Investigating Google Trends to forecast acute febrile illness outbreaks in North India reported through the Integrated Disease Surveillance Program.

backgroundAcute Febrile Illness (AFI) like Malaria, Dengue, Chikungunya, and Enteric fever still remain the most common cause of seeking healthcare in low-middle-income countries and need to be constantly monitored for any impending outbreak. Digital epidemiology promises to assist traditional health surveillance. The health data (including AFI) collated by Google using specialised platforms like Google Trends (GT) is known to correlate with actual disease trends. The present study thus aims to assess the potential of GT to support routine surveillance system and forecast AFI outbreaks reported through the Indian Integrated Disease Surveillance Programme (IDSP).

methodsWe utilised Haryana's IDSP portal to retrieve the weekly data of the most commonly reported infectious diseases causing AFI between 2011 and 2020. Internet search trends were downloaded using GT. Descriptive statistics estimated the burden of the AFI and Bland-Altman's plot depicted statistical agreement between the two. We adopted the Box-Jenkins approach to attain the final SARIMA model and explain the time-dependent weekly incidence of AFI.

resultsThe time series plot of the reported AFI displayed trends. Martin- Bland plots depicted acceptable agreement between two datasets for all Chikungunya and Dengue. Among the models evaluated, the Malaria model [SARIMA(1,1,1)(1,1,1)] demonstrated the best performance with a balanced fit and reasonable accuracy, while the Enteric Fever model [SARIMA(0,1,0)(1,1,1)] exhibited low prediction error but weak seasonal significance. In contrast, the Dengue [SARIMA(1,1,0)(1,1,0)] and Chikungunya [ARIMA(1,0,0)(0,0,0)] models had high forecast errors, limiting their predictive reliability. Overall, GT supplemented the prediction performance of the SARIMA models with adjusted R

conclusionsOur study observed modest improvements in GT-based SARIMA forecasting models compared to routine IDSP mechanisms for predicting AFI outbreaks in Haryana, highlighting the potential for further enhancement. As more granular GT data becomes available, its integration with traditional surveillance systems could significantly enhance forecasting accuracy for AFI and other infectious disease outbreaks. At no additional cost to the health system, GT can serve as a valuable, real-time digital epidemiology tool, strengthening public health preparedness and enabling timely interventions for the early containment of emerging diseases.