A discrete-time-evolution model to forecast progress of Covid-19 outbreak

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Forecasting infectious disease propagation, such as Covid-19, is crucial for effective public health interventions.
• Existing models may require complex data inputs or numerous parameters, limiting their widespread implementation.
• Understanding the transmission dynamics, including asymptomatic and symptomatic phases, is key to accurate prediction.

Purpose of the Study:

• To develop and present a user-friendly discrete-time-evolution model for forecasting Covid-19 propagation.
• To create a tool that utilizes easily accessible, daily updated pandemic data.
• To provide a 14-day forecast of total infected individuals with minimal adjustable parameters.

Main Methods:

• A discrete-time-evolution model with a one-day interval was developed.
• The model incorporates disease aspects like asymptomatic/symptomatic phases and a two-week evolution period.
• The model was validated using publicly available daily Covid-19 data from Brazil, the UK, and South Korea.

Main Results:

• The model demonstrates low error rates in predicting the evolution of Covid-19 in Brazil, the UK, and South Korea.
• It successfully forecasts the total number of infected people for the subsequent 14 days.
• The model directly outputs daily total infected case numbers, facilitating health sector implementation.

Conclusions:

• The presented discrete-time model offers a practical and accurate tool for forecasting Covid-19 propagation.
• Its simplicity and reliance on readily available data make it suitable for health sector use.
• The model's low error rates suggest its utility in estimating disease spread and informing public health strategies.