Modeling and Forecasting of COVID-19 Growth Curve in India

Paving initial forecasting COVID-19 spread capabilities by nonexperts: A case study

Objective

The COVID-19 outbreak compelled countries to take swift actions across various domains amidst substantial uncertainties. In Israel, significant COVID-19-related efforts were assigned to the Israeli Home Front Command (HFC). HFC faced the challenge of anticipating adequate resources to efficiently and timely manage its numerous assignments despite the absence of a COVID-19 spread forecast. This paper describes the initiative of a group of motivated, though nonexpert, people to provide the needed COVID-19 rate of spread of the epidemic forecasts.

Methods

To address this challenge, the Planning Chamber, reporting to the HFC Medical Commander, undertook the task of mapping HFC healthcare challenges and resource requirements. The nonexpert team continuously collected public COVID-19-related data published by the Israeli Ministry of Health (MoH) of verified cases, light cases, mild cases, serious condition cases, life-support cases, and deaths, and despite lacking expertise in statistics and healthcare and having no sophisticated statistical packages, generated forecasts using Microsoft® Excel.

Results

The analysis methods and applications successfully demonstrated the desired outcome of the lockdown by showing a transition from exponential to polynomial growth in the spread of the virus. These forecasting activities enabled decision-makers to manage resources effectively, supporting the HFC's operations during the pandemic.

Conclusions

Nonexpert forecasting may become a necessity and be beneficial, and similar analysis efforts can be easily replicated in future events. However, they are inherently short-lived and should persist only until knowledge centers can bridge the expertise gap. It is crucial to identify major events, such as lockdowns, during forecasting due to their potential impact on spread rates. Despite the expertise gap, the Planning Chamber's approach provided valuable resource management insights for HFC's COVID-19 response.

Analytical reference framework to analyze non-COVID-19 events

BACKGROUND

The COVID-19 pandemic has disrupted the healthcare system, leading to delays in detection of other non-COVID-19 diseases. This paper presents ANE Framework (Analytics for Non-COVID-19 Events), a reliable and user-friendly analytical forecasting framework designed to predict the number of patients with non-COVID-19 diseases. Prior to 2020, there were analytical models focused on specific illnesses and contexts. Then, most models have focused on understanding COVID-19 behavior. There is a lack of analytical frameworks that enable disease forecasting for non-COVID-19 diseases.

METHODS

The ANE Framework utilizes time series analysis to generate forecasting models. The framework leverages daily data from official government sources and employs SARIMA models to forecast the number of non-COVID-19 cases, such as tuberculosis and suicide attempts.

RESULTS

The framework was tested on five different non-COVID-19 events. The framework performs well across all events, including tuberculosis and suicide attempts, with a Mean Absolute Percentage Error (MAPE) of up to 20% and the consistency remains independent of the behavior of each event. Moreover, a pairwise comparison of averages can lead to over or underestimation of the impact. The disruption caused by the pandemic resulted in a 17% gap (2383 cases) between expected and reported tuberculosis cases, and a 19% gap (2464 cases) for suicide attempts. These gaps varied between 20 and 64% across different cities and regions. The ANE Framework has proven to be reliable for analyzing several diseases and exhibits the flexibility to incorporate new data from various sources. Regular updates and the inclusion of new associated data enhance the framework's effectiveness.

CONCLUSIONS

Current pandemic shows the necessity of developing flexible models to be adapted to different illness data. The framework developed proved to be reliable for the different diseases analyzed, presenting enough flexibility to update with new data or even include new data from different databases. To keep updated on the result of the project allows the inclusion of new data associated with it. Similarly, the proposed strategy in the ANE framework allows for improving the quality of the obtained results with news events.

Estimated projection of oral squamous cell carcinoma annual incidence from twenty years registry data: a retrospective cross-sectional study in Indonesia

Background

The incidence of oral squamous cell carcinoma (OSCC) has not been well documented in Indonesia. Thus, we aimed to analyze trends and clinicopathological profiles of OSCC cases in Indonesia, focusing on differences between age and sex groups.

Methods

A cross-sectional study was conducted in Indonesia's main referral hospital, analyzing 1,093 registered OSCC cases from 2001 to 2020. Trend analysis was performed using Joinpoint regression analysis to determine the annual percentage change (APC) for overall cases and each case group based on age, sex, and anatomical subsites. APC significance was assessed using a Monte Carlo permutation test. The projection of case numbers for the following 5 years (2021-2025) was estimated using linear/non-linear regression analysis and presented as a mathematical function. The significance of the trend slope was measured using an ANOVA test. Demographic and clinicopathological characteristics of OSCC were analyzed according to age and sex, and their comparative analysis was assessed using Chi-square and its alternatives.

