Persistence of low pathogenic influenza A virus in water: a systematic review and quantitative meta-analysis

Avian influenza viruses are able to persist in the environment, in-between the transmission of the virus among its natural hosts. Quantifying the environmental factors that affect the persistence of avian influenza virus is important for influencing our ability to predict future outbreaks and target surveillance and control methods. We conducted a systematic review and quantitative meta-analysis of the environmental factors that affect the decay of low pathogenic avian influenza virus (LPAIV) in water. Abiotic factors affecting the persistence of LPAIV have been investigated for nearly 40 years, yet published data was produced by only 26 quantitative studies. These studies have been conducted by a small number of principal authors (n = 17) and have investigated a narrow range of environmental conditions, all of which were based in laboratories with limited reflection of natural conditions. The use of quantitative meta-analytic techniques provided the opportunity to assess persistence across a greater range of conditions than each individual study can achieve, through the estimation of mean effect-sizes and relationships among multiple variables. Temperature was the most influential variable, for both the strength and magnitude of the effect-size. Moderator variables explained a large proportion of the heterogeneity among effect-sizes. Salinity and pH were important factors, although future work is required to broaden the range of abiotic factors examined, as well as including further diurnal variation and greater environmental realism generally. We were unable to extract a quantitative effect-size estimate for approximately half (50.4%) of the reported experimental outcomes and we strongly recommend a minimum set of quantitative reporting to be included in all studies, which will allow robust assimilation and analysis of future findings. In addition we suggest possible means of increasing the applicability of future studies to the natural environment, and evaluating the biological content of natural waterbodies.Antonia E. Dalziel, Steven Delean, Sarah Heinrich, Phillip Casse

Seasonal host and ecological drivers may promote restricted water as a viral vector

In climates with seasonally limited precipitation, terrestrial animals congregate at high densities at scarce water sources. We hypothesize that viruses can exploit the recurrence of these diverse animal congrega- tions to spread. In this study, we test the central prediction of this hypothesis — that viruses employing this transmission strategy remain stable and infectious in water. Equid herpesviruses (EHVs) were cho- sen as a model as they have been shown to remain stable and infectious in water for weeks under labo- ratory conditions. Using fecal data from wild equids from a previous study, we establish that EHVs are shed more frequently by their hosts during the dry season, increasing the probability of water source contamination with EHV. We document the presence of several strains of EHVs present in high genome copy number from the surface water and sediments of waterholes sampled across a variety of mamma- lian assemblages, locations, temperatures and pH. Phylogenetic analysis reveals that the different EHV strains found exhibit little divergence despite representing ancient lineages. We employed molecular approaches to show that EHVs shed remain stable in waterholes with detection decreasing with increas- ing temperature in sediments. Infectivity experiments using cell culture reveals that EHVs remain infectious in water derived from waterholes. The results are supportive of water as an abiotic viral vector for EHVacceptedVersio

Virus host shifts in Drosophila: The influences of virus genotype and coinfection on susceptibility within and across host species

Virus host shifts are a major source of outbreaks and emerging infectious diseases, and continue to cause considerable damage to public health, society, and the global economy. Predicting and preventing future virus host shifts has become a primary goal of infectious disease research, and multiple tools and approaches are being developed to work towards this goal. In this thesis, I examine three key aspects of infection that have implications for our wider understanding of virus host shifts and their predictability in natural systems: whether the outcome of infections across species is correlated between related viruses, whether the presence of a coinfecting virus can alter the outcomes of cross-species transmission, and the influence of host genetics and immunity on the outcomes of coinfection. These experiments make use of a large and evolutionarily diverse panel of Drosphilidae host species, and infections with two insect Cripaviruses: Drosophila C virus (DCV) and Cricket Paralysis virus (CrPV), with the outcomes of infection quantified throughout as viral loads via qRT-PCR. In Chapter Two, phylogenetic generalised linear mixed models are applied to data on the outcome of single infections with three isolates of DCV (DCV-C, DCV-EB, DCV-M) and one isolate of CrPV, to look for correlations in viral load across host species. Strong positive corrections were found between DCV isolates and weaker positive correlations between DCV and CrPV, with evidence of host species by virus interactions on the outcome of infection. Of the four viruses tested, the most closely related isolates tended to be the most strongly correlated, with correlation strength deteriorating with the evolutionary distance between isolates, although we lacked the diversity or sample size of viruses to properly determine any effect of evolutionary distance on correlation strength. Together, this suggests that hosts susceptible to one virus are also susceptible to closely related viruses, and that knowledge of one virus may be extrapolated to closely related viruses, at least within the range of evolutionary divergence tested here. In the remainder of this thesis, I examine the outcome of coinfection with DCV-C and CrPV across host species (Chapter Three) and across genotypes and immune mutants of Drosophila melanogaster (Chapter Four). These chapters aim to assess the potential for coinfection to alter the outcomes of cross-species transmission – and so interfere with predictions of virus host shifts – and the potential influence of host genetics and immunity on the outcome of coinfection. Chapter Three finds little evidence of systematic changes in the outcome of single and coinfection for both viruses across species, suggesting that coinfection may not be a required consideration in predictive models of every host-virus system. Effects of coinfection were found in a subset of species but were not recapitulated in a follow-up experiment looking at tissue tropism during coinfection on a subset of host species. Together, this suggests that any effects of coinfection across species with DCV and CrPV are due to stochastic effects within individual hosts. Chapter Four finds small but credible effects of coinfection across genotypes of D. melanogaster, but these effects showed little host genetic basis or effect on the genetic basis of susceptibility to each virus separately. Mutations in several immune genes caused virus-specific changes in viral load between single and coinfection, suggesting that coinfection interactions between viruses can be moderated by the host immune response. This thesis has aimed to explore several fundamental features of cross-species transmission that are relevant to our understanding – and ability to predict – virus host shifts. Both the finding that correlations exist between viruses and the approach used to characterise coinfection across and within host species would now benefit from an increased diversity of experimental pathogens, to better investigate the influence of virus evolutionary relationships on the outcomes of virus host shifts and present a broader understanding of the potential impact of coinfection on the outcomes of cross-species transmission.Natural Environment Research Council (NERC

