Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters

Wathore, Roshan; Rawlekar, Samyak; Anjum, Saima; Gupta, Ankit; Bherwani, Hemant; Labhasetwar, Nitin; Kumar, Rakesh

doi:10.1016/j.gr.2022.03.014

Cited by 11 publications

(8 citation statements)

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“…We also conduct forecasting exercises for January–March 2021 as well, to evaluate model efficacy during periods of significant declines in daily case counts. The need to ensure that training data for forecasting models capture dynamic changes in the spread of the virus has been noted by other studies ( 11 , 25 , 27 ). Employing data from these time periods is further justified given the rise in population vaccination rates from March 2021 onwards, and the widespread use of home testing kits from late 2021 onwards, which impacts the reliability of official statistics, given the possibility of under-reporting of positive tests to health authorities.…”

Section: Introductionmentioning

confidence: 97%

“…Chu and Qureshi ( 1 ), Chen et al ( 2 ), Stevens et al ( 23 ), and Sen et al ( 24 ) find different autoregressive time series and ML models to be capable of comparable or superior short-term out-of-sample forecasts of daily cases relative to SIR models. In terms of cities, Wathore et al ( 25 ) rely on deep learning models such as LSTM to forecast cases for 8 cities in India, U.S., and Sweden. Their study contains a summary of other LSTM based papers.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities

Sen,

Stevens,

Tran

et al. 2023

Front. Public Health

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IntroductionThere is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions.MethodsThis study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021.ResultsConsistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021.DiscussionThe results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities

Sen,

Stevens,

Tran

et al. 2023

Front. Public Health

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“… Wathore et al (2023) showed improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters. The research presents the improved potential for deep learning models incorporated with environmental parameters as inputs for better and improved prediction of the daily COVID-19 cases in the selected locations, consisting of 8 cities across the globe with varying climatic zones.…”

Section: Introductionmentioning

confidence: 97%

Introduction to the special issue ‘‘Environmental impacts of COVID-19 pandemic”

Gautam

Kiran

2023

Gondwana Research

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“…The study used data from 9 cities across India, the USA, and Sweden with varying climatic zones and found that correlations with temperature were generally positive for cold regions and negative for warm regions, while relative humidity showed mixed correlations. The results suggest that the inclusion of environmental parameters could aid in improving the management and preparedness of the healthcare system during the pandemic, although other confounding factors can affect the forecasting power [ 17 ]. Similarly, a novel multi-stage deep learning model has been presented to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast horizon of 1–4 weeks.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

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The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models—specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)—were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

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Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters

Cited by 11 publications

References 50 publications

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities

Introduction to the special issue ‘‘Environmental impacts of COVID-19 pandemic”

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

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