Search

The efficacy of a stabilized oxychloro-based food grade sanitizer to decontaminate seeds destined for sprout production has been evaluated. By using mung bean seeds as a model system, it was demonstrated that the sanitizer could be used to inactivate a five-strain cocktail of Escherichia coli O157:H7 or Salmonella introduced onto beans at 10³ to 104 CFU/g. Salmonella was more tolerant to stabilized oxychloro than was E. coli O157:H7, with sanitizer levels of >150 and >50 ppm, respectively, being required to ensure pathogen-free sprouts. The decontamination efficacy was also found to be dependent on treatment time (>8 h optimal) and the seed-to-sanitizer ratio (>1:4 optimal). Stabilized oxychloro treatment did not exhibit phytotoxic effects, as germination and sprout yields were not significantly (P > 0.05) different as compared with untreated controls. Although human pathogens could be effectively eliminated from mung beans, the aerobic plate count of native microflora on sprouts grown from treated seed was not significantly (P > 0.05) different from the controls. The diversity of microbial populations (determined through 16S rRNA denaturing gradient gel electrophoresis analysis) associated with bean sprouts was not significantly affected by the sanitizer treatment. However, it was noted that Klebsiella and Herbasprillum (both common plant endophytes) were absent in sprouts derived from decontaminated seed but were present in control sprouts. When a further range of seed types was evaluated, it was found that alfalfa, cress, flax, and soybean could be decontaminated with the stabilized oxychloro sanitizer. However, the decontamination efficacy with other seed types was less consistent. It appears that the rate of seed germination and putative activity of sanitizer sequestering system(s), in addition to other factors, may limit the efficacy of the decontamination method. [ABSTRACT FROM AUTHOR]

Normally, science proceeds following a well-established set of principles. Studies are done with an emphasis on correctness, are submitted to a journal editor who evaluates their relevance, and then undergo anonymous peer review by experts before publication in a journal and acceptance by the scientific community via the open literature. This process is slow, but its accuracy has served all fields of science well. In an emergency situation, different priorities come to the fore. Research and review need to be conducted quickly, and the target audience consists of policymakers. Scientists must jostle for the attention of non-specialists without sacrificing rigour, and must deal not only with peer assessment but also with media scrutiny by journalists who may have agendas other than ensuring scientific correctness. Here, we describe how the Royal Society coordinated efforts of diverse scientists to help model the coronavirus epidemic. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of mathematical modelling in informing and advising policy decision-making. Effective practice of mathematical modelling has challenges. These can be around the technical modelling framework and how different techniques are combined, the appropriate use of mathematical formalisms or computational languages to accurately capture the intended mechanism or process being studied, in transparency and robustness of models and numerical code, in simulating the appropriate scenarios via explicitly identifying underlying assumptions about the process in nature and simplifying approximations to facilitate modelling, in correctly quantifying the uncertainty of the model parameters and projections, in taking into account the variable quality of data sources, and applying established software engineering practices to avoid duplication of effort and ensure reproducibility of numerical results. Via a collection of 16 technical papers, this special issue aims to address some of these challenges alongside showcasing the usefulness of modelling as applied in this pandemic. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes. Furthermore, the construction of these models is often monolithic: they do not allow one to readily modify the processes involved or include the new ones, or to combine models at different scales. We show how to construct a simple model of immune response to a respiratory virus and a model of transmission using an easily modifiable set of rules allowing further refining and merging the two models together. The immune response model reproduces the expected response curve of PCR testing for COVID-19 and implies a long-tailed distribution of infectiousness reflective of individual heterogeneity. This immune response model, when combined with a transmission model, reproduces the previously reported shift in the population distribution of viral loads along an epidemic trajectory. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Some social settings such as households and workplaces, have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020, reaching 64.3% seroprevalence within 10 months, and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data, and a network model of people and places represented as a stochastic graph rewriting system, we estimated the relative contribution of transmission in households, schools and religious institutions to the epidemic, and the relative risk of infection in each of these settings. All congregate settings were important for transmission, with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher general-community transmission rate for women (3.3-fold), and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations., (© 2022. The Author(s).)

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.