Bayesian nonparametric methods for individual-level stochastic epidemic models

Simulating from and making inference for stochastic epidemic models are key strategies for understanding and controlling the spread of infectious diseases. Current methods for modelling infection rate functions are exclusively parametric. This often involves making strict assumptions about the way the disease spreads and choices which may lack any biological or epidemiological justification. To remove the need for making such assumptions, we develop a Bayesian nonparametric framework which allows us to learn how the disease spreads directly from the data. In this thesis, we consider individual-level models where the infection rate between each pair of individuals depends on characteristics of their relationship. We begin by considering infectious diseases where the infection rate between any two individuals can be modelled by a function of a single characteristic, for example, the distance between them. We model this function nonparametrically by assigning a Gaussian Process prior distribution to it and then develop an efficient data augmentation Markov Chain Monte Carlo algorithm to infer this function, alongside the prior distribution hyperparameters and the times individuals were infected. We develop this methodology further, first for multi-type outbreaks and then for outbreaks where the infection rate function depends on more than one characteristic. For multi-type outbreaks, where the infection rate between two individuals not only depends on the characteristics, but also the type of individual being infected, we develop a Multi-Output Gaussian Process method. This method allows us to compare how susceptible each type of individual is to infection. We extend our Gaussian Process method into several dimensions for modelling outbreaks where the infection rate between individuals can be modelled as a function of multiple continuous variables. Finally, we demonstrate our results on two data sets, giving new insights and analysis. The first is an outbreak of Avian Influenza in the Netherlands in 2003, where over 30 million birds were culled. Using the posterior predictive distribution of our nonparametric model, we simulate outbreaks of Avian Influenza to assess various control measures. Alongside our nonparametric analysis, we are able to investigate which of the pre-emptively culled farms were infected. The second is an outbreak of Foot and Mouth Disease in Cumbria, UK. We are able to analyse the relationship between the infection rate of farms with different kind of livestock, showing that farms with both cattle and sheep were much more susceptible to the virus than farms with a single type of livestock

Bayesian nonparametric methods for individual-level stochastic epidemic models

Simulating from and making inference for stochastic epidemic models are key strategies for understanding and controlling the spread of infectious diseases. Current methods for modelling infection rate functions are exclusively parametric. This often involves making strict assumptions about the way the disease spreads and choices which may lack any biological or epidemiological justification. To remove the need for making such assumptions, we develop a Bayesian nonparametric framework which allows us to learn how the disease spreads directly from the data. In this thesis, we consider individual-level models where the infection rate between each pair of individuals depends on characteristics of their relationship. We begin by considering infectious diseases where the infection rate between any two individuals can be modelled by a function of a single characteristic, for example, the distance between them. We model this function nonparametrically by assigning a Gaussian Process prior distribution to it and then develop an efficient data augmentation Markov Chain Monte Carlo algorithm to infer this function, alongside the prior distribution hyperparameters and the times individuals were infected. We develop this methodology further, first for multi-type outbreaks and then for outbreaks where the infection rate function depends on more than one characteristic. For multi-type outbreaks, where the infection rate between two individuals not only depends on the characteristics, but also the type of individual being infected, we develop a Multi-Output Gaussian Process method. This method allows us to compare how susceptible each type of individual is to infection. We extend our Gaussian Process method into several dimensions for modelling outbreaks where the infection rate between individuals can be modelled as a function of multiple continuous variables. Finally, we demonstrate our results on two data sets, giving new insights and analysis. The first is an outbreak of Avian Influenza in the Netherlands in 2003, where over 30 million birds were culled. Using the posterior predictive distribution of our nonparametric model, we simulate outbreaks of Avian Influenza to assess various control measures. Alongside our nonparametric analysis, we are able to investigate which of the pre-emptively culled farms were infected. The second is an outbreak of Foot and Mouth Disease in Cumbria, UK. We are able to analyse the relationship between the infection rate of farms with different kind of livestock, showing that farms with both cattle and sheep were much more susceptible to the virus than farms with a single type of livestock

Bayesian nonparametric inference for heterogeneously mixing infectious disease models

Infectious disease transmissionmodels require assumptions about how the pathogen spreads between individuals. These assumptions may be somewhat arbitrary, particularly when it comes to describing how transmission varies between individuals of different types or in different locations, and may in turn lead to incorrect conclusions or policy decisions. We develop a general Bayesian nonparametric framework for transmission modeling that removes the need to make such specific assumptions with regard to the infection process. We use multioutput Gaussian process prior distributions to model different infection rates in populations containing multiple types of individuals. Further challenges arise because the transmission process itself is unobserved, and large outbreaks can be computationally demanding to analyze. We address these issues by data augmentation and a suitable efficient approximationmethod. Simulation studies using synthetic data demonstrate that our framework gives accurate results. We analyze an outbreak of foot and mouth disease in the United Kingdom, quantifying the spatial transmission mechanism between farms with different combinations of livestock

A Bayesian nonparametric analysis of the 2003 outbreak of highly pathogenic avian influenza in the Netherlands

