The accurate inference of pathogen movements among locations during an epidemic is crucial for measuring infectious disease spread and for informing effective control strategies. Phylogeographic methods can reconstruct historical patterns of disease dissemination by combining the evolutionary history of sampled pathogen genomes with geographic information. Despite a substantial expansion of pathogen genomics during and after the COVID-19 pandemic, only a small fraction of infections are typically sampled and sequenced, leading to underestimation of the true intensity of viral importation. Here, we seek to understand the sampling processes underlying this underestimation. We show that the coupling of viral importation and local transmission dynamics can result in local transmission lineages with different size distributions, influencing the probability that individual viral importation events will be detected. Using analytical and simulation approaches, we show that both the proportion of importation events detected and the temporal patterns of inferred importation are highly sensitive to importation dynamics and local transmission parameters, resulting in substantial biases, particularly under low-intensity sampling. Our findings highlight the importance of interpreting phylogeographic estimates in the context of outbreak conditions, particularly when comparing viral movements across time and among epidemic settings characterised by rapid spatial dissemination. These insights are critical for improving the reliability of genomic epidemiology approaches to the design of public health responses.

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI.

Metagenomics is a powerful tool for characterizing viruses, with broad applications across diverse disciplines, from understanding the ecology and evolutionary history of viruses to identifying causative agents of emerging outbreaks with unknown aetiology. Additionally, metagenomic data contain valuable information about the amount of virus present within samples (i.e. viral load), which can provide insights into transmission potential, time since infection and, in turn, epidemic trajectories. However, before we can effectively use metagenomic data to inform transmission, we need to understand the general relationship between sequencing outputs and viral load. Here, using a commercially available probe panel targeting a wide diversity of viruses, we investigated the detection and recovery of virus genomes by spiking known concentrations of DNA and RNA viruses into wild rodent faecal samples. In total, 15 experimental replicates were sequenced with target enrichment sequencing and compared to shotgun sequencing of the same background samples. Target-enriched sequencing recovered all spike-in viruses at every concentration (102, 103 and 105±1 log genome copies) and showed a log-linear relationship between spike-in concentration and mean read depth. Background viruses (including Kobuvirus and Cardiovirus) were recovered consistently across all biological and technical replicates and by shotgun sequencing, but genome coverage was variable between virus genera and likely reflected the composition of the target enrichment probe panel. Overall, our study highlights the strengths and weaknesses of using commercially available panels to quantify and characterize wildlife viromes and underscores the importance of probe panel design for accurately interpreting coverage and read depth. To advance the use of metagenomics for understanding virus transmission, further research will be needed to elucidate how sequencing strategy (e.g. library depth and pooling), virome composition and probe design influence viral read counts and genome coverage.

The effectiveness of poultry vaccination in preventing the transmission of highly pathogenic avian influenza viruses (AIVs) has been debated, and its impact on wild birds remains uncertain. Here, we reconstruct the movements of H5 subtype AIV lineages among vaccinated poultry, unvaccinated poultry, and wild birds, worldwide, from 1996 to 2023. We find that there is a time lag in viral transmission among different host populations and that movements from wild birds to unvaccinated poultry were more frequent than those from wild birds to vaccinated poultry. Furthermore, our findings suggest that the HA (hemagglutinin) gene of the AIV lineage that circulated predominately in Chinese poultry experienced greater nonsynonymous divergence and adaptive fixation than other lineages. Our results indicate that the epidemiological, ecological, and evolutionary consequences of widespread AIV vaccination in poultry may be linked in complex ways and that much work is needed to better understand how such interventions may affect AIV transmission to, within, and from wild birds.

Accurately estimating the prevalence and transmissibility of an infectious disease is an important task in genetic infectious disease epidemiology. However, generating accurate estimates of these quantities, that make use of both epidemic time series and pathogen genome sequence data, is a challenging problem. Phylogenetic birth-death processes are a popular choice for modelling the transmission of infectious diseases, but it is difficult to estimate the prevalence of infection with them. Here, we extended our approximate likelihood approach, which combines phylogenetic information from sampled pathogen genomes and epidemiological information from a time series of case counts, to estimate historical prevalence in addition to the effective reproduction number. We implement this new method in a BEAST2 package called Timtam. In a simulation study our approximation is seen to be well-calibrated and recovers the parameters of simulated data. To demonstrate how Timtam can be applied to real datasets, we carried out empirical analyses of data from two infectious disease outbreaks: the outbreak of SARS-CoV-2 onboard the Diamond Princess cruise ship in early 2020 and poliomyelitis in Tajikistan in 2010. In both cases we recover estimates consistent with previous analyses.

