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

[This corrects the article DOI: 10.1371/journal.pmed.1003793.].

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

BACKGROUND: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

Social Media and the New World of Scientific Communication During the COVID-19 Pandemic.

The human and social toll of the coronavirus disease 2019 (COVID-19) pandemic has already spurred several major public health "lessons learned," and the theme of effective and responsible scientific communication is among them. We propose that Twitter has played a fundamental-but often precarious-role in permitting real-time global communication between scientists during the COVID-19 epidemic, on a scale not seen before. Here, we discuss 3 key facets to Twitter-enabled scientific exchange during public health emergencies, including some major drawbacks. This discussion also serves as a succinct primer on some of the pivotal epidemiological analyses (and their communication) during the early phases of the COVID-19 outbreak, as seen through the lens of a Twitter feed.

The role of benzathine penicillin G in predicting and preventing all-cause acute respiratory disease in military recruits: 1991-2017.

The adenovirus vaccine and benzathine penicillin G (BPG) have been used by the US military to prevent acute respiratory diseases (ARD) in trainees, though these interventions have had documented manufacturing problems. We fit Poisson regression and random forest models (RF) to 26 years of weekly ARD incidence data to explore the impact of the adenovirus vaccine and BPG prophylaxis on respiratory disease burden. Adenovirus vaccine availability was among the most important predictors of ARD in the RF, while BPG was the ninth most important. BPG was a significant protective factor against ARD (incidence rate ratio (IRR) = 0.68; 95% confidence interval (CI) 0.67-0.70), but less so than either the old or new adenovirus vaccine (IRR = 0.39, 95% CI 0.38-0.39 and IRR = 0.11, 95% CI 0.11-0.11), respectively. These results suggest that BPG is moderately predictive of, and significantly protective against ARD, though to a lesser extent than either the old or new adenovirus vaccine.

Make Data Sharing Routine to Prepare for Public Health Emergencies.

Jean-Paul Chretien and colleagues argue that recent Ebola and Zika virus outbreaks highlight the importance of data sharing in scientific research.

Risks of Death and Severe Disease in Patients With Middle East Respiratory Syndrome Coronavirus, 2012-2015.

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging pathogen, first recognized in 2012, with a high case fatality risk, no vaccine, and no treatment beyond supportive care. We estimated the relative risks of death and severe disease among MERS-CoV patients in the Middle East between 2012 and 2015 for several risk factors, using Poisson regression with robust variance and a bootstrap-based expectation maximization algorithm to handle extensive missing data. Increased age and underlying comorbidity were risk factors for both death and severe disease, while cases arising in Saudi Arabia were more likely to be severe. Cases occurring later in the emergence of MERS-CoV and among health-care workers were less serious. This study represents an attempt to estimate risk factors for an emerging infectious disease using open data and to address some of the uncertainty surrounding MERS-CoV epidemiology.

Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China.

BACKGROUND: Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of 'line lists' with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus. METHODS: We collated and compared six different line lists of laboratory-confirmed human cases of influenza A(H7N9) virus infection in the 2013 outbreak in China, including the official line list constructed by the Chinese Center for Disease Control and Prevention plus five other line lists by HealthMap, Virginia Tech, Bloomberg News, the University of Hong Kong and FluTrackers, based on publicly-available information. We characterized clinical severity and transmissibility of the outbreak, using line lists available at specific dates to estimate epidemiologic parameters, to replicate real-time inferences on the hospitalization fatality risk, and the impact of live poultry market closure. RESULTS: Demographic information was mostly complete (less than 10% missing for all variables) in different line lists, but there were more missing data on dates of hospitalization, discharge and health status (more than 10% missing for each variable). The estimated onset to hospitalization distributions were similar (median ranged from 4.6 to 5.6 days) for all line lists. Hospital fatality risk was consistently around 20% in the early phase of the epidemic for all line lists and approached the final estimate of 35% afterwards for the official line list only. Most of the line lists estimated >90% reduction in incidence rates after live poultry market closures in Shanghai, Nanjing and Hangzhou. CONCLUSIONS: We demonstrated that analysis of publicly-available data on H7N9 permitted reliable assessment of transmissibility and geographical dispersion, while assessment of clinical severity was less straightforward. Our results highlight the potential value in constructing a minimum dataset with standardized format and definition, and regular updates of patient status. Such an approach could be particularly useful for diseases that spread across multiple countries.

Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes.

