities , Delhi came in the first position, followed by Mumbai 2nd , Chennai 3rd , Kolkata 4th , Ahmedabad 5th , Hyderabad 6th based on the concentration of COVID 19 cases in the metropolitan cities. Sanu Dolui | Sayani Chakraborty "Impact of Metropolitanization on Covid-19 Cases in India using Entropy Weights Based Topsis Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32929.pdf Paper Url :https://www.ijtsrd.com/other-scientific-research-area/enviormental-science/32929/impact-of-metropolitanization-on-covid19-cases-in-india-using-entropy-weights-based-topsis-approach/sanu-dolui
Socio economic attributes of covid-19 pandemic in Maharashtra State IndiaSoumik Chakraborty
Discussion about factors affecting Covid-19 spread and situation of virus spread. Relationship and generation of Covid susceptibility map in the region
Impact of COVID-19 caronavirus on poverty in Pakistan: a case study of SindhSubmissionResearchpa
The current research investigated the COVID-19 is spread vigorously in China, USA, France, Italy, Germany, and European countries and Iran Pakistan being as a neighbor country of china & IranOne was for the incoming Pakistani from various countries, such as Iran, China, Afghanistan, and India. The other was arranged inside various hospitals for COVID-19 positive cases. As hundreds and thousands of Pakistani were in Iran for religious purposes, they were. Most of the students and businessmen, inside China, were not allowed to come back. Handling of large scale influx from Iran was the main problem. Out of the total COVID-19 cases, 78 percent of cases were reported from visitors coming from Iran. Pakistan announced the closure of all schools, colleges & universities with a partial lockdown across the country for major cities. by Dr. Faiz Muhammad Shaikh, Ali Raza Memon and Kashaf Shaikh 2020. Impact of COVID-19 caronavirus on poverty in Pakistan: a case study of Sindh . International Journal on Integrated Education. 3, 6 (Jun. 2020), 72-83. DOI:https://doi.org/10.31149/ijie.v3i6.415. https://journals.researchparks.org/index.php/IJIE/article/view/415/391 https://journals.researchparks.org/index.php/IJIE/article/view/415
The prediction of coronavirus disease 2019 outbreak on Bangladesh perspectiv...IJECEIAES
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.
Similar to Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD (20)
COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model
This pdf is about the Schizophrenia.
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What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
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Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
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Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
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Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
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Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
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2022 is driven by spatio-temporal changes in the magnetic connectivity to
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This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD
1. Presented by Amir Mosavi
Coronavirus (COVID-19) Outbreak Prediction Using
Epidemiological Models of Richards Gompertz
Logistic Ratkowsky and SIRD
2. Methods
B. SIRDModel
SIRD model is an extension of SIR model first proposed by Kermack and
McKendrick to study outbreak of contagious epidemics such as plague and cholera [6].
In the SIR model, it is assumed that the population's size is fixed and remain constant
throughout epidemy. It is also assumed that parameters such as age, sex, location and
social behavior has no effect on SIR model. The SIRD model also does not consider
exposed, super-spreader, and asymptomatic populations. We are presently developing
new models to extend and compare with SIRD model, having found insight from the
present work. This is particularly not correct in pandemics such as COVID-
19 because it was observed that social behavior had great impact for controlling and
retarding COVID-19. SIRD model consist of four set of ordinary differential equations
(ODE) for susceptible population (S), infected cases (I), and recovered cases (R), and
deceased cases (D).
3. Daily (a) and cumulative (b) data of COVID-19 pandemic in Iran (24 July 2020).
4. (b) Gompertz Model (e) SIRD Model
Models estimating trends for Wave II of COVID-19 in Iran (24 July 2020).