The rapid evolution of RNA viruses implies that their evolutionary and ecological processes occur on the same time scale. Genome sequences of these pathogens therefore can contain information about the processes that govern their transmission and dispersal. Landscape phylogeographic approaches use phylogeographic reconstructions to investigate the impact of environmental factors and variables on the spatial spread of viruses. Here, we extend and improve existing approaches and develop three novel landscape phylogeographic methods that can test the impact of continuous environmental factors on the diffusion velocity of viral lineages. In order to evaluate the different methods, we also implemented two simulation frameworks to test and compare their statistical performance. The results enable us to formulate clear guidelines for the use of three complementary landscape phylogeographic approaches that have sufficient statistical power and low rates of false positives. Our open-source methods are available to the cientific community and can be used to investigate the drivers of viral spread, with potential benefits for understanding virus epidemiology and designing tailored intervention strategies.

Human mobility is strongly associated with the spread of SARS-CoV-2 via air travel on an international scale and with population mixing and the number of people moving between locations on a local scale. However, these conclusions are drawn mostly from observations in the context of the global north where international and domestic connectivity is heavily influenced by the air travel network; scenarios where land-based mobility can also dominate viral spread remain understudied. Furthermore, research on the effects of nonpharmaceutical interventions (NPIs) has mostly focused on national- or regional-scale implementations, leaving gaps in our understanding of the potential benefits of implementing NPIs at higher granularity. Here, we use Chile as a model to explore the role of human mobility on disease spread within the global south; the country implemented a systematic genomic surveillance program and NPIs at a very high spatial granularity. We combine viral genomic data, anonymized human mobility data from mobile phones and official records of international travelers entering the country to characterize the routes of importation of different variants, the relative contributions of airport and land border importations, and the real-time impact of the country's mobility network on the diffusion of SARS-CoV-2. The introduction of variants which are dominant in neighboring countries (and not detected through airport genomic surveillance) is predicted by land border crossings and not by air travelers, and the strength of connectivity between comunas (Chile's lowest administrative divisions) predicts the time of arrival of imported lineages to new locations. A higher stringency of local NPIs was also associated with fewer domestic viral importations. Our analysis sheds light on the drivers of emerging respiratory infectious disease spread outside of air travel and on the consequences of disrupting regular movement patterns at lower spatial scales.

Anthropogenic land-use change is an important driver of global biodiversity loss and threatens public health through biological interactions. Understanding these landscape–ecological effects at local scales will help achieve the United Nations Sustainable Development Goals by balancing urbanization, biodiversity and the spread of infectious diseases. Here, we address this knowledge gap by analysing a 43-year-long monthly dataset (1980–2022) of synanthropic rodents in Central China during intensive land-use change. We observed a notable increase in the mean patch size, coinciding with a substantial change in rodent community composition and a marked decline in rodent diversity; eight of the nine local rodent species experienced near-extirpation. Our analysis reveals that these irregular species replacements can be attributed to the effect of land consolidation on species competition among rodents, favouring striped field mice, a critical reservoir host of Hantaan virus (HTNV). Consequently, land consolidation has facilitated the proliferation of striped field mice and increased the prevalence of HTNV among them. This study highlights the importance of considering both direct and indirect effects of anthropogenic activities in the management of biodiversity and public health. A 43-year dataset of rodents in the Hu region of China reveals how urbanization-induced changes to land-use configuration affect rodent community composition, including benefitting striped field mice, the primary local hosts of the zoonotic pathogen Hantaan virus.

The global dynamics of seasonal influenza viruses inform the design of surveillance, intervention, and vaccination strategies. The COVID-19 pandemic provided a singular opportunity to evaluate how influenza circulation worldwide was perturbed by human behavioral changes. We combine molecular, epidemiological, and international travel data and find that the pandemic's onset led to a shift in the intensity and structure of international influenza lineage movement. During the pandemic, South Asia played an important role as a phylogenetic trunk location of influenza A viruses, whereas West Asia maintained the circulation of influenza B/Victoria. We explore drivers of influenza lineage dynamics across the pandemic period and reasons for the possible extinction of the B/Yamagata lineage. After a period of 3 years, the intensity of among-region influenza lineage movements returned to pre-pandemic levels, with the exception of B/Yamagata, after the recovery of global air traffic, highlighting the robustness of global lineage dispersal patterns to substantial perturbation.

