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bioRxiv2018

Kaptive Web: user-friendly capsule and lipopolysaccharide serotype prediction for Klebsiella genomes

As whole genome sequencing becomes an established component of the microbiologists toolbox, it is imperative that researchers, clinical microbiologists and public health professionals have access to genomic analysis tools for rapid extraction of epidemiologically and clinically relevant information. For the gram-negative hospital pathogens such as Klebsiella pneumoniae, initial efforts have focused on detection and surveillance of antimicrobial resistance genes and clones. However, with the resurgence of interest in alternative infection control strategies targeting Klebsiella surface polysaccharides, the ability to extract information about these antigens is increasingly important.\n\nHere we present Kaptive Web, an online tool for rapid typing of Klebsiella K and O loci, which encode the polysaccharide capsule and lipopolysaccharide O antigen, respectively. Kaptive Web enables users to upload and analyse genome assemblies in a web browser. Results can be downloaded in tabular format or explored in detail via the graphical interface, making it accessible for users at all levels of computational expertise.\n\nWe demonstrate Kaptive Webs utility by analysis of >500 K. pneumoniae genomes. We identify extensive K and O locus diversity among 201 genomes belonging to the carbapenemase- associated clonal group 258 (25 K and six O loci). Characterisation of a further 309 genomes indicates that such diversity is common among the multi-drug resistant clones and that these loci represent useful epidemiological markers for strain subtyping. These findings reinforce the need for rapid, reliable and accessible typing methods such as Kaptive Web.\n\nKaptive Web is available for use at kaptive.holtlab.net and source code is available at github.com/kelwyres/Kaptive-Web.

microbiology↗

bioRxiv2018

Inferring causal pathways among three or more variables from steady-state correlations in a homeostatic system

Cross-sectional correlations between two variables have limited implications for causality. We show here that in a homeostatic system with three or more inter-correlated variables, it is possible to make causal inferences from steady-state data. Every putative pathway between three variables makes a set of differential predictions that can be tested with steady state data. For example, among 3 variables, A, B and C, the coefficient of determination, [Formula] is predicted by the product of [Formula] and [Formula] for some pathways, but not for others. Residuals from a regression line are independent of residuals from another regression for some pathways, but positively or negatively correlated for certain other pathways. Different pathways therefore have different prediction signatures, which can be used to accept or reject plausible pathways. We apply these principles to test the classical pathway leading to a hyperinsulinemic normoglycemic insulin-resistant, or pre-diabetic state using four different sets of epidemiological data. Currently, a set of indices called HOMA-IR and HOMA-{beta} are used to represent insulin resistance and glucose-stimulated insulin response by {beta} cells respectively. Our analysis shows that if we assume the HOMA indices to be faithful indicators, the classical pathway must in turn, be rejected. Among the populations sampled, the classical pathway and faithfulness of the HOMA indices cannot be simultaneously true. The principles and tools described here can find wide application in inferring plausible regulatory mechanisms in homeostatic systems based on epidemiological data.

physiology↗

bioRxiv2018

Beyond the SNP threshold: identifying outbreak clusters using inferred transmissions

