Objectives  Chikungunya fever, an acute mosquito-borne infectious disease, poses an increasing global epidemic risk. However, data from different sources vary in timeliness and representativeness for outbreak detection. This study aims to compare the performance of open-source surveillance systems and official reporting systems in the early detection of chikungunya fever outbreaks, and to examine the consistency of trends between the former and historical case data.

Methods Reports on chikungunya-related events were collected from the Program for Monitoring Emerging Diseases (ProMED) and the World Health Organization Disease Outbreak News (WHO DON) between 2000 and September 30, 2025. In addition, historical global case data from 2000 to 2024 were compiled based on information publicly released through official websites and the published literature. Large language models were employed to assist in the automated identification and extraction of outbreak events, which were subsequently aggregated and classified by country, time period, and level of economic development. Timeliness was evaluated by comparing the reporting dates of events in ProMED and WHO DON, while the representativeness of ProMED event trends was assessed using reported case numbers as a reference.

Results  The study identified 1,322 ProMED events covering 95 countries and regions, and 33 WHO DON events covering 26 countries and regions. Among the 28 events matched between the two sources, ProMED's median reporting time preceded DON by 13 days, with approximately 68% of events reported earlier in ProMED. Global chikungunya cases peaked three times in 2006, 2014, and 2024, with corresponding significant increases in ProMED event counts during these years, demonstrating consistency between the two during major epidemic phases. The trends showed marked synchrony across low-income, upper-middle-income, and high-income country groups, while exhibiting greater divergence in lower-middle-income countries.

Conclusion ProMED demonstrates superiority over WHO DON in capturing early signals of chikungunya outbreaks, providing more acute detection of endemic and small-scale occurrences, whereas DON places greater emphasis on major events with international implications. ProMED events’ temporal trends exhibit high consistency with historical case data, indicating its effectiveness in reflecting epidemic dynamics at a macro level. Integrating both datasets enhances the timeliness and comprehensiveness of global infectious disease surveillance systems, providing valuable reference for early warning systems against future public health emergencies. The accuracy and completeness of event and case report in this study depend on the accessibility of local open-source information and the robustness of the infectious disease surveillance systems. Particularly in low-income and lower-middle-income countries, these data may not fully reflect the local epidemic situation and need to be integrated with on-site investigations for comprehensive assessment.