Abstract
Background: In recent years, the rise in vector-borne diseases, particularly Dengue and Chikungunya, has posed a significant public health challenge globally. Epidemiological data from the Integrated Disease Surveillance Program (IDSP) is a useful resource for forecasting and early outbreak identification by applying effective models like Sessional Autoregressive Integrated Moving Average (SARIMA) models has proven effective in capturing temporal dependencies in various fields. Objective: To forecast the dengue and chikungunya cases in India using Seasonal Autoregressive Integrated Moving Average models. Methodology: we preprocessing the IDSP dataset to extract relevant temporal patterns and trends. Subsequently, SARIMA models are applied to capture the time dependent dynamics of disease occurrences. The model’s performance is evaluated through rigorous validation processes, including cross-validation techniques, to ensure its reliability and generalizability. Results: This study considered a secondary data set of weekly dengue and chikungunya cases over the period of 2019 to 2023. The time plot shows several spikes in the data, indicating periods where the number of dengue cases increased significantly in the most recent years. Forecast of the future dengue and chikungunya cases was done for the next one year (2024). we observed that the number of dengue and chikungunya cases has fluctuated over time but has generally remained within a certain range. The forecast predicts a significant increase in cases as we move towards the latter half of 2024, with a very sharp spike towards the end of the forecast period. Conclusion: In conclusion, utilizing a secondary dataset spanning from 2019 to 2023, this study employed time series analysis to forecast future Dengue and Chikungunya cases. The forecast predicts a significant increase in dengue cases during 2024 which can lead to potential implications for public health practitioners, policymakers, and researchers involved in infectious disease management.
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