Mathematics, Free Full-Text

Por um escritor misterioso

Descrição

This comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restrictions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in handling complex and dynamic scenarios. Our work applies ARIMA models to a case study using data from Recife, the capital of Pernambuco, Brazil, collected between March and September 2020. The research provides insights into the implications and adaptability of predictive methods in the context of a global pandemic. The findings highlight the ARIMA models’ strength in generating accurate short-term forecasts, crucial for an immediate response to slow down the disease’s rapid spread. Accurate and timely predictions serve as the basis for evidence-based public health strategies and interventions, greatly assisting in pandemic management. Our model selection involves an automated process optimizing parameters by using autocorrelation and partial autocorrelation plots, as well as various precise measures. The performance of the chosen ARIMA model is confirmed when comparing its forecasts with real data reported after the forecast period. The study successfully forecasts both confirmed and recovered COVID-19 cases across the preventive plan phases in Recife. However, limitations in the model’s performance are observed as forecasts extend into the future. By the end of the study period, the model’s error substantially increased, and it failed to detect the stabilization and deceleration of cases. The research highlights challenges associated with COVID-19 data in Brazil, such as under-reporting and data recording delays. Despite these limitations, the study emphasizes the potential of ARIMA models for short-term pandemic forecasting while emphasizing the need for further research to enhance long-term predictions.
[Linda Bostock, A. Shepherd, Sue Chandler] on . *FREE* shipping on qualifying offers. GCSE Higher Mathematics
Mathematics, Free Full-Text
GCSE Higher Mathematics
Mathematics, Free Full-Text
Mathematics Its Contents Methods And Meaning Vol 1 2 and 3
Mathematics, Free Full-Text
Mathematics Day PSD, 2,000+ High Quality Free PSD Templates for
Mathematics, Free Full-Text
Math Contest Guide for Advanced Students
Mathematics, Free Full-Text
Eureka Math/EngageNY Prekindergarten Mathematics Module 2 : Free
Mathematics, Free Full-Text
integral Mathematics equation math formula text Green screen
Mathematics, Free Full-Text
Dan Meyer: “Maths has an obvious perception problem among students
Mathematics, Free Full-Text
SOLUTION: Module 1 mathematics in the modern world pdf free
Mathematics, Free Full-Text
Variational and Free Boundary Problems
Mathematics, Free Full-Text
How To Get Smart in Math
Mathematics, Free Full-Text
4,288 Math Text Box Images, Stock Photos, 3D objects, & Vectors
Mathematics, Free Full-Text
Cl Liu Discrete Mathematics Solution Pdf Free Download - Colaboratory
Mathematics, Free Full-Text
American Institute of Mathematical Sciences
Mathematics, Free Full-Text
80+ Best Math Websites for Teaching and Learning in 2023
Mathematics, Free Full-Text
Complete Mathematics - Home
de por adulto (o preço varia de acordo com o tamanho do grupo)