Verma Madhur, Kishore Kamal, Parija Pragyan Paramita, Sahoo Soumya Swaroop, Gambhir Dolly, Gupta Usha, Kakkar Rakesh
Department of Community & Family Medicine, All India Institute of Medical Sciences Bathinda, Punjab, 151001, India.
Department of Biostatistics, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
BMC Infect Dis. 2025 Mar 28;25(1):431. doi: 10.1186/s12879-025-10801-0.
Acute 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).
We 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.
The 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 of 46%, 50%, 50%, and 52% compared to the original 43%, 49%, 20%, and 48%.
Our 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.
在低收入和中等收入国家，诸如疟疾、登革热、基孔肯雅热和伤寒等急性发热性疾病（AFI）仍然是人们寻求医疗服务的最常见原因，需要持续监测以防范任何即将爆发的疫情。数字流行病学有望辅助传统的健康监测。众所周知，谷歌利用谷歌趋势（GT）等专业平台整理的健康数据（包括AFI数据）与实际疾病趋势相关。因此，本研究旨在评估GT支持常规监测系统以及预测通过印度综合疾病监测计划（IDSP）报告的AFI疫情的潜力。
我们利用哈里亚纳邦的IDSP门户网站检索2011年至2020年间最常报告的导致AFI的传染病的每周数据。使用GT下载互联网搜索趋势。描述性统计估计了AFI的负担，布兰德-奥特曼图描绘了两者之间的统计一致性。我们采用博克斯-詹金斯方法获得最终的季节性自回归整合移动平均（SARIMA）模型，并解释AFI的时间依赖性每周发病率。
报告的AFI的时间序列图显示出趋势。马丁-布兰德图显示所有基孔肯雅热和登革热的两个数据集之间具有可接受的一致性。在评估的模型中，疟疾模型[SARIMA(1,1,1)(1,1,1)]表现最佳，拟合良好且准确性合理，而伤寒模型[SARIMA(0,1,0)(1,1,1)]预测误差低但季节性意义较弱。相比之下，登革热模型[SARIMA(1,1,0)(1,1,0)]和基孔肯雅热模型[ARIMA(1,0,0)(0,0,0)]预测误差高，限制了它们的预测可靠性。总体而言，GT补充了SARIMA模型的预测性能，调整后的R值分别为46%、50%、50%和52%，而原始值分别为43%、49%、20%和48%。
我们的研究观察到，与哈里亚纳邦预测AFI疫情的常规IDSP机制相比，基于GT的SARIMA预测模型有适度改进，凸显了进一步提升的潜力。随着更多粒度的GT数据可用，将其与传统监测系统整合可显著提高AFI和其他传染病疫情的预测准确性。在不增加卫生系统成本的情况下，GT可作为一种有价值的实时数字流行病学工具，加强公共卫生防范，并为及时控制新发疾病实现早期干预提供支持。
