COVID-19 Cases In India Research Articles

Forecasting of COVID-19 Cases in India: A Predictive Study

An outbreak of new coronavirus (COVID-19) originated by SARS-CoV has reached 212 countries throughout the world. India is the second-highest populated country, so it is critical to forecasting the confirmed cases and deaths due to pandemic. To fulfil the purpose, three machine learning models Linear Regression, Multilayer Perceptron, and Sequential Minimal Optimization Regression are used. The predictive data of three geographic regions (India, Maharashtra, and Tamil Nadu) are compared with the data considered to be adequate in practice. The analysis concluded that Sequential Minimal Optimization Regression can be adopted for possible pandemic predictions such as COVID-19.

Statistical Analysis of Future Prospective in COVID-19 Cases in India

Statistical Analysis of Future Prospective in COVID-19 Cases in India - written by Ibrahim Abubakar Sadiq , Sanjeev Kumar Sharma , Rabilu Ado published on 2020/12/09 download full article with reference data and citations

Predictive Models and Analysis of Peak and Flatten Curve Values of CoVID-19 Cases in India

Worldwide increasing cases of COVID-19 are putting high pressure on healthcare services The coronavirus epidemic caused announcing emergency cases in India The virus started with one infected case by 30th January, 2020, in Kerala, where the first death due to corona in Karnataka and 73 announced cases were reported by 12th March, 2020 1024 announced cases were reported by 29th March, 2020 This indicates that the number of confirmed cases is increasing rapidly, causing national crises for India This study aims is to fill a gap between previous studies and the current development of COVID-19 spreading, by extracting a relationship between corona positives as independent and corresponding deaths as a dependent variable This research statistically analyses the mortality in 10 days of every month A mathematical model to predict the new deceased cases corresponding infected cases in a practical scenario is proposed An approximate prediction of mortality corresponding to new predicted cases can be easily performed using the proposed model As most of the other countries have reached their peaks, confirmed cases start decreasing By analyzing these countries’ data considering the lockdown, the peak ratio is identified using all countries’ population and the decreasing rate of confirmed cases after the peak has been achieved The same calculation has been done for death and recovered cases This average peak ratio is used to identify India’s COVID patients’ peak value The decreasing rate is also used to define the rate of confirmed cases and mortality after the peak has occurred The model has also been verified in different countries to identify the significance of the model © 2020, Novel Carbon Resource Sciences All rights reserved

WITHDRAWN: Recurrent neural network based prediction of number of COVID-19 cases in India

COVID-19 has become the most devastating disease of the current century and is pandemic. As per WHO report, there are globally 31,174,627 confirmed cases including 962,613 deaths as of 22nd September 2020. The disease is spreading through outbreaks despite the availability of latest technologies for treatment of patients. In this paper, we proposed a neural network-based prediction of number of cases in India due to COVID-19. Recurrent neural network (RNN) based LSTM is applied on India dataset for prediction. LSTM networks are a type of RNN capable of learning order dependence in sequence forecasting problems. We analyze the performance of the network and then compare it with two parameter reduced variants of LSTM, obtained by elimination of hidden unit signals, bias and input signal. For performance evaluation, we used the MSE measure.

Covid -19 forecasting using regression analysis

Covid-19 is a viral infection started in china and became pandemic by spreading all over the world. The goal of the study is to forecast the number of confirmed, recoevered, active and deaths of covid19 cases in India by December 2020 through regression analysis. According to our study the confirmed cases may raise to 43,67,580, and the active cases may rise to 14,32,583 and deaths may rise to 86,530 by 31 December of 2020

A Steady Trickle-Down from Metro Districts and Improving Epidemic-Parameters Characterize the Increasing COVID-19 Cases in India

