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bioRxiv2019

Multi-scale spatial genetic structure of a vector-borne plant pathogen in orchards and wild habitat

Inferring the dispersal processes of vector-borne plant pathogens is a great challenge because the plausible epidemiological scenarios often involve complex spread patterns at multiple scales. European stone fruit yellows (ESFY), a disease caused by Candidatus Phytoplasma prunorum and disseminated via planting material and vectors belonging to the species Cacopsylla pruni, is a major threat for stone fruit production throughout Europe. The spatial genetic structure of the pathogen was investigated at multiple scales by the application of a combination of statistical approaches to a large dataset obtained through the intensive sampling of the three ecological compartments hosting the pathogen (psyllids, wild and cultivated Prunus) in three Prunus-growing regions in France. This work revealed new haplotypes of Ca. P. prunorum, and showed that the prevalence of the different haplotypes of this pathogen is highly uneven between all regions, and within two of them. In addition, we identified a significant clustering of similar haplotypes within a radius of at most 50 km, but not between nearby wild and cultivated Prunus. We also provide evidence that the two species of the C. pruni complex are unevenly distributed but can spread the pathogen, and that infected plants are transferred between production areas. Altogether, this work supports a main epidemiological scenario where Ca. P. prunorum is endemic in, and mostly acquired from, wild Prunus by immature C. pruni (of both species) who then migrate to \"shelter plants\" that epidemiologically connect sites less than 50 km apart by later providing infectious mature C. pruni to their \"migration basins\", which differ in their haplotypic composition. We argue that such multiscale studies would be very useful for other pathosystems.

ecology↗
bioRxiv2019

Identification of Acinetobacter baumannii loci for capsular polysaccharide (KL) and lipooligosaccharide outer core (OCL) synthesis in genome assemblies using curated reference databases compatible with Kaptive

Multiply antibiotic resistant Acinetobacter baumannii infections are a global public health concern and accurate tracking of the spread of specific lineages is needed. Variation in the composition and structure of capsular polysaccharide (CPS), a critical determinant of virulence and phage susceptibility, makes it an attractive epidemiological marker. The outer core (OC) of lipooligosaccharide also exhibits variation. To take better advantage of the untapped information available in whole genome sequences, we have created a curated reference database of the 92 publicly available gene clusters at the locus encoding proteins responsible for biosynthesis and export of CPS (K locus), and a second database for the 12 gene clusters at the locus for outer core biosynthesis (OC locus). Each entry has been assigned a unique KL or OCL number, and is fully annotated using a simple, transparent and standardised nomenclature. These databases are compatible with Kaptive, a tool for in silico typing of bacterial surface polysaccharide loci, and their utility was validated using a) >630 assembled A. baumannii draft genomes for which the KL and OCL regions had been previously typed manually, and b) 3386 A. baumannii genome assemblies downloaded from NCBI. Among the previously typed genomes, Kaptive was able to confidently assign KL and OCL types with 100% accuracy. Among the genomes retrieved from NCBI, Kaptive detected known KL and OCL in 87% and 90% of genomes, respectively indicating that the majority of common KL and OCL types are captured within the databases; 13 KL were not detected in any public genome assembly. The failure to assign a KL or OCL type may indicate incomplete or poor-quality genomes. However, further novel variants may remain to be documented. Combining outputs with multi-locus sequence typing (Institut Pasteur scheme) revealed multiple KL and OCL types in collections of a single sequence type (ST) representing each of the two predominant globally-distributed clones, ST1 of GC1 and ST2 of GC2, and in collections of other clones comprising >20 isolates each (ST10, ST25, and ST140), indicating extensive within-clone replacement of these loci. The databases are available at https://github.com/katholt/Kaptive and will be updated as further locus types become available. Data Summary1. Databases including fully annotated gene cluster sequences for A. baumannii K loci and OC loci are available for download at https://github.com/katholt/Kaptive 2. The Kaptive software, which can be used to screen new genomes against the K and O locus database is available at https://github.com/katholt/Kaptive (command-line code) and http://kaptive.holtlab.net/ (interactive web service). 3. Details of the Kaptive search results validating in silico serotyping of K and O loci using our approach are provided as supplementary files, Dataset 1 (92 KL reference sequences and 12 OCL reference sequences), Dataset 2 (642 genomes assembled from reads available in NCBI SRA) and Dataset 3 (3415 genome assemblies downloaded from NCBI GenBank). Impact statementThe ability to identify and track closely related isolates is key to understanding, and ultimately controlling, the spread of multiply antibiotic resistant A. baumannii causing difficult to treat infections, which are an urgent public health threat. Extensive variation in the KL and OCL gene clusters responsible for biosynthesis of capsule and the outer core of lipooligosaccharide, respectively, are potentially highly informative epidemiological markers. However, clear, well-documented identification of each variant and simple-to-use tools and procedures are needed to reliably identify them in genome sequence data. Here, we present curated databases compatible with the available web-based and command-line Kaptive tool to make KL and OCL typing readily accessible to assist epidemiological surveillance of this species. As many bacteriophage recognise specific properties of the capsule and attach to it, capsule typing is also important in assessing the potential of specific phage for therapy on a case by case basis.

