SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is thought to spread from person to person primarily by the respiratory route and mainly through close contact (1). Community mitigation strategies can lower the risk for disease transmission by limiting or preventing personto-person interactions (2). U.S. states and territories began implementing various community mitigation policies in March 2020. One widely implemented strategy was the issuance of orders requiring persons to stay home, resulting in decreased population movement in some jurisdictions (3). Each state or territory has authority to enact its own laws and policies to protect the public's health, and jurisdictions varied widely in the type and timing of orders issued related to stay-at-home requirements. To identify the broader impact of these stay-athome orders, using publicly accessible, anonymized location data from mobile devices, CDC and the Georgia Tech Research Institute analyzed changes in population movement relative to stay-at-home orders issued during March 1-May 31, 2020, by all 50 states, the District of Columbia, and five U.S. territories.* During this period, 42 states and territories issued mandatory stay-at-home orders. When counties subject to mandatory state-and territory-issued stay-at-home orders were stratified along rural-urban categories, movement decreased significantly relative to the preorder baseline in all strata. Mandatory stayat-home orders can help reduce activities associated with the spread of COVID-19, including population movement and close person-to-person contact outside the household. Data on state and territorial stay-at-home orders were obtained from government websites containing executive or administrative orders or press releases for each jurisdiction. Each order was analyzed and coded into one of five mutually exclusive categories: 1) mandatory for all persons; 2) mandatory only for persons in certain areas of the jurisdiction; 3) mandatory only for persons at increased risk in the jurisdiction; 4) mandatory only for persons at increased risk in certain areas of the jurisdiction; or 5) advisory or recommendation (i.e., nonmandatory). Jurisdictions that did not issue an order were coded as having no state-or territory-issued

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There is known gender disparity and inequity of women leaders in technology and STEM fields. A rapid gender decline in these burgeoning fields has sparked a national renewed interest in purposefully attracting and mentoring more women to roles in technology leadership. The gender disparity is not only in attracting young women to consider a technology or STEM career but it is in women staying engaged once they choose a career in these areas. Efforts have been made to improve the sustainability of women in technology leadership roles. Books, articles, and manuscripts have been written, formal and informal meetings and corporate awareness programs have been conducted and mentorship programs abound to attract girls to consider technology as a career choice. Further, identifying women role models has been a strategy employed to promote gender awareness. Within the chapter, the qualitative content analysis study investigates four women roles models and identifies leadership characteristics of these known women leaders in technology. It answers the following questions: What are the leadership characteristics of known women role models in technology? What do these leaders value? How do their differences impact their leadership in the field? Finally, what have they identified as propelling them towards innovation and discovery?

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