5. Coronavirus (COVID-19) Outbreak Prediction Using
Epidemiological Models of Richards Gompertz
Logistic Ratkowsky and SIRD
Ahmad Sedaghat
School of Engineering,
Australian College of Kuwait
Safat 13015, Kuwait
a.sedaghat@ack.edu.kw
Seyed Amir Abbas Oloomi
Department of Mechanical
Engineering, Yazd Branch
Azad University, Yazd, Iran
Amiroloomi@iauyazd.ac.ir
Mahdi Ashtian Malayer
Young Researchers and Elite
Club, Yazd Branch, Azad
University, Yazd, Iran
Shahab Band
Future TechnologyResearch
Center, National Yunlin
University of Science and
Technology, Yunlin, Taiwan
Nima Rezaei
School of Medicine, Tehran
University of Medical Sciences
Tehran, Iran
Amir Mosavi
School of Economics and
Business, Norwegian University
of Life Sciences, Norway
amir.mosavi@kvk.uni-obuda.hu
Laszlo Nadai
Kando Kalman Faculty of
Electrical Engineering
Obuda University, 1034
Budapest, Hungary
Abstract—On 30 July 2020, a total number of 301,530
diagnosed COVID-19 cases were reported in Iran, with 261,200
recovered and 16,569 dead. The COVID-19 pandemic started with
2 patients in Qom city in Iran on 20 February 2020. Accurate
prediction of the end of the COVID-19 pandemic and the total
number of populations affected is challenging. In this study,
several widely used models, including Richards, Gompertz,
Logistic, Ratkowsky, and SIRD models, are used to project
dynamics of the COVID-19 pandemic in the future of Iran by
fitting the present and the past clinical data. Iran is the only
country facing a second wave of COVID-19 infections, which
makes its data difficult to analyze. The present study's main
contribution is to forecast the near-future of COVID-19 trends to
allow non-pharmacological interventions (NPI) by public health
authorities and/or government policymakers. We have divided
the COVID-19 pandemic in Iran into two waves, Wave I, from
February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020,
to the present. Two statistical methods, i.e., Pearson correlation
coefficient (R) and the coefficient of determination (R2), are used
to assess the accuracy of studied models. Results for Wave I
Logistic, Ratkowsky, and SIRD models have correctly fitted
COVID-19 data in Iran. SIRD model has fitted the first peak of
infection very closely on April 6, 2020, with 34,447 cases (The
actual peak day was April 7, 2020, with 30,387 active infected
patients) with the re-production number R0=3.95. Results of Wave
II indicate that the SIRD model has precisely fitted with the second
peak of infection, which was on June 20, 2020, with 19,088 active
infected cases compared with the actual peak day on June 21, 2020,
with 17,644 cases. In Wave II, the re-production number R0=1.45
is reduced, indicating a lower transmission rate. We aimed to
provide even a rough project future trends of COVID-19 in Iran
for NPI decisions. Between 180,000 to 250,000 infected cases anda
death toll of between 6,000 to 65,000 cases are expected in
Wave II of COVID-19 in Iran. There is currently no analytical
method to project more waves of COVID-19 beyond Wave II.
Keywords— Coronavirus disease, COVID-19, SARS-CoV-2,
outbreak model, compartmental model, epidemiological model
I. INTRODUCTION
Severe acute respiratory syndrome coronavirus 2 (SARS-
CoV-2) which is responsible for novel coronavirus disease
(COVID-19) was first observed in Wuhan-China in late
December 2019 [1,2]. By 4 June 2020, COVID-19 was spread
worldwide in 215 countries with 6,638,912 infected cases,
3,204,292 recovered, and 389,816 deceased cases [3]. The
outbreak of COVID-19 in Iran was first observed in the City of
Qom, a pilgrims’ center in Iran, which promptly extended to
Tehran. Tehran, the Capital of Iran, is currently the center of
outbreaks [4]. The study by Ahmadi et al. [4] suggested the
dense population in Tehran and nearby cities stemmed from high
initial growth rates of 36.3 to 161.6 in Iran. The Iranian
government then implemented measures on banning movement
between densely populated provinces and cities as an effective
method to reduce the spread of the COVID-19 outbreaks. The
maximum infection day was 3,186 cases on 30 March 2020. On
31 May 2020, 151,491 individuals were reported to be infected.
Among them, 118,848 recovered, and 7,804 died in Iran [3].
