Compartmental models of infectious disease transmission make assumptions about human behaviors. Specifically, they parameterize interactions across population groups, assumed to have distinct epidemiologically-relevant behavioral patterns, primarily through contact matrices stratified by demographic variables such as age, gender, or socioeconomic status. Although such demographic characteristics are readily measurable, they may inadequately capture the social and psychological forces that govern protective behaviors. Drawing on 20 waves of a national survey conducted throughout the COVID-19 pandemic in the United States, we show that institutional trust – particularly trust in public health agencies, physicians, and hospitals – is a dominant predictor of protective behavior adoption. For mask wearing during periods of strongest pandemic activity, for example, institutional trust explains more behavioral variance across population groups than age, income, education, and partisan affiliation combined. In unadjusted analyses, the difference in protective behavior adoption between individuals with the highest and lowest trust in the CDC was four- to six-fold larger than the corresponding differences by age, income, or educational attainment, and exceeded the difference between Democratic and Republican respondents. This association was institutionally specific (e.g., the relationship attenuates for trust in banks), and behaviorally specific (e.g., trust in the CDC is associated with protective behaviors but not visiting a doctor). The latter suggests that trust modifies voluntary compliance with public health recommendations rather than access to or use of healthcare. We conclude that compartmental models of disease transmission would be substantially improved by incorporating institutional trust as a stratifying variable. We additionally offer a trust-integrated mathematical modeling framework and recommendations for the data infrastructure needed for its implementation. ### Competing Interest Statement This work was supported by CDC (CDC-RFA-FT-23-0069), NSF (SES-2029292, SES-2029792, SES-2116465, SES-2116189, SES-2116458, SES-211663, SES-2241884, SES-2241885, SES-2241886, SES-2241887), the Knight Foundation, Amazon Web Services, and the Peterson Foundation. AI language model assistance Claude (claude-sonnet-4-6, Anthropic) was used in editing and proofreading the manuscript text; all intellectual content and scientific claims were developed and verified by the authors. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The institutional review board of Harvard University deemed this study exempt as only deidentified data were used and no participant contact was required. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Anonymized numeric and ordinal survey data from the CHIP50 survey are deposited at https://github.com/MIGHTE\_lab/trust\_pandemic_driver and will be made publicly available upon journal acceptance. [https://github.com/MIGHTE\_lab/trust\_pandemic_driver][1] Centers for Disease Control and Prevention, https://ror.org/042twtr12, CDC-RFA-FT-23-0069 U.S. National Science Foundation, https://ror.org/021nxhr62, SES-2029292, SES-2029792, SES-2116465, SES-2116189, SES-2116458, SES-211663, SES-2241884, SES-2241885, SES-2241886, SES-2241887 John S. and James L. Knight Foundation, https://ror.org/00mn6be63 Amazon Web Services Peterson Foundation [1]: https://github.com/MIGHTE_lab/trust_pandemic_driver

Both academic researchers and political pundits have generally accepted two over-time features of persuasion by partisan media: that the persuasive effects of partisan media might be temporary and decay quickly after a single exposure, and that these effects accumulate from multiple exposures. That effects decay may serve to ameliorate concerns about the broad impact of such media on partisan polarization. Yet the assumption that persuasive effects accumulate may raise larger concerns from real-world repeat exposure. To explore these possibilities, we implement a novel set of multiwave experiments that allow us to examine concerns about media effects over time. We present estimates from three studies suggesting that the persuasive effect of exposure to just a short article or video clip can persist for up to a week. In contrast to this persistence, our results suggest that an experiment adequately powered to detect the cumulative effect from multiple doses of partisan media—let alone one powered to detect cumulative effects among subgroups of the population—would require an unrealistic number of respondents. These cumulative effects are thus difficult to test in an experimental setting with limited resources.