Adaptation of bacteriophage to variable environments

Because a virus is an obligate cellular parasite, the host is a key part of its environment. Viruses may expand their ecological niche by switching host. Successful host switching can be influenced by ecological and evolutionary factors, genetic constraints and fitness within new hosts. An outcome of host switching is reduced fitness exhibited by viruses, a phenomenon observed in the evolution of viral disease emergence and resistance. To understand the genetic basis of this cost, investigations are required at the genotypic and phenotypic level. A host switching paradigm was developed using the model bacteriophage φX174 which was propagated with its laboratory bacterial host Escherichia coli C and with the novel host Salmonella enterica serovar Typhimurium, LT2 strain IJ750 or Escherichia coli K-12 mutant strain JWO196-2 designated as E. coli K-12gmhB-mut. A chemostat was used to achieve steady-state conditions for propagation of φX174 and bacterial cells. Two experiments were performed using this approach. In the first, φX174 was cultured on E. coli K-12gmhB-mut for 3 days (~206 generations). In the second experiment, φX174 was cultured on E. coli C and S. Typhimurium for four consecutive periods of 10 days (~720 generations), alternating between the two hosts. For the second chemostat experiment, the fitness and attachment rates of each viral population were measured using qPCR in liquid culture in order to identify and characterise fitness costs associated with host-switching. Deep sequencing of chemostat samples was also carried out to identify allelic changes occurring before and after host switches. Viral samples were chosen to capture substitutions associated with each host across the experiment (which might explain observed changes in fitness) and time series were picked to identify the dynamics of adaptation on a new host. Bacterial host strains were not sampled in this study. The phenotype measures indicated the pleiotropic costs of host switching, that is a reduction in phage fitness was observed when this was tested on the host used prior to switching, and this may be explained by changes in the attachment rate. The genotype data revealed sets of changes that could be identified as signatures of adaptation to each host, although control data indicate that these may arise during DNA preparation, implicating synthesis of replicative form DNA in the host as a source of selective constraint. Some host-specific alleles and some shared alleles were identified and their fitness effects were examined in isolation after reconstruction of these alleles in the ancestor via targeted mutagenesis. The fitness effects observed for reconstructed mutants were in the direction expected although they do not fully account for the observed costs of host switching. By analysing different phenotypes and genotypes produced during evolution, a detailed view of φX174’s adaptation to different hosts was obtained. The results support the idea that costs associated with pathogen-host adaptation may be host-specific, associated with specific mutations, acquired early and persist. Examining these is relevant for understanding emerging infectious diseases

A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics:influenza A as a case study

Multi-scale epidemic forecasting models have been used to inform population-scale predictions with within-host models and/or infection data collected in longitudinal cohort studies. However, most multi-scale models are complex and require significant modelling expertise to run. We formulate an alternative multi-scale modelling framework using a compartmental model with multiple infected stages. In the large-compartment limit, our easy-to-use framework generates identical results compared to previous more complicated approaches. We apply our framework to the case study of influenza A in humans. By using a viral dynamics model to generate synthetic patient-level data, we explore the effects of limited and inaccurate patient data on the accuracy of population-scale forecasts. If infection data are collected daily, we find that a cohort of at least 40 patients is required for a mean population-scale forecasting error below 10%. Forecasting errors may be reduced by including more patients in future cohort studies or by increasing the frequency of observations for each patient. Our work, therefore, provides not only an accessible epidemiological modelling framework but also an insight into the data required for accurate forecasting using multi-scale models

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease

Wildlife infectious disease dynamics in the context of seasonality and bird migration

The thesis contains chapters that elucidate questions with regards to wildlife infectious disease dynamics - avian influenza in particular - and how those dynamics are affected by seasonality and avian migration

Challenges for modelling interventions for future pandemics

Funding: This work was supported by the Isaac Newton Institute (EPSRC grant no. EP/R014604/1). MEK was supported by grants from The Netherlands Organisation for Health Research and Development (ZonMw), grant number 10430022010001, and grant number 91216062, and by the H2020 Project 101003480 (CORESMA). RNT was supported by the UKRI, grant number EP/V053507/1. GR was supported by Fundação para a Ciência e a Tecnologia (FCT) project reference 131_596787873. and by the VERDI project 101045989 funded by the European Union. LP and CO are funded by the Wellcome Trust and the Royal Society (grant 202562/Z/16/Z). LP is also supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1) and by The Alan Turing Institute for Data Science and Artificial Intelligence. HBS is funded by the Wellcome Trust and Royal Society (202562/Z/16/Z), and the Alexander von Humboldt Foundation. DV had support from the National Council for Scientific and Technological Development of Brazil (CNPq - Refs. 441057/2020-9, 424141/2018-3, 309569/2019-2). FS is supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). EF is supported by UKRI (Medical Research Council)/Department of Health and Social Care (National Insitute of Health Research) MR/V028618/1. JPG's work was supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care.Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.Publisher PDFPeer reviewe