Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19's impact on healthcare resources and capacity
The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation....
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| Veröffentlicht in: | PloS one 2021-11, Vol.16 (11), p.e0260310-e0260310 |
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| description | The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC. |
| doi_str_mv | 10.1371/journal.pone.0260310 |
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By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0260310</identifier><identifier>PMID: 34793573</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adaptation ; Biology and Life Sciences ; Calibration ; Computer and Information Sciences ; Control ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; Delivery of Health Care ; Disease ; Engineering and Technology ; Epidemics ; Evaluation ; Forecasting ; Forecasting techniques ; Health care ; Health care facilities ; Health care industry ; Hospital Bed Capacity - statistics & numerical data ; Hospitalization ; Hospitalization - trends ; Hospitals ; Humans ; Impact prediction ; Intensive Care Units - trends ; Iterative methods ; Mathematical models ; Medical care ; Medicine and Health Sciences ; Modelling ; North Carolina - epidemiology ; Pandemics ; Pandemics - statistics & numerical data ; Probability ; Public health ; Quality management ; Resource allocation ; Severe acute respiratory syndrome coronavirus 2 ; Social Sciences ; Supervision</subject><ispartof>PloS one, 2021-11, Vol.16 (11), p.e0260310-e0260310</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Endres-Dighe et al. 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By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. 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By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. 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| subjects | Adaptation Biology and Life Sciences Calibration Computer and Information Sciences Control Coronaviruses COVID-19 COVID-19 - epidemiology Delivery of Health Care Disease Engineering and Technology Epidemics Evaluation Forecasting Forecasting techniques Health care Health care facilities Health care industry Hospital Bed Capacity - statistics & numerical data Hospitalization Hospitalization - trends Hospitals Humans Impact prediction Intensive Care Units - trends Iterative methods Mathematical models Medical care Medicine and Health Sciences Modelling North Carolina - epidemiology Pandemics Pandemics - statistics & numerical data Probability Public health Quality management Resource allocation Severe acute respiratory syndrome coronavirus 2 Social Sciences Supervision |
| title | Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19's impact on healthcare resources and capacity |
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