Lin et al. [2] applied several predictive models, including
Gompertz, Logistic, Bertalanffy, and Gompertz, to study
COVID-19 in China. Roda et al. [5] explained why it is hard to
predict a pandemic from a mathematical point of view. Zhang et
6. al. [6] studied the duration and outbreak of COVID-19 in several
countries in Europe and America, using power-law and
exponential law models. Li et al. [7] applied SIR mixture model
to tackle double peak epidemy situations. The method is
cumbersome and not easy to implement. Mummert et al. [8]
proposed a simpler multiple-waves model to study pandemics.
However, it is not clear if the method can be implemented in
typical ODE equation models, such as the SIR method. Another
possible candidate for a double peak pandemic may be found in
Sazonov et al. [9], yet, the method is cumbersome andcomplex.
In the present study, we have analyzed several widely used
methods in epidemiology or microbiology, such as Richards,
Gompertz, Logistic, and Ratkowsky, to fit present and previous
trends of COVID-19 in Iran. We applied the Pearson correlation
coefficient (R) and the coefficient of determination (R2) with the
aid of an optimization tool in MATLAB to evaluate the
goodness of fitted data. Results of the studied models are used
to project future trends of COVID-19 in Iran, including the total
number and the end date of different COVID-19 populations,
i.e., infected, recovered, and dead population. The objective is
not to meddle with these models analytically, instead to get the
best of these models using optimization to find their optimized
parameters and to provide further insight to health authorities or
policymakers on non-pharmacological interventions (NPI)
decisions.
II. MATERIALS ANDMETHDOS
SIR The COVID-19 pandemic outbreak in Iran started on
20 February 2020 with 2 infected cases in Qom city. Qom city
has approximately 1.2 million populations and is considered as
wholly land for Shia Muslims with several pilgrimage shrines
in Iran. The city's social structure and travelers into and from
the city have made COVID-19 propagation in Iran unique and
hard to analyze. In the present study, we have applied several
mathematical models widely used in epidemiological studies,
including Richards, Gompertz, Logistic, Ratkowsky, and SIRD
models, to understand dynamics of COVID-19 in Iran better.
The Richards, Gompertz, Logistic, and Ratkowsky models only
deal with single population; therefore, we have used each model
three times to obtain fitting functions for infected, recovered,
and deceased clinical data. Obtaining parameters of each model
is not easy task and has to be done using programming in
MATLAB; although the results of fitting parameters are initial
value dependent and should be obtained carefully.
A. General Epidemiologymodels
We have adopted several widely used models, which use
exponential functions to express the nature of
endemic/pandemic fast-growing contagious diseases. Follow
are listed these models [10-13]. We have divided COVID-19
clinical data into two waves, because only one peak can be dealt
by the models described here. Wave I finish after passing first
peak and reaching to the minimum value. Wave 2 starts from
the minimum value of Wave I. To obtain the parameters of
these models, we adopted an optimization technique to
minimize error between the model values and clinical data. In
literature, some constant numbers were suggested for these
models which are not necessarily the best fit coefficients.
Richards model
[10]
(1)R(t) = Rnas(1 + exp(b
− ct))
–1/d
G(t) = Gnas exp(−exp(b − ct)) (2)
n a sL(t) = L (1 + b.exp(−ct))
–1 (3)
Gompertz model
[11]
Logistic model
[12]
Ratkowsky
model [13]
RK(t) = RKnas (1
+ exp(b
− ct))
–1
(4)
In equation (1), R(t) expresses temporal change of a
pandemic population such as infected, recovered, deceased and
so on. Rnas is the maximum size of studied population,
parameters b, c, and d are model parameters and time, t, is
usually expressed by number of days from outbreak of
infectious disease.
