A modeling framework for the analysis of the SARS-CoV2 transmission dynamics

Despite the progress in medical data collection the actual burden of SARS-CoV-2 remains unknown due to under-ascertainment of cases. This was apparent in the acute phase of the pandemic and the use of reported deaths has been pointed out as a more reliable source of information, likely less prone to under-reporting. Since daily deaths occur from past infections weighted by their probability of death, one may infer the total number of infections accounting for their age distribution, using the data on reported deaths. We adopt this framework and assume that the dynamics generating the total number of infections can be described by a continuous time transmission model expressed through a system of nonlinear ordinary differential equations where the transmission rate is modeled as a diffusion process allowing to reveal both the effect of control strategies and the changes in individuals behavior. We develop this flexible Bayesian tool in Stan and study 3 pairs of European countries, estimating the time-varying reproduction number (Rt) as well as the true cumulative number of infected individuals. As we estimate the true number of infections we offer a more accurate estimate of Rt. We also provide an estimate of the daily reporting ratio and discuss the effects of changes in mobility and testing on the inferred quantities

Winter 2022–23 influenza vaccine effectiveness against influenza-related hospitalised aLRTD: A test-negative, case-control study

Influenza activity in the UK started early during the winter 2022-23 season, with most surveillance systems reporting high levels of hospitalisation, intensive care unit influenza admission and GP influenza-like illness (ILI) consultation rates. Laboratory confirmed positivity rates were comparable to those seen pre-pandemic, between the end of November 2022 and the end of January 2023, exceeding 25% as they did during the 2019-2020 season [1,2]. Annual vaccination against influenza is recommended in the UK to eligible higher-risk groups: adults ≥65 years(y); children and adults in at-risk groups (including during pregnancy); and, pre-school, primary and secondary school-aged children[3]. However, in 2022-23 the offer of seasonal influenza immunisation was extended to healthy 50-64y olds[4]. The vaccines used were quadrivalent, containing one influenza A(H1N1) virus, one influenza A(H3N2) virus, one influenza B/Victoria lineage virus, and one influenza B/Yamagata lineage virus[5]. Public health measures aiming to reduce the transmission of SARS-CoV-2 had affected the transmission of respiratory viruses like influenza during the previous two seasons, with the 22-23 season being the first one where social mixing returned to pre-pandemic levels. Systematic monitoring of the effectiveness of the seasonal flu vaccine (VE) is a public health priority as influenza activity returns to pre-pandemic levels