microbiology↗
bioRxiv2026

DENcode: A model for haplotype-informed transmission probability of dengue virus

Dengue virus transmission networks are often only partially resolved, due to gaps in sampling, unobserved mosquito-mediated transmission, and using methods (phylogenetics) that describe evolutionary relatedness but not explicit, probabilistic transmission links between individual infections. We developed DENcode, a framework to estimate the relative likelihood of vector-mediated transmission between pairs of dengue cases by combining a temperature- and time-modulated epidemiological kernel, which captures the extrinsic incubation period and human infectiousness, with a phylogenetically informed genetic similarity kernel derived from patristic distances between viral haplotypes or consensus sequences. Validation with a real-life dataset of 90 dengue infections sampled from Colombo, Sri Lanka between 2017 - 2020 and sequenced to resolve within-host haplotypes, DENcode estimates were stable across 100 Monte Carlo iterations, yielding narrow credible intervals (median width <0.001) and consistent top-ranked transmission pairs. Sensitivity analyses using ablation experiments showed that removing either the genetic or epidemiological component substantially altered the distribution of linkage probabilities, indicating that both contribute meaningfully to the inferred transmission structure. Serotype-specific transmission networks constructed from pairwise linkage probabilities from DENcode were analysed using degree- and path-based centrality measures at probability thresholds of 0.1 and 0.5, revealing relative importance of cases to disease transmission within the community. Haplotype-derived networks were more informative than consensus-based networks (x 3.6 and x 1.6 times more edges for DENV2 and 3 respectively). DENcode is a robust framework to explore dengue transmission within a community that provides an output of network of transmission probabilities informed by pathogen genetic similarity and clinical epidemiological parameters. Author summaryTracing epidemics of dengue in setting where dengue transmission happens continuously poses many challenges especially with limited availability of genomic surveillance. Here we introduce a model that uses genomic data together with time and location data to calculate a probability of two cases of dengue being related to each other. Using data from the Colombo dengue study, from 2017 to 2020, Sri Lanka, we evaluated the model. We used haplotype level sequences that correspond to the viral variation within the human host and consensus level sequences that average the data from a single human host into a single sequence. We constructed transmission probability networks for each dengue serotype and were able to identify patients who played key roles in the corresponding networks. We were able to show that this model is robust and will be a valuable tool in the context of dengue control.

bioinformatics↗
bioRxiv2026

Dynamics and control of highly pathogenic H5 avian influenza in a threatened pelican population