An enormous body of literature argues that recommendation algorithms drive political polarization by creating "filter bubbles" and "rabbit holes." Using four experiments with nearly 9,000 participants, we show that manipulating algorithmic recommendations to create these conditions has limited effects on opinions. Our experiments employ a custom-built video platform with a naturalistic, YouTube-like interface presenting real YouTube videos and recommendations. We experimentally manipulate YouTube's actual recommendation algorithm to simulate filter bubbles and rabbit holes by presenting ideologically balanced and slanted choices. Our design allows us to intervene in a feedback loop that has confounded the study of algorithmic polarization-the complex interplay between supply of recommendations and user demand for content-to examine downstream effects on policy attitudes. We use over 130,000 experimentally manipulated recommendations and 31,000 platform interactions to estimate how recommendation algorithms alter users' media consumption decisions and, indirectly, their political attitudes. Our results cast doubt on widely circulating theories of algorithmic polarization by showing that even heavy-handed (although short-term) perturbations of real-world recommendations have limited causal effects on policy attitudes. Given our inability to detect consistent evidence for algorithmic effects, we argue the burden of proof for claims about algorithm-induced polarization has shifted. Our methodology, which captures and modifies the output of real-world recommendation algorithms, offers a path forward for future investigations of black-box artificial intelligence systems. Our findings reveal practical limits to effect sizes that are feasibly detectable in academic experiments.

The transmission of communicable diseases in human populations is known to be modulated by behavioral patterns. However, detailed characterizations of how population-level behaviors change over time during multiple disease outbreaks and spatial resolutions are still not widely available. We used data from 431,211 survey responses collected in the United States, between April 2020 and June 2022, to provide a description of how human behaviors fluctuated during the first 2 y of the COVID-19 pandemic. Our analysis suggests that at the national and state levels, people's adherence to recommendations to avoid contact with others (a preventive behavior) was highest early in the pandemic but gradually-and linearly-decreased over time. Importantly, during periods of intense COVID-19 mortality, adaption to preventive behaviors increased-despite the overall temporal decrease. These spatial-temporal characterizations help improve our understanding of the bidirectional feedback loop between outbreak severity and human behavior. Our findings should benefit both computational modeling teams developing methodologies to predict the dynamics of future epidemics and policymakers designing strategies to mitigate the effects of future disease outbreaks.

Scientists provide important information to the public. Whether that information influences decision-making depends on trust. In the USA, gaps in trust in scientists have been stable for 50 years: women, Black people, rural residents, religious people, less educated people and people with lower economic status express less trust than their counterparts (who are more represented among scientists). Here we probe the factors that influence trust. We find that members of the less trusting groups exhibit greater trust in scientists who share their characteristics (for example, women trust women scientists more than men scientists). They view such scientists as having more benevolence and, in most cases, more integrity. In contrast, those from high-trusting groups appear mostly indifferent about scientists' characteristics. Our results highlight how increasing the presence of underrepresented groups among scientists can increase trust. This means expanding representation across several divides-not just gender and race/ethnicity but also rurality and economic status.

Conspiratorial thoughts as a cognitive aspect are understudied outside small clinical cohorts. We conducted a 50-state non-probability internet survey of respondents age 18 and older, who completed the American Conspiratorial Thinking Scale (ACTS) and the 9-item Patient Health Questionnaire (PHQ-9). Across the 6 survey waves, there were 123,781 unique individuals. After reweighting, a total of 78.6 % somewhat or strongly agreed with at least one conspiratorial idea; 19.0 % agreed with all four of them. More conspiratorial thoughts were reported among those age 25-54, males, individuals who finished high school but did not start or complete college, and those with greater levels of depressive symptoms. Endorsing more conspiratorial thoughts was associated with a significantly lower likelihood of being vaccinated against COVID-19. The extent of correlation with non-vaccination suggests the importance of considering such thinking in designing public health strategies.