Whole genome sequencing (WGS) is increasingly used to aid in understanding pathogen transmission [1]. Very often the number of single nucleotide polymorphisms (SNPs) separating isolates collected during an epidemiological study are used to identify sets of cases that are potentially linked by direct transmission. However, there is little agreement in the literature as to what an appropriate SNP cut-off threshold should be, or indeed whether a simple SNP threshold is appropriate for identifying sets of isolates to be treated as \"transmission clusters\". The SNP thresholds that have been adopted for inferring transmission vary widely even for one pathogen. As an alternative to reliance on a strict SNP threshold, we suggest that the key inferential target when studying the spread of an infectious disease is the number of transmission events separating cases. Here we describe a new framework for deciding whether two pathogen genomes should be considered as part of the same transmission cluster, based jointly on the number of SNP differences and the length of time over which those differences have accumulated. Our approach allows us to probabilistically characterize the number of inferred transmission events that separate cases. We show how this framework can be modified to consider variable mutation rates across the genome (e.g. SNPs associated with drug resistance) and we indicate how the methodology can be extended to incorporate epidemiological data such as spatial proximity. We use recent data collected from tuberculosis studies from British Columbia, Canada and the Republic of Moldova to apply and compare our clustering method to the SNP threshold approach. In the British Columbia data, different cases break off from the main clusters as cut-off thresholds are lowered; the transmission-based method obtains slightly different clusters than the SNP cut-offs. For the Moldova data, straightforward application of the methods shows no appreciable difference, but when we take into account the fact that resistance conferring sites likely do not follow the same mutation clock as most sites due to selection, the transmission-based approach differs from the SNP cut-off method. Outbreak simulations confirm that our transmission based method is at least as good at identifying direct transmissions as a SNP cut-off. We conclude that the new method is a promising step towards establishing a more robust identification of outbreaks.

genomics↗

bioRxiv2018

Deconvoluting Virome-Wide Antiviral Antibody Profiling Data

The ability to comprehensively characterize exposures and immune responses to viral infections will be critical to better understanding human health and disease. We previously described the VirScan system, a phage-display based technology for profiling antibody binding to a comprehensive library of peptides designed to represent the human virome. The previous VirScan analytical approach did not fully account for disproportionate representation of viruses in the library or for antibody cross-reactivity among sequences shared by related viruses. Here we present the AntiViral Antibody Response Deconvolution Algorithm ( AVARDA), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection at species-level resolution by considering alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. By comparing acute and convalescent sera, AVARDA successfully confirmed or detected antibody responses to human herpesviruses 1, 3, 4, 5, and 6, thereby improving the rate of diagnosing viral encephalitis in this cohort by 62.5%. We further assessed AVARDAs utility in the setting of an epidemiological study, demonstrating its ability to determine infections acquired in a child followed prospectively from infancy. We consider ways in which AVARDAs conceptual framework may be further developed in the future and describe how its analyses may be extended beyond investigations of viral infection. AVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases.

bioinformatics↗

bioRxiv2018

Co-circulating mumps lineages at multiple geographic scales

Despite widespread vaccination, eleven thousand mumps cases were reported in the United States (US) in 2016-17, including hundreds in Massachusetts, primarily in college settings. We generated 203 whole genome mumps virus (MuV) sequences from Massachusetts and 15 other states to understand the dynamics of mumps spread locally and nationally, as well as to search for variants potentially related to vaccination. We observed multiple MuV lineages circulating within Massachusetts during 2016-17, evidence for multiple introductions of the virus to the state, and extensive geographic movement of MuV within the US on short time scales. We found no evidence that variants arising during this outbreak contributed to vaccine escape. Combining epidemiological and genomic data, we observed multiple co-circulating clades within individual universities as well as spillover into the local community. Detailed data from one well-sampled university allowed us to estimate an effective reproductive number within that university significantly greater than one. We also used publicly available small hydrophobic (SH) gene sequences to estimate migration between world regions and to place this outbreak in a global context, but demonstrate that these short sequences, historically used for MuV genotyping, are inadequate for tracing detailed transmission. Our findings suggest continuous, often undetected, circulation of mumps both locally and nationally, and highlight the value of combining genomic and epidemiological data to track viral disease transmission at high resolution.

genomics↗

bioRxiv2018

TreeN93: a non-parametric distance-based method for inferring viral transmission clusters

SummaryHighly-used methods for identifying transmission clusters of rapidly-evolving pathogens from molecular data require a user-determined distance threshold. The choice of threshold is often motivated by epidemiological information known a priori, which may be unfeasible for epidemics without rich epidemiological information. TreeN93 is a fully non-parametric distance-based method for transmission cluster identification that scales polynomially.\n\nAvailability and implementationTreeN93 is implemented in Python 3 and is freely available at https://github.com/niemasd/TreeN93/.\n\nContactniemamoshiri@gmail.com

bioinformatics↗

bioRxiv2018

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer.

Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis. Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD. These shared transcriptomic alterations could help explain epidemiological observations suggesting direct and inverse comorbid associations between ASD and particular cancer types.

systems biology↗

bioRxiv2018

Temporal population structure of invasive Group B Streptococcus during a period of rising disease incidence shows expansion of a CC17 clone

Group B Streptococcus (GBS) is a major cause of neonatal invasive disease worldwide. In the Netherlands, the incidence of the disease increased, despite the introduction of prevention guidelines in 1999. This was accompanied by changes in pathogen genotype distribution, with a significant increase in the prevalence of isolates belonging to clonal complex (CC) 17. To better understand the mechanisms of temporal changes in the epidemiology of GBS genotypes that correlated with the rise in disease incidence, we applied whole genome sequencing (WGS) to study a national collection of invasive GBS isolates. A total of 1345 isolates from patients aged 0 - 89 days and collected between 1987 and 2016 in the Netherlands were sequenced and characterised. The GBS population contained 5 major lineages representing CC17 (39%), CC19 (25%), CC23 (18%), CC10 (9%), and CC1 (7%). There was a significant rise in the prevalence of isolates representing CC17 and CC23 among cases of early-and late-onset disease, due to expansion of discrete sub-lineages. The most prominent was shown by a CC17 sub-lineage, identified here as CC17-1A, which experienced a major clonal expansion at the end of the 1990s. The CC17-1A expansion correlated with the emergence of a novel phage carrying a gene encoding a putative adhesion protein, named here StrP. The first occurrence of this phage (designated phiStag1) within the collection in 1997, was followed by multiple, independent acquisitions by CC17 and parallel clonal expansions of CC17-1A and another cluster, CC17-1B. The CC17-1A clone was identified in external datasets, and represents a globally distributed invasive sub-lineage of CC17. Our work describes how a sudden change in the epidemiology of specific GBS sub-lineages, in particular CC17-1A, correlates with the rise in the disease incidence, and indicates a putative key role of a novel phage in driving the expansion of this CC17 clone.\n\nAuthor summaryGroup B Streptococcus (GBS) is a commensal organism of the gastrointestinal and genitourinary tracts. However, it is also an opportunistic pathogen and a major cause of neonatal invasive disease, which can be classified into early-onset (0 - 6 days of life) or late-onset (7 - 89 days of life). Current disease prevention strategy involves intrapartum antibiotic prophylaxis (IAP), which aims to prevent the transmission of GBS from mother to baby during labour. Many developed countries adapted national IAP guidelines. In the Netherlands, these were introduced in 1999. However, the incidence of GBS disease increased after IAP introduction. In this study we applied whole genome sequencing to characterise a nationwide collection of invasive GBS from cases of neonatal disease that occurred between 1987 and 2016. Analysis of GBS population structure involving phylogenetic partitioning of individual lineages revealed that the rise in disease incidence involved the expansion of specific clusters from two major GBS lineages, CC17 and CC23. Our study provides new insights into the recent evolution of the hypervirulent CC17 and describes a rapid expansion of a discrete, pre-existing sub-lineage that occurred after acquisition of a novel phage carrying a putative adhesion protein gene, underscoring the major role of CC17 in neonatal diseases.

genomics↗

bioRxiv2018

Rapid, Affordable, Collection and Analysis of Bioaerosol Viral Pathogens (Begomoviruses and Whitefly Vectors)