The ongoing epizootic of highly pathogenic avian influenza (HPAI) continues to cause massive deaths in wildlife. Fundamental understanding of its disease ecology in natural populations is urgently needed. This knowledge has been hindered by the difficulty of acquiring data on epidemic dynamics. Here, using data collected from a threatened population of Dalmatian pelicans (Pelecanus crispus), we recover the epidemiological and evolutionary history of one of the largest HPAI wildlife mortality events. The results show that this devastating outbreak was likely seeded by a single introduction associated with movement of the species. By estimating epidemiological features of two consecutive outbreaks in the same population, we show that panzootic H5N1 since 2022 likely exhibits higher transmissibility and longer shedding time in non-reservoir birds, compared to previous H5NX subtypes. We also evaluate effectiveness of past and future control measures: carcass removal during the outbreak is shown to have surprisingly little impact on mitigating the mortality; and current H5 vaccines relying on capture and injection to deliver cannot establish herd immunity in a wildlife population. The results provide the first field evidence supporting the hypothesis that viral fitness difference of H5N1 to previous H5NX subtypes is the key cause of the expanded epizootic and panzootic since 2022, and on highly debated HPAI management strategies in wildlife populations. Author SummarySince late 2021, a panzootic of H5N1 highly pathogenic avian influenza (HPAI) has caused unprecedented mass mortality in wildlife. Many severely affected species are critical for ecosystem functions, including several threatened and endangered species. However, fundamental knowledge of HPAI disease ecology in natural populations is still lacking, and the effectiveness of potential controls is under debate. Here, using data collected from one of the largest HPAI outbreak in wild animals - over 1700 deaths (80% of the population) in a threatened population of Dalmatian pelicans in Greece, for the first time we recover the transmission dynamics of H5N1 in a migratory bird population. Based on the recovered dynamics, we show that removing carcasses during the outbreak was surprisingly ineffective, and future potential vaccination would require a novel delivery method to establish population immunity in wildlife. Our study provides new insight in the epidemiology of HPAI clade 2.3.4.4b in wildlife, and provides a foundation for assessing interventions within this complicated system.

ecology↗
bioRxiv2026

Species-specific transformer models of bacterial gene order and content for genomic surveillance tasks

Transformer models enable functionally meaningful representation of complex biological data, such as nucleotide or protein sequences. Existing foundation transformer models are trained on large multi-domain corpuses of unlabelled DNA or protein data, showing unmatched task generalisation. However, these foundation models are often outperformed on domain-specific tasks by models trained on taxonomically-constrained data, such as prokaryote gene annotation. By extension, species-specific transformer models hold promise for targeted analyses, given sufficient training data are available. Epidemiological analysis of bacterial pathogens exemplifies the use case of species-specific transformers, due to the wealth of genome data available, coupled with pathogen-specific analyses carried out during routine and outbreak surveillance. Here, we trained a transformer model, PanBART, on the gene content and gene order of two important and biologically distinct bacterial pathogens, Escherichia coli and Streptococcus pneumoniae, benchmarking against state-of-the-art non-transformer approaches for genomic epidemiology. We show PanBART learns representations of population structure in an unsupervised manner, and can be used to accurately assign genomes to biologically-meaningful sequence clusters. PanBART is also able to identify emergent lineages, differentiating them from pre-existing lineages, and can accurately predict genomes likely to uptake genes involved in antibiotic resistance before a transfer event has occurred. Finally, PanBART can be used to conduct co-selection analysis to identify pairs of genes likely to be evolving together. Our work demonstrates that species-specific transformer models can be employed in many critical public health scenarios. We lay the groundwork for wider application of such models in epidemiological analysis, and provide scenarios where such models excel.

bioinformatics↗
bioRxiv2026

Danish profile of soft rot Pectobacteriaceae; A three-year field sampling study proving several clonal clades of soft rot isolates across diverse locations implicating a common origin

