Optimisation within epidemiological systems : exploring the impact and mitigation of disease outbreaks

Disease outbreaks pose significant global challenges, impacting public health, ecosystems, and economies. Globalisation, population growth, urbanisation, and climate change have heightened the frequency and impact of diseases, necessitating effective management strategies to control outbreaks. There is a growing need for mathematical models, particularly epi-economic and bio-economic models, to help understand disease dynamics and evaluate interventions. By integrating economics and epidemiology, these models offer a comprehensive understanding of disease spread, considering individual behaviour and ecological factors. This doctoral thesis explores the use of epidemiological models in understanding disease dynamics, assessing impact, and identifying effective mitigation strategies for different systems. Four paper drafts contribute to this objective. Paper 1 presents a bioeconomic model investigating pests and pathogens’ effect on forest harvesting regimes, offering insights for forest managers in designing effective control strategies. Paper 2 develops a compartmental metapopulation model to analyse COVID-19 transmission in care homes, identifying mitigation strategies for vulnerable communities. Paper 3 explores COVID-19 related sickness absence rates among NHS England staff, guiding resource planning and interventions. Paper 4 introduces a mechanistic compartmental model to estimate COVID-19 sickness absence, evaluating cost-effective interventions and informing workforce management decisions. Several methodological approaches are employed, including; differential equations (compartmental modelling), autoregressive time series models, multivariate regression, and the net present value analysis.Disease outbreaks pose significant global challenges, impacting public health, ecosystems, and economies. Globalisation, population growth, urbanisation, and climate change have heightened the frequency and impact of diseases, necessitating effective management strategies to control outbreaks. There is a growing need for mathematical models, particularly epi-economic and bio-economic models, to help understand disease dynamics and evaluate interventions. By integrating economics and epidemiology, these models offer a comprehensive understanding of disease spread, considering individual behaviour and ecological factors. This doctoral thesis explores the use of epidemiological models in understanding disease dynamics, assessing impact, and identifying effective mitigation strategies for different systems. Four paper drafts contribute to this objective. Paper 1 presents a bioeconomic model investigating pests and pathogens’ effect on forest harvesting regimes, offering insights for forest managers in designing effective control strategies. Paper 2 develops a compartmental metapopulation model to analyse COVID-19 transmission in care homes, identifying mitigation strategies for vulnerable communities. Paper 3 explores COVID-19 related sickness absence rates among NHS England staff, guiding resource planning and interventions. Paper 4 introduces a mechanistic compartmental model to estimate COVID-19 sickness absence, evaluating cost-effective interventions and informing workforce management decisions. Several methodological approaches are employed, including; differential equations (compartmental modelling), autoregressive time series models, multivariate regression, and the net present value analysis

Optimising Assessment and Rehabilitation in People Hospitalised with an Acute Exacerbation of Chronic Obstructive Pulmonary Disease

This research; (i) evaluated the measurement properties of the two-minute walk test (2MWT), including the effect of test repetition, coefficient of repeatability and validity, and compared the cardiorespiratory responses and symptoms reported during the 2MWT and the six-minute walk test, (ii) developed regression equations to estimate the two-minute walk distance and (iii) evaluated the effectiveness of an exercise program in people hospitalised with an AECOPD on exercise capacity, muscle force, functional performance and physical activity

The Impact of Regularity of Primary Care on Emergency Department Presentations and Hospital Admissions

This thesis examines the impact of regular contacts with the general practitioner, and continuity of care with the same general practitioner, on use of hospital and emergency department services among patients with chronic conditions. Through analysing existing clinical and administrative health data, this thesis assesses several causal pathways by which primary care may influence downstream hospitalisation outcomes, and expands upon existing research in this area through testing alternative research designs and analysis methods

Prediction of Covid-19 Multiparametric Biomarkers and Drug Target of Patients for Risk Stratification Using Machine Learning Approach

