Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.
Many socioeconomic factors hinder individuals' ability to obtain and use HA, but these obstacles appeared to be mitigated in part when insurance plans provided adequate HA coverage, or when their family/friends provided encouragement to use HA.
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 multi-model ensemble forecast that combined predictions from dozens of different research 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-week horizon 3-5 times larger than when predicting at a 1-week 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. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.
IntroductionTo design effective national diarrhea control programs, including oral rehydration solution (ORS) and therapeutic zinc supplementation, information is needed on local perceptions of illness, external care seeking behaviors, and home treatment practices.MethodsA cross-sectional, community-based household survey was conducted in the Orodara Health District, Burkina Faso. Caregivers of 10,490 children <27 months were interviewed to assess child diarrhea prevalence and related care practices. Characteristics of households, caregivers, children, and reported illnesses were compared for those caregivers who did or did not recognize the presence of diarrhea, as defined according to clinical criteria (≥3 liquid or semi-liquid stools/day). Multiple logistic regression models were used to examine factors associated with illness recognition and treatment.ResultsClinically defined diarrhea was present in 7.6% (95% CI: 7.1–8.1%) of children during the 24 hours preceding the survey but recognized by only 55% of caregivers. Over half (55%) of the caregivers of 1,067 children with a clinically defined diarrhea episode in the past 14 days sought care outside the home; 78% of those seeking care attended a public sector clinic. Care was sought and treatment provided more frequently for children with fever, vomiting, anorexia, longer illness duration, and those living closer to the health center; and care was sought more frequently for male children. 80% of children with recent diarrhea received some form of treatment; only 24% received ORS, whereas 14% received antibiotics. Zinc was not yet available in the study area.ConclusionsCaregivers frequently fail to recognize children's diarrhea, especially among younger infants and when illness signs are less severe. Treatment practices do not correspond with international recommendations in most cases, even when caregivers consult with formal health services. Child caregivers need additional assistance to recognize diarrhea correctly, and both caregivers and health care providers need updated training on current diarrhea treatment recommendations.
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