ImportanceEfforts to understand the complex association between social media use and mental health have focused on depression, with little investigation of other forms of negative affect, such as irritability and anxiety.ObjectiveTo characterize the association between self-reported use of individual social media platforms and irritability among US adults.Design, Setting, and ParticipantsThis survey study analyzed data from 2 waves of the COVID States Project, a nonprobability web-based survey conducted between November 2, 2023, and January 8, 2024, and applied multiple linear regression models to estimate associations with irritability. Survey respondents were aged 18 years and older.ExposureSelf-reported social media use.Main Outcomes and MeasuresThe primary outcome was score on the Brief Irritability Test (range, 5-30), with higher scores indicating greater irritability.ResultsAcross the 2 survey waves, there were 42 597 unique participants, with mean (SD) age 46.0 (17.0) years; 24 919 (58.5%) identified as women, 17 222 (40.4%) as men, and 456 (1.1%) as nonbinary. In the full sample, 1216 (2.9%) identified as Asian American, 5939 (13.9%) as Black, 5322 (12.5%) as Hispanic, 624 (1.5%) as Native American, 515 (1.2%) as Pacific Islander, 28 354 (66.6%) as White, and 627 (1.5%) as other (ie, selecting the other option prompted the opportunity to provide a free-text self-description). In total, 33 325 (78.2%) of the survey respondents reported daily use of at least 1 social media platform, including 6037 (14.2%) using once a day, 16 678 (39.2%) using multiple times a day, and 10 610 (24.9%) using most of the day. Frequent use of social media was associated with significantly greater irritability in univariate regression models (for more than once a day vs never, 1.43 points [95% CI, 1.22-1.63 points]; for most of the day vs never, 3.37 points [95% CI, 3.15-3.60 points]) and adjusted models (for more than once a day, 0.38 points [95% CI, 0.18-0.58 points]; for most of the day, 1.55 points [95% CI, 1.32-1.78 points]). These associations persisted after incorporating measures of political engagement.Conclusions and RelevanceIn this survey study of 42 597 US adults, irritability represented another correlate of social media use that merits further characterization, in light of known associations with depression and suicidality.

A longstanding literature in American foreign policy holds that the American public’s support for war significantly depends on the number of U.S. casualties in the conflict (their number, rate, trend, proximity, etc.). While a pandemic is clearly not a war, many observers and political leaders have characterized the U.S. public policy response to the COVID-19 pandemic using the metaphor of wartime. This raises the question of whether such characterizations are more than mere metaphor. Has the American public’s response to pandemic-related casualties—cases and deaths—followed similar patterns to those found in the literature on public opinion and war? In this study, we assess the public’s responsiveness to COVID-19 casualties at different stages in the pandemic. Utilizing two large, 50-state surveys conducted during the two largest COVID surges, in winter 2021 and winter 2022, we test several hypotheses from the public opinion and war literature, including that proximity—spatial and temporal—influences public responses and that the public becomes desensitized to casualties over time. We find that in many respects, the public’s response to the pandemic does indeed mirror the patterns found with respect to public opinion and war.

Americans' trust in scientists has been stable and high, relative to other political and social institutions, for the last half century (Krause, Brossard, and Scheufele 2019). Yet, underlying this stability lies a dramatic change such that a partisan gap has emerged, with Democrats exhibiting substantially more trust than Republicans. Fifty years ago, Republicans in fact exhibited more relative trust in scientists. This article explains this continuity and change. First, we demonstrate that the demographic correlates of trust in scientists have been remarkably stable for more than a half century: women, Black, rural, religious, non-college educated, and lower/working-class individuals exhibit less trust than their counterparts. Second, we show that the partisan relationship with trust in scientists has flipped (over that same time period) as low-trusting demographic strata shifted partisan allegiances. This is particularly the case when it comes to education and religiosity. Concomitant with the emergent partisan gap is a massive perceptual gap among Democrats, who perceive a partisan divide more than double its actual size. Democrats vastly underestimate Republicans' trust in scientists. The enduring demographic basis of trust in scientists provides an opportunity to bridge partisan divides by addressing demographic inequities in the practice and application of science.