In equation (2), G(t) is temporal function of a pandemic
population such as infected, recovered, deceased and so on.
Gnas is the maximum size of population and parameters, b and
c, are model constants.
In equation (3), L(t) is Logistic function of time (t), Lnas
is the maximum limit of pandemic population, and b and c are
model parameters.
In equation (4), RK(t) is the Ratkowsky function of time.
RKnas is the maximum value of the pandemic population.
Ratkowsky constants b and c are found by fitting with data.
B. SIRDModel
SIRD model is an extension of SIR model first proposed
by Kermack and McKendrick [14] to study outbreak of
contagious epidemics such as plague and cholera [6]. In the SIR
model, it is assumed that the population's size is fixed and
remain constant throughout epidemy. It is also assumed that
parameters such as age, sex, location and social behavior has no
effect on SIR model. The SIRD model also does not consider
exposed, super-spreader, and asymptomatic populations. We
are presently developing new models to extend and compare
with SIRD model, having found insight from the present work.
This is particularly not correct in pandemics such as COVID-
19 because it was observed that social behavior had great
impact for controlling and retarding COVID-19. SIRD model
consist of four set of ordinary differential equations (ODE) for
susceptible population (S), infected cases (I), and recovered
cases (R), and deceased cases (D) [15]. SIRD model is given by
[16]:
˙ q
N
S = − IS (5)
q
I˙ =
N
IS − (µR +µD)I (6)
7. R˙= µRI
D˙= µDI
(7)
(8)
In equations (5-8), there are 4-set of ODE equations based
on susceptible population (S), infected population (I),
recovered population (R), deceased population (D). Dot-
products are time derivatives such as S˙ = dS⁄dt .The
transmission rate (q) represent growth rate of infected disease.
The recovery rate (µR ) shows the growth rate of recovered
population while the death rate (µD ) dictates growth rate of
deceased population. The removing rate ( µ = µR + µD )
represents the rate of population removed from susceptible
population. The re-production parameter ( RO = q⁄µ > 1) is
defined to show outbreak of an endemic. Also, RO expresses
number of contacts from an infected person to susceptible
people before complete treatment.
Initial total population (N) is assumed a constant number
throughout epidemy and is given by:
N = S + R + I + D = cons. (9)
Total infected cases (TI) should not be mistaken with
active infected population (I), which is described as follows:
TI = I +R +D (10)
To solve 4-set of ODE equations (5-8), a set of initial
conditions are needed as follows (e.g. for total susceptible
population of N=400,000):
S(0) = 399,997; I(0) = 3; R(0) = 0; D(0) = 0 (11)
The 4-set of ODE equations (5-8) are solved using
MATLAB for time intervals of one day. The set of ODE
equations (5-8) are solved using initial conditions (11) in
MATLAB for total time duration of contagious disease.
In Wave I, SIRD model 4-set of ODE equations requires
initial population of COVID-19 in Iran on February 20, 2020
(the first day of infection) as follows:
S(0) = 95,000; I(0) = 2; R(0) = 0; D(0) =2
(12)
The initial susceptible population, S(0), was assumed,
infectious, I(0), recovered, R(0) , and dead, D(0) were
adopted from COVID-19 data of Iran on February 20, 2020 [3].
We consider 5 May 2020 as the start of the Wave II
when the infected cases raise to 1,223 on the second wave of
COVID-19 in Iran. In Wave II, SIRD model 4-set of ODE
equations are solved using initial condition of COVID-19 in
Iran as follows:
S(0) = 340,000; I(0) = 1223; R(0) = 1096; D(0)=
74 (13)
C. Objective functions
Our viewpoint in this work is different. We used MATLAB
optimization to minimize to zero value for the following
coefficients (objective function) while comparing modelvalues
with COVID-19 clinical data and searching for the optimized
best model parameters.
