Knowledge of the origin and reservoir of the coronavirus responsible for the ongoing COVID-19 pandemic is still fragmentary. To date, the closest relatives to SARS-CoV-2 have been detected in Rhinolophus bats sampled in the Yunnan province, China. Here we describe the identification of SARS-CoV-2 related coronaviruses in two Rhinolophus shameli bats sampled in Cambodia in 2010. Metagenomic sequencing identifies nearly identical viruses sharing 92.6% nucleotide identity with SARS-CoV-2. Most genomic regions are closely related to SARS-CoV-2, with the exception of a region of the spike, which is not compatible with human ACE2-mediated entry. The discovery of these viruses in a bat species not found in China indicates that SARS-CoV-2 related viruses have a much wider geographic distribution than previously reported, and suggests that Southeast Asia represents a key area to consider for future surveillance for coronaviruses.

Nipah virus (NiV), a highly lethal virus in humans, circulates in Pteropus bats throughout South and Southeast Asia. Difficulty in obtaining viral genomes from bats means we have a poor understanding of NiV diversity. We develop phylogenetic approaches applied to the most comprehensive collection of genomes to date (N = 257, 175 from bats, 73 from humans) from 6 countries over 22 years (1999–2020). We divide the 4 major NiV sublineages into 15 genetic clusters. Using Approximate Bayesian Computation fit to a spatial signature of viral diversity, we estimate the presence and the average size of genetic clusters per area. We find that, within any bat roost, there are an average of 2.4 co-circulating genetic clusters, rising to 5.5 clusters at areas of 1500–2000 km². We estimate that each genetic cluster occupies an average area of 1.3 million km² (95% confidence interval [CI], .6–2.3 million km²), with 14 clusters in an area of 100 000 km² (95% CI, 6–24 km²). In the few sites in Bangladesh and Cambodia where genomic surveillance has been concentrated, we estimate that most clusters have been identified, but only approximately 15% of overall NiV diversity has been uncovered. Our findings are consistent with entrenched co-circulation of distinct lineages, even within roosts, coupled with slow migration over larger spatial scales.

Bats have been recognized as an exceptional viral reservoir, especially for coronaviruses. At least three bat zoonotic coronaviruses (SARS-CoV, MERS-CoV and SARS-CoV-2) have been shown to cause severe diseases in humans and it is expected more will emerge. One of the major features of CoVs is that they are all highly prone to recombination. An extreme example is the insertion of the P10 gene from reoviruses in the bat CoV GCCDC1, first discovered in Rousettus leschenaultii bats in China. Here, we report the detection of GCCDC1 in four different bat species (Eonycteris spelaea, Cynopterus sphinx, Rhinolophus shameli and Rousettus sp.) in Cambodia. This finding demonstrates a much broader geographic and bat species range for this virus and indicates common cross-species transmission. Interestingly, one of the bat samples showed a co-infection with an Alpha CoV most closely related to RsYN14, a virus recently discovered in the same genus (Rhinolophus) of bat in Yunnan, China, 2020. Taken together, our latest findings highlight the need to conduct active surveillance in bats to assess the risk of emerging CoVs, especially in Southeast Asia.

Nipah virus (NiV), a highly lethal virus in humans, circulates silently in Pteropus bats throughout South and Southeast Asia. Difficulty in obtaining genomes from bats means we have a poor understanding of NiV diversity, including how many lineages circulate within a roost and the spread of NiV over increasing spatial scales. Here we develop phylogenetic approaches applied to the most comprehensive collection of genomes to date (N=257, 175 from bats, 73 from humans) from six countries over 22 years (1999–2020). In Bangladesh, where most human infections occur, we find evidence of increased spillover risk from one of the two co-circulating sublineages. We divide the four major NiV sublineages into 15 genetic clusters (emerged 20-44 years ago). Within any bat roost, there are an average of 2.4 co-circulating genetic clusters, rising to 5.5 clusters at areas of 1,500-2,000 km ² . Using Approximate Bayesian Computation fit to a spatial signature of viral diversity, we estimate that each genetic cluster occupies an average area of 1.3 million km ² (95%CI: 0.6-2.3 million), with 14 clusters in an area of 100,000 km ² (95%CI: 6-24). In the few sites in Bangladesh and Cambodia where genomic surveillance has been concentrated, we estimate that most of the genetic clusters have been identified, but only ∼15% of overall NiV diversity has been uncovered. Our findings are consistent with entrenched co-circulation of distinct lineages, even within individual roosts, coupled with slow migration over larger spatial scales.

Background: Tropical low- and middle-income countries are highly vulnerable to zoonoses and vector-borne diseases, with risks amplified by climatic events, environmental change, and limited surveillance capacity. Cambodia is particularly exposed due to its ecological diversity, seasonal flooding, and rapidly changing land use. Globally, however, field based One Health approaches remain under-implemented, limiting practical evidence on how to address these complex threats. Methods: This protocol describes a longitudinal One Health study conducted in three villages of Battambang province, Cambodia, designed to investigate the prevalence and transmission dynamics of zoonotic and potentially zoonotic pathogens at the human–animal–environment interface. The study examines how vector density, diversity, and pathogen circulation are influenced by hydrological variation and seasonality, and assesses the socio-demographic, behavioral, and environmental factors shaping transmission. Integrated data will be collected through serological and molecular analyses in humans and animals, environmental sampling, and entomological surveillance, enabling cross-compartmental and spatiotemporal analyses. Expected Results: The study will generate integrated, cross-sectoral data to characterize pathogen exposure patterns, identify high-risk populations and practices, and inform targeted public health, veterinary, and environmental interventions. Conclusions: By sharing this protocol, the work addresses a global methodological gap in operationalizing One Health in the field and supports the development of integrated surveillance strategies in climate-sensitive, resource-limited settings.