An exploration of COVID-19's relationship with Neglected Tropical Disease

背景、背景和影响本研究项目侧重于统计学和流行病学的交叉。它的主要目标是对新冠肺炎、被忽视的热带病(NTDS)等传染病和其他深刻影响社会的病毒病原体进行建模。这样的统计和生物数学模型可以让我们更好地了解传染病，因为我们分析和预测了：i)人口一级的流行动态，ii)来自早期入侵动态的长期疾病趋势，以及iii)公共卫生干预的影响。近年来，数学建模的重要性与日俱增，特别是在研究解决新冠状病毒-19病毒所带来的大量问题和社会挑战方面。然而，随着全球大流行，这一研究领域对全球传染病的重要性与日俱增，我们现在遇到的数据数量和种类都越来越多。目的和目标本项目的主要目的是提供一个至关重要的、提高水平的对传染病的了解，例如新冠肺炎。要实现这一理解，就要提出新颖的统计方法，利用各种数据来源和跨学科的知识。这些方法可以帮助回答问题，包括准确测量疾病流行率，评估控制措施的有效性，以及疾病的传播性。通过这样做，该项目渴望向决策者和公共卫生当局提供有关大流行准备、最佳控制策略和测量疾病流行率的信息。关于传播研究成果，该项目旨在向从技术专家到更广泛的公众的广泛受众解释其结果。这种公众参与对控制传染病至关重要，因为传染病的传播会对社会所有成员产生重大影响。技术专家将采取统计学和流行病学专家的形式，与免疫学、动物学、公共卫生、政策和经济学的跨学科合作者一起。最后，这个项目以不可预测的流行病学未来为条件，可能针对一种新出现的病原体进行实时暴发分析。在这里，从早期分析推断的疾病动力学将至关重要地寻求估计疾病的传染性，并对可能随后流行的不同潜在规模进行建模。方法论新奇在每一种设置中，将提出新的统计和生物数学模型，每个模型都受到特定应用的启发。自然，这些将根据疾病的流行病学和生态学知识、相关人口和可用的数据进行微调，所有这些都将影响我们如何对模型动态进行参数化。具体地说，就我们的第一个废水研究而言，这可能需要空间建模、时间建模和随机方面，因为我们试图将临床病例数据与我们模型基于间接监测的输出所获得的数据进行比较。最后，对于任何新病原体的实时爆发，这些也需要新的建模技术。与EPSRC的战略和研究领域保持一致本项目属于EPSRC统计与应用概率和数学科学的研究领域。根据这一研究领域的核心目标，该项目将提出各种受应用启发的新的统计建模方法，这些方法存在于统计流行病学的范围内。为此，该项目还直接支持数学科学主题的一个重要主题--开展跨学科研究。

Background, Context and ImpactThis research project focuses on the intersection of statistics and epidemiology. Its primary objectiveis to model infectious diseases such as COVID-19, Neglected Tropical Diseases (NTDs), and other viralpathogens which profoundly affect society. Such statistical and bio-mathematical models can allowus to better understand infectious diseases, as we analyse and predict i) population-level epidemicdynamics, ii) long-term disease trends from early invasion dynamics, and iii) the impact of publichealth interventions. In recent years, mathematical modelling has been of increasing importance,particularly in research addressing the vast number of questions and societal challenges posed byCOVID-19. Nevertheless, alongside the global pandemic, this research area is of increasingimportance for infectious diseases globally, and we are now encountering an increasing volume andvariety of data.Aims and ObjectivesThe primary aim of this project is to provide a crucial, enhanced level of understanding aboutinfectious diseases, such as COVID-19. This understanding is to be achieved by proposing novelstatistical methodology which harness a variety of data sources and knowledge from acrossdisciplines. These methods can help to answer questions including accurate measurement of diseaseprevalence, appraisal of the effectiveness of control measures, and the transmissibility of diseases.In doing so, this project aspires to inform both decision-makers and public health authorities withrespect to pandemic preparedness, optimal control strategies, and measuring disease prevalence.With respect to communicating the research output, this project aims to explain its findings to awide audience, ranging from technical experts to the wider public. This public engagement is pivotalfor infectious diseases whose transmission can drastically impact all members of society. Technicalexperts will take the form of specialists across statistics and epidemiology, alongside cross-disciplinary collaborators in immunology, zoology, public health, policy, and economics. Finally, thisproject, conditional on an unpredictable epidemiological future, may target a real-time outbreakanalysis for an emerging pathogen. Here, the inferred disease dynamics from an early-stage analysiswould crucially seek to estimate the infectiousness of the disease and model the varying potentialscales of possible ensuing epidemics.Novelty of MethodologyAcross each setting, novel statistical and biomathematical models will be proposed, each inspired bythe specific application. Naturally, these will be fine-tuned according to the epidemiological andecological knowledge of the disease, the population in question, and the data available, all of whichwill influence how we parameterise the model dynamics. Specifically in terms of our first wastewaterstudy, this is likely to entail spatial modelling, temporal modelling, and a stochastic aspect, as weattempt to compare clinical case data with that attained by our model's indirect surveillance-basedoutput. Finally, with respect to any real-time outbreaks of novel pathogens, these also demandnovel modelling techniques. Model dynamics will likewise be informed strongly by the emergingevidence for the epidemiology and ecology of the pathogen.Alignment to EPSRC's strategies and research areasThis project falls within the EPSRC research area of Statistics and Applied Probability and the themeof Mathematical Sciences. In line with this research area's core objective, the project will propose avariety of novel statistical modelling methodologies inspired by applications, which here lie in therealm of statistical epidemiology. To this end, this project also directly supports an importantobjective of the Mathematical Sciences theme; to carry out cross-disciplinary research.