D. Pearson correlation
Pearson correlation (R) is a statistical measure of
goodness of fit between M number of model function values (y)
and COVID-19 data (z) as follows [17]:
=
M ∑ yz − ∑ y ∑ z
ƒ(M ∑ z2 − (∑ z)2)(M ∑ y2 − (∑ y)2)
0bj1 = R (14)
Better fits provide Pearson (R) values close to one.
E. The regressioncoefficient
The regression coefficient (R2) is another statistical
measure for goodness of fit between M number of projected
values (y) and COVID-19 data (z), although we used it as
follows [18-19]:
0bj2 = 1 − R2 =
∑(y–z)2
∑(z–z̅)2
(15)
In equation (15), z̅ is the average of COVID-19 data and
better fits provide the regression coefficient (R2) values close to
unity and Obj2 to zero value
F. Optimization inMATLAB
To fit model parameters based on available COVID-19
data, the optimization toolbox in MATLAB is used. Fminsearch
routine in MATLAB [20] uses a nonlinear programming solver
to search for the minimum of an objective function. Routine 1
shows an optimization program in MATLAB to return
optimized parameters of a fitting model by minimizing error.
function error = opt(x,y_covid)
par1=x(1);
par2=x(2);
...
% Integrate system
[t,y] =
ode45(@(t,y)YourFunction(t,y,par1,par2,par3,par4),tspan,y0)
...
% Calculate error
error = rms(y-y_covid)
end
%% Run optimizer
...
f = @(x)opt(x,y_covid);
[par,fval] = fminsearch(f,x0)
Routine 1: MATLAB routine to optimize model
parameters against COVID-19 data.
As shown in Routine 1, the unknown model parameters par1,
par2, … are optimized by minimizing an error estimator to
8. minimize differences between model parameter (y) value
against the COVID-19 clinical value (y_covid).
.
III. RESULTS
Iran population reported 81.8 million in 2020 [3].
Selecting initial susceptible population S(0) and depends on
parameters such as social behavior and government actions to
control. This is particularly difficult when some patterns such
as double peaks of infections observed. In this study, we
divided COVID-19 data of Iran into 2 phases. SIRD model was
applied for the first and second expected peaks to assess
dynamics of COVID-19 in Iran. Trends of COVID-19 in Iran is
presented. The daily and cumulative data of COVID-19
outbreak in Iran are obtained from European Centre for Disease
Prevention and Control, COVID-19 situation update worldwide
section [21] and are shown in Fig. 1. From daily data, it is
observed that Iran has passed the first daily peak after 45 days
(April 5, 2020) and reached the minimum after 74 days (May
4, 2020) and then the daily infections has increased towards a
second peak after 106 days on June 5, 2020. From cumulative
data in Fig. 1, Wave I of COVID-19 in Iran is identified from
start of the outbreak on February 20, 2020 to May 4, 2020 with
the peak of infectious on April 5, 2020. Wave II is identified
from May 5, 2020 to present with the second peak of infectious
on June 21, 2020. The daily and cumulative data of COVID-19
outbreak in Iran are shown in Fig. 1.
(a) Daily COVID-19 data in Iran (b) Cumulative COVID-19 data inIran
Figure 1. Daily (a) and cumulative (b) data of COVID-19 pandemic in Iran (24 July 2020).
Next, we applied several mathematical models described
in the methodology section for studying Wave I & II of
COVID-19 in Iran.
A. Wave I of COVID-19 inIran
Wave I of COVID-19 in Iran starts from February 20,
2020 to May 4, 2020. Table 1 summarizes optimum parameters
for all models described in methodology section. Accuracy of
fitting COVI-19 data with these models are shown by R and R2
values in Table 1.
Richards Model
Richards model is used to fit total infected (TI), recovered
(R), deceased (D) data from Wave I of COVID-19 in Iran.
Richards model optimized parameters are listed in Table 1.
Fgure 2a shows reslts of Richards model for total ifected cases,
recovered, and deceased cases in Wave I of COVID-19 in Iran.