Background: By mid-September of 2020, the number of daily new infections in India have crossed 95,000. To facilitate an intuition for the spatio-temporal development of the pandemic and to help resource deployment planning, we analyze and describe how the disease burden almost-predictably shifted from large metropolitan districts to sub-urban districts. Methods: We gathered the publicly available granular data from 186 different districts (equivalent of counties) on their COVID-19 infections and deaths during the 15 April to 31 August 2020 period. These districts presented an active case burden of 559,566 and a cumulative 2,715,656 infections as of August 31. The epidemiological data of these districts was fit to a susceptible-asymptomatic-infected-recovered-dead (SAIRD) model and the underlying epidemic parameters for each of these districts during the course of 4 months was estimated. We validated these parameters against known epidemiological characteristic distributions and analyzed them to understand their changes in space-time during the pandemic. Findings: The center of the burden of the current-active infections which on May 15 was in the large metro districts with most international access shifted continuously and smoothly shifted towards districts which could be accessed by domestic airports and by trains. A linear trend-analysis showed a continuous improvement in most epidemic parameters consistently across the districts with four categories of accessibility from an international travel perspective - large metro, metro, urban and sub-urban districts. The reproduction numbers improved from 1.77±0.58 on May 15 to 1.07±0.13 on August 31 in large metro districts (p-Value of trend 0.0001053); and from 1.58±0.39 on May 15 to 0.94±0.11 on August 31 in sub-urban districts (p-Value of trend 0.0067). The recovery rate per infected person per day improved from 0.0581±0.009 on May 15 to 0.091±0.010 on August 31 in large metro districts (p-Value of trend 0.26x10-12; and from 0.059±0.011 on May 15 to 0.100±0.010 on August 31 in sub-urban districts (p-Value of trend 0.12±10-16). The death rate of symptomatic individuals which includes the case-fatality-rate as well as the time from symptoms to death, consistently decreased from 0.0025±0.0014 on May 15 to 0.0013±0.0003 on August 31 in large metro districts (p-Value of trend 0.0010); and from 0.0018±0.0008 on May 15 to 0.0014±0.0003 on August 31 in sub-urban districts (p-Value of trend 0.2789). Interpretation: As the daily infections continue to rise at a national level, it is important to notice a "local-flattening" in larger metro districts, and a shift of the pandemic-burden towards smaller sized districts in a clear hierarchical fashion of accessibility from an international travel perspective. The pandemic burden shifting towards remotely accessible regions, with possibly lesser health care facilities, is a call for attention to the re-organization of resources.Funding Statement: None.Declaration of Interests: The authors declare no conflicts of interest.

Modeling and Forecasting of COVID-19 Growth Curve in India

In this article, we analyze the growth pattern of COVID-19 pandemic in India from March 4 to July 11 using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as exponential smoothing and Holt–Winters models. We found that the growth of COVID-19 cases follows a power regime of ({t}^{2}, t,...) after the exponential growth. We found the optimal change points from where the COVID-19 cases shifted their course of growth from exponential to quadratic and then from quadratic to linear. After that, we saw a sudden spike in the course of the spread of COVID-19 and the growth moved from linear to quadratic and then to quartic, which is alarming. We have also found the best fitted regression models using the various criteria, such as significant p-values, coefficients of determination and ANOVA, etc. Further, we search the best-fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and provide the forecast of COVID-19 cases for future days. We also use usual exponential smoothing and Holt–Winters models for forecasting purpose. We further found that the ARIMA (5, 2, 5) model is the best-fitting model for COVID-19 cases in India.

Statistical Analyses of COVID-19 Cases in India

COVID-19 outbreak was first reported in Wuhan, China and has spread most of the countries of the world. The outbreak spreads are largely influenced by each country's policy and social responsibility. According to WHO, the estimate of attack the rate on 23, June, 2020 to be between 1.4 to 2.5. In India the situation is somehow controllable compared to the situations of developed countries.

Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning

The research paper proposes a methodology to predict the extension of lockdown in order to eradicate COVID-19 from India. All the concepts related to Coronavirus, its history, prevention and cure is explained in the research paper. Concept used to predict the number of active cases, deaths and recovery is Linear Regression which is an application of machine learning. Extension of lockdown is predicted on the basis of predicted number of active cases, deaths and recovery all over India. To predict the number of active cases, deaths and recovery, date wise analysis of current data was done and necessary parameters like daily recovery, daily deaths, increase rate of covid-19 cases were included. Graphical representation of each analysis and prediction was done in order to make predicted results more understandable. The combined analysis was performed at the end which included the final result of total cases of coronavirus in India. Combined analysis included the no. of cases from start of COVID-19 to the predicted end of cases all over India.

Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study.

