Publications
68 results found
Bhatia S, Parag KV, Wardle J, et al., 2023, Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts, PLOS ONE, Vol: 18, ISSN: 1932-6203
Parag K, Cowling B, Lambert B, 2023, Angular reproduction numbers improve estimates of transmissibility when disease generation times are misspecified or time-varying, Proceedings of the Royal Society B: Biological Sciences, Vol: 290, ISSN: 0962-8452
We introduce the angular reproduction number Ω, which measures time-varying changes in epidemic transmissibility resulting from variations in both the effective reproduction number R, and generation time distribution w. Predominant approaches for tracking pathogen spread infer either R or the epidemic growth rate r. However, R is biased by mismatches between the assumed and true w, while r is difficult to interpret in terms of the individual-level branching process underpinning transmission. R and r may also disagree on the relative transmissibility of epidemics or variants (i.e. rA > rB does not imply RA > RB for variants A and B). We find that Ω responds meaningfully to mismatches and time-variations in w while mostly maintaining the interpretability of R. We prove that Ω > 1 implies R > 1 and that Ω agrees with r on the relative transmissibility of pathogens. Estimating Ω is no more difficult than inferring R, uses existing software, and requires no generation time measurements. These advantages come at the expense of selecting one free parameter. We propose Ω as complementary statistic to R and r that improves transmissibility estimates when w is misspecified or time-varying and better reflects the impact of interventions, when those interventions concurrently change R and w or alter the relative risk of co-circulating pathogens.
Policarpo JMP, Ramos AAGF, Dye C, et al., 2023, Scale-free dynamics of COVID-19 in a Brazilian city, Applied Mathematical Modelling: simulation and computation for engineering and environmental systems, Vol: 121, Pages: 166-184, ISSN: 0307-904X
A common basis to address the dynamics of directly transmitted infectious diseases, such as COVID-19, are compartmental (or SIR) models. SIR models typically assume homogenous population mixing, a simplification that is convenient but unrealistic. Here we validate an existing model of a scale-free fractal infection process using high-resolution data on COVID-19 spread in São Caetano, Brazil. We find that transmission can be described by a network in which each infectious individual has a small number of susceptible contacts, of the order of 2-5. This model parameter correlated tightly with physical distancing measured by mobile phone data, such that in periods of greater distancing the model recovered a lower average number of contacts, and vice versa. We show that the SIR model is a special case of our scale-free fractal process model in which the parameter that reflects population structure is set at unity, indicating homogeneous mixing. Our more general framework better explained the dynamics of COVID-19 in São Caetano, used fewer parameters than a standard SIR model and accounted for geographically localized clusters of disease. Our model requires further validation in other locations and with other directly transmitted infectious agents.
Pakkanen MS, Miscouridou X, Penn MJ, et al., 2023, Unifying incidence and prevalence under a time-varying general branching process, Journal of Mathematical Biology, Vol: 87, ISSN: 0303-6812
Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a time-varying variant of the Crump–Mode–Jagers branching process. This model accommodates a time-varying reproduction number and a time-varying distribution for the generation interval. We then derive renewal-like integral equations for incidence, cumulative incidence and prevalence under this model. We show that the equations for incidence and prevalence are consistent with the so-called back-calculation relationship. We analyse two particular cases of these integral equations, one that arises from a Bellman–Harris process and one that arises from an inhomogeneous Poisson process model of transmission.We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling. We present a numerical discretisation scheme to solve these equations, and use this scheme to estimate rates of transmission from serological prevalence of SARS-CoV-2 in the UK and historical incidence data on Influenza, Measles, SARS and Smallpox.
Parag KV, Obolski U, 2023, Risk averse reproduction numbers improve resurgence detection, PLoS Computational Biology, Vol: 19, ISSN: 1553-734X
The effective reproduction number R is a prominent statistic for inferring the transmissibility of infectious diseases and effectiveness of interventions. R purportedly provides an easy-to-interpret threshold for deducing whether an epidemic will grow (R>1) or decline (R<1). We posit that this interpretation can be misleading and statistically overconfident when applied to infections accumulated from groups featuring heterogeneous dynamics. These groups may be delineated by geography, infectiousness or sociodemographic factors. In these settings, R implicitly weights the dynamics of the groups by their number of circulating infections. We find that this weighting can cause delayed detection of outbreak resurgence and premature signalling of epidemic control because it underrepresents the risks from highly transmissible groups. Applying E-optimal experimental design theory, we develop a weighting algorithm to minimise these issues, yielding the risk averse reproduction number E. Using simulations, analytic approaches and real-world COVID-19 data stratified at the city and district level, we show that E meaningfully summarises transmission dynamics across groups, balancing bias from the averaging underlying R with variance from directly using local group estimates. An E>1generates timely resurgence signals (upweighting risky groups), while an E<1ensures local outbreaks are under control. We propose E as an alternative to R for informing policy and assessing transmissibility at large scales (e.g., state-wide or nationally), where R is commonly computed but well-mixed or homogeneity assumptions break down.
