An approach to forecast impact of Covid‐19 using supervised machine learning model

Abstract

The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

Estimating salt consumption in 49 low- and middle-income countries: Development, validation and application of a machine learning model

“…17 The estimates from our ML model for Fiji (2011) and Samoa (2013) suggested that the mean salt consumption was 8.9 g/day and 9.6 g/day, respectively. A survey in Vanuatu in 2016 based on 24-hour urine sample informed that the mean salt intake was 5.9 g/day; 18 our estimate for the year 2011was 8.6 g/day. In 2009 in Vietnam a survey with SU samples revealed that the mean salt consumption was 9.9 g/day; 19 our prediction for the year 2015 was 7.9.…”

“…ML models have been used extensively to predict relevant clinical outcomes (e.g., mortality) and epidemiological indicators (e.g., forecasting COVID-19 cases). [20][21][22][23][24] Furthermore, ML algorithms have proven to be useful for understanding complex outcomes (e.g., identifying clusters of people with diabetes) based on simple predictors (e.g., BMI) in nationally-representative survey data. [25][26][27] Our work complements the current evidence on ML algorithms by demonstrating its use in a relevant field: population salt consumption.…”

“…7 Similarly, researchers have summarized all the available evidence in specific world regions. 8,9 Although the global endeavor was based on the gold standard method to assess sodium/salt intake (i.e., 24-hour urine sample), their estimates were up to 2010. 7 Therefore, robust and comparable sodium/salt intake estimates for all countries lack for the last ten years.…”

“…[11][12][13][14] However, these equations have been used in few WHO STEPS and other national health surveys, 15 leaving several countries without data to quantify the local sodium/salt consumption because they do not have access to SU samples. 16 If we could (accurately) estimate sodium/salt intake based on variables that are routinely available in national health surveys (e.g., weight or blood pressure), mean sodium/salt intake in countries that currently lack urine data (i.e., 24-hour or spot) could be computed using these available predictors. Advanced analytic techniques like machine learning (ML) could make accurate predictions, and inform about the population sodium/salt intake in LMICs.…”

Advanced data integration in banking, financial, and insurance software in the age of COVID‐19

“…In the near future innovative information integration system software design could be the solution to achieve sustainability during pandemic like situations. 75 , 76 , 77 , 78 , 79 …”