Novel respiratory pathogens have pandemic potential, making epidemiologic surveillance of acute lower respiratory tract infections (acute LRTI) a global public health priority. Monitoring acute LRTI among international travelers provides an important underutilized opportunity to complement existing surveillance systems, although reliable denominator data on travel volume are often unavailable. Using GeoSentinel data from 2015-2019, capturing syndromic and etiologic LRTI cases, we modeled baseline epidemiology in travelers by comparing generalized linear mixed models (GLMMs) using out-of-sample metrics. A Shewhart control-chart framework, accounting for increases in travel volume under non-epidemic conditions, was applied to detect deviations from expected trends. The preferred hybrid autoregressive model incorporated country-specific fixed effects, random seasonal effects, and a latent temporal autocorrelation structure, and was evaluated for goodness-of-fit in pre-pandemic (2015-2019) and post-pandemic (2023-2024) periods before retrospective application to 2020 data to identify early COVID-19 signals. The hybrid autoregressive GLMM performed best for modeling baseline epidemiology. Applied retrospectively to early 2020 data from 64 countries, the framework detected an early syndromic signal in China under the conservative assumption of up to a threefold increase in travel volume, consistent with COVID-19 emergence. A conservative signal was also detected in Italy, though driven primarily by influenza A and B rather than novel syndromic cases. Combining traveler surveillance with this statistical framework—integrating GLMMs for baseline modeling and Shewhart charts for outbreak detection—may support early detection of acute LRTI outbreaks despite absent denominator data, positioning GeoSentinel as a valuable complementary network for global health security and pandemic preparedness.

The authors have declared no competing interest.

The International Society of Travel Medicine (ISTM) and the Centers for Disease Control and Prevention (CDC) support GeoSentinel, the Global Surveillance Network of ISTM, through a Cooperative Agreement (1 U01 CK000632-05). Additional support was provided by the Public Health Agency of Canada, the GeoSentinel Foundation, and the Italian Ministry of Health through Ricerca Corrente (Linea 1) funds awarded to IRCCS Sacro Cuore Don Calabria.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study used routine public health surveillance data from the GeoSentinel Surveillance Network, involving no intervention beyond standard clinical care. This activity was reviewed by the Centers for Disease Control and Prevention (CDC), deemed not research, and conducted consistent with applicable federal law and CDC policy. Institutional review board approval was therefore not required.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Aggregate epidemiological and clinical data supporting the findings of this study are included in the article. De-identified individual-level data are not publicly available because they were collected through routine public health surveillance and are subject to data protection and institutional restrictions. R functions used for statistical modeling can be found at https://github.com/SHeidema/ilitools.

https://github.com/SHeidema/ilitools