An Intelligent Prediction Model of COVID-19 in India using Hybrid Epidemic Model

Kumar, Manoj; Bareja, Jatin; Singh, Manjot; Sharma, Rupanshu

doi:10.1109/icosec49089.2020.9215426

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Additionally, One of the greatest challenges with any model is determining the current location and time on the epidemic curve [19]. To tackle this dilemma, [3,[20][21][22] addressed several predictive models to pinpoint the location. However, the impacts of predictor variables are totally ignored.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID 19 and Its Economic Impact in Africa

Ht¹

2021

Preprint

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This paper presents a two-way factor design incorporating both spatial and temporal variation in the prediction of COVID 19 in Africa. In line with this, the impact of COVID-19on the GDP in Africa is well scrutinized. In contrast to the existing works [1–3], this work also extends the two-factor design into the one-way factor design through incorporating covariates into spatial effects. The data rely on the spatial and temporal obtained from WHO datasets [4, 5]. The one-factor design with more covariates is taken into consideration to identify the major potential predictor variables responsible for the deaths and confirmed cases due to COVID 19 in Africa. The MANCOVA considered population density, temperature, humidity; perception, and wind are all considered as co-variates. Simulations show that the two-way analysis of variance has shown that there is a statistically significant difference between the spatial (Fcal= 8.2704, Pvalue= 3.099&lowast;10−6)and temporal (Fcal= 48.7964, Pvalue= 9.147&lowast;10−16) effects. South Africa and Nigeria are highly influencing due to the pandemic where their GDP also relatively mostly declined. A significant economic change is observed before the pandemic and after the outbreak of the pandemic(tcal= 2.9548, Pvalue= 0.01805). COVID 19 negatively influenced the economy of1 most of the African countries. The population density, temperature, and wind are found to be statistically significantly associated with COVID 19 cases and deaths.

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Section: Introductionmentioning

confidence: 99%

Prediction of COVID 19 and Its Economic Impact in Africa

Ht¹

2021

Preprint

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COVID-19 Spread Prediction Using Prophet and Data Fusion Algorithm

Sangeetha

Evangeline

Sinthuja

2022

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Today, technology plays a vital role in the healthcare industry. In the traditional way, physicians' minds were predicting the unknown disease based on their expertise and experience. Use of new technology like predictive analytics is transforming the healthcare industry. Predictive analytics in healthcare uses historical data (demographic information, person's past medical history and behaviors) to make predictions about the future. In this chapter, a predictive model is proposed to predict COVID-19 using prophet algorithm. A novel approach based on longitudinal data fusion approach will maintain temporal data from time to time. Sparse regularization regression uses data source and feature level to predict the spread of virus. The proposed model designed using longitudinal data fusion offers better clinical insights. Predictions will be very beneficial to government and healthcare groups to provoke suitable measures in controlling coronavirus. It is also beneficial to pharmaceutical companies to fabricate pills at a quicker rate.

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An Intelligent Prediction Model of COVID-19 in India using Hybrid Epidemic Model

Cited by 2 publications

References 13 publications

Prediction of COVID 19 and Its Economic Impact in Africa

Prediction of COVID 19 and Its Economic Impact in Africa

COVID-19 Spread Prediction Using Prophet and Data Fusion Algorithm

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