This study aimed to characterize the prevalence of irritability among U.S. adults, and the extent to which it co-occurs with major depressive and anxious symptoms. A non-probability internet survey of individuals 18 and older in 50 U.S. states and the District of Columbia was conducted between November 2, 2023, and January 8, 2024. Regression models with survey weighting were used to examine associations between the Brief Irritability Test (BITe5) and sociodemographic and clinical features. The survey cohort included 42,739 individuals, mean age 46.0 (SD 17.0) years; 25,001 (58.5%) identified as women, 17,281 (40.4%) as men, and 457 (1.1%) as nonbinary. A total of 1218(2.8%) identified as Asian American, 5971 (14.0%) as Black, 5348 (12.5%) as Hispanic, 1775 (4.2%) as another race, and 28,427 (66.5%) as white. Mean irritability score was 13.6 (SD 5.6) on a scale from 5 to 30. In linear regression models, irritability was greater among respondents who were female, younger, had lower levels of education, and lower household income. Greater irritability was associated with likelihood of thoughts of suicide in logistic regression models adjusted for sociodemographic features (OR 1.23, 95% CI 1.22-1.24). Among 1979 individuals without thoughts of suicide on the initial survey assessed for such thoughts on a subsequent survey, greater irritability was also associated with greater likelihood of thoughts of suicide being present (adjusted OR 1.17, 95% CI 1.12-1.23). The prevalence of irritability and its association with thoughts of suicide suggests the need to better understand its implications among adults outside of acute mood episodes.

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.

The Civic Health and Institutions Project: A 50-State Survey (CHIP50) 4Opioid addiction in our social networksThe opioid crisis has worsened dramatically through the 21st century, with an estimated 107,543 people in the U.S. dying of overdoses in 2023 (according to data from the CDC). Here we look at a broader picture of who is affected by this crisis, asking our respondents if they know someone who struggles with opioid addiction.We note, in interpreting these data, that there are multiple possible biases in interpreting responses to the question “Do you personally know someone who struggles with opioid addiction?” Responses are driven by three factors: (1) the underlying prevalence of opioid addiction; (2) the extent to which people are aware of addiction in their social network; and (3) the willingness of the respondent to share that they know someone who is addicted. It is plausible, for example, that the respondent may not know whether someone they know is struggling with addiction. It is also plausible that some people would rather not reveal that they know someone addicted to opioids. Consequently, the figures we present below are likely conservative estimates of the prevalence of addiction in our social networks.

2024 is the first time since 1892 that an incumbent President is facing a predecessor. This yields a potentially distinct dynamic. Elections may be viewed in significant part as an act of assessment of the incumbent, per Reagan’s famous query: are you better off than you were four years ago? The choice confronting the American voter in 2024 is, in a sense, a comparative assessment of two presidencies, that of Biden and that of Trump. CHIP50 has been tracking the approval of Biden and Trump for the last two years. KEY FINDINGS●Approval of both Biden and Trump has been low and fairly stable for the entire period, with more people disapproving than approving of both for the last two years.●Approval of Trump has been higher than approval of Biden for the entire period. Disapproval of both was about the same in June 2022. However, Biden’s disapproval has slightly increased, while Trump’s disapproval has gradually declined during this time, yielding a substantially lower disapproval rate for Trump currently (43%) than for Biden (52%) . ●The largest shifts towards Trump and away from Biden in approval have been in the younger cohorts. For example, for the 18 to 24-year-old cohort, the percentage of people disapproving of Biden has jumped from 40% in June 2022 to 57% now, while the disapproval of Trump has dropped from 55% to 42%.●The shift in approval of Biden and Trump has been matched by a substantial shift in partisanship towards Republicans in the youngest cohort. In June 2022, Democrats had a 46% to 21% advantage in partisan identity; this has dropped to a 38% to 32% margin.●Trump significantly outperforms Biden in swing states, with a net approval that is 19% greater than Biden’s (in this report, we consider as swing states all states that were decided by less than a 3-point margin in 2020: Arizona, Georgia, Michigan, North Carolina, Nevada, Pennsylvania, and Wisconsin).