Infectious diseases on farms pose both public and animal health risks, so understanding how they spread between farms is crucial for developing disease control strategies to prevent future outbreaks. We develop novel Bayesian nonparametric methodology to fit spatial stochastic transmission models in which the infection rate between any two farms is a function that depends on the distance between them, but without assuming a specified parametric form. Making nonparametric inference in this context is challenging since the likelihood function of the observed data is intractable because the underlying transmission process is unobserved. We adopt a fully Bayesian approach by assigning a transformed Gaussian process prior distribution to the infection rate function, and then develop an efficient data augmentation Markov Chain Monte Carlo algorithm to perform Bayesian inference. We use the posterior predictive distribution to simulate the effect of different disease control methods and their economic impact. We analyse a large outbreak of avian influenza in the Netherlands and infer the between-farm infection rate, as well as the unknown infection status of farms which were pre-emptively culled. We use our results to analyse ring-culling strategies, and conclude that although effective, ring-culling has limited impact in high-density areas

The Bayesian Spatial Bradley–Terry model: Urban deprivation modelling in Tanzania

Identifying the most deprived regions of any country or city is key if policy makers are to design successful interventions. However, locating areas with the greatest need is often surprisingly challenging in developing countries. Due to the logistical challenges of traditional household surveying, official statistics can be slow to be updated; estimates that exist can be coarse, a consequence of prohibitive costs and poor infrastructures; and mass urbanization can render manually surveyed figures rapidly out-of-date. Comparative judgement models, such as the Bradley–Terry model, offer a promising solution. Leveraging local knowledge, elicited via comparisons of different areas' affluence, such models can both simplify logistics and circumvent biases inherent to household surveys. Yet widespread adoption remains limited, due to the large amount of data existing approaches still require. We address this via development of a novel Bayesian Spatial Bradley–Terry model, which substantially decreases the number of comparisons required for effective inference. This model integrates a network representation of the city or country, along with assumptions of spatial smoothness that allow deprivation in one area to be informed by neighbouring areas. We demonstrate the practical effectiveness of this method, through a novel comparative judgement data set collected in Dar es Salaam, Tanzania

Limitations and pitfalls of using family letters to communicate genetic risk: a qualitative study with patients and healthcare professionals

European genetic testing guidelines recommend that healthcare professionals (HCPs) discuss the familial implications of any test with a patient and offer written material to help them share the information with family members. Giving patients these “family letters” to alert any relatives of their risk has become part of standard practice and has gone relatively unquestioned over the years. Communication with at-risk relatives will become an increasingly pressing issue as mainstream and routine practice incorporates broad genome tests and as the number of findings potentially relevant to relatives increases. This study therefore explores problems around the use of family letters to communicate about genetic risk. We conducted 16 focus groups with 80 HCPs, and 35 interviews with patients, recruited from across the UK. Data were analyzed thematically and we constructed four themes: 1) HCPs writing family letters: how to write them and why?, 2) Patients’ issues with handing out family letters, 3) Dissemination becomes an uncontrolled form of communication, and 4) When the relative has the letter, is the patient’s and HCP’s duty discharged? We conclude by suggesting alternative and supplementary methods of communication, for example through digital tools, and propose that in comparison to communication by family letter, direct contact by HCPs might be a more appropriate and successful option

Characterization of the cork oak transcriptome dynamics during acorn development

Background: Cork oak (Quercus suber L.) has a natural distribution across western Mediterranean regions and is a keystone forest tree species in these ecosystems. The fruiting phase is especially critical for its regeneration but the molecular mechanisms underlying the biochemical and physiological changes during cork oak acorn development are poorly understood. In this study, the transcriptome of the cork oak acorn, including the seed, was characterized in five stages of development, from early development to acorn maturation, to identify the dominant processes in each stage and reveal transcripts with important functions in gene expression regulation and response to water. Results: A total of 80,357 expressed sequence tags (ESTs) were de novo assembled from RNA-Seq libraries representative of the several acorn developmental stages. Approximately 7.6 % of the total number of transcripts present in Q. suber transcriptome was identified as acorn specific. The analysis of expression profiles during development returned 2,285 differentially expressed (DE) transcripts, which were clustered into six groups. The stage of development corresponding to the mature acorn exhibited an expression profile markedly different from other stages. Approximately 22 % of the DE transcripts putatively code for transcription factors (TF) or transcriptional regulators, and were found almost equally distributed among the several expression profile clusters, highlighting their major roles in controlling the whole developmental process. On the other hand, carbohydrate metabolism, the biological pathway most represented during acorn development, was especially prevalent in mid to late stages as evidenced by enrichment analysis. We further show that genes related to response to water, water deprivation and transport were mostly represented during the early (S2) and the last stage (S8) of acorn development, when tolerance to water desiccation is possibly critical for acorn viability. Conclusions: To our knowledge this work represents the first report of acorn development transcriptomics in oaks. The obtained results provide novel insights into the developmental biology of cork oak acorns, highlighting transcripts putatively involved in the regulation of the gene expression program and in specific processes likely essential for adaptation. It is expected that this knowledge can be transferred to other oak species of great ecological value.Fundação para a Ciência e a Tecnologi

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570