Mathematical Mo Del with So Cial Distancing Parameter for Early Estimation of Covid-19 Spread

Chandra, Saroj Kumar; Singh, Avaneesh; Bajpai, Manish Kumar

doi:10.1101/2020.04.30.20086611

Cited by 9 publications

(10 citation statements)

References 15 publications

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“…Total confirmed cases are highly dependent on the cases reported in previous 7 days. Hence, it is supporting the well exist idea of COVID-19 spread chain which is dependent on number of previous cases [22, 23].…”

Section: Resultssupporting

confidence: 76%

Kalman Filter Based Short Term Prediction Model for COVID-19 Spread

Kumar

Singh

Dixit

et al. 2020

Preprint

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COVID-19 has emerged as global medical emergency in recent decades. The spread scenario of this pandemic has shown many variations. Keeping all this in mind, this article is written after various studies and analysis on the latest data on COVID-19 spread, which also includes the demographic and environmental factors. After gathering data from various resources, all data are integrated and passed into different Machine Learning Models to check the fit. Ensemble Learning Technique,Random Forest, gives a good evaluation score on the test data. Through this technique, various important factors are recognised and their contribution to the spread is analysed. Also, linear relationship between various features is plotted through heatmap of Pearson Correlation matrix. Finally, Kalman Filter is used to estimate future spread of COVID19, which shows good result on test data. The inferences from Random Forest feature importance and Pearson Correlation gives many similarities and some dissimilarities, and these techniques successfully identify the different contributing factors. The Kalman Filter gives a satisfying result for short term estimation, but not so good performance for long term forecasting. Overall, the analysis, plots, inferences and forecast are satisfying and can help a lot in fighting the spread of the virus.

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Section: Resultssupporting

confidence: 76%

Kalman Filter Based Short Term Prediction Model for COVID-19 Spread

Kumar

Singh

Dixit

et al. 2020

Preprint

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“…Mathematical models have widely been used to inform policy, programme planning, and service delivery related to COVID-19 [4]. Several models predicted the spread of COVID-19 with varying levels of accuracy [5] [6] [7]. These models forecasted the number and progression of infections and predicted the health system resource requirements, with different levels of assumptions about the effect of non-pharmaceutical interventions on the epidemic.…”

Section: Introductionmentioning

confidence: 99%

An Interrupted Time Series Analysis of COVID-19 Positivity before, during and after Lockdown in Four States of India

Tetali¹,

Jammy²,

Asirvatham³

et al. 2021

OJEpi

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“…Mathematical models are also helpful in understanding the scenario for spreading the disease [52][53][54].…”

Section: Introductionmentioning

confidence: 99%

“…It estimates the number of cases in worst and best-case scenarios. Mathematical models are also helpful in understanding the scenario for spreading the disease[52-54].…”

Section: Introductionmentioning

confidence: 99%

SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

Singh

Bajpai

2020

Preprint

Self Cite

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We have proposed a new mathematical method, SEIHCRD-Model that is an extension of the SEIR-Model adding hospitalized and critical twocompartments. SEIHCRD model has seven compartments: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), recovered (R), and deceased or death (D), collectively termed SEIHCRD. We have studied COVID- 19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. SEIHCRD model is estimating COVID-19 spread and forecasting under uncertainties, constrained by various observed data in the present manuscript. We have first collected the data for a specific period, then fit the model for death cases, got the values of some parameters from it, and then estimate the basic reproduction number over time, which is nearly equal to real data, infection rate, and recovery rate of COVID-19. We also compute the case fatality rate over time of COVID-19 most affected countries. SEIHCRD model computes two types of Case fatality rate one is CFR daily and the second one is total CFR. We analyze the spread and endpoint of COVID-19 based on these estimates. SEIHCRD model is time-dependent hence we estimate the date and magnitude of peaks of corresponding to the number of exposed cases, infected cases, hospitalized cases, critical cases, and the number of deceased cases of COVID-19 over time. SEIHCRD model has incorporated the social distancing parameter, different age groups analysis, number of ICU beds, number of hospital beds, and estimation of how much hospital beds and ICU beds are required in near future.

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Mathematical Mo Del with So Cial Distancing Parameter for Early Estimation of Covid-19 Spread

Cited by 9 publications

References 15 publications

Kalman Filter Based Short Term Prediction Model for COVID-19 Spread

Kalman Filter Based Short Term Prediction Model for COVID-19 Spread

An Interrupted Time Series Analysis of COVID-19 Positivity before, during and after Lockdown in Four States of India

SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

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