COVID-19 Trend Analysis in Mexican States and Cities

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• The COVID-19 pandemic necessitated localized data analysis for effective public health interventions.
• Subnational analysis provides more actionable insights for regional authorities compared to national-level trends.

Purpose of the Study:

• To present a novel trend analysis methodology for the COVID-19 pandemic in Mexico.
• To provide local authorities with accurate, daily updated predictions of the final number of infections.
• To evaluate the model's performance in selected highly populated regions.

Main Methods:

• Developed a trend analysis model focused on predicting the final epidemic size.
• The model utilizes daily updated data for continuous refinement.
• Applied the model to subnational data from four Mexican states and four major cities.

Main Results:

• The model demonstrated a suitable fit for local COVID-19 data in the evaluated regions.
• The trend analysis effectively assessed the accuracy of infection forecasts.
• Results highlight the utility of subnational, predictive modeling for pandemic management.

Conclusions:

• The proposed trend analysis method offers a valuable tool for understanding and managing the COVID-19 pandemic at a local level.
• Predicting the final number of infections provides a more stable and useful metric for authorities than analyzing daily case variations.
• The study validates the model's applicability and accuracy in real-world pandemic scenarios.