OBJECTIVE: Recent availability of "big data" might be used to study whether and how sexual risk behaviors are communicated on real-time social networking sites and how data might inform HIV prevention and detection. This study seeks to establish methods of using real-time social networking data for HIV prevention by assessing 1) whether geolocated conversations about HIV risk behaviors can be extracted from social networking data, 2) the prevalence and content of these conversations, and 3) the feasibility of using HIV risk-related real-time social media conversations as a method to detect HIV outcomes. METHODS: In 2012, tweets (N=553,186,061) were collected online and filtered to include those with HIV risk-related keywords (e.g., sexual behaviors and drug use). Data were merged with AIDSVU data on HIV cases. Negative binomial regressions assessed the relationship between HIV risk tweeting and prevalence by county, controlling for socioeconomic status measures. RESULTS: Over 9800 geolocated tweets were extracted and used to create a map displaying the geographical location of HIV-related tweets. There was a significant positive relationship (p<.01) between HIV-related tweets and HIV cases. CONCLUSION: Results suggest the feasibility of using social networking data as a method for evaluating and detecting Human immunodeficiency virus (HIV) risk behaviors and outcomes.

Case Investigation and Contact Tracing in US State and Local Public Health Agencies: Sustaining Capacities and Applying Lessons Learned From the COVID-19 Pandemic and 2022 Mpox Outbreak.

The COVID-19 pandemic illuminated the lack of resources available to US state and local public health agencies to respond to large-scale health events. Two response activities that were notably underresourced are case investigation and contact tracing (CI/CT), which health agencies routinely employ to control and prevent the transmission of infectious diseases. However, the scale of contact tracing required during the COVID-19 pandemic exceeded available resources, even in high-capacity public health agencies. For both routine outbreak response and epidemic preparedness, health agencies must have CI/CT program capacities in place prior to the detection of an outbreak to be ready to respond. Our research builds on previous work to identify the baseline CI/CT capacities needed in US state and local public health agencies to respond to any type of outbreak. Fifteen public health officials representing 10 public health agencies and 4 experts in CI/CT were interviewed about various aspects of their CI/CT program during the COVID-19 pandemic. The interviews coincided with the beginning of the 2022 mpox epidemic. Discussions on CI/CT during that response were collected to augment the interviews, where possible. Findings revealed that CI/CT capacities were underresourced prior to and during the pandemic, as well as during the mpox outbreak, even after substantial additional resourcing and efforts to scale up. Moreover, state and local health agencies encountered challenges in pivoting their COVID-19 CI/CT capacities for the mpox response, suggesting that CI/CT programs should either be designed with flexibility in mind, or should allow for specialization based on the pathogen's mode of transmission and the population at risk. Federal, state, and local health agency staff and officials should consider lessons learned from this research to plan for readily scalable and sustainable CI/CT programs to ensure readiness for future outbreaks.

Building Case Investigation and Contact Tracing Programs in US State and Local Health Departments: A Conceptual Framework.

OBJECTIVE: At the onset of the COVID-19 pandemic, and to this day, US state, tribal, local, and territorial health departments lacked comprehensive case investigation and contact tracing (CI/CT) guidelines that clearly define the capabilities and capacities of CI/CT programs and how to scale up these programs to respond to outbreaks. This research aims to identify the capabilities and capacities of CI/CT programs and to develop a conceptual framework that represents the relationships between these program components. METHODS: This study conducted a narrative literature review and qualitative interviews with 10 US state and local health departments and 4 public health experts to identify and characterize the capacities and capabilities of CI/CT programs. RESULTS: This research resulted in the first comprehensive analysis of the capabilities and capacities of CI/CT programs and a conceptual framework that illustrates the interrelationships between the capacities, capabilities, outcomes, and impacts of CI/CT programs. CONCLUSIONS: Our findings highlight the need for further guidance to assist jurisdictional health departments in shifting CI/CT program goals as outbreaks evolve. Training the public health workforce on making decisions around CI/CT program implementation during outbreaks is critical to ensure readiness for a variety of outbreak scenarios.

Estimated Demand for US Hospital Inpatient and Intensive Care Unit Beds for Patients With COVID-19 Based on Comparisons With Wuhan and Guangzhou, China.

