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Epidemics ; 39: 100580, 2022 06.
Article in English | MEDLINE | ID: covidwho-1907009


During the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health care capacity and resources. We present a SARIMA time-series model called Gecko developed for this purpose. We evaluate its historical performance using metrics such as mean absolute error, predictive interval coverage, and weighted interval scores, and compare to alternative hospital admission forecasting models. We find that Gecko outperformed baseline approaches and was among the most accurate models for forecasting hospital admissions at the state and national levels from January-May 2021. This work suggests that simple statistical methods can provide a viable alternative to traditional epidemic models for short-term forecasting.

COVID-19 , Lizards , Animals , COVID-19/epidemiology , Forecasting , Hospitals , Humans , Models, Statistical , Pandemics , United States/epidemiology
MMWR Morb Mortal Wkly Rep ; 69(33): 1127-1132, 2020 Aug 21.
Article in English | MEDLINE | ID: covidwho-725246


The geographic areas in the United States most affected by the coronavirus disease 2019 (COVID-19) pandemic have changed over time. On May 7, 2020, CDC, with other federal agencies, began identifying counties with increasing COVID-19 incidence (hotspots) to better understand transmission dynamics and offer targeted support to health departments in affected communities. Data for January 22-July 15, 2020, were analyzed retrospectively (January 22-May 6) and prospectively (May 7-July 15) to detect hotspot counties. No counties met hotspot criteria during January 22-March 7, 2020. During March 8-July 15, 2020, 818 counties met hotspot criteria for ≥1 day; these counties included 80% of the U.S. population. The daily number of counties meeting hotspot criteria peaked in early April, decreased and stabilized during mid-April-early June, then increased again during late June-early July. The percentage of counties in the South and West Census regions* meeting hotspot criteria increased from 10% and 13%, respectively, during March-April to 28% and 22%, respectively, during June-July. Identification of community transmission as a contributing factor increased over time, whereas identification of outbreaks in long-term care facilities, food processing facilities, correctional facilities, or other workplaces as contributing factors decreased. Identification of hotspot counties and understanding how they change over time can help prioritize and target implementation of U.S. public health response activities.

Coronavirus Infections/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Rural Population/statistics & numerical data , Urban Population/statistics & numerical data , COVID-19 , Humans , Incidence , United States/epidemiology