As seen in Fig. 2a, it is estimated that the total infected cases
will reach to 102,570, recovered cases to 81,587 and fatality to
7,018 by the end of Wave I. On May 4, 2020, these values are
97,424 for total infected cases, 79,379 recovered cases, and
6,203 fatality. Richards model have slightly overestimatedtotal
9. infected cases and recovered yet slightly underestimated
fatality in Wave I.
Table 1: Model parameters fitting Wave I of COVID-19 in Iran (24 July 2020).
Richards Model a b c d R R2
Total Infected 102667.56 2.8933 0.08311 0.5519 0.9994 0.9969
Recovered 92599.73 4.0162 0.08629 0.5944 0.9989 0.9939
Deceased 6878.31 0.005444 0.06104 0.09095 0.9997 0.9974
Gompertz Model a b c R R2
Total Infected 113870.85 2.1940 0.05518 0.9989 0.9960
Recovered 111345.09 2.7657 0.05320 0.9970 0.9914
Deceased 7021.99 2.2014 0.05660 0.9997 0.9976
Logistic Model a b c R R2
Total Infected 98087.84 97.4394 0.1059 0.9992 0.9965
Recovered 85400.81 459.1540 0.1162 0.9988 0.9951
Deceased 6166.76 86.6776 0.1041 0.9978 0.9938
Ratkowsky
Model
Total Infected 98087.84 4.5792 0.1059 0.9992 0.9965
Recovered 85400.81 6.1294 0.1162 0.9988 0.9951
Deceased 6167.99 4.4601 0.1041 0.9978 0.9938
SIRD Model Transmission
rate (q)
Recovery
rate (µR)
Death rate
(µD)
Re-production
number (R0)
R2(I) R2(R) R2(D)
0.36513 0.09486 0.00765 3.56 0.8029 0.9577 0.8854
a b c R R2
(a) RichardsModel (d) Ratkowsky Model
10. (b) Gompertz Model (e) SIRD Model
(c) Logistic Model (d) Logistic Model
Figure 2: Models projecting trends for Wave I of COVID-19 in Iran (24 July 2020).
Gompertz Model
Gompertz model is used here for the Wave I of COVID-
19 in Iran with optimized parameters as listed in Table 1. Fgure
2b illustrates results of Gompertz model for estimating Wave I
of COVID-19 in Iran. From Fig. 2b, it is observed that total
population of infected to reach 113,871 cases, population of
recovered to 111,345 cases, and deceased to 7,022 cases by the
end of Wave I pandemic in Iran. Comparing these values by
actual data on May 4, 2020, i.e. total infected cases of 97,424,
recovered cases of 79,379, and deceased cases of 6,203, it is
observed that Gompertz model has overeestimated all the
populations.
Logistic Model
Logistic model is examined here to study Wave I of
COVID-19 in Iran. Best fitted values to parameters of Logistic
model is listed in Table 1 along with accuracy measures of R
and R2. Figure 2c illustrates results of Logistic model which
indicates total infected cases may reach to 98,088, recovered
cases to 85,400,and deceased cases to 6,168 by the end of Wave
I pandemic in Iran. Actual data on May 4, 2020 suggest that
total infected cases were 97,424, recovered cases 79,379, and
deceased cases 6,203. Logistic model has very closely
estimated total infected and deceased cases, but recovered cases
are overestimated.
Ratkowsky Model
Ratkowsky model results are presented for Wave I of
COVID-19 pandemic in Iran in Table 1 and Fig. 2d for infected,
recovered, deceased cases. As seen in Fig. 2d from February
20, 2020 onwards, Ratkowsky model suggests 98,088 infected
cases, 85,400 recovered cases, and 6,168 deceased cases by end
of Wave I. Ratkowsky model compares well with actual data
on May 4, 2020 consist of 97,424 infected, 79,379 recovered,
and 6,203 deceased cases.
