Publication date: Oct 22, 2024
The COVID-19 pandemic has driven the need for accurate data analysis and forecasting to guide public health decisions. In this study, we utilized ARIMA and ARIMAX models to predict short-term trends in confirmed COVID-19 cases across different regions, including the United States, Asia, Europe, Africa, and the Americas. Comparisons were made between ARIMA and auto.arima models, and anomaly detection was performed to investigate discrepancies between predictions and actual data. The study also explored the relationship between vaccination rates and new case numbers, and examined how socioeconomic factors such as GDP per capita, HDI, and healthcare resources influenced COVID-19 incidence rates across countries. Our findings provide insights into the effectiveness of predictive models and the significant impact of socioeconomic factors on the spread of the virus, contributing valuable information for future epidemic prevention and control strategies.
| Concepts | Keywords |
|---|---|
| Healthcare | Arima |
| Mathematics | Auto |
| October | Covid |
| Reliable | Forecast |
| Wileyonlinelibrary | Medrxiv |
| Models | |
| Period | |
| Preprint | |
| Rates | |
| Regression | |
| Rmse | |
| Series | |
| Significant | |
| Vaccination | |
| Values |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | COVID-19 |
| disease | IDO | production |
| disease | IDO | intervention |
| disease | MESH | uncertainty |
| drug | DRUGBANK | Coenzyme M |
| disease | MESH | anomalies |
| disease | MESH | infection |
| disease | IDO | process |
| disease | MESH | causality |
| disease | IDO | country |
| drug | DRUGBANK | Methyl isocyanate |