Soft rot Pectobacteriaceae (SRP) are among the most economically important plant pathogenic bacteria and are especially known to be problematic in potato production. The epidemiology of disease transmission has been investigated for almost a century, and several aspects have been highlighted as plausible infection routes. However, it is generally accepted that the major source of disease is the latently infected mother tuber, but several parameters are still influencing disease prevalence including contaminated equipment, soil water status as well as temperature. Management of the disease is limited to hygiene practices, dry storage and seed certification systems but several studies have also proven biocontrol agents such as bacteriophages (phages) as promising tools. Despite the severity of SRP on potato production, little is known about the genetic diversity of SRPs in Denmark, and since only few isolates are available, the possibility to design a broadly effective phage cocktail is limited. Here we describe a three-year field study utilizing an agri-citizen science approach where Danish farmers provided symptomatic potato plants or tubers, together with metadata such as date, location, potato variety and origin. By using whole genome sequencing (Illumina and Nanopore) together with metadata we were able to investigate and monitor the epidemiological disease spread across the country using 103 complete genomes, sampled across all three years. In this study we provide epidemiological evidence of disease origins and a suite of phages that could be used as a biocontrol tool for early disease intervention. Our results revealed several clonal clades across diverse locations (SNPs < 20) which strongly indicate common origin. A total of 17 Pectobacterium phages were tested and did target > 80% of clonal clades. Based on the clonality across the soft rot isolates we propose the possibility to set in early on using phages targeting strains relevant for soft rot development, with the possibility of a surveillance program together with customizing the phage preference.

microbiology↗
bioRxiv2026

Data gaps of international databases on HPAI H5 in wildlife in the Americas: implications for surveillance, research, and conservation

Global efforts to prevent and mitigate the impacts of high pathogenicity avian influenza (HPAI) H5 on domestic animals, humans, and wildlife rely on timely and transparent information that is both accurate and interpretable across countries and sectors. International epidemiological and genomic databases, such as the World Animal Health Information System (WAHIS), the Global Animal Disease Information System (EMPRES-i+), the Global Initiative on Sharing All Influenza Data (GISAID), and the National Center for Technological Bioinformation Virus Portal (NCBI) provide essential information for surveillance, research, and decision-making. To evaluate how well these resources capture recent wildlife impacts, we consolidated information from these databases and complementary public sources including government reports, scientific literature, and news articles, on wildlife mortality associated with HPAI H5 in the Americas from November 2021 to July 2024. The consolidated dataset comprised 615,883 wild birds (287 spp.) and 63,409 wild mammals (39 spp.). In comparison, WAHIS represented 16,902 wild birds (261 spp.) and 6,323 wild mammals (31 spp.) while EMPRES-i+ captured a substantially smaller portion of affected host diversity for both wild birds (105 spp.) and wild mammals (27 spp.). Genomic databases (GISAID and NCBI) represented 7,027 whole genome equivalents of H5 viruses from wild birds (175 spp.) and 371 from wild mammals (26 spp.). These discrepancies indicate that international databases, while essential, provide an incomplete picture of HPAI impacts on wildlife, with significant geographic and taxonomic asymmetries attributable to differences in surveillance capacity, reporting practices, sequencing effort, and data-sharing pathways. Studies and management strategies relying on these resources without complementary validation may therefore mistake data gaps for real-world epidemiological patterns. Strengthening data reporting standards, improving validation procedures, and integrating international databases with national reports, scientific publications, and other sources will enhance the reliability of epidemiological analyses and support more effective One Health surveillance, risk assessment, and conservation action. Author summaryHigh pathogenicity avian influenza (HPAI) H5 viruses, often called bird flu viruses, can cause severe disease in birds and mammals, including humans. Because of their relevance for human health, livestock production, and wildlife conservation, international databases were established to share information on when and where these viruses are detected, which species are affected, and what virus strains are found. These databases are essential tools for governments, scientists, and conservation practitioners working to track outbreaks, understand how these viruses spread and evolve, and guide surveillance and response. In this study, we compiled and compared information on recent HPAI H5 events in wildlife in the Americas available in international databases with information from other public sources, including reports from governments, scientific literature, and news articles. We found important discrepancies in how countries and species affected were represented across sources. As a result, international databases might not fully capture the actual distribution or conservation impact of HPAI H5 on wildlife. Our findings also show why decision-makers and scientists should interpret database-derived patterns carefully. We provide recommendations to improve international databases to address these gaps and better inform One Health risk assessment and wildlife conservation actions.

microbiology↗
bioRxiv2026

Incremental AMR acquisition driving successive genotype replacements and the rise of extensively drug resistant (XDR) Shigella sonnei in Australia over 20 years.