Results

The incidence of OSCC in female patients and in the tongue and buccal mucosa showed a positive trend (APC 2.06%; 3.48%; 8.62%, respectively). Moreover, the incidence of OSCC overall, and in women with OSCC, is projected to increase significantly in the next 5 years following the quadratic model. The mean age of patients was 51.09 ± 14.36 years, with male patients being younger than female patients. The male-to-female ratio was 1.15, and 36.5% of these patients were categorized as young (≤45 years old). The tongue was the predominantly affected site. Prominent pathologic characteristics included well-differentiation, keratinization, and grade I of Bryne's (1992) cellular differentiation stage. Most patients presented with advanced staging, lymphovascular invasion, and uninvaded margins. Tumor sites and staging varied according to age, while age and tumor sites differed between sexes.

Conclusion

The rising incidence trends of OSCC among Indonesian patients, both in the past and projected future, are concerning and warrant attention. Further research into risk factors should be conducted as preventive measures.

EKF-SIRD model algorithm for predicting the coronavirus (COVID-19) spreading dynamics

In this paper, we study the Covid 19 disease profile in the Algerian territory since February 25, 2020 to February 13, 2021. The idea is to develop a decision support system allowing public health decision and policy-makers to have future statistics (the daily prediction of parameters) of the pandemic; and also encourage citizens for conducting health protocols. Many studies applied traditional epidemic models or machine learning models to forecast the evolution of coronavirus epidemic, but the use of such models alone to make the prediction will be less precise. For this purpose, we assume that the spread of the coronavirus is a moving target described by an epidemic model. On the basis of a SIRD model (Susceptible-Infection-Recovery- Death), we applied the EKF algorithm to predict daily all parameters. These predicted parameters will be much beneficial to hospital managers for updating the available means of hospitalization (beds, oxygen concentrator, etc.) in order to reduce the mortality rate and the infected. Simulations carried out reveal that the EKF seems to be more efficient according to the obtained results.

Dynamic performance-exposure algorithm for falling risk assessment and prevention of falls in community-dwelling older adults

This study aimed to design a dynamic performance-exposure algorithm for falling risk assessment and prevention of falls in community-dwelling older adults. It involved a cross-sectional and follow-up survey assessing retrospective and prospective falls and respective performance-related, exposure and performance-exposure risk factors. In total, 500 Portuguese community-dwelling adults participated. Data modelling showed significant (p<0.05) relationships between the above risk factors and selected nine key ordered outcomes explaining falls to include in the algorithm: previous falls; health conditions; balance; lower strength; perceiving action boundaries; fat mass; environmental hazards; rest periods; and physical activity. Respective high-, moderate- and low-risk cutoffs were established. The results demonstrated a dynamic relationship between older adults' performance capacity and the exposure to fall opportunity, counterbalanced by the action boundary perception, supporting the build algorithm's conceptual framework. Fall prevention measures should consider the factors contributing most to the individual risk of falling and their distance from low-risk safe values.

Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy

False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R 0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was at 11.1 (95% CI: 10.7,11.5) and for deaths at 3.58 (95% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0% (95% CI: 8.7%, 9.3%) and 5.2% (95% CI: 5.0%, 5.6%) of total estimated infections were reported on these two dates, while 27.9% (95% CI: 27.3%, 28.6%) and 22% (95% CI: 21.4%, 23.1%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R 0 and prediction of future infections. A R-package SEIRfansy is developed for broader dissemination.

Mathematical modeling of COVID-19 pandemic in India using Caputo-Fabrizio fractional derivative

The range of effectiveness of the novel corona virus, known as COVID-19, has been continuously spread worldwide with the severity of associated disease and effective variation in the rate of contact. This paper investigates the COVID-19 virus dynamics among the human population with the prediction of the size of epidemic and spreading time. Corona virus disease was first diagnosed on January 30, 2020 in India. From January 30, 2020 to April 21, 2020, the number of patients was continuously increased. In this scientific work, our main objective is to estimate the effectiveness of various preventive tools adopted for COVID-19. The COVID-19 dynamics is formulated in which the parameters of interactions between people, contact tracing, and average latent time are included. Experimental data are collected from April 15, 2020 to April 21, 2020 in India to investigate this virus dynamics. The Genocchi collocation technique is applied to investigate the proposed fractional mathematical model numerically via Caputo-Fabrizio fractional derivative. The effect of presence of various COVID parameters e.g. quarantine time is also presented in the work. The accuracy and efficiency of the outputs of the present work are demonstrated through the pictorial presentation by comparing it to known statistical data. The real data for COVID-19 in India is compared with the numerical results obtained from the concerned COVID-19 model. From our results, to control the expansion of this virus, various prevention measures must be adapted such as self-quarantine, social distancing, and lockdown procedures.