Covid-19 is a highly contagious virus which almost freezes the world along with its economy. Its ability of human-to-human and surface-to-human transmission turns the world into catastrophic phase. In this study, our aim is to predict the future conditions of novel Coronavirus to recede its impact. We have proposed deep learning based comparative analysis of Covid-19 cases in India and USA. The datasets of confirmed and death cases of Covid-19 are taken into consideration. The recurrent neural network (RNN) based variants of long short term memory (LSTM) such as Stacked LSTM, Bi-directional LSTM and Convolutional LSTM are used to design the proposed methodology and forecast the Covid-19 cases for one month ahead. Convolution LSTM outperformed the other two models and predicts the Covid-19 cases with high accuracy and very less error for all four datasets of both countries. Upward/downward trend of forecasted Covid-19 cases are also visualized graphically, which would be helpful for researchers and policy makers to mitigate the mortality and morbidity rate by streaming the Covid-19 into right direction.

Investigation of early epidemiological signals of COVID-19 in India using outbreak surveillance data

Background: The epidemic of novel coronavirus disease (COVID-19) started in Wuhan, China, in December 2019, has been progressed to a pandemic level with high morbidity and mortality affecting many countries globally. Due to varied clinical characteristics of the disease, diagnosis can be missed easily among the affected population. The epidemiological data and early signals of COVID-19 infection are crucial for the disease investigation. Aim: To assess the presence of early signals of COVID-19 in India before official reporting of cases in the country and to compare epidemiological characters using different surveillance sources in the study. Methods: This study uses Epiwatch outbreaks surveillance data from November 2019 to March 2020 was used to determine the trend of pneumonia of unknown causes in India in order to assess if the COVID-19 could have been detected before the first case was reported.The search was carried out using the search terms defined in the study to identify the early signals of COVID-19.The COVID-19 line list extracted from the crowdsourced database (https://www.covid19india.org/) was additionally used to determine the demographic characteristics of cases. A descriptive analysis was conducted. Results: A total of 379 reports was extracted from Epiwatch. In 2019, there were only six reports of pneumonia of unknown causes in India, and the majority of the reports were focused on swine flu or undetected influenza cases. In 2020, there were 312 news reports of unknown pneumonia and COVID-19. The early signals of COVID-19 in India were observed from 24th January 2020 as the trend of pneumonia news had moderately increased. The trend of pneumonia reporting was low before the first confirmed case was reported in India. The study findings also suggested that there was under-reporting of COVID-19 cases in India by the World Health Organization (WHO) and the Ministry of Health, India when compared to data reported by Epiwatch and the line list. Conclusion: The study findings suggested that there were early signals of COVID-19 in India around 24th January when there was a slight increase in the trend of pneumonia reports in the country. The number of COVID-19 cases in India is under-reported by the Ministry of Health and the WHO. The use of online media surveillance reports such as Epiwatch and the line list could provide real-time assessment and timely investigation of emerging diseases globally.

Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods.

Owing to the pandemic scenario of COVID-19 disease cases all over the world, the outbreak prediction has become extremely complex for the emerging scientific research. Several epidemiological mathematical models of spread are increasing daily to forecast the predictions appropriately. In this study, the classical susceptible-infected-recovered (SIR) modeling approach was employed to study the different parameters of this model for India. This approach was analyzed by considering different governmental lockdown measures in India. Some assumptions were considered to fit the model in the Python simulation for each lockdown scenario. The predicted parameters of the SIR model exhibited some improvement in each case of lockdown in India. In addition, the outcome results indicated that extreme interventions should be performed to tackle this type of pandemic situation in the near future.

Effect of meteorological parameters on spread of COVID-19 in India and air quality during lockdown

The novel Coronavirus (COVID-19) was identified in Wuhan, Hubei Province, China, in December 2019 and has created a medical emergency worldwide. In India, it is already reported more than 855 thousand cases and more than 22 thousands deaths due to COVID-19 till July 12, 2020. The role of temperature, humidity, and absolute humidity in the transmission of COVID-19 has not yet been well established. In contrast, for the previous many viral infections like influenza, it is well established. Therefore the study to investigate the meteorological condition for incidence and spread of COVID-19 infection and to provide a scientific basis for prevention and control measures against the new disease is required for India. In this work, we analyze daily averaged meteorological data for the last three years (2017–2019) for March, April and May months and the same for the year 2020 for March 1 to May 31. We found a positive association between daily COVID-19 cases and temperature and a mixed association with relative and absolute humidity over India. We have investigated the association of aerosols (AOD) and other pollutions (NO2) with COVID-19 cases during the study period and also during the lockdown period (25 March-31 May) in India. During the lockdown period, aerosols (AOD) and NO2 reduced sharply with a maximum percentage drop of about 60 and 45, respectively. We have also found the reduction in surface PM2.5 PM10 and NO2 for the six mega cities of India during the lockdown period. Our results suggest that COVID-19 still may spread in warm, humid regions or during summer/monsoon, therefore an effective public health intervention should be implemented across India to slow down the transmission of COVID-19.

