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Development of indicator systems for early warning of acute respiratory infection: a Delphi stduy
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Meng LI, Chao WANG, Yuanyuan KONG, Ning HAN, Jie FENG, Xihong LYU
Chinese Journal of Public Health | 2026, 42(4) : 418 - 423
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Chinese Journal of Public Health | 2026, 42(4): 418-423
• Special Report—Development of Public Health Evaluation Indicator Frameworks •
Development of indicator systems for early warning of acute respiratory infection: a Delphi stduy
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Meng LI, Chao WANG, Yuanyuan KONG, Ning HAN, Jie FENG, Xihong LYU
Affiliations
  • Department of Infectious Disease Control and Prevention, Shanghai Songjiang District Center for Disease Control and Prevention (Shanghai Songjiang District Health Supervision Institute), Shanghai 201620, China
Published: 2026-04-10 doi: 10.11847/zgggws1148645
Outline
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Objective

To establish comprehensive indicator systems for early warning of acute respiratory infection (ARI) based on multi-source data.

Methods

The Delphi method was adopted for expert consultation. Twenty experts were invited to score the necessity, availability, and sensitivity of different early warning indicators, and the weights were determined based on the scoring results.

Results

The two rounds of expert consultation showed the expert positive coefficients being both 100%, the coefficients of expert authority being both 0.79, and the coefficients of concordance being 0.280 and 0.487 (P < 0.05), respectively. According to the characteristics of ARI in the stages of occurrence, transmission, and epidemics, two indicator systems–the early warning indicator system for risk of ARI and the early warning indicator system for potential clustering or outbreak of ARI–were established. The former included 22 indicators in 5 categories, and the latter included 29 indicators in 6 categories. In the early warning indicator system for risk of ARI, the top 3 indicators with the highest weights were immunization coverage rate (0.052), variations of known pathogens (0.051), and variations of dominant prevalent strains (0.051). In the early warning indicator system for potential clustering or outbreak of ARI, the top 3 indicators with the highest weights were surveillance data of respiratory syndrome (0.040), positive rate of specific pathogen (0.040), and number of cases reported in online direct report system (0.040).

Conclusions

The comprehensive indicator systems for early warning of ARI based on multi-source data and Delphi method established in this study have certain scientificity and practicability, which can provide support for improving the multi-point triggered early warning of infectious diseases.

acute respiratory infection  /  early warning  /  Delphi method  /  indicator system
李萌, 王超, 孔园园, 韩宁, 冯杰, 吕锡宏. 急性呼吸道传染病预警综合指标体系构建:基于德尔菲专家咨询法. 中国公共卫生, 2026 , 42 (4) : 418 -423 . DOI: 10.11847/zgggws1148645
Meng LI, Chao WANG, Yuanyuan KONG, Ning HAN, Jie FENG, Xihong LYU. Development of indicator systems for early warning of acute respiratory infection: a Delphi stduy[J]. Chinese Journal of Public Health, 2026 , 42 (4) : 418 -423 . DOI: 10.11847/zgggws1148645
Year 2026 volume 42 Issue 4
126
41
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Article Info
doi: 10.11847/zgggws1148645
  • Receive Date:2025-08-13
  • Online Date:2026-10-23
  • Published:2026-04-10
Article Data
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History
  • Received:2025-08-13
  • Revised:2025-10-17
  • Accepted:2026-01-26
Affiliations
    Department of Infectious Disease Control and Prevention, Shanghai Songjiang District Center for Disease Control and Prevention (Shanghai Songjiang District Health Supervision Institute), Shanghai 201620, China

Corresponding:

LYU Xihong, E-mail:
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表12种不同金属材料的力学参数
科
Family
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Number of
genus
种数
Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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