The feasibility of forecasting influenza epidemics in Cuba

Abstract

A large influenza epidemic took place in Havana during the winter of 1988. The epidemiologic surveillance unit of the Pedro Kourí Institute of Tropical Medicine detected the beginning of the epidemic wave. The Rvachev-Baroyan mathematical model of the geographic spread of an epidemic was used to forecast this epidemic under routine conditions of the public health system. The expected number of individuals who would attend outpatient services, because of influenza-like illness, was calculated and communicated to the health authorities within enough time to permit the introduction of available control measures. The approximate date of the epidemic peak, the daily expected number of individuals attending medical services, and the approximate time of the end of the epidemic wave were estimated. The prediction error was 12%. The model was sufficiently accurate to warrant its use as a practical forecasting tool in the Cuban public health system.

A systematic review of studies on forecasting the dynamics of influenza outbreaks

“…Compared with the other metrics, fewer papers have focused on predicting outbreak duration. Aguirre and Gonzalez 9 correctly predicted the end of the 1988 epidemic in Havana, Cuba. Based on a retrospective study of three pandemic events, Hall et al .…”

“…As noted, measures of accuracy differed across studies. Aguirre and Gonzalez, Viboud et al ,. and Jiang et al .…”

“…retrospectively estimated the peak time for the 1968–1969 Hong Kong influenza pandemic within the 4‐day epidemic peak period for 32% of the cities for which morbidity data were available. Using the same model as that discussed in, Longini et al ., Aguirre and Gonzalez predicted the 1988 influenza epidemic in Havana, Cuba to peak on March 15th. However, the true peak was observed on March 1st, implying a deviation of approximately 2 weeks.…”

A framework for evaluating epidemic forecasts

“…Some of the studies used various distance functions or errors as a measure of closeness between the predicted and observed time-series. For example, Viboud et al [17], Aguirre and Gonzalez [18], and Jiang et al [19] used correlation coefficients to calculate the accuracy of daily or weekly forecasts of influenza case counts. Other studies evaluated the precision and "closeness" of predicted activities to observed values using different statistical measure of errors such as root-mean-square-error (RMSE), percentage error [19,20], etc.…”

“…Epidemic peak is one of the most important quantities of interest in an outbreak, and its magnitude and timing are important from the perspective of health service providers. Consequently, accurately predicting the peak has been the goal of some forecasting studies [23,24,25,26,18,22,27,28,29,30]. Hall et al [24], Aguirre and Gonzalez [18] and Hyder et al [30] predicted the pandemic duration and computed the error between the predicted and real value.…”