COVID-19 Outbreak Prediction using Machine Learning Algorithm | by Wie Kiang H. | Towards Data Science

Note from the editors: Towards Data Science is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional health advice. However, this article will be focus on how machine learning can be used to predict the spread of the pandemic.
Our society is in the era of unbelievable attempts to struggle upon the spread of this life-threatening condition in terms of infrastructure, finance, business, manufacturing, and several other resources. Artificial Intelligence (AI) researchers strengthen their proficiency in developing mathematical paradigms for investigating this pandemic using nationwide distributed data. This article intends to apply the machine learning models simultaneously with the forecast of expected reachability of the COVID-19 over the nations by using the real-time data from the Johns Hopkins dashboard.
Coronavirus spreads are categorized into four stages. The first stage starts with the cases recorded for the people who traveled to or from affected countries or cities, whereas in the second stage, cases are reported regionally among family, friends, and groups who came into contact with the person coming from the affected countries. Therefore, the affected people are identifiable. Next, the third stage causes the circumstance severely as the infected person becomes undetectable and flattens across the individuals who neither have any travel records nor came in connection with the affected person. This condition obliges immediate lockdown across the nation to reduce the social contacts between individuals to measure the movement of the virus. Finally, stage four starts when the transmission converts to endemic and uncontrollable. China is the first country that felt under stage four of the COVID-19 transmission, while most of the developed countries are now in this stage of the transmission and bearing a further number of epidemics and losses compared to China.
Machine learning algorithms play an essential role in the pandemic investigation and forecasting. Furthermore, machine learning techniques help to expose the epidemic patterns. As a result, an immediate response might be prepared to prevent the spread of the virus (Kalipe, Gautham & Behera, 20181; Singh, Singh & Bhatia, 20182). Moreover, machine learning models are utilized to recognize collective behavior together with the prediction of the expected spread of the COVID-19 across the society by employing the real-time data from the Johns Hopkins dashboard.
The dataset retrieved from the official repository of Johns Hopkins University3. This data consists of daily case reports and daily time series summary tables. In the study, we have selected time-series summary tables in CSV format having three tables for confirmed, death, and recovered cases of COVID-19 with six properties. For example, province/state, country/region, last update, confirmed, death, and recovered cases. The CSV data are available in Github4 repositories.
Coronavirus spread has conducted the society under the edge of loss in social lives. Additionally, it is crucial to investigate the transmission growth ahead and predict the future occurrences of the transmission. In concurrent, state-of-the-art mathematical models are chosen based on machine learning for a computational process to predict the spread of the virus, for instance:
Support Vector Regression5 (SVR)
Polynomial Regression6 (PR)
Deep Learning regression models
It is also involving:
Artificial Neural Network7 (ANN)
Recurrent Neural Networks8 (RNN) using Long Short-Term Memory9 (LSTM) cells.
Machine learning and deep learning strategies are performed using the python library to predict the total number of confirmed, recovered, and death cases extensively. This prediction will allow undertaking specific determinations based on transmission growth, such as expanding the lockdown phase, performing the sanitation plan, and providing daily support and supplies.
Regression analysis is a section of machine learning algorithms. It is the leading machine learning algorithm. Think of straight equation line combining any two variables X and Y, which can be declared algebraically as:
Where b is declared the intercept on the y-axis, and a is called the slope of the line. Here, a and b are also called the parameters of regression analysis. These parameters should learn throughout proper learning methods.
Regression analysis contains a set of machine learning methods that enable us to predict a continuous result variable (Y) based on the value of one or multiple predictor variables (X). It pretends a continuing connection between the result and the predictor variables.
The correlation coefficient interpreted as the strength of a linear relationship between two variables. Karl Pearson emphasizes that the coefficient of correlation is a weight or degree of the linear correlation between two variables. He also has been generated a formula known as Correlation Coefficient. The correlation coefficient between two random variables X and Y, generally indicated by a numerical measure of the linear dependence between those variables and is defined as:
Where, i = 1, 2, 3, 4, ...N, is the collection of input and output variables. Some prediction is given below:
If the value of the correlation coefficient is equal to zero, it indicates no correlation between input variables X and output variable Y.If the value of the correlation coefficient is equal to positive one, it indicates there is a strong relationship between the input variable and the output variable. In other words, if the input variable is increased then the output variable is also increased.If the value of the correlation coefficient is equal to negative, it indicates the input variable is increased then the output variable is also decreased and so on.
If the value of the correlation coefficient is equal to zero, it indicates no correlation between input variables X and output variable Y.
If the value of the correlation coefficient is equal to positive one, it indicates there is a strong relationship between the input variable and the output variable. In other words, if the input variable is increased then the output variable is also increased.
If the value of the correlation coefficient is equal to negative, it indicates the input variable is increased then the output variable is also decreased and so on.
Those variables that have a small or no linear correlation might have a strong nonlinear relationship. On the other hand, estimating linear correlation before fitting a model is a valuable way to recognize variables with a simple relationship. In this proposed study, we have measured the correlation coefficient between date and number of confirmed cases of COVID-2019 spread nationwide.
Our environment is under the control of the COVID-19 virus. This article intended to employ the machine learning models for pandemic analysis through a dataset from Johns Hopkins. In conclusion, the method of Polynomial Regression (PR) generated a minimum Root Mean Square Error (RMSE) amount over other methods in projecting the COVID-19 transmission. However, if the spread mimics the prognosticated trend of the PR model, then it would lead to extensive loss of lives as it presents the incredible growth of the transmission globally. As perceived in China, the increased case of COVID-19 can be degraded by lessening the number of sensitive individuals from infected people. This new normal is obtainable by becoming unsocial and supporting the lockdown regulation with control.
References#1 Predicting Malarial Outbreak using Machine Learning and Deep Learning Approach: A Review and Analysis#2 Sentiment analysis using machine learning techniques to predict outbreaks and epidemics#3 Johns Hopkins University#4 Johns Hopkins University: COVID-19 Data Repository#5 Support Vector Regression#6 Polynomial Regression#7 Artificial Neural Network#8 Recurrent Neural Networks#9 Long Short-Term MemoryDisclaimerThis is for education and information purposes only, additional research in the machine learning algorithm needed to give the exact amount of prediction data from the real-time dataset. The source code of the experiment can be access here on GitHub.