Ho F, Parag KV, Adam DC, et al., 2023, Accounting for the Potential of Overdispersion in Estimation of the Time-varying Reproduction Number, EPIDEMIOLOGY, Vol: 34, Pages: 201-205, ISSN: 1044-3983
Sahadeo NSD, Nicholls S, Moreira FRR, et al., 2023, Implementation of genomic surveillance of SARS-CoV-2 in the Caribbean: Lessons learned for sustainability in resource-limited settings, PLOS Global Public Health, Vol: 3, ISSN: 2767-3375
The COVID-19 pandemic highlighted the importance of global genomic surveillance to monitor the emergence and spread of SARS-CoV-2 variants and inform public health decision-making. Until December 2020 there was minimal capacity for viral genomic surveillance in most Caribbean countries. To overcome this constraint, the COVID-19: Infectious disease Molecular epidemiology for PAthogen Control & Tracking (COVID-19 IMPACT) project was implemented to establish rapid SARS-CoV-2 whole genome nanopore sequencing at The University of the West Indies (UWI) in Trinidad and Tobago (T&T) and provide needed SARS-CoV-2 sequencing services for T&T and other Caribbean Public Health Agency Member States (CMS). Using the Oxford Nanopore Technologies MinION sequencing platform and ARTIC network sequencing protocols and bioinformatics pipeline, a total of 3610 SARS-CoV-2 positive RNA samples, received from 17 CMS, were sequenced in-situ during the period December 5th 2020 to December 31st 2021. Ninety-one Pango lineages, including those of five variants of concern (VOC), were identified. Genetic analysis revealed at least 260 introductions to the CMS from other global regions. For each of the 17 CMS, the percentage of reported COVID-19 cases sequenced by the COVID-19 IMPACT laboratory ranged from 0·02% to 3·80% (median = 1·12%). Sequences submitted to GISAID by our study represented 73·3% of all SARS-CoV-2 sequences from the 17 CMS available on the database up to December 31st 2021. Increased staffing, process and infrastructural improvement over the course of the project helped reduce turnaround times for reporting to originating institutions and sequence uploads to GISAID. Insights from our genomic surveillance network in the Caribbean region directly influenced non-pharmaceutical countermeasures in the CMS countries. However, limited availability of associated surveillance and clinical data made it challenging to contextualise the observed SARS
Creswell R, Robinson M, Gavaghan D, et al., 2023, A Bayesian nonparametric method for detecting rapid changes in disease transmission, JOURNAL OF THEORETICAL BIOLOGY, Vol: 558, ISSN: 0022-5193
Stolerman LM, Clemente L, Poirier C, et al., 2023, Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States, SCIENCE ADVANCES, Vol: 9, ISSN: 2375-2548
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- Citations: 1
Sahadeo NSD, Nicholls S, Moreira FRR, et al., 2023, Correction: Implementation of Genomic Surveillance of SARS-CoV-2 in the Caribbean: Lessons learned for sustainability in resource-limited settings., PLOS Glob Public Health, Vol: 3
[This corrects the article DOI: 10.1371/journal.pgph.0001455.].
Inward RPD, Jackson F, Dasgupta A, et al., 2022, Impact of spatiotemporal heterogeneity in COVID-19 disease surveillance on epidemiological parameters and case growth rates, EPIDEMICS, Vol: 41, ISSN: 1755-4365
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- Citations: 1
Raghwani J, Faust CL, Francois S, et al., 2022, Seasonal dynamics of the wild rodent faecal virome, MOLECULAR ECOLOGY, ISSN: 0962-1083
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- Citations: 4
Parag K, Thompson R, Donnelly C, 2022, Are epidemic growth rates more informative than reproduction numbers?, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol: 185, Pages: S5-S15, ISSN: 0964-1998
Summary statistics, often derived from simplified models of epidemic spread, inform public health policy in real time. The instantaneous reproduction number, Rt, , is predominant among these statistics, measuring the average ability of an infection to multiply. However, Rt, encodes no temporal information and is sensitive to modelling assumptions. Consequently, some have proposed the epidemic growth rate, rt, that is, the rate of change of the log-transformed case incidence, as a more temporally meaningful and model-agnostic policy guide. We examine this assertion, identifying if and when estimates of rt are more informative than those ofRt. We assess their relative strengths both for learning about pathogen transmission mechanisms and for guiding public health interventions in real time.