Highly pathogenic avian influenza virus (HPAIV) A H5, particularly clade 2.3.4.4, has caused worldwide outbreaks in domestic poultry, occasional spillover to humans, and increasing deaths of diverse species of wild birds since 2014. Wild bird migration is currently acknowledged as an important ecological process contributing to the global dispersal of HPAIV H5. However, this mechanism has not been quantified using bird movement data from different species, and the timing and location of exposure of different species is unclear. We sought to explore these questions through phylodynamic analyses based on empirical data of bird movement tracking and virus genome sequences of clade 2.3.4.4 and 2.3.2.1. First, we demonstrate that seasonal bird migration can explain salient features of the global dispersal of clade 2.3.4.4. Second, we detect synchrony between the seasonality of bird annual cycle phases and virus lineage movements. We reveal the differing exposed bird orders at geographical origins and destinations of HPAIV H5 clade 2.3.4.4 lineage movements, including relatively under-discussed orders. Our study provides a phylodynamic framework that links the bird movement ecology and genomic epidemiology of avian influenza; it highlights the importance of integrating bird behavior and life history in avian influenza studies.

Foot-and-mouth disease is a highly contagious disease affecting cloven-hoofed animals, resulting in considerable economic losses. Its causal agent is foot-and-mouth disease virus (FMDV), a picornavirus. Due to its error-prone replication and rapid evolution, the transmission and evolutionary dynamics of FMDV can be studied using genomic epidemiological approaches. To analyze FMDV evolution and identify possible transmission routes in an Argentinean region, field samples that tested positive for FMDV by PCR were obtained from 21 farms located in the Mar Chiquita district. Whole FMDV genome sequences were obtained by PCR amplification in seven fragments and sequencing using the Sanger technique. The genome sequences obtained from these samples were then analyzed using phylogenetic, phylogeographic, and evolutionary approaches. Three local transmission clusters were detected among the sampled viruses. The dataset was analyzed using Bayesian phylodynamic methods with appropriate coalescent and relaxed molecular clock models. The estimated mean viral evolutionary rate was 1.17 × 10− 2 substitutions/site/year. No significant differences in the rate of viral evolution were observed between farms with vaccinated animals and those with unvaccinated animals. The most recent common ancestor of the sampled sequences was dated to approximately one month before the first reported case in the outbreak. Virus transmission started in the south of the district and later dispersed to the west, and finally arrived in the east. Different transmission routes among the studied herds, such as non-replicating vectors and close contact contagion (i.e., aerosols), may be responsible for viral spread.

During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing non-pharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases, deaths and hospitalizations at the municipality level in Mexico to investigate how behavioural changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March - June 2020). We find that the epidemic dynamics in Mexico were initially driven by SARS-CoV-2 exports from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronised. Our results provide actionable and dynamic insights into how to use network science and epidemiological modelling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.

We report the complete genomes of four ssDNA viruses: a circular replication-associated protein-encoding single-stranded DNA virus belonging to a clade previously detected only in mammals, and three chaphamaparvoviruses, which were detected by viromic surveillance of mute swan (Cygnus olor) fecal samples from the United Kingdom.

Abstract Background Early in the coronavirus disease 2019 (COVID-19) pandemic, Sunda pangolins (Manis javanica) involved in the illegal wildlife trade in mainland China were identified as hosts of severe acute respiratory syndrome-related coronaviruses (SARSr-CoVs). Although it is unconfirmed whether pangolins or other traded wildlife served as intermediate hosts for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the trafficking of pangolins presents a clear risk for transmission of viruses with zoonotic and epizootic potential regardless. We have investigated the origins of pangolin carcasses seized in Hong Kong and have evaluated their potential exposure to SARSr-CoVs, other coronaviruses, and paramyxoviruses, aiming to address a gap in our knowledge with regard to the role of wildlife trade in the maintenance and emergence of pathogens with zoonotic and epizootic potential. Results Using a combination of virological and wildlife forensics tools, we investigated 89 Sunda pangolin carcasses seized by Hong Kong authorities during anti-smuggling operations in the territory conducted in 2013 (n = 1) and 2018 (n = 88). Swabs, organ tissues, blood, and other body fluids were collected during post-mortem examination. Two enzyme-linked immunosorbent assays (ELISAs), which employ a double-antigen sandwich format, were used to detect antibodies reactive against SARSr-CoVs. One individual was found to be seropositive with support from both methods, while five individuals exhibited a putatively seropositive result from one ELISA method. Polymerase chain reaction (PCR) screening for coronavirus and paramyxovirus ribonucleic acid (RNA) did not yield any positives. Based on genomic data, the seropositive individual was determined to have likely originated from Java, while the putatively seropositive individuals were determined to have originated from populations in Borneo, Java, and Singapore/Sumatra. Conclusions While the role of pangolins in the evolution and ecology of SARS-CoV-2 is uncertain, our results suggest susceptibility and potential exposure of pangolins to SARSr-CoVs, occurring naturally or associated with the illegal trafficking of these animals. Complex dynamics between natural populations, traded individuals, and pathogen susceptibility complicate conclusions about the role of pangolins, as well as other host species, in the ecology of SARSr-CoVs and potentially zoonotic viruses with risk of future emergence.

