Predicting the COVID-19 positive cases in India with concern to Lockdown by using Mathematical and Machine Learning based Models

Pasayat, Ajit Kumar; Pati, Satya Narayan; Maharana, Aashirbad

doi:10.1101/2020.05.16.20104133

Cited by 15 publications

(21 citation statements)

References 3 publications

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“…Fig. 11 demonstrates the results of Linear Regression technique proposed by the researchers [20] and the research used the official number of cases from February 11, 2020 to May 11, 2020 as the input data to train the model. The predicted duration was of 33 days starting from May 12, 2020 to June 30, 2020.…”

Section: Resultsmentioning

confidence: 99%

Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdowns

Gola

Arya

Animesh

et al. 2020

Preprint

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COVID-19 is spreading widely across the globe right now. Majority of the countries are relying on models and studies such as stochastic simulations, AceMod model, neural networks-based models, exponential growth model,Weibull distribution model, and so on to forecast the number of COVID-19 cases in the coming months. The objective on utilizing these models is to ensure that strict measures can be enacted to contain the virus spread and also predict the resources required to deal with the pandemic as the disease spreads. In the past few months, several models were used to predict the infection rate for COVID-19. These models predicted the infection rates, recovery rate or death rates for the COVID-19 patients. All these different models took different approaches and different scenarios to predict the future rates. Now, that we know the real cases, we can check how accurate these models were. Some of these models were able to predict the near future quite close to the reality but most of them went astray. In this study, we review major forecasting models that were used in the context of India during country-wise lockdown and compare them. From these comparisons, we can see that while the advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic, effects can be catastrophic if poorly fitting models are used for predictions.

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Section: Resultsmentioning

confidence: 99%

Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdowns

Gola

Arya

Animesh

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The insights include the infectious force, the rate of a mild infection becoming serious, estimates for asymptomatic infections and predictions of new infections over time. In [467] , the exponential growth model is used to derive the epidemic curve, and then a linear regression model is proposed to predict the epidemic curve. The logistic model is used in [468] to fit the cap of the epidemic trend and then feed the cap value in to a FbProphet model, a machine learning modeling algorithm proposed to model the epidemic curves.…”

Section: Applications Of Ai In Epidemiologymentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

156

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“…Modelling novel coronavirus disease has then become of extreme importance. Many researchers around the world have studied the patterns of Covid-19 pandemic and several mathematical, computational, clinical and examination studies have been put forward for modelling, prediction, treatment and control of the disease [6,7,8,9,10,11,12,13,14,15,16]. This growing interest of scientists has resulted in a deluge of studies predicting the dynamics of Covid-19, and summarizing trends in these studies is necessary.…”

Section: Introductionmentioning

confidence: 99%

On the reliability of predictions on Covid-19 dynamics: a systematic and critical review of modelling techniques

Gnanvi

Salako

Kotanmi

et al. 2020

Preprint

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Since the beginning of the new coronavirus 2019-nCoV disease (Covid-19) in December 2019, there has been an exponential number of studies using diverse modelling techniques to assess the dynamics of transmission of the disease, predict its future course and determine the impact of different control measures. In this study, we conducted a global systematic literature review to summarize trends in the modelling techniques used for Covid-19 from January 1st 2020 to June 30th 2020. We further examined the reliability and correctness of predictions by comparing predicted and observed values for cumulative cases and deaths. From an initial 2170 peer-reviewed articles and preprints found with our defined keywords, 148 were fully analyzed. We found that most studies on the modelling of Covid-19 were from Asia (52.70%) and Europe (25%). Most of them used compartmental models (SIR and SEIR) (57%) and statistical models (growth models and time series) (28%) while few used artificial intelligence (5%) and Bayesian approach (3%). For cumulative cases, the ratio predicted/observed values and the ratio of the amplitude of confidence interval (CI) or credibility interval (CrI) of predictions and the central value were on average larger than 1 (4.49-9.98 and 1.10-1.94, respectively) indicating cases of incorrect predictions, large uncertainty on predictions, and large variation across studies. There was no clear difference among models used for these two ratios. However, the ratio predicted/observed values was relatively smaller for SIR models than for SEIR models, indicating that more complex models might not be more accurate for predictions. We further found that values of both ratios decreased with the number of days covered by studies, indicating that the wider the time covered by the data, the higher the correctness and accuracy of predictions. In 21.62% of studies, observed values fall within the CI or CrI of the cumulative cases predicted by studies. Only six of the 148 selected studies (4.05%) predicted the number of deaths. For 33.3% of these predictions, the ratio of predicted to actual number of deaths was close to 1. We also found that the Bayesian model made predictions closer to reality than the compartmental and the statistical models, although these differences are only suggestive due to the small size of the data. Our findings suggest that while predictions made by the different models are useful to understand the pandemic course and guide policy-making, there should be cautious in their usage.

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Predicting the COVID-19 positive cases in India with concern to Lockdown by using Mathematical and Machine Learning based Models

Cited by 15 publications

References 3 publications

Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdowns

Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdowns

Applications of artificial intelligence in battling against covid-19: A literature review

On the reliability of predictions on Covid-19 dynamics: a systematic and critical review of modelling techniques

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