Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

BACKGROUND: Anthrax is a toxin-mediated zoonotic disease caused by Bacillus anthracis, with a worldwide distribution recognized for millennia. Bacillus anthracis is considered a potential biowarfare agent. METHODS: We completed a systematic review for clinical and demographic characteristics of adults and children hospitalized with anthrax (cutaneous, inhalation, ingestion, injection [from contaminated heroin], primary meningitis) abstracted from published case reports, case series, and line lists in English from 1880 through 2018, assessing treatment impact by type and severity of disease. We analyzed geographic distribution, route of infection, exposure to anthrax, and incubation period. RESULTS: Data on 764 adults and 167 children were reviewed. Most cases reported for 1880 through 1915 were from Europe; those for 1916 through 1950 were from North America; and from 1951 on, cases were from Asia. Cutaneous was the most common form of anthrax for all populations. Since 1960, adult anthrax mortality has ranged from 31% for cutaneous to 90% for primary meningitis. Median incubation periods ranged from 1 day (interquartile range [IQR], 0-4) for injection to 7 days (IQR, 4-9) for inhalation anthrax. Most patients with inhalation anthrax developed pleural effusions and more than half with ingestion anthrax developed ascites. Treatment and critical care advances have improved survival for those with systemic symptoms, from approximately 30% in those untreated to approximately 70% in those receiving antimicrobials or antiserum/antitoxin. CONCLUSIONS: This review provides an improved evidence base for both clinical care of individual anthrax patients and public health planning for wide-area aerosol releases of B. anthracis spores.

BACKGROUND: Bacillus anthracis can cause anthrax and is a potential bioterrorism agent. The 2014 Centers for Disease Control and Prevention recommendations for medical countermeasures against anthrax were based on in vitro data and expert opinion. However, a century of previously uncompiled observational human data that often includes treatment and outcomes is available in the literature for analysis. METHODS: We reviewed treatment outcomes for patients hospitalized with anthrax. We stratified patients by meningitis status, route of infection, and systemic criteria, then analyzed survival by treatment type, including antimicrobials, antitoxin/antiserum, and steroids. Using logistic regression, we calculated odds ratios and 95% confidence intervals to compare survival between treatments. We also calculated hospital length of stay. Finally, we evaluated antimicrobial postexposure prophylaxis (PEPAbx) using data from a 1970 Russian-language article. RESULTS: We identified 965 anthrax patients reported from 1880 through 2018. After exclusions, 605 remained: 430 adults, 145 children, and 30 missing age. Survival was low for untreated patients and meningitis patients, regardless of treatment. Most patients with localized cutaneous or nonmeningitis systemic anthrax survived with 1 or more antimicrobials; patients with inhalation anthrax without meningitis fared better with at least 2. Bactericidal antimicrobials were effective for systemic anthrax; addition of a protein synthesis inhibitor(s) (PSI) to a bactericidal antimicrobial(s) did not improve survival. Likewise, addition of antitoxin/antiserum to antimicrobials did not improve survival. Mannitol improved survival for meningitis patients, but steroids did not. PEPAbx reduced risk of anthrax following exposure to B. anthracis. CONCLUSIONS: Combination therapy appeared to be superior to monotherapy for inhalation anthrax without meningitis. For anthrax meningitis, neither monotherapy nor combination therapy were particularly effective; however, numbers were small. For localized cutaneous anthrax, monotherapy was sufficient. For B. anthracis exposures, PEPAbx was effective.

Background: Population exposure to Bacillus anthracis spores could cause mass casualties requiring complex medical care. Rapid identification of patients needing anthrax-specific therapies will improve patient outcomes and resource use. Objective: To develop a checklist that rapidly distinguishes most anthrax from nonanthrax illnesses on the basis of clinical presentation and identifies patients requiring diagnostic testing after a population exposure. Design: Comparison of published anthrax case reports from 1880 through 2013 that included patients seeking anthrax-related care at 2 epicenters of the 2001 U.S. anthrax attacks. Setting: Outpatient and inpatient. Patients: 408 case patients with inhalation, ingestion, and cutaneous anthrax and primary anthrax meningitis, and 657 control patients. Measurements: Diagnostic test characteristics, including positive and negative likelihood ratios (LRs) and patient triage assignation. Results: Checklist-directed triage without diagnostic testing correctly classified 95% (95% CI, 93% to 97%) of 353 adult anthrax case patients and 76% (CI, 73% to 79%) of 647 control patients (positive LR, 3.96 [CI, 3.45 to 4.55]; negative LR, 0.07 [CI, 0.04 to 0.11]; false-negative rate, 5%; false-positive rate, 24%). Diagnostic testing was needed for triage in up to 5% of case patients and 15% of control patients and improved overall test characteristics (positive LR, 8.90 [CI, 7.05 to 11.24]; negative LR, 0.06 [CI, 0.04 to 0.09]; false-negative rate, 5%; false-positive rate, 11%). Checklist sensitivity and specificity were minimally affected by inclusion of pediatric patients. Sensitivity increased to 97% (CI, 94% to 100%) and 98% (CI, 96% to 100%), respectively, when only inhalation anthrax cases or higher-quality case reports were investigated. Limitations: Data on case patients were limited to nonstandardized, published observational reports, many of which lacked complete data on symptoms and signs of interest. Reporting bias favoring more severe cases and lack of intercurrent outbreaks (such as influenza) in the control populations may have improved test characteristics. Conclusion: A brief checklist covering symptoms and signs can distinguish anthrax from other conditions with minimal need for diagnostic testing after known or suspected population exposure. Primary Funding Source: U.S. Department of Health and Human Services.