Tracking the spread of emerging pathogens is critical to the design of timely and effective public health responses. Policymakers face the challenge of allocating finite resources for testing and surveillance across locations, with the goal of maximizing the information obtained about the underlying trends in prevalence and incidence. We model this decision- making process as an iterative node classification problem on an undirected and unweighted graph, in which nodes represent locations and edges represent movement of infectious agents among them. To begin, a single node is randomly selected for testing and determined to be either infected or uninfected. Test feedback is then used to update estimates of the probability of unobserved nodes being infected and to inform the selection of nodes for testing at the next iterations, until certain test budget is exhausted. Using this framework, we evaluate and compare the performance of previously developed active learning policies for node selection, including Node Entropy and Bayesian Active Learning by Disagreement. We explore the performance of these policies under different outbreak scenarios using simulated outbreaks on both synthetic and empirical networks. Further, we propose a policy that considers the distance- weighted average entropy of infection predictions among neighbors of each candidate node. Our proposed policy outperforms existing ones in most outbreak scenarios given small test budgets, highlighting the need to consider an exploration-exploitation trade- off in policy design. Our findings could inform the design of cost- effective surveillance strategies for emerging and endemic pathogens and reduce uncertainties associated with early risk assessments in resource- constrained situations.

Background: Arboviruses cause both seasonal epidemics (e.g. dengue viruses, DENV) and emerging outbreaks (e.g. chikungunya and Zika viruses, CHIKV and ZIKV) with a significant impact on global health. These viruses share mosquito vector species, often infecting the same host population within overlapping geographic regions. Thus, comparative analyses of their evolutionary and epidemiological dynamics across spatial and temporal scales could reveal convergent transmission trends. Methodology/Principal Findings: Focusing on Mexico as a case study, we generated CHIKV, DENV-1 and DENV-2 genomes from an epidemiological surveillance-derived historical sample collection, and analysed them together with longitudinally-collected genome and epidemiological data from the Americas. Arboviruses endemically circulating within the country were found to be introduced multiple times from lineages predominantly sampled from the Caribbean and Central America. For CHIKV, at least thirteen introductions were inferred over a year, with six of these leading to persistent transmission chains. For both DENV-1 and DENV-2, at least seven introductions were inferred over a decade. Conclusions/Significance: Our results suggest that CHIKV, DENV-1 and DENV-2 in Mexico share similar evolutionary and epidemiological trajectories. The southwest region of the country was determined to be the most likely location for viral introductions from abroad, with a subsequent spread into the Pacific coast towards the north of Mexico. The virus diffusion patterns observed across the country are likely driven by multiple factors, including mobility linked to human migration from Central towards North America. Considering Mexicos economic role and geographic positioning displaying a high human mobility across borders, our results prompt the need to better understand the role of anthropogenic factors in the transmission dynamics of arboviruses, particularly linked to land-based human migration.

