The accuracy of forecasted hospital admission for respiratory tract infections in children aged 0-5 years for 2017/2023.

AIM

Healthcare services are in need of tools that can help to ensure a sufficient capacity in periods with high prevalence of respiratory tract infections (RTIs). During the COVID-19 pandemic, we forecasted the number of hospital admissions for RTIs among children aged 0-5 years. Now, in 2024, we aim to examine the accuracy and usefulness of our forecast models.

METHODS

We conducted a retrospective analysis using data from 753,070 children aged 0-5 years, plotting the observed monthly number of RTI admissions, including influenza coded RTI, respiratory syncytial virus (RSV) coded RTI, COVID-19 coded RTI, and other upper and lower RTI, from January 1st, 2017, until May 31st, 2023. We determined the accuracy of four different forecast models, all based on monthly hospital admissions and different assumptions regarding the pattern of virus transmission, computed with ordinary least squares regression adjusting for seasonal trends. We compared the observed vs. forecasted numbers of RTIs between October 31st, 2021, and May 31st, 2023, using metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and dynamic time warping (DTW).

RESULTS

In our most accurate prediction, we assumed that the proportion of children who remained uninfected and non-hospitalized during the lockdown would be prone to hospitalization in the subsequent season, resulting in increased numbers when lockdown measures were eased. In this prediction, the difference between observed and forecasted numbers at the peak of hospitalizations requiring vs. not requiring respiratory support in November 2021 to January 2022 was 26 (394 vs. 420) vs. 48 (1810 vs. 1762).

CONCLUSION

In scenarios similar to the COVID-19 pandemic, when the transmission of respiratory viruses is suppressed for an extended period, a simple regression model, assuming that non-hospitalized children would be hospitalized the following season, most accurately forecasted hospital admission numbers. These simple forecasts may be useful for capacity planning activities in hospitals.