Implication of ARIMA Time Series Model on COVID-19 Outbreaks in India

This research paper focuses on a Time Series Model to predict COVID-19 Outbreaks in India. COVID-19 Corona virus disease has been recognized as a worldwide hazard, and most of the studies are being conducted using diverse mathematical techniques to forecast the probable evolution of this outbreak. These mathematical models based on various factors and analyses are subject to potential bias. Here, we put forward a natural Times Series (TS) model that could be very useful to predict the spread of COVID-19. Here, a popular method Auto Regressive Integrated Moving Average (ARIMA) TS model is performed on the real COVID-19 data set to predict the outbreak trend of the prevalence and incidence of COVID-19 in India. Every day data of fresh COVID-19 confirmed cases act as an exogenous factor in this frame. Our data envelops the time period from 12th March, 2020 to 27th June, 2020. The time series under study is a non-stationary. Results obtained in the study revealed that the ARIMA model has a strong potential for prediction. In ACF and PACF graphs. Lag 1 and Lag 40 was found to be significant. Regressed values imply Lag 1 and Lag 40 was significant in predicting the present trend. The model predicted maximum COVID-19 cases in India at around 14, 22,337 with an interval (12, 80,352 - 15, 69, 817) during 1st July to 30th July period cumulatively. As per the model, the number of new cases shall increases drastically in India only. The results will help governments to make necessary arrangements as per the estimated cases. This kind of investigation, implications of ARIMA models and fitting procedures are useful in forecasting COVID-19 Outbreaks in India.

Pediatric and geriatric immunity network mobile computational model for COVID-19

PurposeThe computational model proposed in this work uses the data's of COVID-19 cases in India. From the analysis, it can be observed that the proposed immunity model decides the recovery rate of COVID −19 patients; moreover, the recovery rate does not depend on the age of the patients. These analytic models can be used by public health professionals, hospital administrators and epidemiologists for strategic decision-making to enhance health requirements based on various demographic and social factors of those affected by the pandemic. Mobile-based computational model can be used to compute the travel history of the affected people by accessing the near geographical maps of the path traveled.Design/methodology/approachIn this paper, the authors developed a pediatric and geriatric person’s immunity network-based mobile computational model for COVID-19 patients. As the computational model is hard to analyze mathematically, the authors simplified the computational model as general COVID-19 infected people, the computational immunity model. The model proposed in this work used the data's of COVID-19 cases in India.FindingsThis study proposes a pediatric and geriatric people immunity network model for COVID- 19 patients. For the analysis part, the data's on COVID-19 cases in India was used. In this model, the authors have taken two sets of people (pediatric and geriatric), both are facing common symptoms such as fever, cough and myalgia. From the analysis, it was observed and also proved that the immunity level of patients decides the recovery rate of COVID-19 patients and the age of COVID-19 patients has no significant influence on the recovery rate of the patient.Originality/valueCOVID-19 has created a global health crisis that has had a deep impact on the way we perceive our world and our everyday lives. Not only the rate of contagion and patterns of transmission threatens our sense of agency, but the safety measures put in place to contain the spread of the virus also require social distancing. The novel model in this work focus on the Indian scenario and thereby may help Indian health organizations for future planning and organization. The factors model in this work such as age, immunity level, recovery rate can be used by machine leaning models for predicting other useful outcomes.

ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India

In this paper, we have applied the univariate time series model to predict the number of COVID-19 infected cases that can be expected in upcoming days in India. We adopted an Auto-Regressive Integrated Moving Average (ARIMA) model on the data collected from 31st January 2020 to 25th March 2020 and verified it using the data collected from 26th March 2020 to 04th April 2020. A nonlinear autoregressive (NAR) neural network was developed to compare the accuracy of predicted models. The model has been used for daily prediction of COVID-19 cases for next 50 days without any additional intervention. Statistics from various sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/ are used for the study. The results showed an increasing trend in the actual and forecasted numbers of COVID-19 cases with approximately 1500 cases per day, based on available data as on 04th April 2020. The appropriate ARIMA (1,1,0) model was selected based on the Bayesian Information Criteria (BIC) values and the overall highest R2 values of 0.95. The NAR model architecture constitutes ten neurons, which was optimized using the Levenberg-Marquardt optimization training algorithm (LM) with the overall highest R2 values of 0.97.

