INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 11, NOVEMBER 2020 ISSN 2277-8616 15 IJSTR©2020 www.ijstr.org Mathematical Modeling And Forecasting The Spread Of Covid-19 Using Python Mohit Kumar Kakkar, Meenakshi Sood, Bunty Sharma, Jasdev Bhatti* Abstract: Numerous Mathematical models are being produced to predict the trend of spreading of corona virus disease (COVID19) epidemics in INDIA and worldwide, which has become a pandemic. On 23rd Feb 2020, first case of covid-19 was reported, when we were writing this paper cases crossing 70000 in India. We present here data driven models for COVID-19 in India where we used minimal number of parameters to provide insights into the spread of the disease. Here in this paper we are presenting the Susceptible-Infected (SI), Susceptible-Infected-Recovered (SIR), SEIR and SEIR-D models, implemented on Python language with their transition diagrams. All four models presented here are related quantitatively and based on Indian data. In this paper our aim is to deliver an overview of these models and the outcome of simulation by using the dataset of Covid-19. Numbers of plots are presented here for analysis which makes the prediction easy. Index Terms: Susceptible-Infected-Recovered (SIR), SI, SEIR, SEIR-D, Covid-19, Mathematical Models, Python. ——————————  —————————— 1 INTRODUCTION s the whole world is facing a very difficult situation now a days due to COVID19 and our country is also facing the same situation but as the news are coming from the other countries like USA, UK, Italy, Spain, Iran this virus is showing different behavior in different environment. The outbreak of the novel coronavirus disease (Covid-19) brought considerable turmoil all around the world. Statistics show that the mortality of COVID-19 is 20 times higher than seasonal flu so now it should be the priority for every Indian states and central agencies to take efficient measure to limit the transmission of COVID-19. Worldwide, Mathematical Models are playing important role in the making of key policy discussions on these kinds of epidemics like EBOLA, corona, measles and COVID- 19. [2] presented the analyses in epidemiology for plant diseases. [1] discussed a very simple model for Africa regarding EBOLA virus. Similarly [3] presented SIR Model to forecast the Outbreak of Ebola Virus Diseases using Euler and 4 th Order RK method. Lashari et al. (2019) discussed the spread of two successive SIR epidemics through modeling.[5] explained the SIR-type network epidemics based on non- Markovian concept. [6] gave the idea about the epidemics transition in Varroa‐infested honeybee colonies. [7] presented the concept regarding SIR Model for different applications. Here We have discussed some mathematical model like SIR, SEIR, SEIR-D describing the structure of how the infectious disease spreading. This work attempts to use python as a language to implement the classic infectious disease model. Because infectious disease model research belongs to the research direction of infectious disease dynamics, I only use the differential equations in the model to implement in Python2 Procedure for Paper Submission 2 MATHEMATICAL MODELS Mathematical Models are useful tools which are very much capable to make us aware about the trend of disease, may be its spread rate, and effects of policies to be implemented. These mathematical Models are also very useful in scenarios where perfect data are not available with us or collection of such type of data is also not possible, same problems are arising now with data of COVID-19 as this virus is changing day by day and region by region. 2.1 Notations In this paper we have used following symbols to depict the states and transition rate as mentioned in the table-1 TABLE 1 SYMBOLS USED FOR STATES AND TRANSITION RATES S Susceptible I Infectious R NUMBER OF CONTACTS PER UNIT TIME E Exposed human being N Total population β PROBABILITY OF DISEASE TRANSMISSION PER CONTACT (THE CONTAGION RATE OF THE PATHOGEN) ϒ RECOVERY RATE μ DEATH RATE Ω DEATH RATE AFTER INFECTION (IN SEIR- D MODEL) Ф RECOVERY RATE AFTER INFECTION (IN SEIR-D MODE) Α RATE OF PROGRESSION TO INFECTIOUS STATE R 0 REPRODUCTIVE NUMBER 2.2 SI Model To get the information about the trend of infection, we have used various mathematical models. Here in this section first we discuss the simplest model named SI Susceptible- Infectious Model which is mainly applicable to HIV like disease but this model is not applicable for Covid19 type disease A ————————————————  Mohit Kumar Kakkar, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India, mohit.kakkar@chitkara.edu.in  Meenakshi Sood, Chitkara School of Health Sciences, Chitkara University, Punjab, India. meenakshi.sood@chitkara.edu.in  Bunty Sharma, Chitkara School of Health Sciences, Chitkara University, Punjab, India. bunty.sharmad@chitkara.edu.in  Corresponding Author*: Jasdev Bhatti, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India, jasdev.bhatti@chitkara.edu.in βIr/N