The COVID-19 pandemic highlighted the importance of global genomic surveillance to monitor the emergence and spread of SARS-CoV-2 variants and inform public health decision-making. Until December 2020 there was minimal capacity for viral genomic surveillance in most Caribbean countries. To overcome this constraint, the COVID-19: Infectious disease Molecular epidemiology for PAthogen Control & Tracking (COVID-19 IMPACT) project was implemented to establish rapid SARS-CoV-2 whole genome nanopore sequencing at The University of the West Indies (UWI) in Trinidad and Tobago (T&T) and provide needed SARS-CoV-2 sequencing services for T&T and other Caribbean Public Health Agency Member States (CMS). Using the Oxford Nanopore Technologies MinION sequencing platform and ARTIC network sequencing protocols and bioinformatics pipeline, a total of 3610 SARS-CoV-2 positive RNA samples, received from 17 CMS, were sequenced in-situ during the period December 5 th 2020 to December 31 st 2021. Ninety-one Pango lineages, including those of five variants of concern (VOC), were identified. Genetic analysis revealed at least 260 introductions to the CMS from other global regions. For each of the 17 CMS, the percentage of reported COVID-19 cases sequenced by the COVID-19 IMPACT laboratory ranged from 0·02% to 3·80% (median = 1·12%). Sequences submitted to GISAID by our study represented 73·3% of all SARS-CoV-2 sequences from the 17 CMS available on the database up to December 31 st 2021. Increased staffing, process and infrastructural improvement over the course of the project helped reduce turnaround times for reporting to originating institutions and sequence uploads to GISAID. Insights from our genomic surveillance network in the Caribbean region directly influenced non-pharmaceutical countermeasures in the CMS countries. However, limited availability of associated surveillance and clinical data made it challenging to contextualise the observed SARS-CoV-2 diversity and evolution, highlighting the need for development of infrastructure for collecting and integrating genomic sequencing data and sample-associated metadata.

Summary SARS-CoV-2 variants of concern (VOCs) arise against the backdrop of increasingly heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron genomes, we identified >6,000 independent introductions of the antigenically distinct virus into England and reconstructed the dispersal history of resulting local transmission. Travel restrictions on southern Africa did not reduce BA.1 importation intensity as secondary hubs became major exporters. We explored potential drivers of BA.1 spread across England and discovered an early period during which viral lineage movements mainly occurred between larger cities, followed by a multi-focal spatial expansion shaped by shorter distance mobility patterns. We also found evidence that disease incidence impacted human commuting behaviours around major travel hubs. Our results offer a detailed characterisation of processes that drive the invasion of an emerging VOC across multiple spatial scales and provide unique insights on the interplay between disease spread and human mobility. Highlights Over 6,000 introductions ignited the epidemic wave of Omicron BA.1 in England Importations prior to international travel restrictions were responsible for majority of local BA.1 infections but importations continued from sources other than southern Africa Human mobility at regional and local spatial scales shaped dissemination and growth of BA.1 Changes in human commuting patterns are associated with higher case incidence in travel hubs across England

The Alpha, Beta, and Gamma SARS-CoV-2 variants of concern (VOCs) co-circulated globally during 2020 and 2021, fueling waves of infections. They were displaced by Delta during a third wave worldwide in 2021, which, in turn, was displaced by Omicron in late 2021. In this study, we use phylogenetic and phylogeographic methods to reconstruct the dispersal patterns of VOCs worldwide. We find that source-sink dynamics varied substantially by VOC and identify countries that acted as global and regional hubs of dissemination. We demonstrate the declining role of presumed origin countries of VOCs in their global dispersal, estimating that India contributed <15% of Delta exports and South Africa <1%-2% of Omicron dispersal. We estimate that >80 countries had received introductions of Omicron within 100 days of its emergence, associated with accelerated passenger air travel and higher transmissibility. Our study highlights the rapid dispersal of highly transmissible variants, with implications for genomic surveillance along the hierarchical airline network.

Viruses emerging from wildlife can cause outbreaks in humans and domesticated animals. Predicting the emergence of future pathogens and mitigating their impacts requires an understanding of what shapes virus diversity and dynamics in wildlife reservoirs. In order to better understand coronavirus ecology in wild species, we sampled birds within a coastal freshwater lagoon habitat across 5 years, focussing on a large population of mute swans (Cygnus olor) and the diverse species that they interact with. We discovered and characterised the full genome of a divergent gammacoronavirus belonging to the Goose coronavirus CB17 species. We investigated the genetic diversity and dynamics of this gammacoronavirus using untargeted metagenomic sequencing of 223 faecal samples from swans of known age and sex, and RT-PCR screening of 1632 additional bird samples. The virus circulated persistently within the bird community; virus prevalence in mute swans exhibited seasonal variations, but did not change with swan age-class or epidemiological year. One whole genome was fully characterised, and revealed that the virus originated from a recombination event involving an undescribed gammacoronavirus species. Multiple lineages of this gammacoronavirus co-circulated within our study population. Viruses from this species have recently been detected in aquatic birds from both the Anatidae and Rallidae families, implying that host species habitat sharing may be important in shaping virus host range. As the host range of the Goose coronavirus CB17 species is not limited to geese, we propose that this species name should be updated to ‘Waterbird gammacoronavirus 1’. Non-invasive sampling of bird coronaviruses may provide a tractable model system for understanding the evolutionary and cross-species dynamics of coronaviruses.