Parag KV, Thompson RN, Donnelly CA, 2022, Authors' reply to the discussion of 'Are epidemic growth rates more informative than reproduction numbers?' by Parag et al. in Session 1 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 9 June 2021, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 185, Pages: S55-S60, ISSN: 0964-1998
Parag KV, Donnelly CA, Zarebski AE, 2022, Quantifying the information in noisy epidemic curves, Nature Computational Science, Vol: 2, Pages: 584-594, ISSN: 2662-8457
Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to the time series. Here, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections, as well as a metric for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring the instantaneous reproduction number: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.
Rhys I, Parag K, Faria NM, 2022, Using multiple sampling strategies to estimate SARS-CoV-2 epidemiological parameters from genomic sequencing data, Nature Communications, Vol: 13, Pages: 1-14, ISSN: 2041-1723
Predicted heart age profile across 41 countries: A cross-sectional study of nationally representative surveys in six world regions
Parag KV, Donnelly CA, Zarebski AE, 2022, Quantifying the information in noisy epidemic curves
<jats:title>Abstract</jats:title><jats:p>Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters, such as the instantaneous reproduction number, <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> at time <jats:italic>t</jats:italic>, are often inferred from incident time series, with the aim of informing policymakers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to those time series. While studies have proposed corrections for these issues, methodology for formally assessing how these sources of noise degrade <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> estimate quality is lacking. By adapting Fisher information and experimental design theory, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections. This yields a novel metric, defined by the geometric means of reporting and cumulative delay probabilities, for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.</jats:p>
Parag K, Donnelly C, 2022, Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers, PLoS Computational Biology, Vol: 18, ISSN: 1553-734X
We find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where R falls from supercritical to subcritical values) may be ascertained 5–10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.
Raghwani J, du Plessis L, McCrone JT, et al., 2022, Genomic Epidemiology of Early SARS-CoV-2 Transmission Dynamics, Gujarat, India, EMERGING INFECTIOUS DISEASES, Vol: 28, Pages: 751-758, ISSN: 1080-6040
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- Citations: 2
Zarebski AE, du Plessis L, Parag KV, et al., 2022, A computationally tractable birth-death model that combines phylogenetic and epidemiological data., PLoS Comput Biol, Vol: 18
Inferring the dynamics of pathogen transmission during an outbreak is an important problem in infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each type of data provides different, and potentially complementary, insight. Recent studies have recognised that combining data sources can improve estimates of the transmission rate and the number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the unobserved prevalence. Using simulated data, we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace.
Mee P, Alexander N, Mayaud P, et al., 2022, Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis., The Lancet Regional Health - Americas, Vol: 5, Pages: None-None, ISSN: 2667-193X
BACKGROUND: Brazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported by August 2021. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions, and predicting the course of the epidemic, but are often challenging due to different population sizes and structures. METHODS: We describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate the effective reproduction number (Rt ). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence. FINDINGS: After an initial introduction in São Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11·1 days [95% CI:8.9,13.2] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt ) were largely driven by geographic location and the date of local onset. INTERPRETATION: This study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics. FUNDING: This project was supported by a Medical Research Council UK (MRC-UK) -São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/1438
Parag K, Cowling BJ, Donnelly CA, 2021, Deciphering early-warning signals of SARS-CoV-2 elimination and resurgence from limited data at multiple scales, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 18, ISSN: 1742-5689
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- Citations: 9
Parag KV, Donnelly CA, 2021, Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers
<jats:title>Abstract</jats:title><jats:p>We find that epidemic resurgence, defined as an upswing in the effective reproduction number (<jats:italic>R</jats:italic>) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where <jats:italic>R</jats:italic> falls from supercritical to subcritical values) may be ascertained 5–10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>The timely detection of epidemic resurgence (i.e., upcoming waves of infected cases) is crucial for informing public health policy, providing valuable signals for implementing interventions and identifying emerging pathogenic variants or important population-level behavioural shifts. Increases in epidemic transmissibility, parametrised by the time-varying reproduction number, <jats:italic>R</jats:italic>, commonly signify resurgence. While many studies have improved computation
Parag KV, 2021, Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves, PLoS Computational Biology, Vol: 17, Pages: 1-23, ISSN: 1553-734X
We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.