We compared the voting preferences of respondents who had previously participated in the April-May wave of CHIP50 to those of the same individuals reported during the week following the June 27 US presidential debate. Our results indicate a modest churn of voters’ preferences, with no substantial shift in the race between Biden and Trump

ImportanceThe frequent occurrence of cognitive symptoms in post–COVID-19 condition has been described, but the nature of these symptoms and their demographic and functional factors are not well characterized in generalizable populations.ObjectiveTo investigate the prevalence of self-reported cognitive symptoms in post–COVID-19 condition, in comparison with individuals with prior acute SARS-CoV-2 infection who did not develop post–COVID-19 condition, and their association with other individual features, including depressive symptoms and functional status.Design, Setting, and ParticipantsTwo waves of a 50-state nonprobability population-based internet survey conducted between December 22, 2022, and May 5, 2023. Participants included survey respondents aged 18 years and older.ExposurePost–COVID-19 condition, defined as self-report of symptoms attributed to COVID-19 beyond 2 months after the initial month of illness.Main Outcomes and MeasuresSeven items from the Neuro-QoL cognition battery assessing the frequency of cognitive symptoms in the past week and patient Health Questionnaire-9.ResultsThe 14 767 individuals reporting test-confirmed COVID-19 illness at least 2 months before the survey had a mean (SD) age of 44.6 (16.3) years; 568 (3.8%) were Asian, 1484 (10.0%) were Black, 1408 (9.5%) were Hispanic, and 10 811 (73.2%) were White. A total of 10 037 respondents (68.0%) were women and 4730 (32.0%) were men. Of the 1683 individuals reporting post–COVID-19 condition, 955 (56.7%) reported at least 1 cognitive symptom experienced daily, compared with 3552 of 13 084 (27.1%) of those who did not report post–COVID-19 condition. More daily cognitive symptoms were associated with a greater likelihood of reporting at least moderate interference with functioning (unadjusted odds ratio [OR], 1.31 [95% CI, 1.25-1.36]; adjusted [AOR], 1.30 [95% CI, 1.25-1.36]), lesser likelihood of full-time employment (unadjusted OR, 0.95 [95% CI, 0.91-0.99]; AOR, 0.92 [95% CI, 0.88-0.96]) and greater severity of depressive symptoms (unadjusted coefficient, 1.40 [95% CI, 1.29-1.51]; adjusted coefficient 1.27 [95% CI, 1.17-1.38). After including depressive symptoms in regression models, associations were also found between cognitive symptoms and at least moderate interference with everyday functioning (AOR, 1.27 [95% CI, 1.21-1.33]) and between cognitive symptoms and lower odds of full-time employment (AOR, 0.92 [95% CI, 0.88-0.97]).Conclusions and RelevanceThe findings of this survey study of US adults suggest that cognitive symptoms are common among individuals with post–COVID-19 condition and associated with greater self-reported functional impairment, lesser likelihood of full-time employment, and greater depressive symptom severity. Screening for and addressing cognitive symptoms is an important component of the public health response to post–COVID-19 condition.

Conspiracy beliefs can lead to maladaptive and, in rare cases, even violent behaviors. Focusing on conspiracies about the assassination attempt on former President Trump, this report takes the rare step of differentiating exposure to conspiracy theories from belief in them. It finds that there is considerable exposure through social media. Yet, reliance on social media does not correlate with belief in conspiracy theories. Rather, interpersonal relationships play a larger role. Conspiratorial thinking and political motivations also significantly relate to holding a conspiracy belief. The results suggest that corrective interventions would face substantial communicative and psychological hurdles.