BackgroundIn Australia, the burden of shigellosis is predominantly in returning travellers or in men who have sex with men (MSM). Here, we combine genomic data with comprehensive epidemiological data on sexual exposure and international travel to explore population dynamics of Shigella sonnei and the expansion of multi-drug resistant (MDR) and extensively drug-resistant (XDR) sub-lineages. MethodsA population-level study of all cultured Shigella sonnei isolates in the state of Victoria, Australia, was undertaken between January 2002 and December 2024. Antimicrobial susceptibility testing, whole-genome sequencing, and bioinformatic analyses of 1,305 Shigella sonnei isolates were performed at the Microbiological Diagnostic Unit Public Health Laboratory. Enhanced metadata on source attribution including travel and sexual exposure were collected through surveillance forms or by interviews. ResultsThis study highlights significant shifts in Shigella sonnei cases in Victoria from sensitive strains to MDR and then XDR, particularly in the MSM-associated groups but also associated with a large point source outbreak. We describe an historical pattern of shifting genotype prevalence, and replacement to more varied and higher proportions of antimicrobial resistance over the last decade, resulting in the establishment of two distinct but highly concerning XDR sub-lineages within Victoria. ConclusionsOur genomic-epidemiological analyses highlight that drug-resistant Shigella sonnei remains an ongoing public health threat, and the importance of ongoing surveillance. We determined local evolutionary trajectories and identified expanding sub-lineages that informed shifts in clinical management and antimicrobial recommendations over time, including the use of azithromycin and carbapenems. Placing these local dynamics within the broader global epidemiology, we link how regional evolution interconnects with international dissemination, proving valuable context for guiding local, national and global strategies for prevent outbreaks and antimicrobial resistance.

microbiology↗
bioRxiv2026

Shifts in Genetic Diversity of Porcine Reproductive and Respiratory Syndrome Virus 2 in Vietnam Before and After African Swine Fever: Increased Diversity and Novel Sub-lineages

Porcine reproductive and respiratory syndrome virus 2 (PRRSV-2) remains one of the most important transboundary pathogens affecting swine production in Vietnam; however, it remains poorly understood how long-term evolutionary dynamics were impacted by the African swine fever (ASF) epidemic, a period of time where swine population demographics and movement were heavily perturbed. We investigated the molecular epidemiology, evolutionary history, and phylogeographic dynamics of PRRSV-2 circulating in Vietnam between 2007 and 2024 by integrating 366 Vietnamese ORF5 sequences with a globally curated lineage reference. Maximum-likelihood phylogenetic, Bayesian phylodynamic, and discrete phylogeographic analyses revealed that the Vietnamese PRRSV-2 population underwent substantial reshaping after the ASF epidemic, shifting from a predominantly endemic sub-lineage L8E population to a genetically diverse viral community comprising multiple established and newly emerging sub-lineages. Despite these epidemiological changes, the endemic sub-lineage L8E population maintained a relatively stable evolutionary rate across the pre- and post-ASF periods, suggesting that ASF reshaped viral population structure rather than intrinsic evolutionary dynamics. Two previously unclassified viral clusters circulating in Vietnam and Thailand fulfilled all criteria for formal designation and were recognized as the novel sub-lineages L1M and L10B by the International PRRSV-2 Nomenclature Consortium. Phylogeographic reconstruction further demonstrated contrasting transmission patterns among major sub-lineages, including long-term endemic persistence of L8E, repeated unidirectional introductions of sub-lineages L1M and L10B from Thailand, and bidirectional transpacific dissemination of sub-lineage L1A linking Southeast Asia and North America. Collectively, these findings demonstrate that the ASF epidemic coincided with a fundamental reshaping of the PRRSV-2 epidemiological landscape in Vietnam while revealing Southeast Asia as an active center of ongoing viral diversification. This study provides an updated evolutionary framework for PRRSV-2 surveillance and highlights the importance of continuous genomic monitoring and regional collaboration for the early detection and control of emerging transboundary variants.

genetics↗
bioRxiv2026

Allergic airway inflammation protects against SARS-CoV-2 and MERS-CoV disease but promotes fatal SARS-CoV-1 immunopathology

