Statistical data driven approach of COVID-19 in Ecuador: and estimation via new method

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health

Background:

• The COVID-19 pandemic necessitated advanced tracking methods, building upon models like the susceptible-infected-recovered (SIR) framework.
• Reproduction numbers (R0 and Re) are crucial for quantifying epidemic potential and forecasting scenarios.

Purpose of the Study:

• To estimate real-time reproduction numbers for COVID-19 in Ecuador.
• To compare different estimation techniques for transmission parameters, especially with limited data.
• To identify factors contributing to increased transmissibility in specific regions.

Main Methods:

• Employed traditional estimation techniques and a Bayesian approach for real-time reproduction number (Re) calculation.
• Utilized exponential growth and maximum likelihood estimation for reproduction number (R0) due to data limitations.
• Modified the Bettencourt and Ribeiro model with a time window for improved contagious uncertainty modeling.

Main Results:

• Ecuadorian R0 estimates were 3.45 (exponential growth) and 2.93 (maximum likelihood estimation).
• Guayas, Pichincha, and Manabí exhibited the highest COVID-19 case counts.
• Factors like large gatherings, population density, and delayed interventions contributed to high transmissibility.

Conclusions:

• The study presents a novel approach for measuring infection dynamics and outbreak distribution with limited data.
• The model aids in predicting pandemic spread and informing data-driven public health interventions.