Statistical inference in stochastic/deterministic epidemic models to jointly estimate transmission and severity

This thesis explores the joint estimation of transmission and severity of infectious diseases, focussing on the specific case of influenza. Transmission governs the speed and magnitude of viral spread in a population, while severity determines morbidity and mortality and the resulting effect on health care facilities. Their quantification is crucial to inform public health policies, motivating the routine collection of data on influenza cases. The estimation of severity is compromised by the high degree of censoring affecting the data early during the epidemic. The challenge of estimating transmission is that each influenza data source is often affected by noise and selection bias and individually provides only partial information on the underlying process. To address severity estimation with high censored data, new methods, inspired by demographic models and by parametric survival analysis, are formulated. A comprehensive review of these methods and existing methods is also carried out. To jointly estimate transmission and severity, an initial Bayesian epidemic model is fitted to historical data on severe cases, assuming a deterministic severity process and using a single data source. This model is then extended to describe a more stochastic and hence more realistic process of severe events, with the data generating process governed by hidden random variables in a state-space framework. Such increased realism necessitates the use of multiple data sources to enhance parameter identifiability, in a Bayesian evidence synthesis context. In contrast to the literature in the field, the model introduced accounts for dependencies between datasets. The added stochasticity and unmeasured dependencies result in an intractable likelihood. Inference therefore requires a new approach based on Monte Carlo methods. The method proposed proves its potential and usefulness in the concluding application to real data from the latest (2017/18) epidemic of influenza in England.MRC PhD scholarship Cambridge Philosophical Society scholarshi

Automatic Zig-Zag sampling in practice

Novel Monte Carlo methods to generate samples from a target distribution, such as a posterior from a Bayesian analysis, have rapidly expanded in the past decade. Algorithms based on Piecewise Deterministic Markov Processes (PDMPs), non-reversible continuous-time processes, are developing into their own research branch, thanks their important properties (e.g., correct invariant distribution, ergodicity, and super-efficiency). Nevertheless, practice has not caught up with the theory in this field, and the use of PDMPs to solve applied problems is not widespread. This might be due, firstly, to several implementational challenges that PDMP-based samplers present with and, secondly, to the lack of papers that showcase the methods and implementations in applied settings. Here, we address both these issues using one of the most promising PDMPs, the Zig-Zag sampler, as an archetypal example. After an explanation of the key elements of the Zig-Zag sampler, its implementation challenges are exposed and addressed. Specifically, the formulation of an algorithm that draws samples from a target distribution of interest is provided. Notably, the only requirement of the algorithm is a closed-form function to evaluate the target density of interest, and, unlike previous implementations, no further information on the target is needed. The performance of the algorithm is evaluated against another gradient-based sampler, and it is proven to be competitive, in simulation and real-data settings. Lastly, we demonstrate that the super-efficiency property, i.e. the ability to draw one independent sample at a lesser cost than evaluating the likelihood of all the data, can be obtained in practice.Comment: Small edits from previous version following some minor revisions requeste

influence of teeth anatomical characteristics on the efficacy of manual toothbrushing manoeuvres

Abstract Purpose The aim of the study was to investigate the efficacy of two toothbrushing techniques on the amount of plaque accumulation and to evaluate how the changes were correlated to the anatomical characteristics of the anterior maxillary arch. Methods Thirty subjects of both genders were included, they were asked not to brush for 12 hours. Afterwards, they were asked to manually brush the left side of their maxillary arch with the modified Bass technique and the right side adopting the roll technique. The comparison of photographs taken before and after the manoeuvres, using a plaque disclosing agent, allowed the researchers to measure the changes in plaque accumulation measured using the Quigley and Hein plaque scoring classification. Linear regression analysis was used to evaluate the correlation between such changes and the teeth and arch anatomical characteristics. Results A mean reduction of 9.6 +- 5.2% considering both arches after brushing was observed. The changes in plaque accumulation were not different between the two techniques. The length of the line obtained joining the contact point between the central incisors and the contact point between the second premolar and the first molar on the left side and the distance between that line and the lateral incisor on the same side positively correlated to the decrease in the plaque scores (P = 0.046 and P = 0.044, respectively). Conclusion Both tested techniques were effective in plaque removal in the anterior maxillary arches. However, the research for the anatomical factors influencing the amount of efficacy of the toothbrushing manoeuvres was inconclusive. We can hypothesise that the adoption of one adequate technique could be more important than the teeth characteristics

Exploiting routinely collected severe case data to monitor and predict influenza outbreaks

Abstract Background Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are reported weekly to Public Health England. These data are both readily available and have the potential to provide valuable information to estimate and predict the key transmission features of seasonal and pandemic influenza. Methods We propose an epidemic model that links the underlying unobserved influenza transmission process to data on severe influenza cases. Within a Bayesian framework, we infer retrospectively the parameters of the epidemic model for each seasonal outbreak from 2012 to 2015, including: the effective reproduction number; the initial susceptibility; the probability of admission to intensive care given infection; and the effect of school closure on transmission. The model is also implemented in real time to assess whether early forecasting of the number of admissions to intensive care is possible. Results Our model of admissions data allows reconstruction of the underlying transmission dynamics revealing: increased transmission during the season 2013/14 and a noticeable effect of the Christmas school holiday on disease spread during seasons 2012/13 and 2014/15. When information on the initial immunity of the population is available, forecasts of the number of admissions to intensive care can be substantially improved. Conclusion Readily available severe case data can be effectively used to estimate epidemiological characteristics and to predict the evolution of an epidemic, crucially allowing real-time monitoring of the transmission and severity of the outbreak

