rtestim: Time-varying reproduction number estimation with trend filtering DOI Creative Commons

Jiaping Liu,

Zhenglun Cai, Paul Gustafson

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 18, 2023

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges surveillance data collection, model assumptions that are unverifiable with alone, computationally inefficient frameworks critical limitations for existing approaches. We propose a discrete spline-based approach solves convex optimization problem---Poisson trend filtering---using proximal Newton method. It produces locally adaptive estimator number heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications is efficient, large-scale data. The implementation easily accessible in lightweight R package rtestim (dajmcdon.github.io/rtestim/).

Language: Английский

Simulation-based inference of the time-dependent reproduction number from temporally aggregated and under-reported disease incidence time series data DOI Creative Commons

I. Ogi-Gittins,

Nicholas Steyn, Jonathan A. Polonsky

et al.

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Journal Year: 2025, Volume and Issue: 383(2293)

Published: April 2, 2025

During infectious disease outbreaks, the time-dependent reproduction number ( R t ) can be estimated to monitor pathogen transmission. In previous work, we developed a simulation-based method for estimating from temporally aggregated incidence data (e.g. weekly case reports). While that approach is straightforward use, it assumes implicitly all cases are reported and computation slow when applied large datasets. this article, extend our develop computationally efficient in real-time accounting both temporal aggregation of under-reporting (with fixed reporting probability per case). Using simulated data, show failing consider stochastic lead inappropriately precise estimates, including scenarios which true value lies outside inferred credible intervals more often than expected. We then apply 2018 2020 Ebola outbreak Democratic Republic Congo (DRC), again exploring effects under-reporting. Finally, how extended account variations reporting. Given information about level reporting, framework used estimate during future outbreaks with under-reported data. This article part theme issue ‘Uncertainty quantification healthcare biological systems (Part 2)’.

Language: Английский

Citations

2
Inference of epidemic dynamics in the COVID-19 era and beyond DOI Creative Commons
Anne Cori, Adam J. Kucharski

Epidemics, Journal Year: 2024, Volume and Issue: 48, P. 100784 - 100784

Published: July 31, 2024

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats supporting decision making real-time. Motivated by unprecedented volume breadth of data generated during pandemic, we review modern opportunities for analysis to address questions emerge a major epidemic. Following broad chronology insights required - from understanding initial dynamics retrospective evaluation interventions, describe theoretical foundations each approach underlying intuition. Through series case studies, illustrate real life applications, discuss implications future work.

Language: Английский

Citations

6
ern: An R package to estimate the effective reproduction number using clinical and wastewater surveillance data DOI Creative Commons
David Champredon, Irena Papst, Warsame Yusuf

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(6), P. e0305550 - e0305550

Published: June 21, 2024

The effective reproduction number, [Formula: see text], is an important epidemiological metric used to assess the state of epidemic, as well effectiveness public health interventions undertaken in response. When text] above one, it indicates that new infections are increasing, and thus epidemic growing, while below one decreasing, so under control. There several established software packages readily available statistically estimate using clinical surveillance data. However, there comparatively few accessible tools for estimating from pathogen wastewater concentration, a data stream cemented its utility during COVID-19 pandemic. We present package ern aims perform estimation number real-world or aggregated user-friendly way.

Language: Английский

Citations

4
Time-varying reproductive number estimation for practical application in structured populations DOI Creative Commons
Erin Clancey, Eric Lofgren

Epidemiologic Methods, Journal Year: 2025, Volume and Issue: 14(1)

Published: Jan. 1, 2025

Abstract Objectives EpiEstim is a popular statistical framework designed to produce real-time estimates of the time-varying reproductive number, R t ${\mathcal{R}}_{t}$ . However, methods in have not been tested small, non-randomly mixing populations determine if resulting ̂ ${\hat{\mathcal{R}}}_{t}$ are temporally biased. Thus, we evaluate temporal performance when population structure present, and then demonstrate how recover accuracy using an approximation with Methods Following real-world example COVID-19 outbreak small university town, generate simulated case report data from two-population mechanistic model explicit generation interval distribution expression compute true To quantify bias, compare time points estimated fall below critical threshold 1. Results When present but accounted for prematurely incidence aggregated over weeks at later point than daily data, however, does further affect timing differences between data. Last, show it possible correct by lagging subpopulation estimate total Conclusions key parameter used epidemic response. Since can bias near 1, should be prudently applied structured populations.

Language: Английский

Citations

0
Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning DOI Creative Commons
Shan Gao, Amit K. Chakraborty, Russell Greiner

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(2), P. e1012782 - e1012782

Published: Feb. 13, 2025

Forecasting the occurrence and absence of novel disease outbreaks is essential for management, yet existing methods are often context-specific, require a long preparation time, non-outbreak prediction remains understudied. To address this gap, we propose framework using feature-based time series classification (TSC) method to forecast non-outbreaks. We tested our on synthetic data from Susceptible–Infected–Recovered (SIR) model slowly changing, noisy dynamics. Outbreak sequences give transcritical bifurcation within specified future window, whereas (null bifurcation) do not. identified incipient differences, reflected in 22 statistical features 5 early warning signal indicators, infectives leading Classifier performance, given by area under receiver-operating curve (AUC), ranged 0 . 99 large expanding windows training 7 small rolling windows. The further evaluated four empirical datasets: COVID-19 incidence Singapore, 18 other countries, Edmonton, Canada, as well SARS Hong Kong, with two classifiers exhibiting consistently high accuracy. Our results highlight detectable distinguishing outbreak before potential occurrence, both real-world datasets presented study.

