An epidemic model for an evolving pathogen with strain-dependent immunity

Between pandemics, the influenza virus exhibits periods of incremental evolution via a process known as antigenic drift. This process gives rise to a sequence of strains of the pathogen that are continuously replaced by newer strains, preventing a build up of immunity in the host population. In this paper, a parsimonious epidemic model is defined that attempts to capture the dynamics of evolving strains within a host population. The ‘evolving strains’ epidemic model has many properties that lie in-between the Susceptible–Infected–Susceptible and the Susceptible–Infected–Removed epidemic models, due to the fact that individuals can only be infected by each strain once, but remain susceptible to reinfection by newly emerged strains. Coupling results are used to identify key properties, such as the time to extinction. A range of reproduction numbers are explored to characterise the model, including a novel quasi-stationary reproduction number that can be used to describe the re-emergence of the pathogen into a population with ‘average’ levels of strain immunity, analogous to the beginning of the winter peak in influenza. Finally the quasi-stationary distribution of the evolving strains model is explored via simulation

Processing of discharge summaries in general practice: a retrospective record review

Background: There is a need for greater understanding of the epidemiology of primary care patient safety in order to generate solutions to prevent future harm. Aim: To estimate the rate of failures in processing actions requested in hospital discharge summaries and to determine factors associated with these failures.Design and setting: We undertook a retrospective records review. Our study population was emergency admissions for patients aged ?75 years, drawn from ten practices in three areas of England.Method: One GP researcher reviewed the records for 300 patients after hospital discharge to determine the rate of compliance with actions requested in the discharge summary and to estimate the rate of associated harm from non-compliance. Where GPs documented decision making contrary to what was requested, these instances did not constitute failures. Data were also collected on time taken to process discharge communications.Results: There were failures in processing actions requested in 46% (112/246) of discharge summaries (CI 39-52%). Medications changes were not made in 17% (124/750) of requests (CI 14-19%). Tests were not completed for 25% of requests (CI 16-34%) and 27% of requested follow-ups were not arranged (CI 20-33%). The harm rate associated with these failures was 8%. Increased risk of failure to process test requests was significantly associated with the type of clinical IT system and male patients.Conclusion: Failures occurred in the processing of requested actions in almost half of all discharge summaries, and with all types of action requested. Associated harms were uncommon and most were of moderate severity

Diagnosis of helminths depends on worm fecundity and the distribution of parasites within hosts

Helminth transmission and morbidity are dependent on the number of mature parasites within a host; however, observing adult worms is impossible for many natural infections. An outstanding challenge is therefore relating routine diagnostics, such as faecal egg counts, to the underlying worm burden. This relationship is complicated by density-dependent fecundity (egg output per worm reduces due to crowding at high burdens) and the skewed distribution of parasites (majority of helminths aggregated in a small fraction of hosts). We address these questions for the carcinogenic liver fluke Opisthorchis viverrini, which infects approximately 10 million people across Southeast Asia, by analysing five epidemiological surveys (n = 641) where adult flukes were recovered. Using a mechanistic model, we show that parasite fecundity varies between populations, with surveys from Thailand and Laos demonstrating distinct patterns of egg output and density-dependence. As the probability of observing faecal eggs increases with the number of mature parasites within a host, we quantify diagnostic sensitivity as a function of the worm burden and find that greater than 50% of cases are misdiagnosed as false negative in communities close to elimination. Finally, we demonstrate that the relationship between observed prevalence from routine diagnostics and true prevalence is nonlinear and strongly influenced by parasite aggregation

Predicted Impact of COVID-19 on Neglected Tropical Disease Programs and the Opportunity for Innovation

Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases (STH), trachoma, and visceral leishmaniasis, shows that the impact of this disruption will vary across the diseases. Programs face a risk of resurgence, which will be fastest in high-transmission areas. Furthermore, of the mass drug administration diseases, schistosomiasis, STH, and trachoma are likely to encounter faster resurgence. The case-finding diseases (gambiense sleeping sickness and visceral leishmaniasis) are likely to have fewer cases being detected but may face an increasing underlying rate of new infections. However, once programs are able to resume, there are ways to mitigate the impact and accelerate progress towards the 2030 goals.</p

KairosMS: a new solution for the processing of hyphenated ultrahigh resolution mass spectrometry data

The use of hyphenated Fourier transform mass spectrometry (FTMS) methods affords additional information about complex chemical mixtures. Coeluted components can be resolved thanks to the ultrahigh resolving power, which also allows extracted ion chromatograms (EICs) to be used for the observation of isomers. As such data sets can be large and data analyses laborious, improved tools are needed for data analyses and extraction of key information. The typical workflow for this type of data is based upon manually dividing the total ion chromatogram (TIC) into several windows of usually equal retention time, averaging the signal of each window to create a single mass spectrum, extracting a peak list, performing the compositional assignments, visualizing the results, and repeating the process for each window. Through removal of the need to manually divide a data set into many time windows and analyze each one, a time-consuming workflow has been significantly simplified. An environmental sample from the oil sands region of Alberta, Canada, and dissolved organic matter samples from the Suwannee River Fulvic Acid (SRFA) and marine waters (Marine DOM) were used as a test bed for the new method. A complete solution named KairosMS was developed in the R language utilizing the Tidyverse packages and Shiny for the user interface. KairosMS imports raw data from common file types, processes it, and exports a mass list for compositional assignments. KairosMS then incorporates those assignments for analysis and visualization. The present method increases the computational speed while reducing the manual work of the analysis when compared to other current methods. The algorithm subsequently incorporates the assignments into the processed data set, generating a series of interactive plots, EICs for individual components or entire compound classes, and can export raw data or graphics for off-line use. Using the example of petroleum related data, it is then visualized according to heteroatom class, carbon number, double bond equivalents, and retention time. The algorithm also gives the ability to screen for isomeric contributions and to follow homologous series or compound classes, instead of individual components, as a function of time

Osmium Atoms and Os₂ Molecules Move Faster on Selenium-Doped Compared to Sulfur-Doped Boronic Graphenic Surfaces

We deposited Os atoms on S- and Se-doped boronic graphenic surfaces by electron bombardment of micelles containing 16e complexes [Os(p-cymene)(1,2-dicarba-closo-dodecarborane-1,2-diselenate/dithiolate)] encapsulated in a triblock copolymer. The surfaces were characterized by energy-dispersive X-ray (EDX) analysis and electron energy loss spectroscopy of energy filtered TEM (EFTEM). Os atoms moved ca. 26× faster on the B/Se surface compared to the B/S surface (233 ± 34 pm·s-1 versus 8.9 ± 1.9 pm·s-1). Os atoms formed dimers with an average Os-Os distance of 0.284 ± 0.077 nm on the B/Se surface and 0.243 ± 0.059 nm on B/S, close to that in metallic Os. The Os2 molecules moved 0.83× and 0.65× more slowly than single Os atoms on B/S and B/Se surfaces, respectively, and again markedly faster (ca. 20×) on the B/Se surface (151 ± 45 pm·s-1 versus 7.4 ± 2.8 pm·s-1). Os atom motion did not follow Brownian motion and appears to involve anchoring sites, probably S and Se atoms. The ability to control the atomic motion of metal atoms and molecules on surfaces has potential for exploitation in nanodevices of the future.</p

Quantifying the multi-scale performance of network inference algorithms

Graphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the “wisdom of crowds” network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales