Abstract
Mexico has experienced recurrent viral epidemics of substantial intensity, including hyperendemic dengue, COVID-19, and recent reports of avian influenza A (H5N1) infections in birds, which pose an ongoing risk of zoonotic transmission. Mexico was also the location for the earliest detection of the pdmH1N1 virus during the 2009 influenza A pandemic. Under a One Health framework, markets represent a unique opportunity for low-cost virus monitoring at the human-animal interface. Under the hypothesis that these represent sentinel sites for an early virus detection, we implemented a pilot surveillance program at the central market of Merida city, Yucatan, Mexico, considered a regional hotspot for multiple and recent viral outbreaks. Longitudinal sampling was carried out over 11 months at 1-to-6-week intervals from April 2022 to February 2023. We used multi-type surveillance in mosquitoes, live poultry, and wastewater. All samples were screened using RT-qPCR. Positive samples for DENV, SARS-CoV-2 and avian influenza A were further sequenced and analysed under a phylogenetic and epidemiological approach. Through our entomological surveillance, we report the earliest detection of DENV-3 III-B3.2 (genotype III American II lineage, considered a major public health concern in Latin America) in Mexico, overlapping with the resurgence of DENV-3 as the predominant serotype driving the 2023 national epidemic, which showed an increased severity. Through wastewater surveillance, we consistently detect SARS-CoV-2 RNA in wastewater samples, coinciding with the two infection waves officially recorded at a city and state level. Finally, cloacal swabs taken from two juvenile birds at the market suggest that avian influenza A viruses circulated in live poultry sold at the market. These findings show that our market-based surveillance framework is effective for an early detection and monitoring of pathogenic viruses in urban settings, and could complement official epidemiological surveillance in low- and middle-income countries to strengthen early-outbreak warning systems.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This study was supported by the John Fell OUP Research Grant ATD00390 (M.E.Z and M.U.G.K), the Wellcome Infectious Disease Award ?317324/Z/24/Z (M.G.K, H.P.G and M.E.Z), the Secretaria de Ciencia, Humanidades, TecnologĂa e InovaciĂ³n award (SECIHTI, Mexico) through the PRONACES Health grant (PRONAII project number 303002, G.S) and the Ciencia BĂ¡sica y de Frontera programme (CBF2023-2024-3184, M.G.K), and the UKRI Innovation BSRC/EPSRC/NIHR 971557 grant (A.R.S). M.G.K is funded through a Sanger International Fellowship award. M.E.Z is funded by a UCL Rosetrees Excellence Fellowship UCL2024\2. P.M.D was funded through the doctoral program at ‘Posgrado en Ciencias de la Produccion y de la Salud Animal-UNAM’ through the SECIHTI doctoral scholarship. M.U.G.K. acknowledges funding from The Rockefeller Foundation (PC-2022-POP-005), Health AI Programme from Google.org, the Oxford Martin School Programmes in Pandemic Genomics & Digital Pandemic Preparedness, European Union's Horizon Europe programme projects MOOD (#874850) and E4Warning (#101086640), Wellcome Trust grants 303666/Z/23/Z, 226052/Z/22/Z & 228186/Z/23/Z, the United Kingdom Research and Innovation (#APP8583), the Medical Research Foundation (MRF- RG-ICCH-2022-100069), UK International Development (301542-403), the Bill & Melinda Gates Foundation (INV-063472) and Novo Nordisk Foundation (NNF24OC0094346). B.G is further funded by Wellcome Trust grants 303666/Z/23/Z, 226052/Z/22/Z & 228186/Z/23/Z. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission or the other funders. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Source:
Link: https://www.medrxiv.org/content/10.64898/2025.12.22.25342882v1
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