A multi-method approach to modeling COVID-19 disease dynamics in the United States.

In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model's state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model's two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.

A Community Health Record: Improving Health Through Multisector Collaboration, Information Sharing, and Technology.

We present a framework for developing a community health record to bring stakeholders, information, and technology together to collectively improve the health of a community. It is both social and technical in nature and presents an iterative and participatory process for achieving multisector collaboration and information sharing. It proposes a methodology and infrastructure for bringing multisector stakeholders and their information together to inform, target, monitor, and evaluate community health initiatives. The community health record is defined as both the proposed framework and a tool or system for integrating and transforming multisector data into actionable information. It is informed by the electronic health record, personal health record, and County Health Ranking systems but differs in its social complexity, communal ownership, and provision of information to multisector partners at scales ranging from address to zip code.

Summary of notifiable diseases--United States, 2012.

The Summary of notifiable diseases--United States, 2012 contains the official statistics, in tabular and graphic form, for the reported occurrence of nationally notifiable infectious diseases in the United States for 2012. Unless otherwise noted, the data are final totals for 2012 reported as of June 30, 2013. These statistics are collected and compiled from reports sent by state health departments and territories to the National Notifiable Diseases Surveillance System (NNDSS), which is operated by CDC in collaboration with the Council of State and Territorial Epidemiologists (CSTE). The Summary is available at http://www.cdc.gov/mmwr/mmwr_nd/index.html. This site also includes Summary publications from previous years.

Syndromic surveillance in an ICD-10 world.

The Centers for Disease Control and Prevention's BioSense program is an integrated national public health surveillance system that uses electronic medical record (EMR) data to provide situational awareness for all-hazard health-related events. Because the system leverages International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coded data from EMRs for syndromic surveillance, the upcoming Health and Human Services-mandated transition from ICD-9-CM to ICD-10-CM will have a significant impact. To translate across the two encoding systems, we developed a Mapping Reference Table (MRT) for the ICD-9/10 transition. We extracted ICD-9-CM codes binned to predefined syndromes and mapped each to its corresponding ICD-10-CM code(s). Then, we translated the output ICD-10-CM codes back to ICD-9-CM through a reverse translation validation process. Throughout the translation process, we examined outputs manually and incorporated annotated results into the MRT. The resulting MRT can be used to refine and update each existing syndromic surveillance definition in BioSense to be compatible with ICD-10-CM and consistently classify or bin any given emergency department visit into the correct syndrome regardless of coding system.

Summary of Notifiable Diseases - United States, 2011.

The Summary of Notifiable Diseases - United States, 2011 contains the official statistics, in tabular and graphic form, for the reported occurrence of nationally notifiable infectious diseases in the United States for 2011. Unless otherwise noted, the data are final totals for 2011 reported as of June 30, 2012. These statistics are collected and compiled from reports sent by state health departments and territories to the National Notifiable Diseases Surveillance System (NNDSS), which is operated by CDC in collaboration with the Council of State and Territorial Epidemiologists (CSTE).

BING: biomedical informatics pipeline for Next Generation Sequencing.

High throughput parallel genomic sequencing (Next Generation Sequencing, NGS) shifts the bottleneck in sequencing processes from experimental data production to computationally intensive informatics-based data analysis. This manuscript introduces a biomedical informatics pipeline (BING) for the analysis of NGS data that offers several novel computational approaches to 1. image alignment, 2. signal correlation, compensation, separation, and pixel-based cluster registration, 3. signal measurement and base calling, 4. quality control and accuracy measurement. These approaches address many of the informatics challenges, including image processing, computational performance, and accuracy. These new algorithms are benchmarked against the Illumina Genome Analysis Pipeline. BING is the one of the first software tools to perform pixel-based analysis of NGS data. When compared to the Illumina informatics tool, BING's pixel-based approach produces a significant increase in the number of sequence reads, while reducing the computational time per experiment and error rate (<2%). This approach has the potential of increasing the density and throughput of NGS technologies.