Winter 2023-24 has seen an unusual confluence of a variety of respiratory illnesses, ranging from flu to RSV (Respiratory Syncytial Virus) and COVID-19. Between December 21, 2023 and January 29, 2024, we surveyed 30,460 individuals aged 18 and older across all 50 states plus the District of Columbia. We asked them if they had experienced an Influenza-like Illness (ILI) defined as experiencing a fever and cough, or a fever and sore throat, and/or if they had been diagnosed with COVID-19, over the previous month. Amongst those who responded yes to such questions, we asked them whether or not they had sought medical attention. In this report, we summarize our findings across a variety of demographic subgroups, including age, race, education, income, gender, and geography.

Hamas, as well as its implications for antisemitism and Islamophobia. A series of polls from October and December 2023, for example, reported that those between 18 and 24 were split 50/50 on whether they supported Israel or Hamas. In fact, this result gained so much traction that candidate for the GOP nomination for president, Vivek Ramaswamy cited it at a campaign event in November 2023. However, national surveys usually lack the sample size to confidently report percentages of small subgroups, like 18 to 24 year olds, raising questions about the validity of findings like these.Between December 21, 2023 and January 29, 2024, we surveyed 30,460 individuals aged 18 and older across all 50 states plus the District of Columbia. We included feeling thermometers for Israel, Palestine, Jews, and Muslims, asking people to separately rate how they feel about each group on a 0 to 100 scale, where 0 indicates feeling very unfavorable or cold, 50 indicates not feeling particularly warm or cold toward that group, and 100 indicates feeling very favorable or warm.Our survey’s sample size allows us to zoom in on subgroups within the population. For instance, it included 3,294 Americans between ages 18 and 24. This allows us to look within this age group and break young Americans down by partisanship, as our survey includes 1101 18-24-year-old Democrats and 676 18-24-year-old Republicans. In this report, we examine how responses to these four thermometer ratings varied by age, race, education, religion, and partisanship, as well as how the different thermometer scores are correlated with one another.

Importance Trust in physicians and hospitals has been associated with achieving public health goals, but the increasing politicization of public health policies during the COVID-19 pandemic may have adversely affected such trust. Objective To characterize changes in US adults' trust in physicians and hospitals over the course of the COVID-19 pandemic and the association between this trust and health-related behaviors. Design, Setting, and Participants This survey study uses data from 24 waves of a nonprobability internet survey conducted between April 1, 2020, and January 31, 2024, among 443 455 unique respondents aged 18 years or older residing in the US, with state-level representative quotas for race and ethnicity, age, and gender. Main Outcome and Measure Self-report of trust in physicians and hospitals; self-report of SARS-CoV-2 and influenza vaccination and booster status. Survey-weighted regression models were applied to examine associations between sociodemographic features and trust and between trust and health behaviors. Results The combined data included 582 634 responses across 24 survey waves, reflecting 443 455 unique respondents. The unweighted mean (SD) age was 43.3 (16.6) years; 288 186 respondents (65.0%) reported female gender; 21 957 (5.0%) identified as Asian American, 49 428 (11.1%) as Black, 38 423 (8.7%) as Hispanic, 3138 (0.7%) as Native American, 5598 (1.3%) as Pacific Islander, 315 278 (71.1%) as White, and 9633 (2.2%) as other race and ethnicity (those who selected "Other" from a checklist). Overall, the proportion of adults reporting a lot of trust for physicians and hospitals decreased from 71.5% (95% CI, 70.7%-72.2%) in April 2020 to 40.1% (95% CI, 39.4%-40.7%) in January 2024. In regression models, features associated with lower trust as of spring and summer 2023 included being 25 to 64 years of age, female gender, lower educational level, lower income, Black race, and living in a rural setting. These associations persisted even after controlling for partisanship. In turn, greater trust was associated with greater likelihood of vaccination for SARS-CoV-2 (adjusted odds ratio [OR], 4.94; 95 CI, 4.21-5.80) or influenza (adjusted OR, 5.09; 95 CI, 3.93-6.59) and receiving a SARS-CoV-2 booster (adjusted OR, 3.62; 95 CI, 2.99-4.38). Conclusions and Relevance This survey study of US adults suggests that trust in physicians and hospitals decreased during the COVID-19 pandemic. As lower levels of trust were associated with lesser likelihood of pursuing vaccination, restoring trust may represent a public health imperative.