Δοκίμια πάνω σε επιδημικά μοντέλα και τη στατιστική τους ανάλυση

Public health related decisions concerning infectious diseases are characterized by the use of complex mathematical models in order to understand the dynamics of infectious diseases and design intervention strategies. Efficient modelling and inference procedures for learning the model parameters from data are of central interest. In this thesis, a comprehensive review of two new and efficient statistical machine learning methods, namely Hamiltonian Monte Carlo and Variational Inference, as implemented in the freely available Stan software, was carried out. We explored how Stan could be used to fit a class of epidemic models based upon systems of ordinary differential equations and demonstrated its potential in an application to real data. In the light of the COVID-19 pandemic, this thesis revolved around model-based approaches to estimate the transmissibility of SARS-CoV2, focusing on two different classes of epidemic models. Shortcomings in global epidemiological surveillance, led to the use of indirect estimations of infections, through deaths. This approach was adopted to fit an extension of the deterministic SEIR (susceptible-exposed-infected-recovered) compartmental model, where the transmission rate is a diffusion process, allowing to reveal both the effect of control strategies and the changes in individuals behaviour. We proceeded with a suitably tailored chain-binomial epidemic model which was later extended to include population heterogeneity, introducing contact uncertainty into the inference structure in a highly hierarchical setting, trying to reveal the age distribution of infections through aggregate deaths. In the main, careful consideration of data combined with the use of contemporary developments in statistics, can be an essential tool for advanced analysis based on realistically complex models.Οι αποφάσεις που αφορούν τη δημόσια υγεία σχετικά με τις μολυσματικές ασθένειες χαρακτηρίζονται από τη χρήση πολύπλοκων μαθηματικών μοντέλων προκειμένου να κατανοηθεί η δυναμική των μολυσματικών ασθενειών και να σχεδιαστούν στρατηγικές παρέμβασης. Η αποτελεσματική μοντελοποίηση και οι διαδικασίες εξαγωγής συμπερασμάτων για την εκμάθηση των παραμέτρων του μοντέλου μέσω των δεδομένα έχουν κεντρικό ενδιαφέρον. Σε αυτή τη διατριβή, πραγματοποιήθηκε μια ολοκληρωμένη ανασκόπηση δύο νέων και αποτελεσματικών στατιστικών μεθόδων μηχανικής μάθησης, ονομαστικά των μεθόδων Hamiltonian Monte Carlo και Variational Inference, όπως υλοποιούνται στο ελεύθερα διαθέσιμο λογισμικό Stan. Εξερευνήσαμε πώς το Stan θα μπορούσε να χρησιμοποιηθεί για την προσαρμογή μιας κατηγορίας μοντέλων επιδημίας που βασίζονται σε συστήματα συνήθων διαφορικών εξισώσεων και δείξαμε τις δυνατότητές του σε μια εφαρμογή σε πραγματικά δεδομένα.Υπό το πρίσμα της πανδημίας COVID-19, αυτή η διατριβή επικεντρώθηκε σε προσεγγίσεις που βασίζονται σε μοντέλα για την εκτίμηση της μεταδοτικότητας του SARS-CoV2, εστιάζοντας σε δύο διαφορετικές κατηγορίες επιδημικών μοντέλων. Αδυναμίες στην παγκόσμια επιδημιολογική επιτήρηση, οδήγησαν στη χρήση έμμεσων εκτιμήσεων των λοιμώξεων, μέσω των θανάτων. Αυτή η προσέγγιση υιοθετήθηκε για να εκτιμηθεί μια επέκταση του ντετερμινιστικού μοντέλου SEIR (ευπαθείς-εκτεθειμένοι-μολυσμένοι-ανοσοποιημένοι/διαγραμμένοι), όπου ο ρυθμός μετάδοσης είναι μια στοχαστική διαδικασία, επιτρέποντας στο μοντέλο να αντικατοπτρίσει τόσο την επίδραση των στρατηγικών ελέγχου όσο και των αλλαγών στη συμπεριφορά των ατόμων. Στη συνέχεια χρησιμοποιήθηκε ένα κατάλληλα προσαρμοσμένο επιδημικό μοντέλο που ανήκει στην ευρύτερη κλάση των διωνυμικών-αλυσιδωτών (chain-binomial) μοντέλων, το οποίο αργότερα επεκτάθηκε για να συμπεριλάβει την ετερογένεια του πληθυσμού, εισάγοντας αβεβαιότητα γύρω από το μέσο αριθμό επαφών σε ένα άκρως ιεραρχικό πλαίσιο, προσπαθώντας να αποκαλύψει την ηλικιακή κατανομή των λοιμώξεων μέσω των αθροιστικών θανάτων. Επί της ουσίας, η προσεκτική εξέταση των δεδομένων σε συνδυασμό με τη χρήση των σύγχρονων εξελίξεων στη στατιστική, μπορεί να είναι ένα ουσιαστικό εργαλείο για προηγμένη ανάλυση που βασίζεται σε ρεαλιστικά πολύπλοκα μοντέλα

anastasiachtz/COMMAND_stan/

R code associated with the paper, "Contemporary statistical inference for infectious disease models using Stan"

Contemporary statistical inference for infectious disease models using Stan

This paper is concerned with the application of recent statistical advances to inference of infectious disease dynamics. We describe the fitting of a class of epidemic models using Hamiltonian Monte Carlo and variational inference as implemented in the freely available Stan software. We apply the two methods to real data from outbreaks as well as routinely collected observations. Our results suggest that both inference methods are computationally feasible in this context, and show a trade-off between statistical efficiency versus computational speed. The latter appears particularly relevant for real-time applications.</p