Language: Английский

Citations

0
A flexible framework for local-level estimation of the effective reproductive number in geographic regions with sparse data DOI Creative Commons

Md Sakhawat Hossain,

RK Goyal, Natasha K. Martin

et al.

BMC Medical Research Methodology, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 18, 2025

Our research focuses on local-level estimation of the effective reproductive number, which describes transmissibility an infectious disease and represents average number individuals one person infects at a given time. The ability to accurately estimate in geographically granular regions is critical for disaster planning resource allocation. However, not all have sufficient outcome data; this lack data presents significant challenge accurate estimation. To overcome challenge, we propose two-step approach that incorporates existing $$\:{R}_{t}$$ procedures (EpiEstim, EpiFilter, EpiNow2) using from geographic with (step 1), into covariate-adjusted Bayesian Integrated Nested Laplace Approximation (INLA) spatial model predict sparse or missing 2). flexible framework effectively allows us implement any procedure coarse entirely data. We perform external validation simulation study evaluate proposed method assess its predictive performance. applied our $$\:{R}_{t}\:$$ South Carolina (SC) counties ZIP codes during first COVID-19 wave ('Wave 1', June 16, 2020 – August 31, 2020) second 2', December March 02, 2021). Among three methods used step, EpiNow2 yielded highest accuracy prediction Median county-level percentage agreement (PA) was 90.9% (Interquartile Range, IQR: 89.9–92.0%) 92.5% (IQR: 91.6–93.4%) Wave 1 2, respectively. zip code-level PA 95.2% 94.4–95.7%) 96.5% 95.8–97.1%) Using EpiEstim, ensemble-based median ranging 81.9 90.0%, 87.2-92.1%, 88.4-90.9%, respectively, across both waves granularities. These findings demonstrate methodology useful tool small-area , as yields high

Language: Английский

Citations

0
Analysis Insights to Support the Use of Wastewater and Environmental Surveillance Data for Infectious Diseases and Pandemic Preparedness DOI Creative Commons
Kathleen O’Reilly, Matthew J. Wade, Kata Farkas

et al.

Epidemics, Journal Year: 2025, Volume and Issue: unknown, P. 100825 - 100825

Published: March 1, 2025

Wastewater-based epidemiology is the detection of pathogens from sewage systems and interpretation these data to improve public health. Its use has increased in scope since 2020, when it was demonstrated that SARS-CoV-2 RNA could be successfully extracted wastewater affected populations. In this Perspective we provide an overview recent advances pathogen within wastewater, propose a framework for identifying utility sampling suggest areas where analytics require development. Ensuring both collection analysis are tailored towards key questions at different stages epidemic will inference made. For analyses useful methods determine absence infection, early reliably estimate trajectories prevalence, detect novel variants without reliance on consensus sequences. This research area included many innovations have improved collected optimistic innovation continue future.

Language: Английский

Citations

0
Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting DOI Creative Commons

I. Ogi-Gittins,

Jonathan A. Polonsky,

M. Keita

et al.

Infectious Disease Modelling, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
SARS-CoV-2 epidemiology, kinetics, and evolution: A narrative review DOI Creative Commons
Samuel Alizon, Mircea T. Sofonea

Virulence, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 8, 2025

Since winter 2019, SARS-CoV-2 has emerged, spread, and evolved all around the globe. We explore 4 y of evolutionary epidemiology this virus, ranging from applied public health challenges to more conceptual biology perspectives. Through review, we first present spread lethality infections it causes, starting its emergence in Wuhan (China) initial epidemics world, compare virus other betacoronaviruses, focus on airborne transmission, containment strategies ("zero-COVID" vs. "herd immunity"), explain phylogeographical tracking, underline importance natural selection epidemics, mention within-host population dynamics. Finally, discuss how pandemic transformed (or should transform) surveillance prevention viral respiratory identify perspectives for research COVID-19.

Language: Английский

Citations

0
A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data DOI Creative Commons

I. Ogi-Gittins,

William S. Hart, Jiao Song

et al.

Epidemics, Journal Year: 2024, Volume and Issue: 47, P. 100773 - 100773

Published: May 14, 2024

Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure time-dependent reproduction number, which has been estimated in real-time a range pathogens from incidence time series data. While commonly used approaches estimating number can be reliable when recorded frequently, such data are often aggregated temporally (for example, numbers cases may reported weekly rather than daily). As we show, methods unreliable timescale transmission shorter recording. To address this, here develop simulation-based approach involving Approximate Bayesian Computation We first use simulated dataset representative situation daily unavailable only summary values reported, demonstrating that our method provides accurate estimates under circumstances. then apply to two outbreak datasets consisting influenza case 2019-20 2022-23 Wales (in United Kingdom). Our simple-to-use will allow obtained outbreaks.

Language: Английский

Citations

3