Time series analysis and modelling of MIS-C cases: can we predict MIS-C cases by new cases in the 0–18 age group?

Background : Multisystem Inflammatory Syndrome in Children (MIS-C) is a rare but severe complication following SARS-CoV-2 infection. Early recognition and forecasting may support preparedness and clinical resource allocation. Objective : We aimed to model the temporal relationship between COVID-19 incidence and MIS-C cases using time series analysis. Materials and methods : We retrospectively analyzed data from 51 children with MIS-C (age 0–18 years) hospitalized in a single center between November 2020 and April 2021. A regression model with ARIMA(1,0,0) errors was applied to predict MIS-C cases based on weekly COVID-19 incidence in individuals aged 0–18 years. Results : The majority of children were male (n = 37, 72.5%) and older than 5 years (n = 40, 78%), with a mean age of 8.82 ± 4.16 years. Abdominal symptoms were predominant (n = 38, 74%), and nearly half experienced respiratory (n = 23, 45%) or cardiac (n = 26, 51%) involvement. The ARIMA model identified a lag of 5–6 weeks between COVID-19 cases and MIS-C incidence (R² = 0.41). A selection-aware Monte Carlo simulation confirmed the robustness of the model’s significant regressors when extrapolated beyond the original data period. Conclusion : The moderate predictive power of the ARIMA model supports the potential of syndromic surveillance to forecast MIS-C clusters, aiding early intervention. Larger datasets could further enhance prediction accuracy.