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Sero-surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can reveal trends and differences in subgroups and capture undetected or unreported infections that are not included in case-based surveillance systems.Cross-sectional, convenience samples of remnant sera from clinical laboratories from 51 U.S. jurisdictions were assayed for infection-induced SARS-CoV-2 antibodies biweekly from October 25, 2020, to July 11, 2021, and monthly from September 6, 2021, to February 26, 2022. Test results were analyzed for trends in infection-induced, nucleocapsid-protein seroprevalence using mixed effects models that adjusted for demographic variables and assay type.Analyses of 1,469,792 serum specimens revealed U.S. infection-induced SARS-CoV-2 seroprevalence increased from 8.0% (95% confidence interval (CI): 7.9%-8.1%) in November 2020 to 58.2% (CI: 57.4%-58.9%) in February 2022. The U.S. ratio of the change in estimated seroprevalence to the change in reported case prevalence was 2.8 (CI: 2.8-2.9) during winter 2020-2021, 2.3 (CI: 2.0-2.5) during summer 2021, and 3.1 (CI: 3.0-3.3) during winter 2021-2022. Change in seroprevalence to change in case prevalence ratios ranged from 2.6 (CI: 2.3-2.8) to 3.5 (CI: 3.3-3.7) by region in winter 2021-2022.Ratios of the change in seroprevalence to the change in case prevalence suggest a high proportion of infections were not detected by case-based surveillance during periods of increased transmission. The largest increases in the seroprevalence to case prevalence ratios coincided with the spread of the B.1.1.529 (Omicron) variant and with increased accessibility of home testing. Ratios varied by region and season with the highest ratios in the midwestern and southern United States during winter 2021-2022. Our results demonstrate that reported case counts did not fully capture differing underlying infection rates and demonstrate the value of sero-surveillance in understanding the full burden of infection. Levels of infection-induced antibody seroprevalence, particularly spikes during periods of increased transmission, are important to contextualize vaccine effectiveness data as the susceptibility to infection of the U.S. population changes.This work was supported by the Centers for Disease Control and Prevention, Atlanta, Georgia.

This paper reports on an empirical study of the cognitive processes involved in program comprehension. Verbal protocols were gathered from professional programmers as they were engaged in a program-understanding task. Based on analysis of these protocols, several types of interesting cognitive events were identified. These include asking questions and conjecturing facts about the code. We describe these event types and use them to derive a computational model of the programmers' mental processes.

Understanding how a program is constructed and how it functions are significant components of the task of maintaining or enhancing a computer program. We have analyzed vidoetaped protocols of experienced programmers as they enhanced a personnel data base program. Our analysis suggests that there are two strategies for program understanding, the systematic strategy and the as-needed strategy. The programmer using the systematic strategy traces data flow through the program in order to understand global program behavior. The programmer using the as-needed strategy focuses on local program behavior in order to localize study of the program. Our empirical data show that there is a strong relationship between using a systematic approach to acquire knowledge about the program and modifying the program successfully. Programmers who used the systematic approach to study the program constructed successful modifications; programmers who used the as-needed approach failed to construct successful modifications. Programmers who used the systematic strategy gathered knowledge about the causal interactions of the program's functional components . Programmers who used the as-needed strategy did not gather such causal knowledge and therefore failed to detect interactions among components of the program.