Epidemiological, clinical, and experimental data suggest that allergic asthma may have protective effects against severe Coronavirus Disease 2019 (COVID-19) but are often conflicting between different models of infection and epidemiological analyses. Allergic asthma is characterized by type 2 (T2) inflammation, mucus hypersecretion, and airway remodeling. We used a house dust mite (HDM) respiratory exposure model to determine how preexisting allergic inflammation alters the pathogenesis of three highly pathogenic coronaviruses, SARS-CoV-1, SARS-CoV-2, and MERS-CoV. HDM treatment induced canonical features of allergic asthma, including goblet cell hyperplasia, mucin hypersecretion, eosinophil recruitment, M2 macrophage polarization, and induction of IL-13. Across multiple SARS-CoV-2 variants, HDM treated mice exhibited reduced viral titers, less weight loss, diminished lung immunopathology, and improved survival rates. Similar protection was observed in a mouse model of MERS-CoV, with reduced lethality, lower viral burden, and blunted cytokine storm signatures in HDM treated mice. In contrast, allergic airway inflammation exacerbated SARS-CoV-1 pathogenesis, as HDM-treated mice developed fulminant alveolar inflammation, severe weight loss, elevated pro-inflammatory cytokines and chemokines, and increased mortality despite having reduced virus titer. Thus, in mice, allergic airway inflammation exerts opposing virus specific effects on coronavirus pathogenesis. These results reconcile conflicting epidemiological and mechanistic studies across coronavirus epidemics and highlight the need to consider host allergic status as a key variable influencing disease outcome from emerging coronaviruses.

microbiology↗
bioRxiv2026

Estimation of the transmission dynamics of H5N1 HPAI outbreak in a dairy herd using a modeling approach

The emergence of Highly Pathogenic Avian Influenza (HPAI 2.3.4.4b) in dairy herds in 2024 across 19 states in the United States of America has raised concerns regarding the potential national and global zoonotic impact. All recent modeling efforts implemented homogenous cattle-to-cattle (both intra and inter-herd) transmission models which did not capture the real-world heterogeneity in mixing of animals, individual variations in susceptibility and infectiousness and clinical incidences across pens and lactation groups. The aim of this study was to develop a heterogenous transmission model to estimate the epidemiological parameters for intra-herd HPAI transmission on Californian dairies. We developed a validated stochastic agent-based model to estimate the epidemiological parameters for intra-herd HPAI transmission on California dairies. The hierarchical agent-based model also parameterized stochastic cattle movements within-herd to simulate real dairy management practices. A cow-level SEIR transmission approach was assumed during the outbreak. A novel Bayesian Optimizer with Gaussian Process (BO-GP) was fitted to the agent-based model for validation which converged within 25-40 iterations (out of 100 per farm) with minimal loss over two distinct error metrics, namely, Poisson loss function and temporal distance metric. Our optimized simulations estimated an average R0 was around 10.7-10.8 across all farms within the first 15 days of observed outbreak on four dairy farms with a mean effective transmission rate of 4.5% per contact between susceptible and infectious cows within each pen. Our model demonstrated that the movement of cows between pens ensured localized clusters of outbreaks within the sub-herds (pen population) that prolonged the overall outbreak within farms. We estimated the total duration of infection between 14.5 and 28 days, which is higher than the estimates from the homogenous models. With an integrated hierarchical agent-based model combined with Bayesian approximation, we produced actionable insights on the epidemiology of intra-farm spread of HPAI within cow herds, thereby guiding both future model development and applied disease control strategy.

systems biology↗
bioRxiv2016

How human behavior drives the propagation of an emerging infection: the case of the 2014 Chikungunya outbreak in Martinique

Understanding the spatio-temporal dynamics of endemic infections is of critical importance for a deeper understanding of pathogen transmission, and for the design of more efficient public health strategies. However, very few studies in this domain have focused on emerging infections, generating a gap of knowledge that hampers epidemiological response planning. Here, we analyze the case of a Chikungunya outbreak that occurred in Martinique in 2014. Using time series estimates from a network of sentinel practitioners covering the entire island, we first analyze the spatio-temporal dynamics and show that the largest city has served as the epicenter of this epidemic. We further show that the epidemic spread from there through two different propagation waves moving northwards and southwards, probably by individuals moving along the road network. We then develop a mathematical model to explore the drivers of the temporal dynamics of this mosquito-borne virus. Finally, we show that human behavior, inferred by a textual analysis of messages published on the social network Twitter, is required to explain the epidemiological dynamics over time. Overall, our results suggest that human behavior has been a key component of the outbreak propagation, and we argue that such results can lead to more efficient public health strategies specifically targeting the propagation process.

