An effective and economic estimation of population mean in stratified random sampling using a linear cost function.

Improvement in the estimation of population mean has been an area of interest in sampling theory. So many estimators have been suggested for elevated estimation of the population mean in stratified random sampling, but there is still a gap for more closely estimating the population mean. In this paper, the authors propose a ratio-product-cum-exponential-cum-logarithmic type estimator for the enhanced estimation of population mean by implying one auxiliary variable in stratified random sampling using conventional ratio, exponential ratio, and logarithmic ratio type estimators. The suggested estimator is a generalization of ratio, exponential ratio, and logarithmic ratio type estimators, and therefore these are special cases of the proposed estimator. The proposed estimator's bias and MSE are determined and compared with those of influential estimators, with the linear cost function being used to investigate and compare alternatives. Use Cramer's rule to determine the optimal value of the proposed estimator. The proposed estimator is more effective than other existing estimators, according to theoretical observations. For various applications, we suggest using a proposed estimator with the minimal MSE, which is verified by a numerical example, to have practical applicability of theoretical conclusions in real life.

A computational strategy for estimation of mean using optimal imputation in presence of missing observation.

In this study, we suggest an optimal imputation strategy for the elevated estimation of the population mean of the primary variable utilizing the known auxiliary parameters for the missing observations. Under this strategy, we suggest a new modified Searls type estimator, and we study its sampling properties, mainly bias and mean squared error (MSE), for an approximation of order one. The introduced estimator is compared theoretically with the estimators of population mean in competition under the imputation method. The efficiency conditions for the introduced estimator to be more efficient than the estimators in the competition are derived. To be sure about the efficiencies, these efficiency conditions are verified through the three natural populations. We have also conducted a simulation study and generated an artificial population with the same parameters as a natural population. The estimator with minimum MSE and the highest Percentage Relative Efficiency (PRE) is recommended for practical use in different areas of applications.

Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces.

SARS-CoV-2, the virus responsible for COVID-19, posed a significant threat to the world. We analyzed COVID-19 dissemination data in the top ten Indian provinces by infection incidences using the Susceptible-Infectious-Removed (SIR) model, an Autoregressive Integrated Moving Average (ARIMA) time series model, a machine learning model based on the Random Forest, and distribution fitting. Outbreaks are expected to continue if the Basic Reproduction Number (R0) > 1, and infection waves are anticipated to end if the R0 < 1, as determined by the SIR model. Different parametric probability distributions are also fitted. Data collected from December 12, 2021, to March 31, 2022, encompassing data from both before and during the implementation of strict control measures. Based on the estimates of the model parameters, health agencies and government policymakers can develop strategies to combat the spread of the disease in the future, and the most effective technique can be recommended for real-world application for other outbreaks of COVID-19. The best method out of these could be also implemented further on the epidemiological data of other similar infectious agents.

Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models.

The global impact of COVID-19 has heightened concerns about emerging viral infections, among which monkeypox (MPOX) has become a significant public health threat. To address this, our study employs a comprehensive approach using three statistical techniques: Distribution fitting, ARIMA modeling, and Random Forest machine learning to analyze and predict the spread of MPOX in the top ten countries with high infection rates. We aim to provide a detailed understanding of the disease dynamics and model theoretical distributions using country-specific datasets to accurately assess and forecast the disease's transmission. The data from the considered countries are fitted into ARIMA models to determine the best time series regression model. Additionally, we employ the random forest machine learning approach to predict the future behavior of the disease. Evaluating the Root Mean Square Errors (RMSE) for both models, we find that the random forest outperforms ARIMA in six countries, while ARIMA performs better in the remaining four countries. Based on these findings, robust policy-making should consider the best fitted model for each country to effectively manage and respond to the ongoing public health threat posed by monkeypox. The integration of multiple modeling techniques enhances our understanding of the disease dynamics and aids in devising more informed strategies for containment and control.

Modeling Global COVID-19 Dissemination Data After the Emergence of Omicron Variant Using Multipronged Approaches.

The COVID-19 pandemic has followed a wave pattern, with an increase in new cases followed by a drop. Several factors influence this pattern, including vaccination efficacy over time, human behavior, infection management measures used, emergence of novel variants of SARS-CoV-2, and the size of the vulnerable population, among others. In this study, we used three statistical approaches to analyze COVID-19 dissemination data collected from 15 November 2021 to 09 January 2022 for the prediction of further spread and to determine the behavior of the pandemic in the top 12 countries by infection incidence at that time, namely Distribution Fitting, Time Series Modeling, and Epidemiological Modeling. We fitted various theoretical distributions to data sets from different countries, yielding the best-fit distribution for the most accurate interpretation and prediction of the disease spread. Several time series models were fitted to the data of the studied countries using the expert modeler to obtain the best fitting models. Finally, we estimated the infection rates (ß), recovery rates (γ), and Basic Reproduction Numbers ([Formula: see text]) for the countries using the compartmental model SIR (Susceptible-Infectious-Recovered). Following more research on this, our findings may be validated and interpreted. Therefore, the most refined information may be used to develop the best policies for breaking the disease's chain of transmission by implementing suppressive measures such as vaccination, which will also aid in the prevention of future waves of infection.

Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread.

In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with predictive monitoring approaches, and epidemiological modeling are illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and the best-fitted distribution is chosen for the prediction of the spread of the disease. The infectious diseases develop over time and we have data on the single variable that is the number of infections that happened, therefore, time series models are fitted and the prediction is done based on the best-fitted model. Monitoring approaches may also be applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. We came up with, how to improve the existing modeling methods, the use of fuzzy variables, and detection of fraud in the available data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.