Daily timing of low tide drives seasonality in intertidal emersion mortality risk

Sea level exerts a fundamental influence on the intertidal zone, where organisms are subject to immersion and emersion at varying timescales and frequencies. While emersed, intertidal organisms are exposed to atmospheric stressors which show marked diurnal and seasonal variability, therefore the daily and seasonal timing of low water is a key determinant of survival and growth in this zone. Using the example of shallow coral reefs, the coincidence of emersion with selected stressors was investigated for eight locations around the Australian coastline. Hourly water levels (1992 – 2016) from a high-resolution sea level hindcast (http://sealevelx.ems.uwa.edu.au), were linked to maximum surface solar radiation data from the Copernicus ERA5 atmospheric model and minimum atmospheric temperature observations from the Australian Bureau of Meteorology to identify seasonal patterns and historical occurrence of coral emersion mortality risk. Local tidal characteristics were found to dictate the time of day when low water, and therefore emersion mortality risk occurs, varying on a seasonal and regional basis. In general, risk was found to be greatest during the Austral spring when mean sea levels are lowest and a phase change in solar tidal constituents occurs. For all Great Barrier Reef sites, low tide occurs close to midday during winter and midnight in the summer, which may be fundamental factor supporting the historical bio-geographical development of the reef. Interannual variability in emersion mortality risk was mostly driven by non-tidal factors, particularly along the West Coast where El Niño events are associated with lower mean sea levels. This paper highlights the importance of considering emersion history when assessing intertidal environments, including shallow coral reef platform habitats, where critical low water events intrinsically influence coral health and cover. The study addresses a fundamental knowledge gap in both the field of water level science and intertidal biology in relation to the daily timing of low tide, which varies predictably on a seasonal and regional basis

Mathematical Models for a New Era of Malaria Eradication

Maciej Boni and colleagues discuss a new model exploring how a switch in antimalarial drug use to artemisinin-based combination therapies will affect malaria prevalence and incidence in endemic regions

Analysis of symmetries in models of multi-strain infections

In mathematical studies of the dynamics of multi-strain diseases caused by antigenically diverse pathogens, there is a substantial interest in analytical insights. Using the example of a generic model of multi-strain diseases with cross-immunity between strains, we show that a significant understanding of the stability of steady states and possible dynamical behaviours can be achieved when the symmetry of interactions between strains is taken into account. Techniques of equivariant bifurcation theory allow one to identify the type of possible symmetry-breaking Hopf bifurcation, as well as to classify different periodic solutions in terms of their spatial and temporal symmetries. The approach is also illustrated on other models of multi-strain diseases, where the same methodology provides a systematic understanding of bifurcation scenarios and periodic behaviours. The results of the analysis are quite generic, and have wider implications for understanding the dynamics of a large class of models of multi-strain diseases

Digital Epidemiology

Mobile, social, real-time: the ongoing revolution in the way people communicate has given rise to a new kind of epidemiology. Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world. The rapid, unprecedented increase in the availability of relevant data from various digital sources creates considerable technical and computational challenges

Long-term evolution of antigen repertoires among carried meningococci

Most studies of bacterial pathogen populations have been based on isolates collected from individuals with disease, or their contacts, over short time periods. For commensal organisms that occasionally cause disease, such as Neisseria meningitidis, however, the analysis of isolates from long-term asymptomatic carriage is necessary to elucidate their evolution and population structure. Here, we use mathematical models to analyse the structuring and dynamics of three vaccine-candidate antigens among carried meningococcal isolates collected over nearly 30 years in the Czech Republic. The data indicate that stable combinations of antigenic alleles were maintained over this time period despite evidence for high rates of recombination, consistent with theoretical models in which strong immune selection can maintain non-overlapping combinations of antigenic determinants in the presence of recombination. We contrast this antigenic structure with the overlapping but relatively stable combinations of the housekeeping genes observed among the same isolates, and use a novel network approach to visualize these relationships

Introduction of rubella-containing-vaccine to Madagascar: implications for roll-out and local elimination

Few countries in Africa currently include rubella-containing vaccination (RCV) in their immunization schedule. The Global Alliance for Vaccines Initiative (GAVI) recently opened a funding window that has motivated more widespread roll-out of RCV. As countries plan RCV introductions, an understanding of the existing burden, spatial patterns of vaccine coverage, and the impact of patterns of local extinction and reintroduction for rubella will be critical to developing effective programmes. As one of the first countries proposing RCV introduction in part with GAVI funding, Madagascar provides a powerful and timely case study. We analyse serological data from measles surveillance systems to characterize the epidemiology of rubella in Madagascar. Combining these results with data on measles vaccination delivery, we develop an age-structured model to simulate rubella vaccination scenarios and evaluate the dynamics of rubella and the burden of congenital rubella syndrome (CRS) across Madagascar. We additionally evaluate the drivers of spatial heterogeneity in age of infection to identify focal locations where vaccine surveillance should be strengthened and where challenges to successful vaccination introduction are expected. Our analyses indicate that characteristics of rubella in Madagascar are in line with global observations, with an average age of infection near 7 years, and an impact of frequent local extinction with reintroductions causing localized epidemics. Modelling results indicate that introduction of RCV into the routine programme alone may initially decrease rubella incidence but then result in cumulative increases in the burden of CRS in some regions (and transient increases in this burden in many regions). Deployment of RCV with regular supplementary campaigns will mitigate these outcomes. Results suggest that introduction of RCV offers a potential for elimination of rubella in Madagascar, but also emphasize both that targeted vaccination is likely to be a lynchpin of this success, and the public health vigilance that this introduction will require

Genealogical typing of Neisseria meningitidis

Despite the increasing popularity of multilocus sequence typing (MLST), the most appropriate method for characterizing bacterial variation and facilitating epidemiological investigations remains a matter of debate. Here, we propose that different typing schemes should be compared on the basis of their power to infer clonal relationships and investigate the utility of sequence data for genealogical reconstruction by exploiting new statistical tools and data from 20 housekeeping loci for 93 isolates of the bacterial pathogen Neisseria meningitidis. Our analysis demonstrated that all but one of the hyperinvasive isolates established by multilocus enzyme electrophoresis and MLST were grouped into one of six genealogical lineages, each of which contained substantial variation. Due to the confounding effect of recombination, evolutionary relationships among these lineages remained unclear, even using 20 loci. Analyses of the seven loci in the standard MLST scheme using the same methods reproduced this classification, but were unable to support finer inferences concerning the relationships between the members within each complex

Changes in serogroup and genotype prevalence among carried meningococci in the United Kingdom during vaccine implementation.

BACKGROUND: Herd immunity is important in the effectiveness of conjugate polysaccharide vaccines against encapsulated bacteria. A large multicenter study investigated the effect of meningococcal serogroup C conjugate vaccine introduction on the meningococcal population. METHODS: Carried meningococci in individuals aged 15-19 years attending education establishments were investigated before and for 2 years after vaccine introduction. Isolates were characterized by multilocus sequence typing, serogroup, and capsular region genotype and changes in phenotypes and genotypes assessed. RESULTS: A total of 8462 meningococci were isolated from 47 765 participants (17.7%). Serogroup prevalence was similar over the 3 years, except for decreases of 80% for serogroup C and 40% for serogroup 29E. Clonal complexes were associated with particular serogroups and their relative proportions fluctuated, with 12 statistically significant changes (6 up, 6 down). The reduction of ST-11 complex serogroup C meningococci was probably due to vaccine introduction. Reasons for a decrease in serogroup 29E ST-254 meningococci (from 1.8% to 0.7%) and an increase in serogroup B ST-213 complex meningococci (from 6.7% to 10.6%) were less clear. CONCLUSIONS: Natural fluctuations in carried meningococcal genotypes and phenotypes a can be affected by the use of conjugate vaccines, and not all of these changes are anticipatable in advance of vaccine introduction

THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites.

As many malaria-endemic countries move towards elimination of Plasmodium falciparum, the most virulent human malaria parasite, effective tools for monitoring malaria epidemiology are urgent priorities. P. falciparum population genetic approaches offer promising tools for understanding transmission and spread of the disease, but a high prevalence of multi-clone or polygenomic infections can render estimation of even the most basic parameters, such as allele frequencies, challenging. A previous method, COIL, was developed to estimate complexity of infection (COI) from single nucleotide polymorphism (SNP) data, but relies on monogenomic infections to estimate allele frequencies or requires external allele frequency data which may not available. Estimates limited to monogenomic infections may not be representative, however, and when the average COI is high, they can be difficult or impossible to obtain. Therefore, we developed THE REAL McCOIL, Turning HEterozygous SNP data into Robust Estimates of ALelle frequency, via Markov chain Monte Carlo, and Complexity Of Infection using Likelihood, to incorporate polygenomic samples and simultaneously estimate allele frequency and COI. This approach was tested via simulations then applied to SNP data from cross-sectional surveys performed in three Ugandan sites with varying malaria transmission. We show that THE REAL McCOIL consistently outperforms COIL on simulated data, particularly when most infections are polygenomic. Using field data we show that, unlike with COIL, we can distinguish epidemiologically relevant differences in COI between and within these sites. Surprisingly, for example, we estimated high average COI in a peri-urban subregion with lower transmission intensity, suggesting that many of these cases were imported from surrounding regions with higher transmission intensity. THE REAL McCOIL therefore provides a robust tool for understanding the molecular epidemiology of malaria across transmission settings

Bacterial microevolution and the Pangenome

The comparison of multiple genome sequences sampled from a bacterial population reveals considerable diversity in both the core and the accessory parts of the pangenome. This diversity can be analysed in terms of microevolutionary events that took place since the genomes shared a common ancestor, especially deletion, duplication, and recombination. We review the basic modelling ingredients used implicitly or explicitly when performing such a pangenome analysis. In particular, we describe a basic neutral phylogenetic framework of bacterial pangenome microevolution, which is not incompatible with evaluating the role of natural selection. We survey the different ways in which pangenome data is summarised in order to be included in microevolutionary models, as well as the main methodological approaches that have been proposed to reconstruct pangenome microevolutionary history