IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiology of coronavirus disease 2019 (COVID-19), is readily transmitted person to person. Optimal control of COVID-19 depends on directing resources and health messaging to mitigation efforts that are most likely to prevent transmission, but the relative importance of such measures has been disputed. OBJECTIVE To assess the proportion of SARS-CoV-2 transmissions in the community that likely occur from persons without symptoms. DESIGN, SETTING, AND PARTICIPANTS This decision analytical model assessed the relative amount of transmission from presymptomatic, never symptomatic, and symptomatic individuals across a range of scenarios in which the proportion of transmission from people who never develop symptoms (ie, remain asymptomatic) and the infectious period were varied according to published best estimates. For all estimates, data from a meta-analysis was used to set the incubation period at a median of 5 days. The infectious period duration was maintained at 10 days, and peak infectiousness was varied between 3 and 7 days (−2 and +2 days relative to the median incubation period). The overall proportion of SARS-CoV-2 was varied between 0% and 70% to assess a wide range of possible proportions. MAIN OUTCOMES AND MEASURES Level of transmission of SARS-CoV-2 from presymptomatic, never symptomatic, and symptomatic individuals. RESULTS The baseline assumptions for the model were that peak infectiousness occurred at the median of symptom onset and that 30% of individuals with infection never develop symptoms and are 75% as infectious as those who do develop symptoms. Combined, these baseline assumptions imply that persons with infection who never develop symptoms may account for approximately 24% of all transmission. In this base case, 59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms. Under a broad range of values for each of these assumptions, at least 50% of new SARS-CoV-2 infections was estimated to have originated from exposure to individuals with infection but without symptoms. CONCLUSIONS AND RELEVANCE In this decision analytical model of multiple scenarios of proportions of asymptomatic individuals with COVID-19 and infectious periods, transmission from asymptomatic individuals was estimated to account for more than half of all transmissions. In addition to identification and isolation of persons with symptomatic COVID-19, effective control of spread will require reducing the risk of transmission from people with infection who do not have symptoms. These findings suggest that measures such as wearing masks, hand hygiene, social distancing, and strategic testing of people who are not ill will be foundational to slowing the spread of COVID-19 until safe and effective vaccines are available and widely used.

No abstract

Background The COVID-19 pandemic has driven demand for forecasts to guide policy and planning. Previous research has suggested that combining forecasts from multiple models into a single "ensemble" forecast can increase the robustness of forecasts. Here we evaluate the real-time application of an open, collaborative ensemble to forecast deaths attributable to COVID-19 in the U.S. Methods Beginning on April 13, 2020, we collected and combined one- to four-week ahead forecasts of cumulative deaths for U.S. jurisdictions in standardized, probabilistic formats to generate real-time, publicly available ensemble forecasts. We evaluated the point prediction accuracy and calibration of these forecasts compared to reported deaths. Results Analysis of 2,512 ensemble forecasts made April 27 to July 20 with outcomes observed in the weeks ending May 23 through July 25, 2020 revealed precise short-term forecasts, with accuracy deteriorating at longer prediction horizons of up to four weeks. At all prediction horizons, the prediction intervals were well calibrated with 92-96% of observations falling within the rounded 95% prediction intervals. Conclusions This analysis demonstrates that real-time, publicly available ensemble forecasts issued in April-July 2020 provided robust short-term predictions of reported COVID-19 deaths in the United States. With the ongoing need for forecasts of impacts and resource needs for the COVID-19 response, the results underscore the importance of combining multiple probabilistic models and assessing forecast skill at different prediction horizons. Careful development, assessment, and communication of ensemble forecasts can provide reliable insight to public health decision makers.

Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

The economic impact of community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) remains unclear. We developed an economic simulation model to quantify the costs associated with CA-MRSA infection from the societal and third-party payer perspectives. A single CA-MRSA case costs third-party payers $2,277 – $3,200 and society $7,070 – $20,489, depending on patient age. In the United States (US), CA-MRSA imposes an annual burden of $478 million - 2.2 billion on third-party payers and $1.4 billion - 13.8 billion on society, depending on the CA-MRSA definitions and incidences. The US jail system and Army may be experiencing annual total costs of $7 – 11 million ($6 – 10 million direct medical costs) and $15 – 36 million ($14 – 32 million), respectively. Hospitalization rates and mortality are important cost drivers. CA-MRSA confers a substantial economic burden to third-party payers and society, with CA-MRSA-attributable productivity losses being major contributors to the total societal economic burden. Although decreasing transmission and infection incidence would decrease costs, even if transmission were to continue at present levels, early identification and appropriate treatment of CA-MRSA infections before they progress could save considerable costs.

Background: Treatments for health care-associated infections (HAIs) caused by antibiotic-resistant bacteria and Clostridium difficile are limited, and some patients have developed untreatable infections. Evidence-supported interventions are available, but coordinated approaches to interrupt the spread of HAIs could have a greater impact on reversing the increasing incidence of these infections than independent facility-based program efforts. Methods:Data from CDC's National Healthcare Safety Network and Emerging Infections Program were analyzed to project the number of health care-associated infections from antibiotic-resistant bacteria or C. difficile both with and without a large scale national intervention that would include interrupting transmission and improved antibiotic stewardship. As an example, the impact of reducing transmission of one antibiotic-resistant infection (carbapenem-resistant Enterobacteriaceae [CRE]) on cumulative prevalence and number of HAI transmission events within interconnected groups of health care facilities was modeled using two distinct approaches, a large scale and a smaller scale health care network. Results:Immediate nationwide infection control and antibiotic stewardship interventions, over 5 years, could avert an estimated 619,000 HAIs resulting from CRE, multidrug-resistant Pseudomonas aeruginosa, invasive methicillin-resistant Staphylococcus aureus (MRSA), or C. difficile. Compared with independent efforts, a coordinated response to prevent CRE spread across a group of inter-connected health care facilities resulted in a cumulative 74% reduction in acquisitions over 5 years in a 10-facility network model, and 55% reduction over 15 years in a 102-facility network model. Conclusions:With effective action now, more than half a million antibiotic-resistant health care-associated infections could be prevented over 5 years. Models representing both large and small groups of interconnected health care facilities illustrate that a coordinated approach to interrupting transmission is more effective than historical independent facilitybased efforts.Implications for Public Health: Public health-led coordinated prevention approaches have the potential to more completely address the emergence and dissemination of these antibiotic-resistant organisms and C. difficile than independent facility-based efforts.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is an antibiotic resistance threat of the highest priority. Given the limited treatment options for this multidrug-resistant organism (MDRO), there is an urgent need for targeted strategies to prevent transmission. Here, we applied whole-genome sequencing to a comprehensive collection of clinical isolates to reconstruct regional transmission pathways and analyzed this transmission network in the context of statewide patient transfer data and patient-level clinical data to identify drivers of regional transmission. We found that high regional CRKP burdens were due to a small number of regional introductions, with subsequent regional proliferation occurring via patient transfers among health care facilities. While CRKP was predicted to have been imported into each facility multiple times, there was substantial variation in the ratio of intrafacility transmission events per importation, indicating that amplification occurs unevenly across regional facilities. While myriad factors likely influence intrafacility transmission rates, an understudied one is the potential for clinical characteristics of colonized and infected patients to influence their propensity for transmission. Supporting the contribution of high-risk patients to elevated transmission rates, we observed that patients colonized and infected with CRKP in high-transmission facilities had higher rates of carbapenem use, malnutrition, and dialysis and were older. This report highlights the potential for regional infection prevention efforts that are grounded in genomic epidemiology to identify the patients and facilities that make the greatest contribution to regional MDRO prevalence, thereby facilitating the design of precision interventions of maximal impact.

No abstract