Modeling and prediction of COVID-19 pandemic using Gaussian mixture model

Abstract

COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 10 6 and 5.27 × 10 5 , respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.

Impact of meteorological conditions and air pollution on COVID-19 pandemic transmission in Italy

“…Because ARDS requires ICU admission and 97% of the infected people develop symptoms after 11 days of incubation 3 , meteorological and air-pollution data are 20 days back time-shifted. This means that the daily 28 and applied to the COVID-19 pandemic variables. This approach is also followed in 29 .…”

Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.

“…The ARIMA model assumes the trends will continue in the future inde nitely as against the empirical models which assumes convergence. Few studies used non-parametric models like fourier decomposition methods to predict turn around dates of the epidemic and the results were found to agree with popular SIR models [24].…”

The role of tropospheric Ozone in flagging COVID-19 pandemic transmission.

“…This means that the daily number of ICU patients from 24 February 2020 to 14 June 2020 are the result of infections that happened from 10 February 2020 to 31 May 2020. The ICU per-day cases are modeled following the Gaussian Mixture Model (GMM) (Singhal et al, 2020, Lolli et al, 2020. The observational data show different trends with respect to time.…”