Detection of pathogens is critical to monitoring their distribution and spread, and is a key component in the prediction and management of disease epidemiology. Monitoring for pathogens as bioaerosols requires developing techniques which are sensitive, affordable, and time saving before they will have widespread impact. This approach also overcomes private property issues, which are a major pitfall in monitoring diseases in complex agricultural and urban settings. In this study, we have applied an emerging technology of electrostatic sampling to the detection of an insect-transmitted plant pathogen as a bioaerosol. Where insects aggregate in large numbers, as with whiteflies, leafhoppers, psyllids and honey bees, the pathogen (ie. virus or bacteria) becoming aerosolized as thousands of excreta droplets fall from the plants during feeding. Agricultural systems have not fully measured the impact of bioaerosols on disease epidemiology. Electrostatic sampling provides a valuable, affordable, method for monitoring for diseases as bioaerosols, which includes plant, animal and human pathogens. This study shows results which successfully used an electrostatic sampling device to collect an aerosolized begomovirus from the air near whiteflies feeding on virus-infected tomato plants.

pathology↗

bioRxiv2016

Admixture into and within sub-Saharan Africa

Understanding patterns of genetic diversity is a crucial component of medical research in Africa. Here we use haplotype-based population genetics inference to describe gene-flow and admixture in a collection of 48 African groups with a focus on the major populations of the sub-Sahara. Our analysis presents a framework for interpreting haplotype diversity within and between population groups and provides a demographic foundation for genetic epidemiology in Africa. We show that coastal African populations have experienced an influx of Eurasian haplotypes as a series of admixture events over the last 7,000 years, and that Niger-Congo speaking groups from East and Southern Africa share ancestry with Central West Africans as a result of recent population expansions associated with the adoption of new agricultural technologies. We demonstrate that most sub-Saharan populations share ancestry with groups from outside of their current geographic region as a result of large-scale population movements over the last 4,000 years. Our in-depth analysis of admixture provides an insight into haplotype sharing across different geographic groups and the recent movement of alleles into new climatic and pathogenic environments, both of which will aid the interpretation of genetic studies of disease in sub-Saharan Africa.

Genomics↗

bioRxiv2016

Reanalysis of the Anthrax Epidemic in Rhodesia, 1978-84

In the mid-1980s, the largest epidemic of anthrax of the last 200 years was documented in a little known series of studies by Davies in The Central African Journal of Medicine. This epidemic involved thousands of cattle and 10,738 human cases with 200 fatalities in Rhodesia during the Counterinsurgency. Grossly unusual epidemiological features were noted that, to this day, have not been definitively explained. This study performed a historical reanalysis of the data to reveal an estimated geographic involvement of 245,750 km2, with 171,990 cattle and 17,199 human cases. Geospatial time series analysis is suggestive of multiple, independent geotemporal foci of anthrax introduced via an unknown mechanism rather than re-emergence from native endemic foci.

Epidemiology↗

bioRxiv2016

Real-time Zika risk assessment in the United States

BackgroundConfirmed local transmission of Zika Virus (ZIKV) in Texas and Florida have heightened the need for early and accurate indicators of self-sustaining transmission in high risk areas across the southern United States. Given ZIKVs low reporting rates and the geographic variability in suitable conditions, a cluster of reported cases may reflect diverse scenarios, ranging from independent introductions to a self-sustaining local epidemic.\n\nMethodsWe present a quantitative framework for real-time ZIKV risk assessment that captures uncertainty in case reporting, importations, and vector-human transmission dynamics.\n\nResultsWe assessed county-level risk throughout Texas, as of summer 2016, and found that importation risk was concentrated in large metropolitan regions, while sustained ZIKV transmission risk is concentrated in the southeastern counties including the Houston metropolitan region and the Texas-Mexico border (where the sole autochthonous cases have occurred in 2016). We found that counties most likely to detect cases are not necessarily the most likely to experience epidemics, and used our framework to identify triggers to signal the start of an epidemic based on a policymakers propensity for risk.\n\nConclusionsThis framework can inform the strategic timing and spatial allocation of public health resources to combat ZIKV throughout the US, and highlights the need to develop methods to obtain reliable estimates of key epidemiological parameters.

