Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines

Pollett, Simon; Johansson, Michael A.; Reich, Nicholas G.; Brett-Major, David M.; Valle, Sara Y. Del; Venkatramanan, Srinivasan; Lowe, Rachel; Porco, Travis C.; Berry, Irina Maljkovic; Deshpande, Alina; Kraemer, Moritz U. G.; Blazes, David L.; Pan‐Ngum, Wirichada; Vespigiani, Alessandro; Mate, Suzanne; Silal, Sheetal P; Kandula, Sasikiran; Sippy, Rachel; Quandelacy, Talía M.; Morgan, Joseph A.; Ball, Jacob D.; Morton, Lindsay; Althouse, Benjamin M.; Pavlin, Julie A.; Panhuis, Wilbert van; Riley, Steven; Biggerstaff, Matthew; Viboud, Cécile; Brady, Oliver J.; Rivers, Caitlin

doi:10.1371/journal.pmed.1003793

Cited by 59 publications

(48 citation statements)

References 46 publications

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“…Without this information, our ability to synthesize insights from the research to determine best practices is limited. In response to these concerns, the EPIFORGE 2020 guidelines were developed and recommend consistent terminology, a clear definition of study purpose and model targets, identification of prospective versus retrospective work, comparison to a baseline model, a non-technical summary of results, and full documentation of: data sources, data availability, data processing, methods, assumptions, code, model validation, forecast accuracy evaluation, uncertainty, limitations, interpretation, and generalizability 157 . Consistent sharing of this information for epidemiological predictions would improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting.…”

Section: Resultsmentioning

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: Resultsmentioning

confidence: 99%

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

Nixon

Jindal

Parker

et al. 2022

Preprint

Self Cite

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“…We found that in the highly influential field of infectious disease modeling that relies as much on its assumptions and underlying code and data, transparency and reproducibility have large potential for improvement. Yet, there is a growing literature of recommendations and tutorials for researchers and other stakeholders (41)(42)(43)(44), plus the EPIFORGE guidelines (45) which are guidelines for the reporting of epidemic forecasting and prediction research. They all explicitly urge for code sharing, and data sharing and transparency in general.…”

Section: Discussionmentioning

confidence: 99%

A meta-epidemiological assessment of transparency indicators of infectious disease models

Zavalis

Ioannidis

2022

Preprint

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Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Manual assessment of 10% of the articles that were identified by the text mining algorithms as fulfilling transparency indicators showed that 24/29 (82.8%) actually shared code, 29/33 (87.9%) actually shared data; and all had COI and funding statements, but 95.8% disclosed no conflict and 11.7% reported no funding. On manual assessment, 5/6 articles identified as registered had indeed been registered. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired.

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“…In the absence of a dedicated checklist for time-series forecasting, the study was conducted in accordance with TRIPOD and EPIFORGE guidelines for predictive model development where relevant 21 , 29 .…”

Section: Methodsmentioning

confidence: 99%

“…Several examples also exist within the surgical literature in applying algorithms of this kind in order to predict service demand 18 , 19 , but few have been trained on acute surgical data 20 . Fewer still justify and compare their choice of forecasting models and parameters, as is recommended for other clinical domains 21 .…”

Section: Introductionmentioning

confidence: 99%

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Pandit

Jalal

Toma

et al. 2022

Sci Rep

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Characterizing acute service demand is critical for neurosurgery and other emergency-dominant specialties in order to dynamically distribute resources and ensure timely access to treatment. This is especially important in the post-Covid 19 pandemic period, when healthcare centers are grappling with a record backlog of pending surgical procedures and rising acute referral numbers. Healthcare dashboards are well-placed to analyze this data, making key information about service and clinical outcomes available to staff in an easy-to-understand format. However, they typically provide insights based on inference rather than prediction, limiting their operational utility. We retrospectively analyzed and prospectively forecasted acute neurosurgical referrals, based on 10,033 referrals made to a large volume tertiary neurosciences center in London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021 through the use of a novel AI-enabled predictive dashboard. As anticipated, weekly referral volumes significantly increased during this period, largely owing to an increase in spinal referrals (p < 0.05). Applying validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point, with Prophet demonstrating the best test and computational performance. Using a mixed-methods approach, we determined that a dashboard approach was usable, feasible, and acceptable among key stakeholders.

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Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines

Cited by 59 publications

References 46 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

A meta-epidemiological assessment of transparency indicators of infectious disease models

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

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