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

背景、背景和影响本研究项目的重点是统计学和流行病学的交叉。其主要目标是模拟传染病，如COVID-19、被忽视的热带病（NTDs）和其他严重影响社会的病毒性病原体。这种统计和生物数学模型可以让我们更好地了解传染病，因为我们可以分析和预测：(1)人口层面的流行病动态，(2)早期入侵动态的长期疾病趋势，以及(3)公共卫生干预措施的影响。近年来，数学建模越来越重要，特别是在解决covid -19带来的大量问题和社会挑战的研究中。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日益增加，我们现在遇到的数据量和种类都在增加。目的和目标该项目的主要目的是提供对COVID-19等传染病的重要的、增强的了解。这种理解是通过提出利用各种数据源和跨学科知识的新颖统计方法来实现的。这些方法有助于回答包括准确测量患病率、评估控制措施的有效性以及疾病的传播性等问题。为此，该项目希望向决策者和公共卫生当局提供关于大流行防范、最佳控制战略和衡量疾病流行的信息。在交流研究成果方面，该项目旨在向广泛的受众解释其发现，从技术专家到更广泛的公众。这种公众参与对于传染病至关重要，因为传染病的传播可能对所有社会成员产生重大影响。技术专家将采用统计学和流行病学专家的形式，以及免疫学、动物学、公共卫生、政策和经济学方面的跨学科合作者。最后，该项目以不可预测的流行病学未来为条件，可能针对新出现的病原体进行实时爆发分析。在这里，从早期分析中推断出的疾病动态将至关重要地寻求估计疾病的传染性，并为可能随后发生的流行病的不同潜在规模建立模型。方法的新颖性在每种情况下，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。当然，这些将根据疾病的流行病学和生态学知识、相关人群和可用数据进行微调，所有这些都将影响我们如何参数化模型动力学。特别是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的模型间接监测输出所获得的数据进行比较。最后，对于任何新型病原体的实时暴发，这也需要新的建模技术。同样，模型动力学也将被病原体的流行病学和生态学的新证据强有力地告知。与EPSRC的战略和研究领域保持一致该项目属于EPSRC的统计和应用概率研究领域以及数学科学主题。根据该研究领域的核心目标，该项目将提出各种受应用启发的新颖统计建模方法，这些方法在这里属于统计流行病学领域。为此，该项目还直接支持数学科学主题的一个重要目标；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.