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

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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.