In the situation of Coronavirus disease 2019 (COVID-19), forecasting disease progression and identifying therapeutic drug targets is critical, especially given the nonattendance of a viable approach for treating severe cases. The preparation cohort revealed promising biomarkers, which were then precisely measured and employed to assess prediction accuracy across validation cohorts. This approach holds significant potential in enhancing understanding of severe COVID-19 and aiding the development of effective treatments. However, ultrasound-guided MRI (US-MRI) is an emerging modality that can noninvasively acquire multi-parametric information on COVID-19 and function without the need for contrast agents. This shows that neural network analysis of US-MRI transports exclusive prognosis data and this significantly improved prognosis performance. Consequently, the research proposed a deep neural network model of an Ensemble Multi-Relational Graph Neural Network (EMR-GNN) to determine the optimal model for predicting vascular biomarkers (CRP, IL-6, ferritin). In the nonappearance of a tailored treatment for this emerging virus, scientists are actively investigating various strategies to curb its replication. This work focuses on identifying potential drug targets, drawing from proteins abundant in lung material and those targeted by FDA-approved drugs as catalogued in HPA. This effort reflects a broader initiative within the methodical unrestricted to develop effective means of limiting virus replication. Accordingly, recognized five lung-improved proteins, comprising MRC1, SG3A1, CCL18, histone H4, and CLEC3B, were annotated as “drug targets”. For this, the researcher proposes a Heterogeneous Graph Structural Attention Neural Network (HGS-ANN) model to learn topological information of composite molecules and a Dilated Causal CNN-LSTM model with U-Net layers for modelling spatial-sequential information in Simplified Molecular-Input Line-Entry System (SMILES) sequences of drug data. The COVID-19 datasets are downloaded from the GEO database. These data are evaluated using Matlab software. The proposed work evaluated that the AUC of the work is 0.995, however, the AUC is measured based on sex, age, and chronic diseases. This model has a 0.933 accuracy in the subgroup of slices thicker than 1mm. However, the AUC curve and the classification outcome of the proposed method are compared with the existing rad model, deeper, and KNN models. In comparison to existing methods, the proposed model demonstrates superior performance. This research not only identifies potential therapeutic targets nonetheless also serves to uncover biomarkers crucial for comprehending the pathogenesis of undecorated COVID-19

Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches

Introduction: Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design. Methods: Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year follow-up, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay. Results: i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”. ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively. iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features. Conclusions: Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people

Improving risk prediction model quality in the critically ill:data linkage study

Background: A previous National Institute for Health and Care Research study [Harrison DA, Ferrando-Vivas P, Shahin J, Rowan KM. Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients. Health Serv Deliv Res 2015;3(41)] identified the need for more research to understand risk factors and consequences of critical care and subsequent outcomes. Objectives: First, to improve risk models for adult general critical care by developing models for mortality at fixed time points and time-to-event outcomes, end-stage renal disease, type 2 diabetes, health-care utilisation and costs. Second, to improve risk models for cardiothoracic critical care by enhancing risk factor data and developing models for longer-term mortality. Third, to improve risk models for in-hospital cardiac arrest by enhancing risk factor data and developing models for longer-term mortality and critical care utilisation. Design: Risk modelling study linking existing data. Setting: NHS adult critical care units and acute hospitals in England. Participants: Patients admitted to an adult critical care unit or experiencing an in-hospital cardiac arrest. Interventions: None. Main outcome measures: Mortality at hospital discharge, 30 days, 90 days and 1 year following critical care unit admission; mortality at 1 year following discharge from acute hospital; new diagnosis of end-stage renal disease or type 2 diabetes; hospital resource use and costs; return of spontaneous circulation sustained for > 20 minutes; survival to hospital discharge and 1 year; and length of stay in critical care following in-hospital cardiac arrest. Data sources: Case Mix Programme, National Cardiac Arrest Audit, UK Renal Registry, National Diabetes Audit, National Adult Cardiac Surgery Audit, Hospital Episode Statistics and Office for National Statistics. Results: Data were linked for 965,576 critical care admissions between 1 April 2009 and 31 March 2016, and 83,939 in-hospital cardiac arrests between 1 April 2011 and 31 March 2016. For admissions to adult critical care units, models for 30-day mortality had similar predictors and performance to those for hospital mortality and did not reduce heterogeneity. Models for longer-term outcomes reflected increasing importance of chronic over acute predictors. New models for end-stage renal disease and diabetes will allow benchmarking of critical care units against these important outcomes and identification of patients requiring enhanced follow-up. The strongest predictors of health-care costs were prior hospitalisation, prior dependency and chronic conditions. Adding pre- and intra-operative risk factors to models for cardiothoracic critical care gave little improvement in performance. Adding comorbidities to models for in-hospital cardiac arrest provided modest improvements but were of greater importance for longer-term outcomes. Limitations: Delays in obtaining linked data resulted in the data used being 5 years old at the point of publication: models will already require recalibration. Conclusions: Data linkage provided enhancements to the risk models underpinning national clinical audits in the form of additional predictors and novel outcomes measures. The new models developed in this report may assist in providing objective estimates of potential outcomes to patients and their families. Future work: (1) Develop and test care pathways for recovery following critical illness targeted at those with the greatest need; (2) explore other relevant data sources for longer-term outcomes; (3) widen data linkage for resource use and costs to primary care, outpatient and emergency department data