and Utah. Portions of the population in Colorado (49%), Minnesota (55%), New Mexico (61%), and Utah (35%) and the whole population of Maryland are included as part of the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET). https://www.cdc.gov/coronavirus/2019-ncov/covid-data/ covid-net/purpose-methods.html † A COVID-19 case (confirmed or probable) was defined as the detection of SARS-CoV-2 RNA or antigen in a respiratory specimen collected from a person aged ≥18 years per the Council of State and Territorial Epidemiologists' update to the standardized surveillance case definition and national notification for 2019 novel coronavirus disease (COVID-19) (21-ID-01

Across decades of co-circulation in humans, influenza A subtypes H1N1 and H3N2 have caused seasonal epidemics characterized by different age distributions of cases and mortality. H3N2 causes the majority of severe, clinically attended cases in high-risk elderly cohorts, and the majority of overall deaths, whereas H1N1 causes fewer deaths overall, and cases shifted towards young and middle-aged adults. These contrasting age profiles may result from differences in childhood imprinting to H1N1 and H3N2 or from differences in evolutionary rate between subtypes. Here we analyze a large epidemiological surveillance dataset to test whether childhood immune imprinting shapes seasonal influenza epidemiology, and if so, whether it acts primarily via homosubtypic immune memory or via broader, heterosubtypic memory. We also test the impact of evolutionary differences between influenza subtypes on age distributions of cases. Likelihood-based model comparison shows that narrow, within-subtype imprinting shapes seasonal influenza risk alongside age-specific risk factors. The data do not support a strong effect of evolutionary rate, or of broadly protective imprinting that acts across subtypes. Our findings emphasize that childhood exposures can imprint a lifelong immunological bias toward particular influenza subtypes, and that these cohort-specific biases shape epidemic age distributions. As a consequence, newer and less "senior" antibody responses acquired later in life do not provide the same strength of protection as responses imprinted in childhood. Finally, we project that the relatively low mortality burden of H1N1 may increase in the coming decades, as cohorts that lack H1N1-specific imprinting eventually reach old age.

On October 6, 2020, this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr). Mitigating the spread of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), requires individual, community, and state public health actions to prevent person-to-person transmission. Community mitigation measures can help slow the spread of COVID-19; these measures include wearing masks, social distancing, reducing the number and size of large gatherings, pausing operation of businesses where maintaining social distancing is challenging, working from or staying at home, and implementing certain workplace and educational institution controls (1-4). The Arizona Department of Health Services' (ADHS) recommendations for mitigating exposure to SARS-CoV-2 were informed by continual monitoring of patient demographics, SARS-CoV-2 community spread, and the pandemic's impacts on hospitals. To assess the effect of mitigation strategies in Arizona, the numbers of daily COVID-19 cases and 7-day moving averages during January 22-August 7, 2020, relative to implementation of enhanced community mitigation measures, were examined. The average number of daily cases increased approximately 151%, from 808 on June 1, 2020 to 2,026 on June 15, 2020 (after stay-at-home order lifted), necessitating increased preventive measures. On June 17, local officials began implementing and enforcing mask wearing (via county and city mandates),* affecting approximately 85% of the state population. Statewide mitigation measures included limitation of public events; closures of bars, gyms, movie theaters, and water parks; reduced restaurant dine-in capacity; and voluntary resident action to stay at home and wear masks (when and where not mandated). The number of COVID-19 cases in Arizona peaked during June 29-July 2, stabilized during July 3-July 12, and further declined by approximately 75% during July 13-August 7. Widespread implementation and enforcement of sustained community mitigation measures informed by state and local officials' continual data monitoring and collaboration can help prevent transmission of SARS-CoV-2 and decrease the numbers of COVID-19 cases. * Mandates and ordinances varied and were county-and city-specific. Enforcement types included educating persons on the dangers of COVID-19 spread, issuing fines to persons and businesses who refused to comply with mandates, and loss of licenses for businesses not enforcing rules or mandates. Public pools (e.g., at hotels; limited capacity) Jun 29, Jul 23 Private pools in public areas (e.g., multihousing complexes; limited capacity) Jun 29, Jul 23 Public events (<50 persons) Mar 15, Jun 29, Jul 23 Wearing masks (mandatory) Local officials able to mandate and enforce wearing masks Jun 17

Problem/ConditionCoccidioidomycosis (Valley fever) is an infection caused by the environmental fungus Coccidioides spp., which typically causes respiratory illness but also can lead to disseminated disease. This fungus typically lives in soils in warm, arid regions, including the southwestern United States.Reporting Period2011–2017.Description of SystemCoccidioidomycosis has been nationally notifiable since 1995 and is reportable in 26 states and the District of Columbia (DC), where laboratories and physicians notify local and state public health departments about possible coccidioidomycosis cases. Health department staff determine which cases qualify as confirmed cases according to the definition established by Council of State and Territorial Epidemiologists and voluntarily submit basic case information to CDC through the National Notifiable Diseases Surveillance System.ResultsDuring 2011–2017, a total of 95,371 coccidioidomycosis cases from 26 states and DC were reported to CDC. The number of cases decreased from 2011 (22,634 cases) to 2014 (8,232 cases) and subsequently increased to 14,364 cases in 2017; >95% of cases were reported from Arizona and California. Reported incidence in Arizona decreased from 261 per 100,000 persons in 2011 to 101 in 2017, whereas California incidence increased from 15.7 to 18.2, and other state incidence rates stayed relatively constant. Patient demographic characteristics were largely consistent with previous years, with an overall predominance among males and among adults aged >60 years in Arizona and adults aged 40–59 years in California.InterpretationCoccidioidomycosis remains an important national public health problem with a well-established geographic focus. The reasons for the changing trends in reported cases are unclear but might include environmental factors (e.g., temperature and precipitation), surveillance artifacts, land use changes, and changes in the population at risk for the infection.Public Health ActionHealth care providers should consider a diagnosis of coccidioidomycosis in patients who live or work in or have traveled to areas with known geographic risk for Coccidioides and be aware that those areas might be broader than previously recognized. Coccidioidomycosis surveillance provides important information about the epidemiology of the disease but is incomplete both in terms of geographic coverage and data availability. Expanding surveillance to additional states could help identify emerging areas that pose a risk for locally acquired infections. In Arizona and California, where most cases occur, collecting systematic enhanced data, such as more detailed patient characteristics and disease severity, could help clarify the reasons behind the recent changes in incidence and identify additional opportunities for focused prevention and educational efforts.

Coccidioidomycosis is a debilitating fungal disease caused by inhalation of arthroconidia. We developed a novel approach for detection of airborne Coccidioides and used it to investigate the distribution of arthroconidia across the Phoenix, Arizona, metropolitan area. Air filters were collected daily from 21 stationary air-sampling units across the area: the first set collected before, during and after a large dust storm on August 25, 2015, and the second over the 45-day period September 25–November 8, 2016. Analysis of DNA extracted from the filters demonstrated that the day of the dust storm was not associated with increase of Coccidioides in air samples, although evidence of the low-level polymerase chain reaction (PCR) inhibition was observed in DNA extracted from samples collected on the day of the dust storm. Testing over 45 days identified uneven geographic distribution suggesting Coccidioides hot spots. In 2016, highest daily concentration of arthroconidia was observed between September 25–October 20, and only sporadic low levels were detected after that. These results provide evidence of seasonality and uneven spatial distribution of Coccidioides in the air. Our results demonstrate that routine air monitoring for arthroconidia is possible and provides an important tool for Coccidioides surveillance, which can address important questions about environmental exposure and human infection.