ABSTRACT
Patient lines are frequent in medical care, and one indicator of access to healthcare is waiting time. We explain queuing theory - an empirical method that provides service providers with a lot of experience in the design and management of existing services. This paper focuses on the pattern of arrival and the facilities available in the hospital in Vijayanagara District. The most important purpose of this research was to provide policymakers with knowledge to contribute to the well-being of the population by reducing waiting time for service, in this study, the waiting time of patients in the ambient department was first analyzed with the M / M/1 queuing method. The entire world is spreading of coronavirus-COVID-19 has increased exponentially across the globe, and still, no vaccine is available for the treatment of patients. The crowd has grown tremendously in the hospitals where the facilities are minimal. The queue theory is applied for the Single-server system and its self-similarity existence in a queue used to identify the queue time, waiting time, and Hurst parameter by different patient arrivals methods Health care center in our local area located in Hospet, Vijayanagara district, Karnataka. © 2022 Author(s).
ABSTRACT
COVID-19 is the infectious disease caused by the most recently discovered corona virus. This new virus and disease were unknown before the outbreak began in Wuhan, China, in December 2019. This paper focuses on a Time Series Model to predict COVID-19 Outbreaks 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 01st Sep, 2020 to 9th Dec, 2020. 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. 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. 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 Sep to 9th Dec period cumulatively. As per the model, the number of new cases shall fluctuate drastically in India. The results will help governments to make necessary arrangements as per the estimated cases. This kind of analysis and implications of ARIMA models and fitting procedures are useful in forecasting COVID-19 Outbreaks in India. © 2022 Author(s).
ABSTRACT
Patient queues are frequent in medical care, and one indicator of access to healthcare is waiting time. We explain queuing theory - an empirical method that provides service providers with a lot of experience in the design and management of existing services. This paper focuses on the pattern of arrival and the facilities available in the hospital in Vijayanagara District. The most important purpose of this research was to provide policymakers with knowledge to contribute to the well-being of the population by reducing waiting time for service, since long queues in exceeding cases will delay effective decisions about a particular disease that may cause death while waiting for service by patients. In this study, the waiting time of patients in the ambient department was first analyzed with the M / M/1 queuing method after measuring the mean number of patients served per hour and their median number in the hour. Additional findings of the workers questionnaire were evaluated to know. © 2022 Author(s).
ABSTRACT
The entire world is spreading of coronavirus-COVID-19 has increased exponentially across the globe, and still, no vaccine is available for the treatment of patients. The crowd has grown tremendously in the hospitals where the facilities are minimal. The queue theory is applied for the Single-server system and its self-similarity existence in a queue used to identify the queue time, waiting time, and Hurst parameter by different patient arrivals methods Health care center in our local area located in Hosapete, Ballari district, Karnataka. Due to more arrivals to the health care center for the identification and confirmation of disease covid-19. This study paper presents a sequential queuing model for estimating infections' detection and identification in severe loading conditions. The goal is to offer a simplified probabilistic model to determine the general behavior to predict how long the treatment cycle will diagnose and classify people already tested and get negative or positive results. For this type of Method, there are some graphical representations of the various measurement criteria. The modelling results showed that the patient's waiting period in the course of inquiries, detections, detecting, or treating COVID-19 in the event of imbalances in the system as a whole rise following the logarithm rule. © 2021 Karadeniz Technical University. All rights reserved.