Re-shaping models to forecast faecal pathogen risk to humans

自20世纪初以来，在医学、食品生物技术和环境微生物学等不同的研究领域，人们一直使用简单的方法(称为一级动力学)来描述细菌种群的下降。当用来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，在粪便沉积后，随着时间的推移，转移到更广泛的环境和人类的任何潜在风险被认为是减少的，因此模型和政策反映了这一点。然而，这个项目将把新的数据与模型相结合，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。这将揭示出对从牛到土壤和水质的扩散源细菌风险的严重低估，对公共健康的威胁增加，如果结合预期的气候变化结果，这种威胁可能会恶化。微生物动力学知识是片面的，因为许多工作都集中在受控的实验室实验上。与现场相关的死亡概况需要纳入一套被认为影响微生物持久性的气候驱动因素的相互作用(例如，温度、紫外线、间歇性再湿)。在野外条件下的新研究推测，一旦离开动物宿主，粪便材料中的细菌生长可能是延长细菌持久性的一个重要因素。然而，这类研究的一个关键限制是只分析了排便后立即阶段的几个粪便样本。为了更好地了解增长潜力，高分辨率采样势在必行。我们的目标是“重塑”模拟的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轮廓中反映更合适的生长表示。该项目以牛的粪便为例，在计算牧场粪便细菌的预算时，量化了两种计算细菌增长或忽略细菌增长的方法之间的误差程度。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，以解释乳制品粪便中的细菌生长。我们的方法通过每个季节性实验收集16个时间序列样本来得出大肠杆菌(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天内以高分辨率收集的，包括在第0天以半天为间隔收集的两个样本。这与以前的低频抽样制度相比有了显着的变化。我们使用不同的假设农场场景对细菌死亡的建模近似进行了关键检查，以评估修订的“死亡”模式对牧场细菌预算的累积影响。为了促进这一点，我们使用了一个现有的经验模型(假设一阶‘死亡’和牲畜排泄率)，并修改了现有的模型代码，以反映通过田间实验观察到的生长。这一成果是利用实地相关数据量化与一阶下降假设相关的误差程度的第一次尝试之一，并将开创先例，承认放牧牛造成的陆地粪便细菌库潜在的低估或高估。该项目评估了两个主要问题：(1)通过对比季节，牧场粪便中的粪便来源细菌的数量增加的幅度；(Ii)如果在微生物下降的模型近似中忽略人口增加阶段，对土地的粪便细菌负担的低估或高估程度。

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.