Predicting COVID-19 statistics using machine learning regression model: Li-MuLi-Poly

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World

Coronavirus disease 2019 (COVID-2019) has spread rapidly all over the world and it is known that the most effective way to eliminate the disease is vaccination. Although the traditional vaccine development process is quite long, more than ten COVID-19 vaccines have been approved for use in about a year. The COVID-19 vaccines that have been administered are highly effective enough, but achieving herd immunity is required to end the pandemic. The motivation of this study is to contribute to review the countries’ vaccine policies and adjusting the manufacturing plans of the vaccine companies. In this study, the total number of people fully vaccinated against COVID-19 was forecasted in the US, Asia, Europe, Africa, South America, and the World with the Autoregressive Integrated Moving Average (ARIMA) model, which is a new approach in vaccination studies. Additionally, for herd immunity, the percentage of fully vaccinated people in these regions at the beginning of 2021 summer was determined. ARIMA results show that in the US, Asia, Europe, Africa, South America, and the World will reach 139 million, 109 million, 127 million, 8 million, 38 million, and 441 million people will be fully vaccinated on 1 June 2021, respectively. According to these results, 41.8% of the US, 2.3% of Asia, 17% of Europe, 0.6% of Africa, 8.8% of South America, and 5.6% of the World population will be fully vaccinated people against the COVID-19. Results show that countries are far from the herd immunity threshold level desired to reach for safely slow or stop the COVID-19 epidemic.

Analysis of Second Wave of COVID-19 in Different Countries

We analyse the evolution of the second wave of the COVID-19 pandemic in several countries by using a logistic model. The model uses a regression analysis based on the least-squares fitting. In particular, the growth rate of the infection has been fitted as an exponential increase, as compared to a power law increase, reported previously in logistic models. The data shows that the increase in the exponent of the exponential increase is around 0.03 day- 1 , with a standard deviation of 0.01  day- 1 . The present results suggest that duration of the peaking of the second wave is almost same for several countries considered. The growth rate is also on the same order of several countries regardless of the total number of infections in a particular country. Since the decay of the growth rate is self-similar to that during the increase in the second wave of several countries, we can predict the end of the second wave in India. The model suggests that the second wave will end in the first week of August 2021, with a growth rate of 0.1% day- 1 at that time.

Sen's Innovative Method for Trend Analysis of Epidemic: A Case Study of Covid-19 Pandemic in India

Analysis of trend of epidemiological data helps to appreciate the progression of an epidemic and to develop monitoring and control strategies by the government agencies. Sen's Innovative Method suggests a graphical analysis, which can overcome many limitations of data such as short length, non-Gaussian nature, skewness or serial correlation. In this article, this method is applied for the first time on epidemiological data. For the case study, Covid-19 or SARS-CoV-2 data from India were employed. The results show that Sen's Innovative Method is capable of indicating the shift in epidemiological trend quite efficiently, before it is reflected in the time series or moving average plots. The graphical analysis worked particularly well in comparing the trends of monthly data. It is concluded that this method would be especially suitable for monitoring the epidemiological trend by breaking up the data into smaller segments, as was illustrated in the study.

Universal Epidemic Curve for COVID-19 and Its Usage for Forecasting

We construct a universal epidemic curve for COVID-19 using the epidemic curves of eight nations that have reached saturation for the first phase and then fit an eight-degree polynomial that passes through the universal curve. We take India's epidemic curve up to January 1, 2021 and match it with the universal curve by minimizing square-root error between the model prediction and actual value. The constructed curve has been used to forecast epidemic evolution up to February 25, 2021. The predictions of our model and those of supermodel for India (Agrawal et al. in Indian J Med Res, 2020; Vidyasagar et al. in https://www.iith.ac.in/~m_vidyasagar/arXiv/Super-Model.pdf, 2020) are reasonably close to each other considering the uncertainties in data fitting.

Predicting mortality rate and associated risks in COVID-19 patients

The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which was later declared as a global pandemic in view of its exponential rise and spread around the world. Resultantly, the scientific and medical research communities around the globe geared up to curb its spread. In this manuscript, authors claim competence of AI-mediated methods to predict mortality rate. Efficient prediction model enables healthcare professionals to be well prepared to handle this unpredictable situation. The prime focus of the study is to investigate efficient prediction model. In order to determine the most effective prediction model, authors perform comparative analysis of numerous models. The performance of various prediction models is compared using various error metrics viz. Root mean square error, mean absolute error, mean square error and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}^{2}$$\end{document}R2. During comparative analysis, Auto seasonal auto regressive integrated moving average model proves its competence over comparative models.

CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time

Because of the lack of reliable information on the spread parameters of COVID-19, there is an increasing demand for new approaches to efficiently predict the dynamics of new virus spread under uncertainty. The study presented in this paper is based on the Case-Based Reasoning method used in statistical analysis, forecasting and decision making in the field of public health and epidemiology. A new mathematical Case-Based Rate Reasoning model (CBRR) has been built for the short-term forecasting of coronavirus spread dynamics under uncertainty. The model allows for predicting future values of the increase in the percentage of new cases for a period of 2–3 weeks. Information on the dynamics of the total number of infected people in previous periods in Italy, Spain, France, and the United Kingdom was used. Simulation results confirmed the possibility of using the proposed approach for constructing short-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Russia showed that the deviation of the predicted total number of confirmed cases from the actual one was within 0.3%. For the USA, the deviation was 0.23%.