During the First World War, anthrax cases in the United States and England increased greatly and seemed to be associated with use of new shaving brushes. Further investigation revealed that the source material and origin of shaving brushes had changed during the war. Cheap brushes of imported horsehair were being made to look like the preferred badger-hair brushes. Unfortunately, some of these brushes were not effectively disinfected and brought with them a nasty stowaway: Bacillus anthracis. A review of outbreak summaries, surveillance data, and case reports indicated that these cases originated from the use of ineffectively disinfected animal-hair shaving brushes. This historical information is relevant to current public health practice because renewed interest in vintage and animal-hair shaving brushes has been seen in popular culture. This information should help healthcare providers and public health officials answer questions on this topic.

Health officials lack field-implementable tools for forecasting the effects that a large-scale release of Bacillus anthracis spores would have on public health and hospitals. We created a modeling tool (combining inhalational anthrax caseload projections based on initial case reports, effects of variable postexposure prophylaxis campaigns, and healthcare facility surge capacity requirements) to project hospitalizations and casualties from a newly detected inhalation anthrax event, and we examined the consequences of intervention choices. With only 3 days of case counts, the model can predict final attack sizes for simulated Sverdlovsk-like events (1979 USSR) with sufficient accuracy for decision making and confirms the value of early postexposure prophylaxis initiation. According to a baseline scenario, hospital treatment volume peaks 15 days after exposure, deaths peak earlier (day 5), and recovery peaks later (day 23). This tool gives public health, hospital, and emergency planners scenario-specific information for developing quantitative response plans for this threat.

BACKGROUND: Bacillus anthracis, the causative agent of anthrax, is a potential bioterrorism agent. Anthrax meningitis is a common manifestation ofB. anthracisinfection, has high mortality, and requires more aggressive treatment than anthrax without meningitis. Rapid identification and treatment of anthrax meningitis are essential for successful management of an anthrax mass casualty incident. METHODS: Three hundred six published reports from 1880 through 2013 met pre-defined inclusion criteria. We calculated descriptive statistics for abstracted cases and conducted multivariable regression on separate derivation and validation cohorts to identify clinical diagnostic and prognostic factors for anthrax meningitis. RESULTS: One hundred thirty-two of 363 (36%) cases with systemic anthrax met anthrax meningitis criteria. Severe headache, altered mental status, meningeal signs, and other neurological signs at presentation independently predicted meningitis in the derivation cohort and were tested as a four-item assessment tool for use during anthrax mass casualty incidents. Presence of any one factor on admission had a sensitivity for finding anthrax meningitis of 89% (83%) in the adult (pediatric) validation cohorts. Anthrax meningitis was unlikely in the absence of any of these signs or symptoms (LR-=0.12 [0.19] for adult [pediatric] cohorts), while presence of two or more made meningitis very likely (LR+=26.5 [30.0]). Survival of anthrax meningitis was predicted by treatment with a bactericidal agent (P=0.005) and use of multiple antimicrobials (P=0.01). CONCLUSIONS: We developed an evidence-based assessment tool for screening patients for meningitis during an anthrax mass casualty incident; its use could improve both patient outcomes and resource allocation in such an event.

BACKGROUND: Understanding how hospitals functioned during the 2009 influenza A(H1N1)pdm09 pandemic may improve future public health emergency response, but information about its impact on US hospitals remains largely unknown. RESEARCH DESIGN: We matched hospital and emergency department (ED) discharge data from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project with community-level influenza-like illness activity during each hospital's pandemic period in fall 2009 compared with a corresponding calendar baseline period. We compared inpatient mortality for sentinel conditions at high-surge versus nonsurge hospitals. RESULTS: US hospitals experienced a doubling of pneumonia and influenza ED visits during fall 2009 compared with prior years, along with an 18% increase in overall ED visits. Although no significant increase in total inpatient admissions occurred overall, approximately 10% of all study hospitals experienced high surge, associated with higher acute myocardial infarction and stroke case fatality rates. These hospitals had similar characteristics to other US hospitals except that they had higher mortality for acute cardiac illnesses before the pandemic. After adjusting for 2008 case fatality rates, the association between high-surge hospitals and increased mortality for acute myocardial infarction and stroke patients persisted. CONCLUSIONS: The fall 2009 pandemic period substantially impacted US hospitals, mostly through increased ED visits. For a small proportion of hospitals that experienced a high surge in inpatient admissions, increased mortality from selected clinical conditions was associated with both prepandemic outcomes and surge, highlighting the linkage between daily hospital operations and disaster preparedness.

