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

Abstract

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.

Recognizing the importance of COVID-19 data wrangling

“…She has emphasized the need for immediate access to data, in machine-readable formats, that can be used by public health experts for planning and modeling as well as by the general public at large ( 10 ). She is also participating in efforts to examine the quality of prospective COVID-19 modeling during the pandemic and has recently released a preprint systematically reviewing over 100 papers with data-driven modeling studies on population-level dynamics of COVID-19 ( 11 ).…”

A Deep Learning Approach to Forecast Short-Term COVID-19 Cases and Deaths in the US

“…Whatever the method, a recognized shortcoming in the existing COVID-19 modeling literature is the lack of rigorous and robust evaluation, which is critical to assess and compare model performance. 23 On October 19 th 2021, the CDC COVID-19 Forecast Hub published the EPIFORGE guidelines to attempt to improve the quality of models, highlighting the importance of consistency, interpretability, reproducibility, and comparability of models. 24 However, most model evaluation presented in the published literature remains incomprehensive.…”

“…24 However, most model evaluation presented in the published literature remains incomprehensive. 23 Many models are evaluated for a single forecasting period, according to a single error metric, and sometimes not evaluated retrospectively at all. 23 Furthermore, many of the existing studies do not account for critical factors, such as human behavior, which are available through mobility data and/or real-time survey data.…”

“…23 Many models are evaluated for a single forecasting period, according to a single error metric, and sometimes not evaluated retrospectively at all. 23 Furthermore, many of the existing studies do not account for critical factors, such as human behavior, which are available through mobility data and/or real-time survey data. Additionally, there is a substantial gap between model development and model implementation for real-time forecasting, and many of the models mentioned above lack guidance on when and where each model would be most suitable, let alone information on if, when and where they were applied.…”

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

“…6 However, even within the scientific community, the sheer volume of information obstructs efficient synthesis of the literature to establish best practices. 9 Efforts to address some of these problems exist, such as recruiting researchers to conduct rapid and publicly available reviews of papers. 10 Nevertheless, these disparate efforts (including informal reviews on social media) still leave information scattered and difficult to synthesise.…”