Epidemiology↗
bioRxiv2016

Quantitative, model-based estimates of variability in the serial interval of Plasmodium falciparum malaria

Background: The serial interval is a fundamentally important quantity in infectious disease epidemiology that has numerous applications to inferring patterns of transmission from case data. Many of these applications are apropos to efforts to eliminate Plasmodium falciparum (Pf) malaria from locations throughout the world, yet the serial interval for this disease is poorly understood quantitatively.\n\nResults: To obtain a quantitative estimate of the serial interval for Pf malaria, we took the sum of components of the Pf malaria transmission cycle based on a combination of mathematical models and empirical data. During this process, we identified a number of factors that account for substantial variability in the serial interval across different contexts. Treatment with antimalarial drugs roughly halves the serial interval, seasonality results in different serial intervals at different points in the transmission season, and variability in within-host dynamics results in many individuals whose serial intervals do not follow average behavior.\n\nConclusions: These results have important implications for epidemiological applications that rely on quantitative estimates of the serial interval of Pf malaria and other diseases characterized by prolonged infections and complex ecological drivers.

Epidemiology↗
bioRxiv2016

Inference of transmission network structure from HIV phylogenetic trees

Phylogenetic inference is an attractive mean to reconstruct transmission histories and epidemics. As the interest lies in how HIV-1 spread in a human population, many previous studies have ignored details about the evolutionary process of the pathogen. Because phylogenetics investigates the evolutionary history of the pathogen rather than the spread between hosts per se, we first investigated the effects of including a within-host evolutionary model in epidemiological simulations. In particular, we investigated if the resulting phvlogenv could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivitv across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phvlogenv (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phvlogenv reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivitv dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.

Epidemiology↗
bioRxiv2016

Animals in the Zika virus life cycle: what to expect from megadiverse Latin American countries.

Zika virus (ZIKV) was first isolated in 1947 in primates in Uganda, West Africa. The virus remained confined to the equatorial regions of Africa and Asia, cycling between infecting monkeys, arboreal mosquitoes, and occasional humans. The ZIKV Asiatic strain was probably introduced into Brazil in 2013. In the current critical human epidemic in the Americas, ZIKV is transmitted primarily by Aedes aegypti mosquitoes, especially where the human population density is combined with poor sanitation. Presently, ZIKV is in contact with the rich biodiversity in all Brazilian biomes, bordering on other Latin American countries. Infections in Brazilian primates have been reported recently, but the overall impact of this virus on wildlife in the Americas is still unknown. The current epidemic in the Americas requires knowledge on the role of mammals, especially non-human primates, in ZIKV transmission to humans. The article discusses the available data on ZIKV in host animals, besides issues of biodiversity, rapid environmental change, and impact on human health in megadiverse Latin American countries. The authors reviewed scientific articles and recent news stories on ZIKV in animals, showing that 47 animal species from three orders (mammals, reptiles, and birds) have been investigated for the potential to establish a sylvatic cycle. The review aims to contribute to epidemiological studies and the knowledge on the natural history of ZIKV. The article concludes with questions that require urgent attention in epidemiological studies involving wildlife in order to understand their role as ZIKV hosts and to effectively control the epidemic.

