Search

Planning for a catastrophe involving a disease outbreak with the potential for mass casualties is a significant challenge for emergency managers. Public health experts at the US Centers for Disease Control and Prevention (CDC) teamed with operations researchers to address important aspects of mass dispensing: medical supply distribution, locations of dispensing facilities, optimal facility staffing and resource allocation, routing of the population, and dispensing methods. Simulation-optimization technology was integrated into a decision support and data management suite, RealOpt©, for tactical and strategic operational planning. RealOpt has enabled the CDC to provide modern tools that support dynamic planning for emergencies and that establish a knowledge data bank to provide feedback about the deployment of various techniques. The RealOpt suite now has a US user base of over 6,500 public health and emergency directors covering all states, plus many international users. RealOpt has been applied in hundreds of drills and dispensing events, including anthrax preparedness, and for seasonal flu and H1N1 vaccination events. [ABSTRACT FROM AUTHOR]

In this study, we describe the development and use of a biological-behavior-intervention computational informatics framework that combines disease modelling for infectious virus with stratifications for social behavior and employment, and resource logistics. The framework incorporates heterogeneous group behavior and interaction dynamics, and optimizes intervention and resources for effective containment. We demonstrate its usage by analyzing and optimizing containment strategies for the 2014-2016 West Africa Ebola outbreak, and its implementation for responses to the 2020 COVID-19 pandemic in the United States. Our analysis shows that timely action within 1.5 months from the onset of confirmed cases can cut down 90% of overall infections and bring rapid containment within 6-8 months. The additional medical resources required are minor and would ensure proper treatment and quarantine of patients while reducing the risk of infections among healthcare workers. The benefit (in infection / death control) would be reduced by 10 to over 100 fold and time to containment would increase by 2-4 fold when intervention and medical resources are injected within 5 months. In contrast, the additional resources needed to bring down the overall infection in a delayed intervention are significant, with inferior results. The disease module can be tailored for different pathogens. It expands the well-used SEIR model to include social and intervention activities, asymptomatic and post-recovery transmission, hospitalization, outcome of recovery, and funeral events. The model also examines the transmission rate of health care workers and allows for heterogenous infection factors among different groups. It also captures time-variant human behavior during the horizon of the outbreak. The framework optimizes the intervention timeline and resource allocation during an infectious disease outbreak and offers insights on how resource availability in time and quantity can affect the disease trends and containment significantly. This can inform policy, disease management and resource allocation. While focusing on bed availability for quarantine and treatment appears to be simplistic, their necessity for Ebola responses cannot be overemphasized. We link these insights to a web-based tool to provide quick and intuitive observations for decision making and investigation of the disease outbreak situation. Subsequent use of the system to determine the optimal timing and effectiveness and tradeoffs analysis of various non-pharmaceutical intervention strategies for COVID-19 provide a foundation for policy makers to execute the first-step response. These results have been implemented on the ground since March 2020. The web-based tool pinpoints accurately the import of disease from global travels and associated disease spread and health burdens. This prospectively affirms the importance of such a real-time computational system, and its availability before onset of a pandemic., (©2021 AMIA - All rights reserved.)

Avian influenza viruses have caused infections and deaths in wild birds, commercial poultry, and humans. It poses an increasing threat of a pandemic. To understand the transmission dynamics of avian influenza viruses and assess the effectiveness of different containment strategies, we develop a flexible modeling framework based on multi-layer compartmental models for digital disease surveillance and response in combating pandemics. The model can accommodate other disease outbreaks under diverse settings. We demonstrate its usage on avian influenza and derive the basic reproduction number and spread characteristics. We contrast the effectiveness of different containment strategies and their combination effect in protecting both the human and the bird populations. Our system, a digital surveillance and response system (RealOpt-ASSURE), can record, monitor, and predict avian influenza outbreaks. It combines with intervention strategies to return policies and on-the-ground operations/actions that are needed for best population protection. RealOpt-ASSURE can accept heterogeneous types of surveillance data. It can help decision makers to evaluate the risk of a pandemic and choose proper containment strategies to rapidly mitigate the outbreak.

This research follows the Updated Guidelines for Evaluating Public Health Surveillance Systems, Recommendations from the Guidelines Working Group, published by the Centers for Disease Control and Prevention nearly a decade ago. Since then, models have been developed and complex systems have evolved with a breadth of disparate data to detect or forecast chemical, biological, and radiological events that have a significant impact on the One Health landscape. How the attributes identified in 2001 relate to the new range of event-based biosurveillance technologies is unclear. This article frames the continuum of event-based biosurveillance systems (that fuse media reports from the internet), models (ie, computational that forecast disease occurrence), and constructs (ie, descriptive analytical reports) through an operational lens (ie, aspects and attributes associated with operational considerations in the development, testing, and validation of the event-based biosurveillance methods and models and their use in an operational environment). A workshop was held in 2010 to scientifically identify, develop, and vet a set of attributes for event-based biosurveillance. Subject matter experts were invited from 7 federal government agencies and 6 different academic institutions pursuing research in biosurveillance event detection. We describe 8 attribute families for the characterization of event-based biosurveillance: event, readiness, operational aspects, geographic coverage, population coverage, input data, output, and cost. Ultimately, the analyses provide a framework from which the broad scope, complexity, and relevant issues germane to event-based biosurveillance useful in an operational environment can be characterized.