SIRD Model
Table 1 shows fit parameters of SIRD model to the data. As
observed in Table 1, the accuracy of SIRD model to fit COVID-
11. 19 data in Iran is acceptable. It is also observed that the re-
production number is relatively high (R0=3.56) in the Wave I
of COVID-19 in Iran.
Figure 2e shows the projected results of SIRD model for Wave
I of COVID-19 in Iran. As shown in the figure 2e, the peak day
of active infectious cases obtained by SIRD model is on April
6, 2020 with 34,447 cases nearly exact match with the actual
peak day of infectious which was April 7, 2020 with 30,387
active infected cases. Total recovered cases of 85,117 and total
deceased cases of 6,865 can be compared with actual data on
May 4, 2020; i.e. 79,379 recovered cases and 6,203 deceased
cases. SIRD model is the only model among studied models
that can simultaneously fit all 4-set of COVID-19 populations.
In addition, SIRD model is the only model to fit peak day of
infectious. Although SIRD model has slightly overestimated all
exposed populations in Wave I of COVID-19 in Iran.
Table 2: Model parameters estimating Wave II of COVID-19 in Iran (24 July 2020).
Richards a b c d R R2
Model
Total Infected 245237.13 -1.2040 0.03208 0.07686 0.9992 0.9964
Recovered 255016.26 -0.3181 0.03041 0.1487 0.9984 0.9970
Deceased 51399.22 -0.001340 0.01563 0.1428 0.9992 0.9964
Gompertz Model a b c R R2
TotalInfected 251469.95 1.2501 0.02976 0.9985 0.9967
Recovered 282636.64 1.3555 0.02525 0.9973 0.9973
Deceased 65018.52 1.6443 0.01185 0.9994 0.9966
LogisticModel a b c R R2
TotalInfected 200911.73 14.7874 0.05958 0.9972 0.9922
Recovered 198723.02 19.5511 0.05731 0.9982 0.9945
Deceased 17319.55 35.3241 0.04485 0.9973 0.9959
Ratkowsky
Model
Total Infected 200911.73 2.6938 0.05958 0.9982 0.9922
Recovered 198723.02 2.9730 0.05731 0.9983 0.9945
Deceased 17319.55 3.5646 0.04485 0.9972 0.9959
SIRD Model Transmission
rate (q)
Recovery Death rate
rate (µR) (µD)
Re-production
number (R0)
R2(I) R2(R) R2(D)
0.30339 0.20026 0.00846 1.45 0.87631 0.95694 0.94292
a b c R R2
(a) Richards Model (d) Ratkowsky Model
12. (b) Gompertz Model (e) SIRD Model
(c) Logistic Model (d) LogisticModel
Figure 3: Models estimating trends for Wave II of COVID-19 in Iran (24 July 2020).
B. Wave II of COVID-19 inIran
We consider May 5, 2020 as the beginning of the Wave II
when the infected cases start rising from 1,223 on the second
wave of COVID-19 in Iran. Table 2 illustrates model
parameters for Wave II optimized to provide accuracies as
enlisted by R and R2 values.
Richards Model
In Wave 2 of COVID-19 in Iran, Richards model results
are presented in Fig. 3a. As shown in Fig. 3a, Richards model
estimates maximum 245,237 infected cases, 255,016 recovered
cases, and 51,399 deceased cases by the end of Wave II of
COVID-19 pandemic in Iran. As seen in Fig. 3, Richards and
Gampertz models overestimated these populations compared
with other models.
Gompertz Model
Wave II results of Gompertz model for in Iran is shown
Table 2 and Fig. 3b. Fgure 3b illustrates that total population
of infected to reach 251,470 cases, population of recovered to
282,636 cases, and population of deceased to 65,018 cases by
the end of Wave II. Gompertz model estimations are very high
compared with all other models studied here. It is unlikely that
Gompertz results on affected populations will be realistic.
