Suchergebnisse

Abstract
The process used to determine site suitability for military base camps lacks a formal framework for reducing relative risks to soldier safety and maximise mission effectiveness. Presently, military personnel responsible for determining site suitability of a base camp must assess large amounts of geographic, socioeconomic and logistical data, without a decision analysis framework to aid in the process. By adopting a multicriteria decision analysis (MCDA) framework to determine site suitability of base camps, battlespace commanders can make better, more defensible decisions. This paper surveys US Army officers with recent base camp experience to develop a set of initial criteria and weights relevant to base camp site selection. The developed decision framework is demonstrated using an MCDA methodology in an illustrative example to compare alternative base camp locations within a designated Area of Interest (AoI). Leveraging the site ranking output and/or criteria weights resulting from the methodology provides decision-making support that can be used in the field when time, resources and data may not be readily available.

Abstract
The process used to determine site suitability for military base camps lacks a formal framework for reducing relative risks to soldier safety and maximise mission effectiveness. Presently, military personnel responsible for determining site suitability of a base camp must assess large amounts of geographic, socioeconomic and logistical data, without a decision analysis framework to aid in the process. By adopting a multicriteria decision analysis (MCDA) framework to determine site suitability of base camps, battlespace commanders can make better, more defensible decisions. This paper surveys US Army officers with recent base camp experience to develop a set of initial criteria and weights relevant to base camp site selection. The developed decision framework is demonstrated using an MCDA methodology in an illustrative example to compare alternative base camp locations within a designated Area of Interest (AoI). Leveraging the site ranking output and/or criteria weights resulting from the methodology provides decision-making support that can be used in the field when time, resources and data may not be readily available.

Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the international or within-country variation in hospitalization, ICU admission, or death. Identifying alternative explanations for the limited predictive power of comorbidities and age at the population level should be considered for future research.

Jeffrey C Cegan,1 Benjamin D Trump,1 Susan M Cibulsky,2 Zachary A Collier,3 Christopher L Cummings,4 Scott L Greer,5 Holly Jarman,5 Kasia Klasa,1,5 Gary Kleinman,2 Melissa A Surette,6 Emily Wells,1 Igor Linkov1 1US Army Engineer Research and Development Center, US Army Corps of Engineers, Vicksburg, MS, USA; 2US Department of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response, Boston, MA, USA; 3Radford University, Davis College of Business and Economics, Department of Management, Radford, VA, USA; 4North Carolina State University, Genetic Engineering and Society Center, Raleigh, NC, USA; 5University of Michigan, School of Public Health, Department of Health Management and Policy, Ann Arbor, MI, USA; 6Federal Emergency Management Agency, Region I, Boston, MA, USACorrespondence: Jeffrey C Cegan; Igor LinkovUS Army Engineer Research and Development Center, US Army Corps of Engineers, 696 Virginia Road, Concord, MA, 01742, USATel +1-978-318-8881; +1-617-233-9869Email Jeffrey.C.Cegan@usace.army.mil; Igor.Linkov@usace.army.milAbstract: Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the international or within-country variation in hospitalization, ICU admission, or death. Identifying alternative explanations for the limited predictive power of comorbidities and age at the population level should be considered for future research.Keywords: comorbidity, health outcomes, COVID-19, mortality rates

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.