Identification and Evaluation of Epidemic Prediction and Forecasting Reporting Guidelines: A Systematic Review and a Call for Action

INTRODUCTION: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. METHODS: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. RESULTS: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. CONCLUSIONS: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health

Identification and evaluation of epidemic prediction and forecasting reporting guidelines : a systematic review and a call for action

NGR reports funding by NIGMS grant R35GM119582. BMA is supported by Bill and Melinda Gates Foundation through the Global Good Fund. SP and IMB were funded by the Armed Forces Health Surveillance Branch (GEIS: P0116_19_WR_03.11).Introduction: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. Methods: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. Results: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. Conclusions: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.Publisher PDFPeer reviewe

An Overview of Foodborne Sample-Initiated Retrospective Outbreak Investigations and Interagency Collaboration in the United States

Foodborne outbreak investigations have traditionally included the detection of a cluster of illnesses first, followed by an epidemiologic investigation to identify a food of interest. The increasing use of whole genome sequencing (WGS) subtyping technology for clinical, environmental, and food isolates of foodborne pathogens, and the ability to share and compare the data on public platforms, present new opportunities to identify earlier links between illnesses and their potential sources. We describe a process called sample-initiated retrospective outbreak investigations (SIROIs) used by federal public health and regulatory partners in the United States. SIROIs begin with an evaluation of the genomic similarity between bacterial isolates recovered from food or environmental samples and clusters of clinical isolates while subsequent and parallel epidemiologic and traceback investigations are initiated to corroborate their connection. SIROIs allow for earlier hypothesis generation, followed by targeted collection of information about food exposures and the foods and manufacturer of interest, to confirm a link between the illnesses and their source. This often leads to earlier action that could reduce the breadth and burden of foodborne illness outbreaks. We describe two case studies of recent SIROIs and present the benefits and challenges. Benefits include insight into foodborne illness attribution, international collaboration, and opportunities for enhanced food safety efforts in the food industry. Challenges include resource intensiveness, variability of epidemiologic and traceback data, and an increasingly complex food supply chain. SIROIs are valuable in identifying connections among small numbers of illnesses that may span significant time periods; detecting early signals for larger outbreaks or food safety issues associated with manufacturers; improving our understanding of the scope of contamination of foods; and identifying novel pathogen/commodity pairs

Identification and evaluation of epidemic prediction and forecasting reporting guidelines:a systematic review and a call for action

INTRODUCTION: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. METHODS: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. RESULTS: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. CONCLUSIONS: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health

Multinational Outbreak of Listeria monocytogenes Infections Linked to Enoki Mushrooms Imported from the Republic of Korea 2016–2020

Keeping the global food supply safe necessitates international collaborations between countries. Health and regulatory agencies routinely communicate during foodborne illness outbreaks, allowing partners to share investigational evidence. A 2016–2020 outbreak of Listeria monocytogenes infections linked to imported enoki mushrooms required a multinational collaborative investigation among the United States, Canada, Australia, and France. Ultimately, this outbreak included 48 ill people, 36 in the United States and 12 in Canada, and was linked to enoki mushrooms sourced from one manufacturer located in the Republic of Korea. Epidemiologic, laboratory, and traceback evidence led to multiple regulatory actions, including extensive voluntary recalls by three firms in the United States and one firm in Canada. In the United States and Canada, the Korean manufacturer was placed on import alert while other international partners provided information about their respective investigations and advised the public not to eat the recalled enoki mushrooms. The breadth of the geographic distribution of this outbreak emphasizes the global reach of the food industry. This investigation provides a powerful example of the impact of national and international coordination of efforts to respond to foodborne illness outbreaks and protect consumers. It also demonstrates the importance of fast international data sharing and collaboration in identifying and stopping foodborne outbreaks in the global community. Additionally, it is a meaningful example of the importance of food sampling, testing, and integration of sequencing results into surveillance databases

Trajectory Analysis of Serum Biomarker Concentrations Facilitates Outcome Prediction after Pediatric Traumatic and Hypoxemic Brain Injury

Traumatic brain injury (TBI) and hypoxic ischemic encephalopathy (HIE) are leading causes of morbidity and mortality in children. Several studies over the past several years have evaluated the use of serum biomarkers to predict outcome after pediatric brain injury. These studies have all used simple point estimates such as initial and peak biomarker concentrations to predict outcome. However, this approach does not recognize patterns of change over time. Trajectory analysis is a type of analysis which can capture variance in biomarker concentrations over time and has been used with success in the social sciences. We used trajectory analysis to evaluate the ability of the serum concentrations of 3 brain-specific biomarkers – S100B, neuron-specific enolase (NSE) and myelin basic protein (MBP) – to predict poor outcome (Glasgow Outcome Scale scores 3–5) after pediatric TBI and HIE. Clinical and biomarker data from 100 children with TBI or HIE were evaluated. For each biomarker, we validated 2-, 3- and 4-group models for outcome prediction, using sensitivity and specificity. For S100B, the 3-group model predicted poor outcome with a sensitivity of 59% and specificity of 100%. For NSE, the 3-group model predicted poor outcome with a sensitivity of 48% and specificity of 98%. For MBP, the 3-group model predicted poor outcome with a sensitivity of 73% and specificity of 61%. Thus, when the models predicted a poor outcome, there was a very high probability of a poor outcome. In contrast, 17% of subjects with a poor outcome were predicted to have a good outcome by all 3 biomarker trajectories. These data suggest that trajectory analysis of biomarker data may provide a useful approach for predicting outcome after pediatric brain injury