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error

Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, we determine expressions for the sensitivity and specificity of panel tests. In this we identify a counter-intuitive relationship between panel test sensitivity and disease prevalence that means panel tests become more sensitive as prevalence increases. We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for

Syndromic case definitions for lower respiratory tract infection (LRTI) are less sensitive in older age: an analysis of symptoms among hospitalised adults

AbstractBackgroundLower Respiratory Tract Infections (LRTI) pose a serious threat to older adults but may be underdiagnosed due to atypical presentations. Here we assess LRTI symptom profiles and syndromic (symptom-based) case ascertainment in older (≥ 65y) as compared to younger adults (&lt; 65y).MethodsWe included adults (≥ 18y) with confirmed LRTI admitted to two acute care Trusts in Bristol, UK from 1st August 2020- 31st July 2022. Logistic regression was used to assess whether age ≥ 65y reduced the probability of meeting syndromic LRTI case definitions, using patients’ symptoms at admission. We also calculated relative symptom frequencies (log-odds ratios) and evaluated how symptoms were clustered across different age groups.ResultsOf 17,620 clinically confirmed LRTI cases, 8,487 (48.1%) had symptoms meeting the case definition. Compared to those not meeting the definition these cases were younger, had less severe illness and were less likely to have received a SARS-CoV-2 vaccination or to have active SARS-CoV-2 infection. Prevalence of dementia/cognitive impairment and levels of comorbidity were lower in this group.After controlling for sex, dementia and comorbidities, age ≥ 65y significantly reduced the probability of meeting the case definition (aOR = 0.67, 95% CI:0.63–0.71). Cases aged ≥ 65y were less likely to present with fever and LRTI-specific symptoms (e.g., pleurisy, sputum) than younger cases, and those aged ≥ 85y were characterised by lack of cough but frequent confusion and falls.ConclusionsLRTI symptom profiles changed considerably with age in this hospitalised cohort. Standard screening protocols may fail to detect older and frailer cases of LRTI based on their symptoms

Relative vaccine effectiveness of mRNA COVID-19 boosters in people aged at least 75 years during the spring-summer (monovalent vaccine) and autumn-winter (bivalent vaccine) booster campaigns:a prospective test negative case–control study, United Kingdom, 2022

Background:Understanding the relative vaccine effectiveness (rVE) of new COVID-19 vaccine formulations against SARS-CoV-2 infection is a public health priority. A precise analysis of the rVE of monovalent and bivalent boosters given during the 2022 spring-summer and autumn-winter campaigns, respectively, in a defined population remains of interest.Aim:We assessed rVE against hospitalisation for the spring-summer (fourth vs third monovalent mRNA vaccine doses) and autumn-winter (fifth BA.1/ancestral bivalent vs fourth monovalent mRNA vaccine dose) boosters.Methods:We performed a prospective single-centre test-negative design case–control study in ≥ 75-year-old people hospitalised with COVID-19 or other acute respiratory disease. We conducted regression analyses controlling for age, sex, socioeconomic status, patient comorbidities, community SARS-CoV-2 prevalence, vaccine brand and time between baseline dose and hospitalisation.Results:We included 682 controls and 182 cases in the spring-summer booster analysis and 572 controls and 152 cases in the autumn-winter booster analysis. A monovalent mRNA COVID-19 vaccine as fourth dose showed 46.6% rVE (95% confidence interval (CI): 13.9–67.1) vs those not fully boosted. A bivalent mRNA COVID-19 vaccine as fifth dose had 46.7% rVE (95% CI: 18.0–65.1), compared with a fourth monovalent mRNA COVID-19 vaccine dose.Conclusions:Both fourth monovalent and fifth BA.1/ancestral mRNA bivalent COVID-19 vaccine doses demonstrated benefit as a booster in older adults. Bivalent mRNA boosters offered similar protection against hospitalisation with Omicron infection to monovalent mRNA boosters given earlier in the year. These findings support immunisation programmes in several European countries that advised the use of BA.1/ancestral bivalent booster doses