Epidemiology↗

bioRxiv2016

Exposing the diversity of multiple infection patterns

Natural populations often have to cope with genetically distinct parasites that can coexist, or not, within the same hosts. Theoretical models addressing the evolution of virulence have considered two within host infection outcomes, namely superinfection and coinfection. The field somehow became limited by this dichotomy that does not correspond to an empirical reality, as other infection patterns, namely sets of within-host infection outcomes, are possible. We indeed formally prove there are 114 different infection patterns for the sole recoverable chronic infections caused by horizontally-transmitted microparasites. We afterwards highlight eight infection patterns using an explicit modelling of within-host dynamics that captures a large range of ecological interactions, five of which have been neglected so far. To clarify the terminology related to multiple infections, we introduce terms describing these new relevant patterns and illustrate them with existing biological systems. This characterisation of infection patterns opens new perspectives for understanding the epidemiology and the evolution of parasites.

Epidemiology↗

bioRxiv2016

Defining the risk of Zika and chikungunya virus transmission in human population centers of the eastern United States

The recent spread of mosquito-transmitted viruses and associated disease to the Americas motivates a new, data-driven evaluation of risk in temperate population centers. Temperate regions are generally expected to pose low risk for significant mosquito-borne disease, however, the spread of the Asian tiger mosquito (Aedes albopictus) across densely populated urban areas has established a new landscape of risk. We use a model informed by field data to assess the conditions likely to facilitate local transmission of chikungunya and Zika viruses from an infected traveler to Ae. albopictus and then to other humans in USA cities with variable human densities and seasonality.\n\nMosquito-borne disease occurs when specific combinations of conditions maximize virus-to-mosquito and mosquito-to-human contact rates. We develop a mathematical model that captures the epidemiology and is informed by current data on vector ecology from urban sites. The model predicts that one of every two infectious travelers arriving at peak mosquito season could initiate local transmission and > 10% of the introductions could generate a disease outbreak of at least 100 people. Despite Ae. albopictus propensity for biting non-human vertebrates, we also demonstrate that local virus transmission and human outbreaks may occur when vectors feed from humans even just 40% of the time. This work demonstrates how a conditional series of non-average events can result in local arbovirus transmission and outbreaks of disease in humans, even in temperate cities.\n\nAuthor SummaryZika and chikungunya viruses are transmitted by Aedes mosquitoes, including Ae. albopictus, which is abundant in many temperate cities. While disease risk is lower in temperate regions where viral amplification cannot build across years, there is significant potential for localized disease outbreaks in urban populations. We use a model informed by field data to assess the conditions likely to facilitate local transmission of virus from an infected traveler to Ae. albopictus and then to other humans in USA cities with variable human densities and seasonality. The model predicts that one of every two infectious travelers arriving at peak mosquito season could initiate local transmission and > 10% of the introductions could generate a disease outbreak of >100 people.\n\nClassification: Ecology

Epidemiology↗

bioRxiv2016

Insights into mortality patterns and causes of death through a process point of view

Process point of view models of mortality, such as the Strehler-Mildvan and stochastic vitality models, represent death in terms of the loss of survival capacity through challenges and dissipation. Drawing on hallmarks of aging, we link these concepts to candidate biological mechanisms through a framework that defines death as challenges to vitality where distal factors defined the age-evolution of vitality and proximal factors define the probability distribution of challenges. To illustrate the process point of view, we hypothesize that the immune system is a mortality nexus, characterized by two vitality streams: increasing vitality representing immune system development and immunosenescence representing vitality dissipation. Proximal challenges define three mortality partitions: juvenile and adult extrinsic mortalities and intrinsic adult mortality. Model parameters, generated from Swedish mortality data (1751-2010), exhibit biologically meaningful correspondences to economic, health and cause-of-death patterns. The model characterizes the 20th century epidemiological transition mainly as a reduction in extrinsic mortality resulting from a shift from high magnitude disease challenges on individuals at all vitality levels to low magnitude stress challenges on low vitality individuals. Of secondary importance, intrinsic mortality was described by a gradual reduction in the rate of loss of vitality presumably resulting from reduction in the rate of immunosenescence. Extensions and limitations of a distal/proximal framework for characterizing more explicit causes of death, e.g. the young adult mortality hump or cancer in old age are discussed.

