How Mathematical Approaches Could Help Decision-Making to Epidemic Control? The Successful Experience against COVID-19 in Cuba

Abstract

Background. There is a gap for the effective use of mathematical models for real-time decision-making. We aimed to illustrate with the Cuban experience to control the COVID-19, how mathematical models can be put in place to answer key decision-makers´ questions.

Methods. A science-policy partnership was created to mutually define questions, communicate results and facilitate the translation of modeling advice into actions. For forecasting and planning at national level mechanistic models and machine learning based on the epidemic patterns in other countries were used. Statistical models to explain the variability of transmission was used to stratify control actions. The effect of interventions was assessed using branching process models, time varying reproduction number (Rt) and social mixing patterns by location, and by age group.

Findings. The mathematical approach implemented contribute to successful control of the COVID-19 in Cuba. The urbanization, living conditions and the economic index explain the 73% of the variability of the transmission at provincial level. Increased risk of transmission were identified in 33 municipalities mostly in densely populated urban areas with high aging index. Control intervention reduced the transmission from R0=2.84 (95% CI: 1.52 - 4.76) to Rt=0.6 (95% CI:0.2-2.38 ). The highest transmission was detected among adolescents and from people older than 60 years.

Conclusions. Understanding the key questions for decision-making at all times, translating problems into a mathematical language, integrating different approaches to their solution and being able to present the results in an easy-to-understand way is vital to have a timely impact on controlling the epidemic.