Importance: Identifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate its effects, yet it remains a challenging task. Objective: To characterize the ability of non-probability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing. Design: Internet-based non-probability surveys were conducted, using the PureSpectrum survey vendor, approximately every 6 weeks between April 2020 and January 2023. They collected information on COVID-19 infections with representative state-level quotas applied to balance age, gender, race and ethnicity, and geographic distribution. Data from this survey were compared to institutional case counts collected by Johns Hopkins University and wastewater surveillance data for SARS-CoV-2 from Biobot Analytics. Setting: Population-based online non-probability survey conducted for a multi-university consortium -the Covid States Project. Participants: Residents of age 18+ across 50 US states and the District of Columbia in the US. Main Outcomes and Measures: The main outcomes are: (a) survey-weighted estimates of new monthly confirmed COVID-19 cases in the US from January 2020 to January 2023, and (b) estimates of uncounted test-confirmed cases, from February 1, 2022, to January 1, 2023. These are compared to institutionally reported COVID-19 infections and wastewater viral concentrations. Results: The survey spanned 17 waves deployed from June 2020 to January 2023, with a total of 408,515 responses from 306,799 respondents with mean age 42.8 (STD 13) years; 202,416 (66%) identified as women, and 104,383 (34%) as men. A total of 16,715 (5.4%) identified as Asian, 33,234 (10.8%) as Black, 24,938 (8.1%) as Hispanic, 219,448 (71.5%) as White, and 12,464 (4.1%) as another race. 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 of r=0.96; p=1.8 e-12) from April 2020 to January 2022 (50-state correlation average of r=0.88, SD = 0.073). This was before the government-led mass distribution of at-home rapid tests. Following January 2022, correlation was diminished and no longer statistically significant (r=0.55, p=0.08; 50-state correlation average of r=0.48, SD = 0.227). In contrast, survey COVID-19 estimates correlated highly with SARS-CoV-2 viral concentrations in wastewater both before (r=0.92; p=2.2e-09) and after (r=0.89; p=2.3e-04) January 2022. Institutionally reported COVID-19 cases correlated (r = 0.79, p=1.10e-05) with wastewater viral concentrations before January 2022, but poorly (r = 0.31, p=0.35) after, suggesting 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 unaccounted for in official records between January 2022 and January 2023. Conclusions and Relevance: Non-probability 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 healthcare officials to implement accessible and affordable at-home testing for efficient infection monitoring in the future. Trial Registration: NA ### Competing Interest Statement Dr. Santillana has received institutional research funds from the Johnson and Johnson foundation, from Janssen global public health, and from Pfizer Pharmaceuticals, Inc. Dr. Perlis serves as a scientific advisor to Genomind, Vault Health, Psy Therapeutics, Circular Genomics, Swan AI Studios, Belle AI, and Mila Health. ### Funding Statement Dr. Santillana has been funded (in part) by contracts 200-2016-91779 and cooperative agreement CDC-RFA-FT-23-0069 with the Centers for Disease Control and Prevention (CDC). The findings, conclusions, and views expressed are those of the author(s) and do not necessarily represent the official position of the CDC. Dr. Santillana was also partially supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM130668. Drs Ognyanova, Lazer, and Baum were supported by the National Science Foundation. Dr. Perlis was supported by National Institute of Mental Health award number RF132335 ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was determined to be exempt by the Institutional Review Board of Harvard University; all participants signed consent online prior to survey access. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data are available upon reasonable request. <https://www.covidstates.org/>