Modelers published thoughtful articles after the 2003 SARS crisis, but had limited if any real-time impact on the global response and may even have inadvertently contributed to a lingering misunderstanding of the means by which the epidemic was controlled. The impact of any intervention depends on its efficiency as well as efficacy, and efficient isolation of infected individuals before they become symptomatic is difficult to imagine. Nonetheless, in exploring the possible impact of quarantine, the product of efficiency and efficacy was varied over the entire unit interval. Another mistake was repeatedly fitting otherwise appropriate gamma distributions to times to event regardless of whether they were stationary or not, particularly onset-isolation intervals whose progressive reduction evidently contributed to SARS control. By virtue of their unknown biology, newly-emerging diseases are more challenging than familiar human scourges. Influenza, for example, recurs annually and has been modeled more thoroughly than any other infectious disease. Moreover, models were integrated into preparedness exercises, during which working relationships were established that bore fruit during the 2009 A/H1N1 pandemic. To provide the most accurate and timely advice possible, especially about the possible impact of measures designed to control diseases caused by novel human pathogens, we must appreciate the value and difficulty of policy-oriented modeling. Effective communication of insights gleaned from modeling SARS will help to ensure that policymakers involve modelers in future outbreaks of newly-emerging infectious diseases. Accordingly, we illustrate the increasingly timely care-seeking by which, together with increasingly accurate diagnoses and effective isolation, SARS was controlled via heuristic arguments and descriptive analyses of familiar observations.

This article uses the 2009 H1N1 influenza vaccination program experience to introduce a cost analysis approach that may be relevant for planning mass prophylaxis operations, such as vaccination clinics at public health centers, work sites, schools, or pharmacy-based clinics. These costs are important for planning mass influenza vaccination activities and are relevant for all public health emergency preparedness scenarios requiring countermeasure dispensing. We demonstrate how costs vary depending on accounting perspective, staffing composition, and other factors. We also describe a mass vaccination clinic budgeting tool that clinic managers may use to estimate clinic costs and to examine how costs vary depending on the availability of volunteers or donated supplies and on the number of patients vaccinated per hour. Results from pilot tests with school-based H1N1 influenza vaccination clinic managers are described. The tool can also contribute to planning efforts for universal seasonal influenza vaccination.

Public health agencies across the globe are working to mitigate the impact of the 2009 pandemic caused by swine-origin influenza A (H1N1) virus. Prior to the large-scale distribution of an effective vaccine, the primary modes of control have included careful surveillance, social distancing and hygiene measures, strategic school closures, other community measures, and the prudent use of antiviral medications to prevent infection (prophylaxis) or reduce the severity and duration of symptoms (treatment). Here, we use mathematical models to determine the optimal geo-temporal tactics for distributing the U.S. strategic national stockpile of antivirals for treatment of infected cases during the early stages of a pandemic, prior to the wide availability of vaccines.We present a versatile optimization method for efficiently searching large sets of public health intervention strategies, and apply it to evaluating tactics for distributing antiviral medications from the U.S. Strategic National Stockpile (SNS). We implemented the algorithm on a network model of H1N1 transmission within and among U.S. cities to project the epidemiological impacts of antiviral stockpile distribution schedules and priorities. The resulting optimized strategies critically depend on the rates of antiviral uptake and wastage (through misallocation or loss). And while a surprisingly simple pro rata distribution schedule is competitive with the optimized strategies across a wide range of uptake and wastage, other equally simple policies perform poorly.Even as vaccination campaigns get underway worldwide, antiviral medications continue to play a critical in reducing H1N1-associated morbidity and mortality. If efforts are made to increase the fraction of cases treated promptly with antivirals above current levels, our model suggests that optimal use of the antiviral component of the Strategic National Stockpile may appreciably slow the transmission of H1N1 during fall 2009, thereby improving the impact of targeted vaccination. A more aggressive optimized antiviral strategy of this type may prove critical to mitigating future flu pandemics, but may increase the risk of antiviral resistance.

PURPOSE: Mathematical and simulation models are increasingly used to plan for and evaluate health sector responses to disasters, yet no clear consensus exists regarding best practices for the design, conduct, and reporting of such models. The authors examined a large selection of published health sector disaster response models to generate a set of best practice guidelines for such models. METHODS: The authors reviewed a spectrum of published disaster response models addressing public health or health care delivery, focusing in particular on the type of disaster and response decisions considered, decision makers targeted, choice of outcomes evaluated, modeling methodology, and reporting format. They developed initial recommendations for best practices for creating and reporting such models and refined these guidelines after soliciting feedback from response modeling experts and from members of the Society for Medical Decision Making. RESULTS: The authors propose 6 recommendations for model construction and reporting, inspired by the most exemplary models: health sector disaster response models should address real-world problems, be designed for maximum usability by response planners, strike the appropriate balance between simplicity and complexity, include appropriate outcomes that extend beyond those considered in traditional cost-effectiveness analyses, and be designed to evaluate the many uncertainties inherent in disaster response. Finally, good model reporting is particularly critical for disaster response models. CONCLUSIONS: Quantitative models are critical tools for planning effective health sector responses to disasters. The proposed recommendations can increase the applicability and interpretability of future models, thereby improving strategic, tactical, and operational aspects of preparedness planning and response.