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COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis.
Dunfrey Pires Aragão1,2, Andouglas Gonçalves da Silva Junior3, Adriano Mondini4
1Pós-Graduação em Engenharia Elétrica e de Computação, Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, 3000, Lagoa Nova, Natal 59078-970, Brazil.
Summary
Analyzing COVID-19 data in Araraquara, Brazil, this study used mathematical and machine learning models to identify patterns and events influencing case numbers. Fast Fourier Transform (FFT) proved valuable for informing prevention strategies.
Area of Science:
- Epidemiology
- Data Science
- Public Health
Background:
- COVID-19 epidemiology evolved significantly due to variants, interventions, and health service preparedness.
- Continuous assessment of epidemiological features via time-series forecasting is crucial for understanding disease dynamics.
- Identifying factors influencing daily COVID-19 cases is essential for effective control.
Purpose of the Study:
- To analyze COVID-19 epidemiological data in Araraquara, Brazil, identifying patterns and events affecting case numbers.
- To utilize mathematical and machine learning approaches for temporal prospecting and data interpretation.
- To assess the utility of Fast Fourier Transform (FFT) in understanding COVID-19 epidemiological shifts.
Main Methods:
- Analysis of diverse databases including social mobility, epidemiological reports, and mass testing data.
- Application of Fast Fourier Transform (FFT) for event mapping.
- Implementation of machine learning models like Seasonal Auto-regressive Integrated Moving Average (ARIMA) and Neural Networks (NNs) for temporal analysis.
Main Results:
- Identified patterns and events correlating with changes in COVID-19 behavior in Araraquara.
- Achieved a Root-Mean-Square Error (RMSE) of approximately 4.55-5.57 in forecasting.
- Demonstrated the effectiveness of FFT in analyzing epidemiological time-series data.
Conclusions:
- Fast Fourier Transform (FFT) is a valuable tool for analyzing COVID-19 epidemiological data.
- The study provides insights into factors influencing COVID-19 incidence in Araraquara.
- Findings support the development of enhanced prevention and control measures for infectious diseases.
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