Importance: Sustained spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has happened in major US cities. Capacity needs in cities in China could inform the planning of local health care resources. Objectives: To describe and compare the intensive care unit (ICU) and inpatient bed needs for patients with coronavirus disease 2019 (COVID-19) in 2 cities in China to estimate the peak ICU bed needs in US cities if an outbreak equivalent to that in Wuhan occurs. Design, Setting, and Participants: This comparative effectiveness study analyzed the confirmed cases of COVID-19 in Wuhan and Guangzhou, China, from January 10 to February 29, 2020. Exposures: Timing of disease control measures relative to timing of SARS-CoV-2 community spread. Main Outcomes and Measures: Number of critical and severe patient-days and peak number of patients with critical and severe illness during the study period. Results: In Wuhan, strict disease control measures were implemented 6 weeks after sustained local transmission of SARS-CoV-2. Between January 10 and February 29, 2020, patients with COVID-19 accounted for a median (interquartile range) of 429 (25-1143) patients in the ICU and 1521 (111-7202) inpatients with serious illness each day. During the epidemic peak, 19 425 patients (24.5 per 10 000 adults) were hospitalized, 9689 (12.2 per 10 000 adults) were considered in serious condition, and 2087 (2.6 per 10 000 adults) needed critical care per day. In Guangzhou, strict disease control measures were implemented within 1 week of case importation. Between January 24 and February 29, COVID-19 accounted for a median (interquartile range) of 9 (7-12) patients in the ICU and 17 (15-26) inpatients with serious illness each day. During the epidemic peak, 15 patients were in critical condition and 38 were classified as having serious illness. The projected number of prevalent critically ill patients at the peak of a Wuhan-like outbreak in US cities was estimated to range from 2.2 to 4.4 per 10 000 adults, depending on differences in age distribution and comorbidity (ie, hypertension) prevalence. Conclusions and Relevance: Even after the lockdown of Wuhan on January 23, the number of patients with serious COVID-19 illness continued to rise, exceeding local hospitalization and ICU capacities for at least a month. Plans are urgently needed to mitigate the consequences of COVID-19 outbreaks on the local health care systems in US cities.

The demand for inpatient and ICU beds for COVID-19 in the US: lessons from Chinese cities.

Background: Sustained spread of SARS-CoV-2 has happened in major US cities. Capacity needs in Chinese cities could inform the planning of local healthcare resources. Methods: We described the intensive care unit (ICU) and inpatient bed needs for confirmed COVID-19 patients in two Chinese cities (Wuhan and Guangzhou) from January 10 to February 29, 2020, and compared the timing of disease control measures in relation to the timing of SARS-CoV-2 community spread. We estimated the peak ICU bed needs in US cities if a Wuhan-like outbreak occurs. Results: In Wuhan, strict disease control measures were implemented six weeks after sustained local transmission of SARS-CoV-2. Between January 10 and February 29, COVID-19 patients accounted for an average of 637 ICU patients and 3,454 serious inpatients on each day. During the epidemic peak, 19,425 patients (24.5 per 10,000 adults) were hospitalized, 9,689 (12.2 per 10,000 adults) were considered to be in serious condition, and 2,087 patients (2.6 per 10,000 adults) needed critical care per day. In Guangzhou, strict disease control measures were implemented within one week of case importation. Between January 24 and February 29, COVID-19 accounted for an average of 9 ICU patients and 20 inpatients on each day. During the epidemic peak, 15 patients were in critical condition, and 38 were classified as serious. If a Wuhan-like outbreak were to happen in a US city, the need for healthcare resources may be higher in cities with a higher prevalence of vulnerable populations. Conclusion: Even after the lockdown of Wuhan on January 23, the number of seriously ill COVID-19 patients continued to rise, exceeding local hospitalization and ICU capacities for at least a month. Plans are urgently needed to mitigate the effect of COVID-19 outbreaks on the local healthcare system in US cities.

Large-scale influenza vaccination promotion on a mobile app platform: A randomized controlled trial.

While health-care providers have used incentives in an attempt to motivate patients to obtain vaccinations, their effect on vaccination rates has not been systematically evaluated on a large scale. In this study, we examined whether mobile applications may improve population vaccination rates through enhanced communication and incentives education. Our study is the first randomized controlled trial assessing the effect of large-scale messaging combined with individualized incentives on influenza-vaccination rates. In this trial, we delivered messages regarding influenza vaccinations to 50,286 adults, aged 18 through 65, then compared the subsequent vaccination rate, the effectiveness of the message content and the timing. Multiple rounds of messaging occurred over a seven-week period during the 2016 flu season, after which vaccination rates were observed for one week. Participants were randomly assigned to one of three messaging approaches: conspicuous (highlighting the amount of rewards to be received for obtaining a flu shot); generic (promoting vaccinations with no mention of rewards); or no-message. Evidence of vaccination obtainment was indicated by medical and pharmacy claims, augmented by patients self-reporting through the mobile wellness app during the study period. Of the people assigned to receive messaging, 23.2% obtained influenza vaccination, compared to 22.0% of people who obtained vaccination in the no-messaging control arm. This difference was statistically significant (p < 0.01). The research revealed that messaging effectiveness decreased after each successive batch sent, suggesting that most participants responsive to messaging would become activated immediately after receiving one alert. Interestingly, in this large-scale study, there were no significant differences between conspicuous incentives and generic messaging, suggesting an important area for future research. Trial Registration: clinicaltrials.gov identifier: NCT02908893.

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.