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.

自世纪初以来，简单的方法（称为一级动力学）已被用于描述不同的研究领域，如医学，食品生物技术和环境微生物学的细菌种群下降。当用于描述牲畜粪便中的粪便细菌和病原体的种群时，这些动力学通常被称为“死亡”，反映了普遍持有的观点，即粪便沉积后种群数量下降。因此，任何转移到更广泛的环境和人类的潜在风险被认为在粪便沉积后随着时间的推移而减少，因此模型和政策反映了这一点。然而，该项目将联合收割机新数据与模型相结合，以表明用一阶下降来描述人口变化动态是有缺陷的，因为它不能说明在波动的环境条件下发生的人口增长。这将揭示一个重大低估的扩散源细菌的风险，从牛到土壤和水质，增加对公共健康的威胁，如果与预期的气候变化结果相结合，可能会恶化。与实地相关的死亡概况需要纳入一套被认为会影响微生物持久性的气候驱动因素（如温度、紫外线、间歇性复湿）的相互作用。在野外条件下进行的新研究推测，粪便中的细菌生长可能是延长细菌在动物宿主之外的持久性的重要因素。然而，这些研究的一个关键局限性是在排便后的一段时间内只分析了少数粪便样本。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”模拟粪便细菌种群动态的可信度，以确保它们在细菌持久性的数学分析中反映出更合适的生长表示。该项目的重点是牛粪便作为一个例子，并量化的两种方法之间的误差程度占，或忽略，细菌生长时，计算预算的粪便细菌沉积在牧场。该项目利用了重复的实地实验提供的数据。这一经验数据支持了该模型的发展，以解释奶牛粪便中的细菌生长。我们的方法得出的季节性人口变化概况E。大肠杆菌（一种关键的粪便指示生物），每个季节性实验收集16个时间序列样本。在实验的前10天内以高分辨率收集这些样品中的8个，包括在第0天以半天间隔收集的两个样品。这表示与先前的低频采样机制相比的显著变化。我们使用不同的假设农场场景对细菌死亡的模拟近似值进行了严格检查，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增长。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，并将为承认放牧牛导致的陆地粪便细菌库的潜在低估或高估开创先例。该项目评估了两个主要问题：（1）通过对比季节，牧场粪便中粪便衍生细菌的数量增加幅度;（ii）如果在模拟微生物下降的近似值中忽略了数量增加阶段，则对粪便细菌对土地的负担的低估或高估程度。

项目成果

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

季节性气象变量对牲畜粪便中大肠杆菌持久性的影响及其对环境和人类健康的影响。

• 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