Re-shaping models to forecast faecal pathogen risk to humans

重塑模型以预测粪便病原体对人类的风险

• 批准号: NE/J004456/1
• 负责人: David Oliver
• 金额: $ 10.21万
• 依托单位: University of Stirling
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FJ004456%2F1
• 关键词: Re; shaping; models; forecast; faecal

Since the early 20th century simple approaches (termed 1st-order kinetics) have been used to describe the population decline of bacteria in research fields as diverse as medicine, food biotechnology and environmental microbiology. When used to describe populations of faecal bacteria and pathogens in livestock faeces, these kinetics are commonly referred to as 'die-off', reflecting the generally held view that populations decline after faeces has been deposited. Consequently any potential risk of transfers to the wider environment and humans is thought to lessen with the passing of time after faeces deposition and thus models and policies reflect this. However, this project will combine new data with modelling to show that describing population change dynamics in terms of a 1st-order decline is flawed because of it failing to account for population increases that occur under fluctuating environmental conditions. This will reveal a major underestimation of diffuse source bacterial risks from cattle to soil and water quality, with increased threats to public health that may worsen if combined with expected climate change outcomes.Knowledge of microbial dynamics is partial in the sense that much work has focused on controlled laboratory experiments. Field relevant die-off profiles need to incorporate the interaction of a suite of climatic drivers thought to impact on microbial persistence (e.g. temperature, UV, episodic rewetting). New research under field conditions has speculated that bacterial growth in faecal material may be a significant factor for protracting bacterial persistence once outside the animal host. However, a key limitation of such studies is the analysis of only a few faecal samples during the immediate period post defecation. Higher resolution sampling is imperative to understand growth potential better. Our aim is to 'reshape' the credibility of modelled faecal bacteria population dynamics to ensure they reflect a more appropriate representation of growth within the mathematical profiling of bacterial persistence. The project focuses on cattle faeces as an example and quantifies the extent of error between the two approaches of accounting for, or ignoring, bacteria growth when calculating budgets of faecal bacteria deposited on pasture. The project draws on data provided by replicated field experiments. This empirical data underpins the development of the model to account for bacterial growth in dairy faeces. Our approach derives seasonal population change profiles for E. coli (a key faecal indicator organism) by collecting 16 time-series samples per seasonal experiment. Eight of these samples are collected at high resolution within the first 10 days of the experiment, including two samples collected at half day intervals on day 0. This represents a marked change from previous low frequency sampling regimes. We include a critical examination of modelled approximations of bacterial die-off using different hypothetical farm scenarios to evaluate the cumulative implications of the revised 'die-off' pattern on bacterial budgets on pasture. To facilitate this we use an existing empirical model (assuming 1st-order 'die-off' and livestock excretion rates) and amend the existing model code to reflect growth as observed through the field experiments. The output represents one of the first attempts to quantify the degree of error associated with assumptions of 1st-order decline using field relevant data and will set a precedent for acknowledging the potential under- or over- estimation of terrestrial faecal bacteria reservoirs contributed to by grazing cattle. The project assesses two main issues: (1) the magnitude of population increase of faecally derived bacteria within faeces on pasture through contrasting seasons; and (ii) the degree of under- or over- estimation of faecal bacteria burden to land if the population increase phase is ignored in modelled approximations of microbial decline.

自 20 世纪初以来，简单的方法（称为一阶动力学）已被用来描述医学、食品生物技术和环境微生物学等不同研究领域中细菌种群的减少。当用于描述牲畜粪便中的粪便细菌和病原体种群时，这些动力学通常被称为“死亡”，反映了人们普遍认为粪便沉积后种群数量下降的观点。因此，任何转移到更广泛的环境和人类的潜在风险被认为会随着粪便沉积后时间的推移而减少，因此模型和政策反映了这一点。然而，该项目将新数据与建模相结合，以表明用一阶下降来描述人口变化动态是有缺陷的，因为它未能考虑到在波动的环境条件下发生的人口增长。这将揭示对从牛到土壤和水质的扩散源细菌风险的严重低估，对公众健康的威胁增加，如果与预期的气候变化结果相结合，这种威胁可能会恶化。对微生物动力学的了解是片面的，因为许多工作都集中在这一点上受控实验室实验。与现场相关的死亡概况需要纳入一系列被认为会影响微生物持久性的气候驱动因素（例如温度、紫外线、间歇性再润湿）的相互作用。现场条件下的新研究推测，粪便中的细菌生长可能是细菌在离开动物宿主后长期存活的一个重要因素。然而，此类研究的一个关键限制是仅分析排便后不久的少数粪便样本。更高分辨率的采样对于更好地了解增长潜力至关重要。我们的目标是“重塑”粪便细菌种群动态模型的可信度，以确保它们在细菌持久性的数学分析中反映更合适的生长情况。该项目以牛粪便为例，在计算牧场上粪便细菌的预算时，量化了考虑或忽略细菌生长的两种方法之间的误差程度。该项目利用了重复现场实验提供的数据。这些经验数据支持了模型的开发，以解释乳制品粪便中的细菌生长。我们的方法通过在每个季节性实验中收集 16 个时间序列样本，得出大肠杆菌（一种关键的粪便指示生物）的季节性种群变化概况。其中 8 个样本是在实验的前 10 天内以高分辨率采集的，其中包括第 0 天以半天间隔采集的两个样本。这与之前的低频采样方式相比发生了显着变化。我们使用不同的假设农场情景对细菌死亡的近似模型进行了严格的检查，以评估修订后的“死亡”模式对牧场细菌预算的累积影响。为了促进这一点，我们使用现有的经验模型（假设一阶“死亡”和牲畜排泄率）并修改现有的模型代码以反映通过现场实验观察到的生长情况。该输出代表了使用现场相关数据量化与一阶下降假设相关的误差程度的首次尝试之一，并将开创一个先例，承认放牧造成的陆地粪便细菌库可能被低估或高估。牛。该项目评估两个主要问题：（1）不同季节牧场粪便中源自粪便的细菌数量增加的程度； (ii) 如果在微生物下降的近似模型中忽略种群增长阶段，则低估或高估土地粪便细菌负担的程度。

项目成果

Effects of seasonal meteorological variables on E. coli persistence in livestock faeces and implications for environmental and human health.

• DOI: 10.1038/srep37101
• 发表时间: 2016-11-15
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Oliver DM;Page T
• 通讯作者: Page T