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

Using outbreak science to strengthen the use of models during epidemics.

Infectious disease modeling has played a prominent role in recent outbreaks, yet integrating these analyses into public health decision-making has been challenging. We recommend establishing ‘outbreak science’ as an inter-disciplinary field to improve applied epidemic modeling

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

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

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

Data curation during a pandemic and lessons learned from COVID-19

Detailed, accurate data related to a disease outbreak enable informed public health decision making. Given the variety of data types available across different regions, global data curation and standardization efforts are essential to guarantee rapid data integration and dissemination in times of a pandemic.Data availability The underlying dataset for Fig. 1a is available open access from the supplemental material in ref. 5, and datasets for Fig. 1b,c from the UNESCO World Heritage List 2021 in ref. 32.https://www.nature.com/natcomputscihj2023Computer Scienc

Spin crossover equation of state and sound velocities of (Mg_(0.65)Fe_(0.35))O ferropericlase to 140 GPa

We have determined the elastic and vibrational properties of periclase-structured (Mg_(0.65)Fe_(0.35))O (“FP35”), a composition representative of deep mantle “pyrolite” or chondrite-pyroxenite models, from nuclear resonant inelastic x-ray scattering (NRIXS) and x-ray diffraction (XRD) measurements in diamond-anvil cells at 300 K. Combining with in situ XRD measurements, the Debye sound velocity of FP35 was determined from the low-energy region of the partial phonon density of states (DOS) obtained from NRIXS measurements in the pressure range of 70 to 140 GPa. In order to obtain an accurate description of the equation of state (EOS) for FP35, separate XRD measurements were performed up to 126 GPa at 300 K. A new spin crossover EOS was introduced and applied to the full P-V data set, resulting in a zero-pressure volume V_0 = 77.24 ± 0.17 Å^3, bulk modulus K_0 = 159 ± 8 GPa and its pressure-derivative K′_0 = 4.12 ± 0.42 for high-spin FP35 and K_(0,LS) = 72.9 ± 1.3 Å^3, K_(0,LS) = 182 ± 17 GPa with K′_(0,LS) fixed to 4 for low-spin FP35. The high-spin to low-spin transition occurs at 64 ± 3 GPa. Using the spin crossover EOS and Debye sound velocity, we derived the shear (V_S) and compressional (V_P) velocities for FP35. Comparing our data with previous results on (Mg,Fe)O at similar pressures, we find that the addition of iron decreases both V_P and V_S, while elevating their ratio (V_P/V_S). Small amounts (<10%) of low-spin FP35 mixed with silicates could explain moderate reductions in wave speeds near the core mantle boundary (CMB), while a larger amount of FP35 near the CMB would not allow a large structure to maintain neutral buoyancy

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

Funding: MIDAS Coordination Center and the National Institutes of General Medical Sciences (NIGMS 1U24GM132013) for supporting travel to the face-to-face consensus meeting by members of the Working Group. NGR was supported by the National Institutes of General Medical Sciences (R35GM119582). Travel for SV was supported by the National Institutes of General Medical Sciences (1U24GM132013-01). BMA was supported by Bill & Melinda Gates through the Global Good Fund. RL was funded by a Royal Society Dorothy Hodgkin Fellowship.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.Publisher PDFNon peer reviewe