HLA-haploidentical T cell-depleted allogeneic hematopoietic stem cell transplantation in children with fanconi anemia

Abstract We report the outcome of 12 consecutive pediatric patients with Fanconi anemia (FA) who had neither an HLA-identical sibling nor an HLA-matched unrelated donor and who were given T cell–depleted, CD34 + positively selected cells from a haploidentical related donor after a reduced-intensity, fludarabine-based conditioning regimen. Engraftment was achieved in 9 of 12 patients (75%), and the cumulative incidence of graft rejection was 17% (95% confidence interval [CI], 5% to 59%). Cumulative incidences of grades II to IV acute and chronic graft-versus-host disease were 17% (95% CI, 5% to 59%) and 35% (95% CI, 14% to 89%), respectively. The conditioning regimen was well tolerated, with no fatal regimen-related toxicity and 3 cases of grade III regimen-related toxicity. The cumulative incidence of transplant-related mortality was 17% (95% CI, 5% to 59%). The 5-year overall survival, event-free survival, and disease-free survival were 83% (95% CI, 62% to 100%), 67% (95% CI, 40% to 93%), and 83% (95% CI, 62% to 100%), respectively. These data demonstrate that a fludarabine-based conditioning regimen, followed by infusion of high doses of T cell–depleted stem cells, is able to ensure engraftment with good overall survival and disease-free survival, confirming the feasibility of haploidentical hematopoietic stem cell transplantation in FA. To the best of our knowledge, this is the largest series of hematopoietic stem cell transplantation from a haploidentical related donor in FA patients reported to date

Risk factors associated with severe hospital burden of COVID-19 disease in Regione Lombardia: a cohort study.

BACKGROUND: Understanding the risk factors associated with hospital burden of COVID-19 is crucial for healthcare planning for any future waves of infection. METHODS: An observational cohort study is performed, using data on all PCR-confirmed cases of COVID-19 in Regione Lombardia, Italy, during the first wave of infection from February-June 2020. A multi-state modelling approach is used to simultaneously estimate risks of progression through hospital to final outcomes of either death or discharge, by pathway (via critical care or not) and the times to final events (lengths of stay). Logistic and time-to-event regressions are used to quantify the association of patient and population characteristics with the risks of hospital outcomes and lengths of stay respectively. RESULTS: Risks of severe outcomes such as ICU admission and mortality have decreased with month of admission (for example, the odds ratio of ICU admission in June vs March is 0.247 [0.120-0.508]) and increased with age (odds ratio of ICU admission in 45-65 vs 65 + age group is 0.286 [0.201-0.406]). Care home residents aged 65 + are associated with increased risk of hospital mortality and decreased risk of ICU admission. Being a healthcare worker appears to have a protective association with mortality risk (odds ratio of ICU mortality is 0.254 [0.143-0.453] relative to non-healthcare workers) and length of stay. Lengths of stay decrease with month of admission for survivors, but do not appear to vary with month for non-survivors. CONCLUSIONS: Improvements in clinical knowledge, treatment, patient and hospital management and public health surveillance, together with the waning of the first wave after the first lockdown, are hypothesised to have contributed to the reduced risks and lengths of stay over time

Correction to: decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context.

BACKGROUND: The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. METHODS: This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February-June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February-June 2020, with non-missing hospital of admission and non-missing admission date. RESULTS: The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56-80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1-28.0%); and steadily decreased from 34.6% (32.5-36.6%) in February to 7.6% (6.3-10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6-12.3) days, compared to 8.1 (7.8-8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5-22.8) days in February to 5.2 (4.7-5.8) days in June. CONCLUSIONS: The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2 i

Comparison of stochastic and deterministic models for gambiense sleeping sickness at different spatial scales : a health area analysis in the DRC

The intensification of intervention activities against the fatal vector-borne disease gambiense human African trypanosomiasis (gHAT, sleeping sickness) in the last two decades has led to a large decline in the number of annually reported cases. However, while we move closer to achieving the ambitious target of elimination of transmission (EoT) to humans, pockets of infection remain, and it becomes increasingly important to quantitatively assess if different regions are on track for elimination, and where intervention efforts should be focused. We present a previously developed stochastic mathematical model for gHAT in the Democratic Republic of Congo (DRC) and show that this same formulation is able to capture the dynamics of gHAT observed at the health area level (approximately 10,000 people). This analysis was the first time any stochastic gHAT model has been fitted directly to case data and allows us to better quantify the uncertainty in our results. The analysis focuses on utilising a particle filter Markov chain Monte Carlo (MCMC) methodology to fit the model to the data from 16 health areas of Mosango health zone in Kwilu province as a case study. The spatial heterogeneity in cases is reflected in modelling results, where we predict that under the current intervention strategies, the health area of Kinzamba II, which has approximately one third of the health zone’s cases, will have the latest expected year for EoT. We find that fitting the analogous deterministic version of the gHAT model using MCMC has substantially faster computation times than fitting the stochastic model using pMCMC, but produces virtually indistinguishable posterior parameterisation. This suggests that expanding health area fitting, to cover more of the DRC, should be done with deterministic fits for efficiency, but with stochastic projections used to capture both the parameter and stochastic variation in case reporting and elimination year estimations

Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a case study.

BACKGROUND: Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested. METHODS: Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored. RESULTS: The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3-4 of 2018. Estimates for R0 were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R0 across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity. CONCLUSIONS: This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable