Coordinated Strategy for a Model-Based Decision Support Tool for Coronavirus Disease, Utah, USA

Meredith, Hannah R.; Arehart, Emerson; Grantz, Kyra H.; Beams, Alexander; Sheets, Theresa; Nelson, Richard; Zhang, Yue; Vinik, Russell; Barfuss, Darryl; Pettit, Jacob; McCaffrey, Keegan; Dunn, Angela; Good, Michael L.; Frattaroli, Shannon; Samore, Matthew H.; Lessler, Justin; Lee, Elizabeth C.; Keegan, Lindsay T

doi:10.3201/eid2705.203075

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…To advance knowledge of best practices for translation, modelers should prioritize documentation of the process of sharing models with decision-makers when possible. For example, this paper documents the process of modelers working with policymakers in Utah to provide COVD-19 decision support 177 . By neglecting to share their experiences and knowledge on translation of models, modelers are missing an opportunity to harness the collaborative power of academia to identify best practices for translation and boost the utility of their work.…”

Section: Discussion: Commentary On Data Quality and Model Translationmentioning

confidence: 99%

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

Nixon

Jindal

Parker

et al. 2022

Preprint

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SummaryBackgroundInfectious disease modeling can serve as a powerful tool for science-based management of outbreaks, providing situational awareness and decision support for policy makers. Predictive modeling of an emerging disease is challenging due to limited knowledge on its epidemiological characteristics. For COVID-19, the prediction difficulty was further compounded by continuously changing policies, varying behavioral responses, poor availability and quality of crucial datasets, and the variable influence of different factors as the pandemic progresses. Due to these challenges, predictive modeling for COVID-19 has earned a mixed track record.MethodsWe provide a systematic review of prospective, data-driven modeling studies on population-level dynamics of COVID-19 in the US and conduct a quantitative assessment on crucial elements of modeling, with a focus on the aspects of modeling that are critical to make them useful for decision-makers. For each study, we documented the forecasting window, methodology, prediction target, datasets used, geographic resolution, whether they expressed quantitative uncertainty, the type of performance evaluation, and stated limitations. We present statistics for each category and discuss their distribution across the set of studies considered. We also address differences in these model features based on fields of study.FindingsOur initial search yielded 2,420 papers, of which 119 published papers and 17 preprints were included after screening. The most common datasets relied upon for COVID-19 modeling were counts of cases (93%) and deaths (62%), followed by mobility (26%), demographics (25%), hospitalizations (12%), and policy (12%). Our set of papers contained a roughly equal number of short-term (46%) and long-term (60%) predictions (defined as a prediction horizon longer than 4 weeks) and statistical (43%) versus compartmental (47%) methodologies. The target variables used were predominantly cases (89%), deaths (52%), hospitalizations (10%), and Rt (9%). We found that half of the papers in our analysis did not express quantitative uncertainty (50%). Among short-term prediction models, which can be fairly evaluated against truth data, 25% did not conduct any performance evaluation, and most papers were not evaluated over a timespan that includes varying epidemiological dynamics. The main categories of limitations stated by authors were disregarded factors (39%), data quality (28%), unknowable factors (26%), limitations specific to the methods used (22%), data availability (16%), and limited generalizability (8%). 36% of papers did not list any limitations in their discussion or conclusion section.InterpretationPublished COVID-19 models were found to be consistently lacking in some of the most important elements required for usability and translation, namely transparency, expressing uncertainty, performance evaluation, stating limitations, and communicating appropriate interpretations. Adopting the EPIFORGE 2020 guidelines would address these shortcomings and improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. We also discovered that most of the operational models that have been used in real-time to inform decision-making have not yet made it into the published literature, which highlights that the current publication system is not suited to the rapid information-sharing needs of outbreaks. Furthermore, data quality was identified to be one of the most important drivers of model performance, and a consistent limitation noted by the modeling community. The US public health infrastructure was not equipped to provide timely, high-quality COVID-19 data, which is required for effective modeling. Thus, a systematic infrastructure for improved data collection and sharing should be a major area of investment to support future pandemic preparedness.

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Section: Discussion: Commentary On Data Quality and Model Translationmentioning

confidence: 99%

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

Nixon

Jindal

Parker

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Section: Introductionmentioning

confidence: 99%

“…We consider that this work, of construction of the models, in an iterative and continuous process and in close collaboration with those in charge of epidemiological surveillance, can be valuable, as it enables other countries in similar circumstances to adapt these models to their local conditions. 12 , 14 , 15 …”

Section: Introductionmentioning

confidence: 99%

“…We use successive models to explain the epidemic which originated in imported cases in a first phase, describe the occurrence of community transmission in a second phase, and an increase in severe cases in a third phase, with discussing the epidemiological team in real-time. 11,12 In Colombia, the first COVID-19 case was confirmed in Bogotá in a passenger from Italy on March 6, 2020. 13 The first case from Medellín was confirmed on March 9, 2020, corresponding to an imported case from Spain.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring COVID-19 in Colombia during the first year of the pandemic

Ospina

Hincapié-Palacio

Ochoa

et al. 2022

Journal of Public Health Research

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Background: COVID-19 cases in Medellín, the second largest city in Colombia, were monitored during the first year of the pandemic using both mathematical models based on transmission theory and surveillance from each observed epidemic phase. Design and Methods: Expected cases were estimated using mandatory reporting data from Colombia’s national epidemiological surveillance system from March 7, 2020 to March 7, 2021. Initially, the range of daily expected cases was estimated using a Borel-Tanner stochastic model and a deterministic Susceptible-Infected-Removed (SIR) model. A subsequent expanded version of the SIR model was used to include asymptomatic cases, severe cases and deaths. The moving average, standard deviation, and goodness of fit of estimated cases relative to confirmed reported cases were assessed, and local transmission in Medellin was contrasted with national transmission in Colombia. Results: The initial phase was characterized by imported case detection and the later phase by community transmission and increases in case magnitude and severity. In the initial phase, a maximum range of expected cases was obtained based on the stochastic model, which even accounted for the reduction of new imported cases following the closure of international airports. The deterministic estimate achieved an adequate fit with respect to accumulated cases until the conclusion of the mandatory national quarantine and gradual reopening, when reported cases increased. The estimated new cases were reasonably fit with the maximum reported incidence. Conclusion: Adequate model fit was obtained with the reported data. This experience of monitoring epidemic trajectory can be extended using models adapted to local conditions.

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“…In addition, documenting the process of sharing models with decision makers is crucial to advance knowledge of best practices for science translation. 7 One aspect of modelling that could be redesigned for easier interpretation and use by various stakeholders and the public is the selection of prediction targets. These targets have predominantly been the numbers of incident cases and deaths, despite poor forecast performance for these data during crucial moments for decision making, as discussed in the Forecasters blog.…”

mentioning

confidence: 99%

Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation

Nixon

Jindal²,

Parker³

et al. 2022

The Lancet Digital Health

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Coordinated Strategy for a Model-Based Decision Support Tool for Coronavirus Disease, Utah, USA

Cited by 8 publications

References 6 publications

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

Monitoring COVID-19 in Colombia during the first year of the pandemic

Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation

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