A Study to Analyse Covid-19 Outbreak Using Multiple Linear Regression: A Supervised Machine Learning Approach

Introduction: Globally, COVID-19 have impacted people's quality of life Machine learning have recently be-come popular for making predictions because of their precision and adaptability in identifying diseases. This study aims to identify significant predictors for daily active cases and to visualise trends in daily active, positive cases, and immunisations. Material and methods: This paper utilized secondary data from Covid-19 health bulletin of Uttarakhand and multiple linear regression as a part of supervised machine learning is performed to analyse dataset. Results: Multiple Linear Regression model is more accurate in terms of greater score of R2 (=0.90) as com-pared to Linear Regression model with R2 =0.88. The daily number of positive, cured, deceased cases are significant predictors for daily active cases (p <0.001). Using time series linear regression approach, cumulative number of active cases is forecasted to be 6695 (95% CI: 6259-7131) on 93rd day since 18 Sep 2022, if similar trend continues in upcoming 3 weeks in Uttarakhand. Conclusion: Regression models are useful for forecasting COVID-19 instances, which will help governments and health organisations address this pandemic in future and establish appropriate policies and recommen-dations for regular prevention. © 2023 National Journal of Community Medicine.

Global COVID-19 development trend forecast based on machine learning

The outbreak of COVID-19 not only affects people's health, but also hinders the pace of economic progress of various countries. Our goal was to develop a prediction model based on machine learning, which could be used to predict development trend of COVID-19 in the future. It can provide governments and health authorities with useful information conducive to decision-making. Considering that the propagation of COVID-19 is affected by many factors and a single prediction model lacks all-round monitoring of the data set, the ARIMA-SVM integration model was established by using the global cumulative number of confirmed cases. The individual models of ARIMA and SVM were used to predict the COVID-19 trend. Based on the prediction results of the above prediction model, a new integration forecast model was formed through a combination of weighted weights. Finally, the forecast results of the combined model and the individual model were compared. The prediction performance of models were compared according to Mean Absolute Percentage Error (MAPE). The prediction results showed that the MAPE values of ARIMA model, SVM model and ARIMA-SVM integration model were 15.843%, 1.251%, 1.132% respectively. Compared with the traditional machine learning models ARIMA and SVM, the combined model has reduced the average absolute error percentage by 92.103% and 9.51%, respectively, and can achieve more accurate and reliable COVID-19 trend prediction. It used two single models to complement each other, reduced the systematic error of the prediction model, and significantly improved the prediction effect. © 2022 IEEE.

The forecast of COVID-19 spread risk at the county level.

The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people's lives and restart the economy quickly and safely. People's social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p = 0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <1000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also can help with early and effective management of possible future pandemics. The code used for this study was made publicly available on https://github.com/Murtadha44/covid-19-spread-risk. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40537-021-00491-1.

Assessment of COVID-19 Hospitalization Forecasts from a Simplified SIR Model

We propose the SH model, a simplified version of the well-known SIR compartmental model of infectious diseases. With optimized parameters and initial conditions, this time-invariant two-parameter two-dimensional model is able to fit COVID-19 hospitalization data over several months with high accuracy (e.g., the root rela-tive squared error is below 10% for Belgium over the period from 2020-03-15 to 2020-07-15). Moreover, we observed that, when the model is trained on a suitable three-week period around the first hospitalization peak for Belgium, it forecasts the subsequent two months with mean absolute percentage error (MAPE) under 4%. We repeated the experiment for each French department and found 14 of them where the MAPE was below 20%. However, when the model is trained in the in-crease phase, it is less successful at forecasting the subsequent evolution. © 2021, Intercollegiate Biomathematics Alliance. All rights reserved.

Modelling COVID-19 Scenarios for the States and Federal Territories of Malaysia.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease, which has become pandemic since December 2019. In the recent months, among five countries in the Southeast Asia, Malaysia has the highest per-capita daily new cases and daily new deaths. A mathematical modelling approach using a Singular Spectrum Analysis (SSA) technique was used to generate data-driven 30-days ahead forecasts for the number of daily cases in the states and federal territories in Malaysia at four consecutive time points between 27 July 2021 and 26 August 2021. Each forecast was produced using SSA prediction model of the current major trend at each time point. The objective is to understand the transition dynamics of COVID-19 in each state by analysing the direction of change of the major trends during the period of study. The states and federal territories in Malaysia were grouped in four categories based on the nature of the transition. Overall, it was found that the COVID-19 spread has progressed unevenly across states and federal territories. Major regions like Selangor, Kuala Lumpur, Putrajaya and Negeri Sembilan were in Group 3 (fast decrease in infectivity) and Labuan was in Group 4 (possible eradication of infectivity). Other states e.g. Pulau Pinang, Sabah, Sarawak, Kelantan and Johor were categorised in Group 1 (very high infectivity levels) with Perak, Kedah, Pahang, Terengganu and Melaka were classified in Group 2 (high infectivity levels). It is also cautioned that SSA provides a promising avenue for forecasting the transition dynamics of COVID-19; however, the reliability of this technique depends on the availability of good quality data.

Fractional unit-root tests allowing for a fractional frequency flexible Fourier form trend: predictability of Covid-19.

In this study we propose a fractional frequency flexible Fourier form fractionally integrated ADF unit-root test, which combines the fractional integration and nonlinear trend as a form of the Fourier function. We provide the asymptotics of the newly proposed test and investigate its small-sample properties. Moreover, we show the best estimators for both fractional frequency and fractional difference operator for our newly proposed test. Finally, an empirical study demonstrates that not considering the structural break and fractional integration simultaneously in the testing process may lead to misleading results about the stochastic behavior of the Covid-19 pandemic.