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.

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

Published: Nov. 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

Language: Английский

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
An Institutional Framework for Enhanced Food Security Amidst the COVID-19 Pandemic: Strategic Implementation and Outcomes DOI Creative Commons

Akbar Akbar,

Rahim Darma, Andi Irawan

et al.

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: unknown, P. 101833 - 101833

Published: March 1, 2025

Language: Английский

Citations

0
Machine Learning Approaches for Real-Time ZIP Code and County-Level Estimation of State-Wide Infectious Disease Hospitalizations Using Local Health System Data DOI Creative Commons
Tanvir Ahammed, Md Sakhawat Hossain, Christopher S. McMahan

et al.

Epidemics, Journal Year: 2025, Volume and Issue: 51, P. 100823 - 100823

Published: April 6, 2025

The lack of conventional methods estimating real-time infectious disease burden in granular regions inhibits timely and efficient public health response. Comprehensive data sources (e.g., state department data) typically needed for such estimation are often limited due to 1) substantial delays reporting 2) geographic granularity provided researchers. Leveraging local system presents an opportunity overcome these challenges. This study evaluates the effectiveness machine learning statistical approaches using estimate current previous COVID-19 hospitalizations South Carolina. Random Forest models demonstrated consistently higher average median percent agreement accuracy compared generalized linear mixed weekly across 123 ZIP codes (72.29 %, IQR: 63.20-75.62 %) 28 counties (76.43 70.33-81.16 with sufficient coverage. To account underrepresented populations systems, we combined Classification Regression Trees (CART) imputation. was 61.02 % (IQR: 51.17-72.29 all 72.64 66.13-77.69 counties. Median cumulative over 6 months 80.98 68.99-89.66 81.17 68.55-91.33 These findings emphasize utilizing burden. Moreover, methodologies developed this can be adapted other diseases, offering a valuable tool officials respond swiftly effectively various crises.

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.

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

Published: Nov. 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

Language: Английский

Citations

0