Analysis of COVID 2019 by using Weighted Moving Averages

This research analyses and forecasts covid-19 case trends using weighted moving averages (WMA), a time series approach that prioritises recent data. The research examines how WMA can catch short-term patterns. This study examines the trajectory of COVID-19 utilising Weighted Moving Averages (WMA) to estimate short-term trending in case numbers. When compared to older approaches such as Simple Moving Averages (SMA), WMA is more sensitive and accurate in smoothing and predicting daily infections because it prioritises recent data items. The study uses publicly available COVID-19 time series data to assess the effectiveness of WMA in capturing real-world patterns, providing insights for public health planning and action. In order to predict daily case trends, this study uses weighted moving averages (WMA) using COVID-19 time series data. WMA provides better short-term prediction than conventional techniques by focussing on current data. In epidemiology, time series analysis is a useful technique that supports traditional epidemiological models in two ways: forecasting and prediction. Prediction is the process of interpreting historical and present data using a variety of internal and external factors that may or may not be causal. Exploring potential future values based on the model's predictive power and projected future values of internal and/or external effects is known as forecasting. The time series analysis technique has the benefit of being simpler to apply (for more simplistic and linear models like auto-Regressive integrated moving average). Nonetheless, it has a limited predicting time, unlike conventional models such as susceptible-exposed-removed. Its usefulness in predicting stems from its superior accuracy in short-term prediction. It may be used to estimate morality or hospital risk based on new cases, conduct seroprevalence studies, examine the characteristics of emerging variations, and estimate excess morality and its relevance to pandemic conditions.