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. 2025 May 27;16(1):4900.
doi: 10.1038/s41467-025-59322-z.

Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics

Dhammika Leshan Wannigama  1   2   3   4   5   6 , Mohan Amarasiri #  7   8 , Phatthranit Phattharapornjaroen #  7   9   10 , Cameron Hurst #  7   11   12   13 , Charin Modchang  14   15   16 , John Jefferson V Besa  17   18 , Kazuhiko Miyanaga  19 , Longzhu Cui  19 , Stefan Fernandez  20 , Angkana T Huang  21 , Puey Ounjai  22 , W K C P Werawatte  23 , Ali Hosseini Rad S M  24 , Porames Vatanaprasan  25 , Dylan John Jay  7 , Thammakorn Saethang  26 , Sirirat Luk-In  27 , Phitsanuruk Kanthawee  28 , Wanwara Thuptimdang  29 , Ratana Tacharoenmuang  30 , Bernadina Cynthia  31 , S P H Spencer Vitharana  32 , Natharin Ngamwongsatit  33 , Hitoshi Ishikawa  34 , Takashi Furukawa  35 , Yangzhong Wang  36 , Andrew C Singer  37 , Naveen Kumar Devanga Ragupathi  38   39 , Tanittha Chatsuwan  40 , Kazunari Sei  35 , Asuka Nanbo  41 , Asada Leelahavanichkul  40   42 , Talerngsak Kanjanabuch  43   44   45   46 , Hiroshi Hamamoto  47 , Paul G Higgins  48   49   50 , Daisuke Sano  8   51 , Anthony Kicic  52   53   54   55 , José O Valdebenito  56   57   58 , Jonas Bonnedahl  59 , Sam Trowsdale  60 , Parichart Hongsing #  47   7   61   62 , Aisha Khatib  63 , Kenji Shibuya  64 , Shuichi Abe  47   17
Affiliations

Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics

Dhammika Leshan Wannigama et al. Nat Commun. .

Abstract

Avian influenza viruses (AIVs) pose a growing global health threat, particularly in low- and middle-income countries (LMICs), where limited surveillance capacity and under-resourced healthcare systems hinder timely detection and response. Migratory birds play a significant role in the transboundary spread of AIVs, yet data from key regions along migratory flyways remain sparse. To address these surveillance gaps, we conducted a study between December 2021 and February 2023 using fresh bird guano collected across 10 countries in the Global South. Here, we show that remote, uninhabited regions in previously unsampled areas harbor a high diversity of AIV strains, with H5N1 emerging as the most prevalent. Some of these H5N1 samples also carry mutations that may make them less responsive to the antiviral drug oseltamivir. Our findings documented the presence of AIVs in several underrepresented regions and highlighted critical transmission hotspots where viral evolution may be accelerating. These results underscore the urgent need for geographically targeted surveillance to detect emerging variants, inform public health interventions, and reduce the risk of zoonotic spillover.

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Conflict of interest statement

Competing interests: The authors declare no competing interests. Ethics: The institutional review board at Yamagata Prefectural Central Hospital, Yamagata, Japan, waived ethical approval for this work as it determined that our project is exempt under the type of environmental surveillance study.

Figures

Fig. 1
Fig. 1. Avian influenza surveillance using bird guano in the Global South.
a Map of sampling countries with bird migration flyways (Papua New Guinea (n = 5792), Sri Lanka (n = 2657), Indonesia (n = 6205), Yemen (n = 503), Myanmar (n = 3709), Phillippines (n = 2130), Mozambique (n = 502), Madagascar (n = 1540), Somalia (n = 512), and Maldives (n = 3486)). b Percentage of avian influenza virus (AIV)-positive samples collected from different bird guano sampling locations. Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Avian influenza surveillance using bird guano in the Global South.
a The boxplot of AIV RNA concentrations (copies/ml) in positive bird guano samples Papua New Guinea (n = 41), Sri Lanka (n = 38), Indonesia (n = 40), Yemen (n = 32), Myanmar (n = 45), Phillippines (n = 33), Mozambique (n = 29), Madagascar (n = 34), Somalia (n = 33), and Maldives (n = 35)). The lower and upper boundaries of the box (interquartile) represent the 25th and the 75th percentile, respectively. The line within the box corresponds to the median, while the whiskers indicate the highest and the lowest AIV RNA copy values, except for the outliers that are represented by the rounds outside the whiskers. b Distribution of avian influenza virus subtypes as percentages among the positive samples Papua New Guinea (n = 41), Sri Lanka (n = 38), Indonesia (n = 40), Yemen (n = 32), Myanmar (n = 45), Phillippines (n = 33), Mozambique (n = 29), Madagascar (n = 34), Somalia (n = 33), and Maldives (n = 35)).
Fig. 3
Fig. 3. Phylogenetic analysis of avian influenza virus (AIV) hemagglutinin (HA) gene sequences obtained from bird guano samples, alongside reference sequences from GISAID.
Panels show phylogenies for the following HA subtypes: a H5, b H1, c H6, d H2, e H3, f H4, g H8, h H7, i H9, j H10, k H11, and l H12. Evolutionary history was inferred using the Maximum Likelihood method and the Hasegawa-Kishino-Yano substitution model for the hemagglutinin (HA) and neuraminidase (NA) genes. The trees with the highest log likelihood are shown. Nextclade (https://clades.nextstrain.org) were used for clade assignment, mutation calling and quality control for viral genomes.
Fig. 4
Fig. 4. Phylogenetic analysis of avian influenza virus (AIV) neuraminidase (NA) gene sequences obtained from bird guano samples, alongside reference sequences from GISAID.
Panels show phylogenies for the following NA subtypes: a N1, b N2, c N3, d N4, e N5, f N7, g N6, and h N9. Evolutionary history was inferred using the Maximum Likelihood method and the Hasegawa-Kishino-Yano substitution model for the hemagglutinin (HA) and neuraminidase (NA) genes. The trees with the highest log likelihood are shown. Nextclade (https://clades.nextstrain.org) were used for clade assignment, mutation calling, and quality control for viral genomes.
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