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ImportanceIdentifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate the pandemic’s effects, yet it remains a challenging task. ObjectiveTo characterize the ability of nonprobability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing. Design, Setting, and ParticipantsInternet-based online nonprobability surveys were conducted among residents aged 18 years or older across 50 US states and the District of Columbia, using the PureSpectrum survey vendor, approximately every 6 weeks between June 1, 2020, and January 31, 2023, for a multiuniversity consortium—the COVID States Project. Surveys collected information on COVID-19 infections with representative state-level quotas applied to balance age, sex, race and ethnicity, and geographic distribution. Main Outcomes and MeasuresThe main outcomes were (1) survey-weighted estimates of new monthly confirmed COVID-19 cases in the US from January 2020 to January 2023 and (2) estimates of uncounted test-confirmed cases from February 1, 2022, to January 1, 2023. These estimates were compared with institutionally reported COVID-19 infections collected by Johns Hopkins University and wastewater viral concentrations for SARS-CoV-2 from Biobot Analytics. ResultsThe survey spanned 17 waves deployed from June 1, 2020, to January 31, 2023, with a total of 408 515 responses from 306 799 respondents (mean [SD] age, 42.8 [13.0] years; 202 416 women [66.0%]). Overall, 64 946 respondents (15.9%) self-reported a test-confirmed COVID-19 infection. National survey-weighted test-confirmed COVID-19 estimates were strongly correlated with institutionally reported COVID-19 infections (Pearson correlation,r = 0.96;P < .001) from April 2020 to January 2022 (50-state correlation mean [SD] value,r = 0.88 [0.07]). This was before the government-led mass distribution of at-home rapid tests. After January 2022, correlation was diminished and no longer statistically significant (r = 0.55;P = .08; 50-state correlation mean [SD] value,r = 0.48 [0.23]). In contrast, survey COVID-19 estimates correlated highly with SARS-CoV-2 viral concentrations in wastewater both before (r = 0.92;P < .001) and after (r = 0.89;P < .001) January 2022. Institutionally reported COVID-19 cases correlated (r = 0.79;P < .001) with wastewater viral concentrations before January 2022, but poorly (r = 0.31;P = .35) after, suggesting that both survey and wastewater estimates may have better captured test-confirmed COVID-19 infections after January 2022. Consistent correlation patterns were observed at the state level. Based on national-level survey estimates, approximately 54 million COVID-19 cases were likely unaccounted for in official records between January 2022 and January 2023. Conclusions and RelevanceThis study suggests that nonprobability survey data can be used to estimate the temporal evolution of test-confirmed infections during an emerging disease outbreak. Self-reporting tools may enable government and health care officials to implement accessible and affordable at-home testing for efficient infection monitoring in the future. 

There is nearly equal number of male and female student enrollments in primary and secondary level of education in Bangladesh, but at the tertiary level and at the job sector, a sharp drop in the number of women is observed. This paper explores the current status of female students’ enrollment in science, technology, engineering, and mathematics (STEM) at the tertiary education system in Bangladesh. It is followed by explorations of challenges women face in technical workplace. Quantitative data for the paper come from more than 1.18 million students at tertiary level from eight public and private universities for three academic years from 2018 to 2020. In addition, a qualitative study was conducted with 48 participants in pre- and during COVID-19 eras to understand barriers hampering women in STEM-related education and jobs. The paper provides a guideline for future policies to ensure inclusive space for growth and retention for women in STEM.