Epidemiology↗
bioRxiv2016

New method to reconstruct phylogenetic and transmission trees with sequence data from infectious disease outbreaks

Whole-genome sequencing (WGS) of pathogens from host samples becomes more and more routine during infectious disease outbreaks. These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. However, the relationship between transmission events and WGS data is obscured by uncertainty arising from four largely unobserved processes: transmission, case observation, within-host pathogen dynamics and mutation. To properly resolve transmission events, these processes need to be taken into account. Recent years have seen much progress in theory and method development, but applications are tailored to specific datasets with matching model assumptions and code, or otherwise make simplifying assumptions that break up the dependency between the four processes. To obtain a method with wider applicability, we have developed a novel approach to reconstruct transmission trees with WGS data. Our approach combines elementary models for transmission, case observation, within-host pathogen dynamics, and mutation. We use Bayesian inference with MCMC for which we have designed novel proposal steps to efficiently traverse the posterior distribution, taking account of all unobserved processes at once. This allows for efficient sampling of transmission trees from the posterior distribution, and robust estimation of consensus transmission trees. We implemented the proposed method in a new R package phybreak. The method performs well in tests of both new and published simulated data. We apply the model to to five datasets on densely sampled infectious disease outbreaks, covering a wide range of epidemiological settings. Using only sampling times and sequences as data, our analyses confirmed the original results or improved on them: the more realistic infection times place more confidence in the inferred transmission trees.\n\nAuthor SummaryIt is becoming easier and cheaper to obtain whole genome sequences of pathogen samples during outbreaks of infectious diseases. If all hosts during an outbreak are sampled, and these samples are sequenced, the small differences between the sequences (single nucleotide polymorphisms, SNPs) give information on the transmission tree, i.e. who infected whom, and when. However, correctly inferring this tree is not straightforward, because SNPs arise from unobserved processes including infection events, as well as pathogen growth and mutation within the hosts. Several methods have been developed in recent years, but none so generic and easily accessible that it can easily be applied to new settings and datasets. We have developed a new model and method to infer transmission trees without putting prior limiting constraints on the order of unobserved events. The method is easily accessible in an R package implementation. We show that the method performs well on new and previously published simulated data. We illustrate applicability to a wide range of infectious diseases and settings by analysing five published datasets on densely sampled infectious disease outbreaks, confirming or improving the original results.

Epidemiology↗
bioRxiv2016

Containing Emerging Epidemics: a Quantitative Comparison of Quarantine and Symptom Monitoring

Strategies for containing an emerging infectious disease outbreak must be non-pharmaceutical when drugs or vaccines for the pathogen do not yet exist or are unavailable. The success of these non-pharmaceutical strategies will depend not only on the effectiveness of quarantine or other isolation measures but also on the epidemiological characteristics of the infection. However, there is currently no systematic framework to assess the relationship between different containment strategies and the natural history and epidemiological dynamics of the pathogen. Here, we compare the effectiveness of quarantine and symptom monitoring, implemented via contact tracing, in controlling epidemics using an agent-based branching model. We examine the relationship between epidemic containment and the disease dynamics of symptoms and infectiousness for seven case study diseases with diverse natural histories including Ebola, Influenza A, and Severe Acute Respiratory Syndrome (SARS). We show that the comparative effectiveness of symptom monitoring and quarantine depends critically on the natural history of the infectious disease, its inherent transmissibility, and the intervention feasibility in the particular healthcare setting. The benefit of quarantine over symptom monitoring is generally maximized for fast-course diseases, but we show the conditions under which symptom monitoring alone can control certain outbreaks. This quantitative framework can guide policy-makers on how best to use non-pharmaceutical interventions to contain emerging outbreaks and prioritize research during an outbreak of a novel pathogen.\n\nSIGNIFICANCEQuarantine and symptom monitoring of contacts with suspected exposure to an infectious disease are key interventions for the control of emerging epidemics; however, there does not yet exist a quantitative framework for comparing the control performance of each. Here, we use a mathematical model of seven case study diseases to show how the choice of intervention is influenced by the natural history of the infectious disease, its inherent transmissibility, and the intervention feasibility in the particular healthcare setting. We use this information to identify the most important characteristics of the disease and setting that need to be characterized for an emerging pathogen in order to make an informed decision between quarantine and symptom monitoring.

Epidemiology↗
bioRxiv2016

Phylodynamics on local sexual contact networks

Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.

epidemiology↗