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

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

Published: April 1, 2025

Language: Английский

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Time-varying reproduction number estimation: Fusing compartmental models with generalised additive models

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 28, 2024

Abstract The reproduction number, the mean number of secondary cases infected by each primary case, is a central metric in infectious disease epidemiology, and played key role COVID-19 pandemic response. This because it gives an indication effort required to control disease. Beyond well-known basic there are two natural versions, namely effective numbers. As behaviour, population immunity viral characteristics can change with time, these numbers vary over time different regions. Real world data be complex, for example daily variation due weekend surveillance biases as well stochastic noise. such, this work we consider Generalised Additive Model smooth real through explicit incorporation day-of-the-week effects, provide simple measure time-varying growth rate associated data. Converting resulting spline into estimator both requires assumptions on model structure, which here assume compartmental model. calculated based simulated data, compared estimates from already existing tool. derived method estimating effective, efficient comparable other methods. It provides useful alternative approach, included part toolbox models, that particularly apt at smoothing out effects surveillance.

Language: Английский

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

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

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Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the US

Cell Reports, Journal Year: 2024, Volume and Issue: 43(7), P. 114451 - 114451

Published: July 1, 2024

Omicron surged as a variant of concern in late 2021. Several distinct variants appeared and overtook each other. We combined frequencies infection estimates from nowcasting model for US state to estimate variant-specific infections, attack rates, effective reproduction numbers (R

Language: Английский

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Challenges in the case-based surveillance of infectious diseases

Royal Society Open Science, Journal Year: 2024, Volume and Issue: 11(8)

Published: Aug. 1, 2024

To effectively inform infectious disease control strategies, accurate knowledge of the pathogen's transmission dynamics is required. Since timings infections are rarely known, estimates infection incidence, which crucial for understanding dynamics, often rely on measurements other quantities amenable to surveillance. Case-based surveillance, in infected individuals identified by a positive test, predominant form surveillance many pathogens, and was used extensively during COVID-19 pandemic. However, there can be biases present case-based indicators due to, example test sensitivity, changing testing behaviours co-circulation pathogens with similar symptom profiles. Here, we develop mathematical description diseases. By considering realistic epidemiological parameters situations, demonstrate potential common based data. Crucially, find that these (e.g. case numbers, test-positive proportion) heavily biased circulating Future strategies could designed minimize sources bias uncertainty, providing more and, ultimately, targeted application public health measures.

Language: Английский

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Nowcasting and Forecasting the 2022 U.S. Mpox Outbreak: Support for Public Health Decision Making and Lessons Learned

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

Published: April 17, 2023

Abstract In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious models have shown valuable tool decision making during outbreaks; however, model complexity often makes communicating results limitations makers difficult. We performed nowcasting forecasting 2022 mpox outbreak in United States using R package EpiNow2. generated nowcasts/forecasts at national level, by Census region, jurisdictions reporting greatest number cases. Modeling were shared situational awareness within CDC publicly on website. retrospectively evaluated forecast predictions four key phases three metrics, weighted interval score, mean absolute error, prediction coverage. compared performance EpiNow2 with a naïve Bayesian generalized linear (GLM). The had less probabilistic error than GLM every phase except early phase. share our experiences an existing nowcasting/forecasting highlight areas improvement development future tools. also reflect lessons learned regarding data quality issues adapting modeling different audiences.

Language: Английский

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Incorporating testing volume into estimation of effective reproduction number dynamics

Journal of the Royal Statistical Society Series A (Statistics in Society), Journal Year: 2023, Volume and Issue: 187(2), P. 436 - 453

Published: Dec. 13, 2023

Abstract Branching process inspired models are widely used to estimate the effective reproduction number—a useful summary statistic describing an infectious disease outbreak—using counts of new cases. Case data is a real-time indicator changes in number, but challenging work with because cases fluctuate due factors unrelated number infections. We develop model that incorporates diagnostic tests as surveillance covariate. Using simulated and from SARS-CoV-2 pandemic California, we demonstrate incorporating leads improved performance over state art.

Language: Английский

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“Real-time county-aggregated wastewater-based estimates for SARS-CoV-2 effective reproduction numbers”

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 3, 2024

ABSTRACT Background The effective reproduction number ( R e ) serves as a metric of population-wide, time-varying disease spread. During the COVID-19 pandemic, was primarily estimated from clinical surveillance data streams cc ), which have varied in quality and representativeness due to changes testing volume, test-seeking behavior, resource constraints. Deriving alternative sources such wastewater could inform future public health responses. Objectives We county-aggregated, sewershed-restricted wastewater-based SARS-CoV-2 ww May 1, 2022 April 30, 2023 for five counties California varying population sizes, rates, demographics, proportions surveilled by wastewater, sampling frequencies validate reliability real-time metric. Methods produced both instantaneous cohort using smoothed deconvolved concentrations. then population-weighted aggregated these sewershed-level estimates arrive at county-level . Using mean absolute error (MAE), Spearman’s rank correlation (ρ), confusion matrix classification, cross-correlation analyses, we compared timing trajectory two models to: (1) publicly available, ensemble estimates, (2) Results Both demonstrated high concordance with traditional indicated low errors (MAE ≤ 0.09), significant positive Spearman (Spearman ρ ≥ 0.66, p < 0.001), classification accuracy (≥ 0.81). relative timings were less clear, analyses suggesting strong associations wide range temporal lags that county model type. Discussion This estimation methodology provides generalizable, robust, operationalizable framework estimating Our results support additional use an epidemiological tool surveillance. Based on this research, available nowcasts Communicable diseases Assessment Tool https://calcat.covid19.ca.gov/cacovidmodels/ ).

Language: Английский

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A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(11), P. e1012569 - e1012569

Published: Nov. 20, 2024

During pandemics, countries, regions, and communities develop various epidemic models to evaluate spread guide mitigation policies. However, model uncertainties caused by complex transmission behaviors, contact-tracing networks, time-varying parameters, human factors, limited data present significant challenges model-based approaches. To address these issues, we propose a novel framework that centers around reproduction number estimates perform counterfactual analysis, strategy evaluation, feedback control of epidemics. The 1) introduces mechanism quantify the impact testing-for-isolation intervention on basic number. Building this mechanism, 2) proposes method reverse engineer effective under different strengths strategy. In addition, based quantifies number, 3) closed-loop algorithm uses both as indicate severity goal adjustments in intensity intervention. We illustrate framework, along with its three core methods, addressing key questions validating effectiveness using collected during COVID-19 pandemic at University Illinois Urbana-Champaign (UIUC) Purdue University: How severe would an outbreak have been without implemented strategies? What varying strength had outbreak? can adjust current state

Language: Английский

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An Evaluation Framework for Comparing Epidemic Intelligence Systems

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 31880 - 31901

Published: Jan. 1, 2023

In the context of Epidemic Intelligence, many Event-Based Surveillance (EBS) systems have been proposed in literature to promote early identification and characterization potential health threats from online sources any nature. Each EBS system has its own surveillance definitions priorities, therefore this makes task selecting most appropriate for a given situation challenge end-users. work, we propose new evaluation framework address issue. It first transforms raw input epidemiological event data into set normalized events with multi-granularity, then conducts descriptive retrospective analysis based on four objectives: spatial, temporal, thematic source analysis. We illustrate relevance by applying it an Avian Influenza dataset collected selection systems, show how our allows identifying their strengths drawbacks terms epidemic surveillance.

Language: Английский

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