The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research

Reich, Nicholas G.; Cornell, Matthew; Ray, Evan L.; House, Katie; Le, Khoa

doi:10.1038/s41597-021-00839-5

Cited by 13 publications

(19 citation statements)

References 37 publications

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“…One important assumption was the availability of sufficiently accurate estimates of true infections. We based our estimates on the predictions of four prominent infection models, which have been used and relied upon throughout this pandemic and some recent studies have concluded that such models exhibit reasonable levels of prediction accuracy ( 34 , 35 ). Moreover, our ensemble approach, based on the average of these underlying models, can be expected to produce more robust estimates than if we relied upon any single model.…”

Section: Discussionmentioning

confidence: 99%

Estimating Exposure Risk to Guide Behaviour During the SARS-COV2 Pandemic

Smyth

2021

Front. Digit. Health

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The end of 2020 and the beginning of 2021 was a challenging time for many countries in Europe, as the combination of colder weather, holiday celebrations, and the emergence of more transmissible virus variants conspired to create a perfect storm for virus transmission across the continent. At the same time lockdowns appeared to be less effective than they were earlier in the pandemic. In this paper we argue that one contributing factor is that existing ways of communicating risk—case numbers, test positivity rates, hospitalisations etc.—are difficult for individuals to translate into a level of personal risk, thereby limiting the ability of individuals to properly calibrate their own behaviour. We propose an new more direct measure of personal risk, exposure risk, to estimate the likelihood that an individual will come into contact with an infected person, and we argue that it can play an important role, alongside more conventional statistics, to help translate complex epidemiological data into a simple measure to guide pandemic behaviour. We describe how exposure risk can be calculated using existing data and infection prediction models, and use it to evaluate and compare the exposure risk associated with 39 European countries.

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Section: Discussionmentioning

confidence: 99%

Estimating Exposure Risk to Guide Behaviour During the SARS-COV2 Pandemic

Smyth

2021

Front. Digit. Health

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“…The first is the COVID-19 Forecast Hub GitHub repository and the second is an online database, Zoltar, which can be accessed via a REST API. 11 Details about data format and access are documented in the subsequent sections.…”

Section: Methodsmentioning

confidence: 99%

“…The forecasts were automatically synchronized with an online database called Zoltar via calls to a REpresentational State Transfer (REST) application programming interface (API) 11 every six hours (Figure 2). Forecast data may be downloaded directly from GitHub, via the covidHubUtils R package, 12 the zoltr R package 13 or zoltpy python library.…”

Section: Background and Summarymentioning

confidence: 99%

“…15–18 The general structure of this data collection could be applied to additional diseases or forecasting outcome in the future. 11…”

Section: Background and Summarymentioning

confidence: 99%

“…[15][16][17][18] The general structure of this data collection could be applied to additional diseases or forecasting outcome in the future. 11 This large collaborative effort has provided data on short-term forecasts for over a year of forecasting efforts. These data were collected in real-time and therefore are not subject to retrospective biases.…”

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confidence: 99%

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The United States COVID-19 Forecast Hub dataset

Cramer

Huang

Wang

et al. 2021

Preprint

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Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

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Progress and opportunities in advancing near-term forecasting of freshwater quality

Lofton

Howard

Thomas

et al. 2022

Preprint

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Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past five years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in early stages of development (i.e., non-operational), despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed, and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events five days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts necessitates substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

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The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research

Cited by 13 publications

References 37 publications

Estimating Exposure Risk to Guide Behaviour During the SARS-COV2 Pandemic

Estimating Exposure Risk to Guide Behaviour During the SARS-COV2 Pandemic

The United States COVID-19 Forecast Hub dataset

Progress and opportunities in advancing near-term forecasting of freshwater quality

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