Kraemer MUG, Hill V, Ruis C, et al., 2021, Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence, Science, Vol: 373, Pages: 889-895, ISSN: 0036-8075
Understanding the causes and consequences of the emergence of SARS-CoV-2 variants of concern is crucial to pandemic control yet difficult to achieve, as they arise in the context of variable human behavior and immunity. We investigate the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based PCR data. We identify a multi-stage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7's increased intrinsic transmissibility. We further explore how B.1.1.7 spread was shaped by non-pharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates.
Parag K, 2021, Sub-spreading events limit the reliable elimination of heterogeneous epidemics, Journal of the Royal Society Interface, Vol: 18, Pages: 1-10, ISSN: 1742-5662
We show that sub-spreading events i.e., transmission events in which an infection propagates to few or no individuals, can be surprisingly important for defining the lifetime of an infectious disease epidemic and hence its waiting time to elimination or fade-out, measured from the time-point of its last observed case. While limiting super-spreading promotes more effective control when cases are growing, we find that when incidence is waning, curbing sub-spreading is more important for achieving reliable elimination of the epidemic. Controlling super-spreading in this low-transmissibility phase offers diminishing returns over non-selective, population-wide measures. By restricting sub-spreading we efficiently dampen remaining variations among the reproduction numbers of infectious events, which minimises the risk of premature and late end-of-epidemic declarations. Because case-ascertainment or reporting rates can be modelled in exactly the same way as control policies, we concurrently show that the under-reporting of sub-spreading events during waning phases will engender overconfident assessments of epidemic elimination. While controlling sub-spreading may not be easily realised, the likely neglecting of these events by surveillance systems could result in unexpectedly risky end-of-epidemic declarations. Super-spreading controls the size of the epidemic peak but sub-spreading mediates the variability of its tail.
Bhatia S, Parag K, Wardle J, et al., 2021, Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment
<jats:title>Abstract</jats:title> <jats:p>From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for 81 countries with evidence of sustained transmission. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3\% and 45.6\% of the observations lying in the 50\% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9\% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.</jats:p>
Parag KV, Pybus OG, Wu C-H, 2021, Are skyline plot-based demographic estimates overly dependent on smoothing prior assumptions?, Systematic Biology, Vol: 71, ISSN: 1063-5157
In Bayesian phylogenetics, the coalescent process provides an informative framework for inferring changes in the effective size of a population from a phylogeny (or tree) of sequences sampled from that population. Popular coalescent inference approaches such as the Bayesian Skyline Plot, Skyride and Skygrid all model these population size changes with a discontinuous, piecewise-constant function but then apply a smoothing prior to ensure that their posterior population size estimates transition gradually with time. These prior distributions implicitly encode extra population size information that is not available from the observed coalescent data i.e., the tree. Here we present a novel statistic, Ω, to quantify and disaggregate the relative contributions of the coalescent data and prior assumptions to the resulting posterior estimate precision. Our statistic also measures the additional mutual information introduced by such priors. Using Ω we show that, because it is surprisingly easy to over-parametrise piecewise-constant population models, common smoothing priors can lead to overconfident and potentially misleading inference, even under robust experimental designs. We propose Ω as a useful tool for detecting when effective population size estimates are overly reliant on prior assumptions and for improving quantification of the uncertainty in those estimates.
Mee P, Alexander N, Mayaud P, et al., 2021, Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: a longitudinal analysis
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Brazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions and predicting the course of the epidemic, but are often challenging due to different population sizes and structures.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate reproduction number (<jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>After an initial introduction in São Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to Northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11·1 days [95% CI:13·2,8·9] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean<jats:italic>Rt</jats:italic>) were largely driven by geographic location and the date of local onset.</jats:p></jats:
Parag KV, Thompson RN, Donnelly CA, 2021, Are epidemic growth rates more informative than reproduction numbers?
<jats:p>Summary statistics, often derived from simplified models of epidemic spread, inform public health policy in real time. The instantaneous reproduction number, <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>, is predominant among these statistics, measuring the average ability of an infection to multiply. However, <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> encodes no temporal information and is sensitive to modelling assumptions. Consequently, some have proposed the epidemic growth rate, <jats:italic>r</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>, i.e., the rate of change of the log-transformed case incidence, as a more temporally meaningful and model-agnostic policy guide. We examine this assertion, identifying if and when estimates of <jats:italic>r</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> are more informative than those of <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>. We assess their relative strengths both for learning about pathogen transmission mechanisms and for guiding public health interventions in real time.</jats:p>
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