Joint estimation of the effective reproduction number and daily incidence in the presence of aggregated and missing data

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июнь 7, 2024

Abstract Disease surveillance is an integral component of government policy, allowing public health professionals to monitor transmission infectious diseases and appropriately apply interventions. To aid with efforts, there has been extensive development mathematical models help inform policy decisions, However, these rely upon data streams that are expensive often only practical for high income countries. With a growing focus on equitable tools dire need equipped handle the stream challenges prevalent in low middle countries, where incomplete subject aggregation. address this need, we develop model joint estimation effective reproduction number daily incidence disease using aggregated data. Our investigation demonstrates novel robust across variety reduced streams, making it suitable application diverse regions. Author summary Monitoring important part hindered by limitations streams. This especially true countries sectors have less funding. In work enhance overcoming limitations, providing accurate inferences relevant epidemiological parameters.

Язык: Английский

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Interpreting epidemiological surveillance data: A modelling study from Pune City

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Сен. 14, 2024

Abstract Routine epidemiological surveillance data represents one of the most continuous and comprehensive sources during course an epidemic. This is used as inputs to forecasting models well for public health decision making such imposition lifting lockdowns quarantine measures. However, generated testing contact tracing not through randomized sampling which makes it unclear how representative epidemic itself. Using BharatSim simulation framework, we build agent-based model with a detailed algorithm actual strategies employed in Pune city generate synthetic data. We simulate impact different strategies, availability tests efficiencies on resulting The fidelity representing real-time state decision-making explored context city.

Язык: Английский

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A Flexible Framework for Local-Level Estimation of the Effective Reproductive Number in Geographic Regions with Sparse Data

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 7, 2024

Abstract Background 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. Methods 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. Results applied our 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. Conclusion These findings demonstrate methodology useful tool small-area , as yields high

Язык: Английский

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rtestim: Time-varying reproduction number estimation with trend filtering

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Дек. 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/).

Язык: Английский

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