Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.

COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries

In Vitro Doubled Haploid Production of Bacterial Blight Resistant Plants from BC2F1 Plants (Ranbir Basmati X Pau148) Through Anther Culture

Doubled haploid plants are very important for the development of complete homozygous plants from heterozygous parents in one generation as they possess duplicate copy of haploid chromosome. Haploid production is easily obtained from in vitro anther culture. The present study was undertaken with the objective to develop doubled haploids using anthers for in vitro induction of callus on N6 medium supplemented with various combinations and concentrations of 2,4-dichlorophenoxy acetic acid (2,4-D) (0.5-2.5 mg/L), Kinetin (0.5-1.0 mg/L) and Naphthalene acetic acid (NAA) (2.0 mg/L) as callus induction medium (CIM). The highest callus induction frequency was obtained when N6 medium fortified with 2,4-D (2.5 mg/L), Kinetin (0.5 mg/L) and NAA (2 mg/L) of 10.07 per cent. The induced callus was sub cultured for shoot regeneration on Murashige and Skoog medium (MS) supplemented with growth regulators: Kinetin and NAA (0.5 mg/L each) in combination with BAP (0.0 - 2.5 mg/L). MS medium supplemented with NAA (0.5 mg/L), Kinetin (0.5 mg/L) and BAP (1.5 mg/L) was most responsive exhibiting regeneration frequency of 28.1 per cent which resulted in maximum regeneration of green plantlets and only 5.21 per cent of albinos. Individual plantlets were separated and immersed in liquid MS medium augmented with NAA (0.5-1.0 mg/L) and BAP (0.5-1.0 mg/L). Maximum rooting was observed in MS medium with NAA (0.5 mg/L) and BAP (1.0 mg/L). The survival rate of in-vitro raised plants was 51.51 per cent. Of these surviving plants, 21 plants were observed to have the sterility percentage above 50 percent and hence can be considered as the doubled haploid plants. Plant DH8 is susceptible and DH20 is heterozygous for gene Xa21. Two plants are susceptible for gene xa1

COVID-19: Time Series Datasets India versus World

This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details. . [Dataset is updated Thrice a Week

COVID-19: Time Series Datasets India versus World

This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details. . [Dataset is updated Thrice a Week

COVID-19: Time Series Datasets India versus World

This dataset consists of COVID-19 time series data of India since March 24th, 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details. . The Authors can Refer to and CITE our latest Papers on COVID: 1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945. 2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118. 3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50. . [Dataset is updated Once a Week

COVID-19: Time Series Datasets India versus World

This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details. . [Dataset is updated Twice a Week] The Authors can Refer to and CITE our latest Papers on COVID: 1. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Evolutionary Modelling of the COVID-19 Pandemic in Fifteen Most Affected Countries" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.110118 2. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.10994

COVID-19: Time Series Datasets India versus World

This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details. . The Authors can Refer to and CITE our latest Papers on COVID: 1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945. 2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118. 3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50. . [Dataset is updated Once a Week

COVID-19: Time Series Datasets India versus World

This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details. . [Dataset is updated Thrice a Week

COVID-19: Time Series Datasets India versus World

This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details. . [Dataset is updated Twice a Week