Time series models for the description and forecasting of incident cases of dengue: a case study in Londrina- Paraná

Objective: The objective of this study is to describe the temporal behavior of dengue cases and to model the temporal evolution of reported incident cases for the Londrina city, Paraná-Brazil using time series modeling, specifically Seasonal Autoregressive Integrated Moving Average (SARIMA) models from January 2015 to February 2023, with the aim of predicting new cases. Methods: This is an ecological epidemiological study with a time series component. Data were obtained monthly by InfoDengue site in a structured way for the Londrina city- Paraná between January 2015 and February 2023. Time series analysis was used and the SARIMA (Seasonal Autoregressive Integrated Moving Average) model was chosen, since these models incorporate a seasonal component, to fit the natural situation of the disease with recursive periodic peaks and drops over the series, considering thus the intrinsic seasonality of dengue in spring and summer periods with an increase in cases. Results: After several inductive adjustments, the SARIMA model (2,1,2)(1,1,1)12 provided the best fit for dengue incidence data, with good performance, including capturing the oscillation peaks of the year 2020 (period of the coronavirus pandemic). Conclusion: The main contribution of the work is a better understanding of the disease, allowing predictions of the number of cases in periods subsequent to the series studied, which brings support for decision-making in public policies for prevention and educational actions for the population, and possibility of managing the SUS priority health system.