Logistic Model
Logistic model results for Wave II of COVID-19 in Iran
is shown in Table 2 and Fig. 3c. It can be seen from Fig. 3c and
Table 2 that Logistic model projects 200,911 infected cases,
198,723 recovered cases, and 17,320 deceased cases for Wave
II. Results of Logistic model is closer to Ratkowsky and SIRD
models.
Ratkowsky Model
Ratkowsky results for Wave II are shown in Table 2 and
Fig. 3d for COVID-19 in Iran. Figure 3d shows results from
May 5, 2020 to present projecting 200,911 infected cases,
198,723 recovered cases, and 6,168 deceased cases by end of
Wave II. Ratkowsky model results compare well with Logistic
and SIRD models.
13. SIRD Model
Table 2 illustrates fit parameters of SIRD model to the data.
As observed in Table 2, the accuracy of SIRD model to fit
COVID-19 data in Iran is very good. In Wave II, the re-
production number (RO) of 1.45 is obtained. Compare with re-
production in Wave I, it is interesting to see that the second wave
of COVID-19 inWave II is weakened and transmission rate has
slowed down. Figure 3e shows the results of SIRD model for
Wave II of COVID-19 in Iran. From Fig. 3e, the peak day of
infectious is fitted on June 20, 2020 with 19,088 active infected
cases whilst actual peak day was June 21, 2020 with 17,644
active infected cases. By November 18, 2020 in the Wave II of
COVID-19 in Iran, it is expected that total recovered cases reach
to 180,700 whilst total deceased cases reach to 7,655.
IV. CONCLUTIONS
It In this work, Richards, Gompertz, Logistic, Ratkowsky
and SIRD models are applied to estimate dynamics of COVID-
19 in Iran. COVID-19 data from 20 February to 31 May 2020
are used to assess projectability of the studied models on trends
of COVID-19 in Iran. The aim is to have a good comparison of
widely used models to provide us with even rough projection of
future trends of COVID-19 in Iran. This may assist public
health organization or policy makers for NPI decisions. There
is currently no analytical method to project more waves of
COVID-19 beyond Wave II. The outcome of this work may be
summarized as follows:
• In Wave I: Richards and Gompertz model overestimated
whilst Logistic, Ratkowsky, and SIRD models provided closer
projection for COVID-19 data in Iran. WaveSIRD model has
projected with nearly exact match the first peak of infection on
April 6, 2020 with 34,447 cases (Actual peak day was April 7,
2020 with 30,387 active infected cases).The re-production
number (R0) of COVID-19 is calculated 3.95 in Wave I
indicating highly transmittable COVID-19 in Iran.
• In Wave II: Richards and Gompertz model very highly
overestimated trends whilst Logistic, Ratkowsky, and SIRD
models provided close projection to each other.WaveSIRD
model provides near exact match for the peak day of infectious;
i.e. June 20, 2020 with 19,088 active infected cases compared
with actual peak day on June 21, 2020 with 17,644 active
infected cases. The re-production number (R0) for Wave II of
COVID-19 in Iran is estimated 1.45 indicating that COVID-19
transmission rate was slowed down due to current preventative
measure.From results of Wave II, it is observed that the second
wave of COVID-19 may affect between 180,000 to 250,000
population and death tool may reach between 6,000 to 65,000
cases depend on different model discussed here.
From results of Wave I COVID-19 in Iran, it is observed
that Logistic, Ratkowsky, and SIRD models are better fitting
models. Even rough projection of future trends of the selected
models for Wave II of COVID-19 may assist in NPI disease
management by Iranian government and/or health organization
in Iran. There are currently no analytical models available to
predict the number of waves and forecast accurately and
completely trends of COVID-19. Richards, Gompertz, Logistic
and Ratkowsky are models for only modelling one population.
SIRD model is capable of modelling four populations although
the model is based on many simplified assumptions that
disregard sex, age, location, and behavior of individuals.
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