Epidemiology↗

bioRxiv2016

Using simulation to aid trial design: ring-vaccination trials

BackgroundThe 2014-5 West African Ebola epidemic highlights the need for rigorous, rapid clinical trials under difficult circumstances. Challenges include temporally and spatially patchy transmission, and the responsibility to deliver public health interventions during a randomized trial. An innovative design such as ring vaccination with an immediate arm and a delayed arm can address these issues, but complex trials raise complex analysis issues.\n\nMethods and FindingsWe present a stochastic, compartmental model for a ring vaccination trial of a vaccine for an Ebola-like disease. After identification of an index case, a ring of primary contacts is recruited and either vaccinated immediately or after a delay of 21 days. The primary outcome of the trial is effectiveness calculated from cumulative incidence in the two arms, counting cases only from a pre-specified window in which the immediate arm is assumed to be fully protected and the delayed arm is not protected. The results of simulating the trial are used to calculate the sample size necessary for 80% power and the estimates of effectiveness are reported under a variety of assumptions regarding the trial design and implementation.\n\nThe three key components of sample size calculations - attack rate in controls, estimate of incidence difference between the arms, and intracluster correlation coefficient - are dependent on trial design and implementation in a way that can be quantitatively predicted by the model. Under baseline parameter assumptions, we found that a total of 8,900 study participants were needed to achieve 80% power to detect a difference in attack rate between the two arms, whereas a standard approach with the same parameters returns a necessary sample size of 7,100 individuals. Such a study would on average return a vaccine effectiveness estimate of 69.81%, with average 95% confidence interval (41.2%, 84.2%).\n\nWe found that for this design the necessary sample size and estimated effectiveness are sensitive to properties of the vaccine - in particular, pre-exposure and post-exposure efficacy; to two setting-specific parameters over which investigators have little control - rate of infections from outside the ring and overall attack rate in the controls; and to three parameters that are determined by the study design - the time window in which cases are counted, intensity of case-detection and administrative delay in vaccinating individuals.\n\nThis approach replaces assumptions about parameters in the trial with assumptions about disease dynamics and vaccine characteristics at the individual level.\n\nConclusionsIncorporating simulation into the trial design process can improve robustness of sample size calculations. Simulation can identify optimal values for study design parameters that can be controlled. For this specific trial design, vaccine effectiveness depends on properties of the ring vaccination design and on the measurement window, as well as the epidemiologic setting. Rejecting the null likely indicates one or more types of vaccine efficacy at the individual level, but the magnitude of the effect will vary across settings.

Epidemiology↗

bioRxiv2016

Zika virus infection as a cause of congenital brain abnormalities and Guillain-Barre syndrome: systematic review

BackgroundThe World Health Organization stated in March 2016 that there was scientific consensus that the mosquito-borne Zika virus was a cause of the neurological disorder Guillain-Barre syndrome and of microcephaly and other congenital brain abnormalities, based on rapid evidence assessments. Decisions about causality require systematic assessment to guide public health actions. The objectives of this study were: to update and re-assess the evidence for causality through a rapid and systematic review about links between Zika virus infection and a) congenital brain abnormalities, including microcephaly, in the foetuses and offspring of pregnant women and b) Guillain-Barre syndrome in any population; and to describe the process and outcomes of an expert assessment of the evidence about causality.\n\nMethods and findingsThe study had three linked components. First, in February 2016, we developed a causality framework that defined questions about the relationship between Zika virus infection and each of the two clinical outcomes in 10 dimensions; temporality, biological plausibility, strength of association, alternative explanations, cessation, dose-response, animal experiments, analogy, specificity and consistency. Second, we did a systematic review (protocol number CRD42016036693). We searched multiple online sources up to May 30, 2016 to find studies that directly addressed either outcome and any causality dimension, used methods to expedite study selection, data extraction and quality assessment, and summarised evidence descriptively. Third, a multidisciplinary panel of experts assessed the review findings and reached consensus on causality. We found 1091 unique items up to May 30, 2016. For congenital brain abnormalities, including microcephaly, we included 72 items; for eight of 10 causality dimensions (all except dose-response relationship and specificity) we found that more than half the relevant studies supported a causal association with Zika virus infection. For Guillain-Barre syndrome, we included 36 items, of which more than half the relevant studies supported a causal association in seven of ten dimensions (all except dose-response relationship, specificity and animal experimental evidence). Articles identified non-systematically from May 30-July 29, 2016 strengthened the review findings. The expert panel concluded that: a) the most likely explanation of available evidence from outbreaks of Zika virus infection and clusters of microcephaly is that Zika virus infection during pregnancy is a cause of congenital brain abnormalities including 61 microcephaly; and b) the most likely explanation of available evidence from outbreaks of Zika virus infection and Guillain-Barre syndrome is that Zika virus infection is a trigger of Guillain-Barre syndrome. The expert panel recognised that Zika virus alone may not be sufficient to cause either congenital brain abnormalities or Guillain-Barre syndrome but agreed that the evidence was sufficient to recommend increased public health measures. Weaknesses are the limited assessment of the role of dengue virus and other possible co-factors, the small number of comparative epidemiological studies, and the difficulty in keeping the review up to date with the pace of publication of new research.\n\nConclusionsRapid and systematic reviews with frequent updating and open dissemination are now needed, both for appraisal of the evidence about Zika virus infection and for the next public health threats that will emerge. This rapid systematic review found sufficient evidence to say that Zika virus is a cause of congenital abnormalities and is a trigger of Guillain-Barre situation.

Epidemiology↗

bioRxiv2016

Assessing the public health impact of tolerance-based therapies with mathematical models

Disease tolerance is a defense strategy against infections that aims at maintaining host health even at high pathogen replication or load. Tolerance mechanisms are currently intensively studied with the long-term goal of exploiting them therapeutically. Because tolerance-based treatment imposes less selective pressure on the pathogen it has been hypothesised to be \"evolution-proof\". However, the primary public health goal is to reduce the incidence and mortality associated with a disease. From this perspective, tolerance-based treatment bears the risk of increasing the prevalence of the disease, which may lead to increased mortality. We assessed the promise of tolerance-based treatment strategies using mathematical models. Conventional treatment was implemented as an increased recovery rate, while tolerance-based treatment was assumed to reduce the disease-related mortality of infected hosts without affecting recovery. We investigated the endemic phase of two types of infections: acute and chronic. Additionally, we considered the effect of pathogen resistance against conventional treatment. We show that, for low coverage of tolerance-based treatment, chronic infections can cause even more deaths than without treatment. Overall, we found that conventional treatment always outperforms tolerance-based treatment, even when we allow the emergence of pathogen resistance. Our results cast doubt on the potential benefit of tolerance-based over conventional treatment. Any clinical application of tolerance-based treatment of infectious diseases has to consider the associated detrimental epidemiological feedback.\n\nAuthor summaryConventional therapies improve patient health by eliminating the pathogen, or, at least, reducing its burden. Recently, alternative therapies that exploit host tolerance mechanisms have received attention from the medical community as a promising strategy. These treatments aim at reducing the level of illness due to the infection, rather than eliminating the pathogen directly. Using a mathematical model, we show that although these treatments are beneficial at the individual level, they can have undesired public health consequences. In particular we show that tolerance-based treatment gives more time for the disease to spread in the population, which in turn increase its prevalence. Moreover, in the case of a low coverage of the treatment of a chronic infection, the overall mortality can increase.

Epidemiology↗