Epidemic Trend of COVID-19 Transmission in India During Lockdown-1 Phase.

To assess the current epidemic trend of COVID-19/SARS-CoV-2 in India, the epidemic dynamics of COVID-19 cases in India in terms of Case Fatality Rate (CFR), Case Recovery Rate (CRR) and Mortality rate (MR) COVID-19 have been evaluated during Lockdown-1. The analysis includes (i) epidemic curve of Covid-19 cases (ii) demographic analysis (iii) calculation of the CFR and CRR by different methods (iv) calculation of MR (v) Geo-temporal analysis (vi) epidemiological transmission factor (vii) evaluation of the effects and impact of infection, prevention and control in India. A total of 10,815 COVID-19 confirmed cases have been reported in 31 states/union territories as of April 14, 2020 with 9272 active cases (85.73%), 1190 cured/discharged (11%), and 353 deaths (3.23%). Among confirmed cases, most cases (59%) are aged 20–49 which is working age in India and 76% cases are reported for men. The median age of Indian COVID-19 patients found to be 39. As of April 14, the CFR per total cases in India is 3.32% and per closed cases is 23.27%. The CRR per total cases in India is 11.00% and per closed cases is 76.72%, which indicates that the recovery rate of COVID-19 is more than the fatality rate in India. The prevention and control measures taken by the state and central governments at all levels and measure of maintenance of social distancing by people have resulted in effective curbing in the COVID-19 transmission in India.

Significance of geographical factors to the COVID-19 outbreak in India.

Recently, the large outbreak of COVID-19 cases all over the world has whacked India with about 30,000 confirmed cases within the first 3 months of transmission. The present study used long-term climatic records of air temperature (T), rainfall (R), actual evapotranspiration (AET), solar radiation (SR), specific humidity (SH), wind speed (WS) with topographic altitude (E) and population density (PD) at the regional level to investigate the spatial association with the number of COVID-19 infections (NI). Bivariate analysis failed to find any significant relation (except SR) with the number of infected cases within 36 provinces in India. Variable Importance of Projection (VIP) through Partial Least Square (PLS) technique signified higher importance of SR, T, R and AET. However, generalized additive model fitted with the log-transformed value of input variables and applying spline smoothening to PD and E, significantly found high accuracy of prediction (R2 = 0.89), and thus well-explained complex heterogeneity among the association of regional parameters with COVID-19 cases in India. Our study suggests that comparatively hot and dry regions in lower altitude of the Indian territory are more prone to the infection by COVID-19 transmission.Electronic supplementary materialThe online version of this article (10.1007/s40808-020-00838-2) contains supplementary material, which is available to authorized users.

Real-time Forecasting of COVID-19 Prevalence in India using ARIMA Model

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

Investigating the dynamics of COVID-19 pandemic in India under lockdown.

In this paper, we investigate the ongoing dynamics of COVID-19 in India after its emergence in Wuhan, China in December 2019. We discuss the effect of nationwide lockdown implemented in India on March 25, 2020 to prevent the spread of COVID-19. Susceptible-Exposed-Infectious-Recovered (SEIR) model is used to forecast active COVID-19 cases in India considering the effect of nationwide lockdown and possible inflation in the active cases after its removal on May 3, 2020. Our model predicts that with the ongoing lockdown, the peak of active infected cases around 43,000 will occur in the mid of May, 2020. We also predict a 7 to 21% increase in the peak value of active infected cases for a variety of hypothetical scenarios reflecting a relative relaxation in the control strategies implemented by the government in the post-lockdown period. For India, it is an important decision to come up with a non-pharmaceutical control strategy such as nationwide lockdown for 40 days to prolong the higher phases of COVID-19 and to avoid severe load on its public health-care system. As the ongoing COVID-19 outbreak remains a global threat, it is a challenge for all the countries to come up with effective public health and administrative strategies to battle against COVID-19 and sustain their economies.