Managerial and Organizational Challenges in the Age of AI.

This Viewpoint discusses the managerial and organizational challenges that could result from the use of artificial intelligence systems in psychiatric research and care.

Intraday adaptation to extreme temperatures in outdoor activity.

Linkages between climate and human activity are often calibrated at daily or monthly resolutions, which lacks the granularity to observe intraday adaptation behaviors. Ignoring this adaptation margin could mischaracterize the health consequences of future climate change. Here, we construct an hourly outdoor leisure activity database using billions of cell phone location requests in 10,499 parks in 2017 all over China to investigate the within-day outdoor activity rhythm. We find that hourly temperatures above 30 °C and 35 °C depress outdoor leisure activities by 5% (95% confidence interval, CI 3-7%) and by 13% (95% CI 10-16%) respectively. This activity-depressing effect is larger than previous daily or monthly studies due to intraday activity substitution from noon and afternoon to morning and evening. Intraday adaptation is larger for locations and dates with time flexibility, for individuals more frequently exposed to heat, and for parks situated in urban areas. Such within-day adaptation substantially reduces heat exposure, yet it also delays the active time at night by about half an hour, with potential side effect on sleep quality. Combining empirical estimates with outputs from downscaled climate models, we show that unmitigated climate change will generate sizable activity-depressing and activity-delaying effects in summer when projected on an hourly resolution. Our findings call for more attention in leveraging real-time activity data to understand intraday adaptation behaviors and their associated health consequences in climate change research.

Expanding the measurement of culture with a sample of two billion humans.

Culture has played a pivotal role in human evolution. Yet, the ability of social scientists to study culture is limited by the currently available measurement instruments. Scholars of culture must regularly choose between scalable but sparse survey-based methods or restricted but rich ethnographic methods. Here, we demonstrate that massive online social networks can advance the study of human culture by providing quantitative, scalable and high-resolution measurement of behaviourally revealed cultural values and preferences. We employ data across nearly 60 000 topic dimensions drawn from two billion Facebook users across 225 countries and territories. We first validate that cultural distances calculated from this measurement instrument correspond to traditional survey-based and objective measures of cross-national cultural differences. We then demonstrate that this expanded measure enables rich insight into the cultural landscape globally at previously impossible resolution. We analyse the importance of national borders in shaping culture and compare subnational divisiveness with gender divisiveness across countries. Our measure enables detailed investigation into the geopolitical stability of countries, social cleavages within small- and large-scale human groups, the integration of migrant populations and the disaffection of certain population groups from the political process, among myriad other potential future applications.

Projecting the Suicide Burden of Climate Change in the United States.

We quantify and monetize changes in suicide incidence across the conterminous United States (U.S.) in response to increasing levels of warming. We develop an integrated health impact assessment model using binned and linear specifications of temperature-suicide relationship estimates from Mullins and White (2019), in combination with monthly age- and sex-specific baseline suicide incidence rates, projections of six climate models, and population projections at the conterminous U.S. county scale. We evaluate the difference in the annual number of suicides in the U.S. corresponding to 1-6°C of warming compared to 1986-2005 average temperatures (mean U.S. temperatures) and compute 2015 population attributable fractions (PAFs). We use the U.S. Environmental Protection Agency's Value of a Statistical Life to estimate the economic value of avoiding these mortality impacts. Assuming the 2015 population size, warming of 1-6°C could result in an annual increase of 283-1,660 additional suicide cases, corresponding to a PAF of 0.7%-4.1%. The annual economic value of avoiding these impacts is $2 billion-$3 billion (2015 U.S. dollars, 3% discount rate, and 2015 income level). Estimates based on linear temperature-suicide relationship specifications are 7% larger than those based on binned temperature specifications. Accounting for displacement decreases estimates by 17%, while accounting for precipitation decreases estimates by 7%. Population growth between 2015 and the future warming degree arrival year increases estimates by 15%-38%. Further research is needed to quantify and monetize other climate-related mental health outcomes (e.g., anxiety and depression) and to characterize these risks in socially vulnerable populations.

Global evidence of expressed sentiment alterations during the COVID-19 pandemic.

The COVID-19 pandemic has created unprecedented burdens on people's physical health and subjective well-being. While countries worldwide have developed platforms to track the evolution of COVID-19 infections and deaths, frequent global measurements of affective states to gauge the emotional impacts of pandemic and related policy interventions remain scarce. Using 654 million geotagged social media posts in over 100 countries, covering 74% of world population, coupled with state-of-the-art natural language processing techniques, we develop a global dataset of expressed sentiment indices to track national- and subnational-level affective states on a daily basis. We present two motivating applications using data from the first wave of COVID-19 (from 1 January to 31 May 2020). First, using regression discontinuity design, we provide consistent evidence that COVID-19 outbreaks caused steep declines in expressed sentiment globally, followed by asymmetric, slower recoveries. Second, applying synthetic control methods, we find moderate to no effects of lockdown policies on expressed sentiment, with large heterogeneity across countries. This study shows how social media data, when coupled with machine learning techniques, can provide real-time measurements of affective states.

Campus Smoking Policies and Smoking-Related Twitter Posts Originating From California Public Universities: Retrospective Study.

BACKGROUND: The number of colleges and universities with smoke- or tobacco-free campus policies has been increasing. The effects of campus smoking policies on overall sentiment, particularly among young adult populations, are more difficult to assess owing to the changing tobacco and e-cigarette product landscape and differential attitudes toward policy implementation and enforcement. OBJECTIVE: The goal of the study was to retrospectively assess the campus climate toward tobacco use by comparing tweets from California universities with and those without smoke- or tobacco-free campus policies. METHODS: Geolocated Twitter posts from 2015 were collected using the Twitter public application programming interface in combination with cloud computing services on Amazon Web Services. Posts were filtered for tobacco products and behavior-related keywords. A total of 42,877,339 posts were collected from 2015, with 2837 originating from a University of California or California State University system campus, and 758 of these manually verified as being about smoking. Chi-square tests were conducted to determine if there were significant differences in tweet user sentiments between campuses that were smoke- or tobacco-free (all University of California campuses and California State University, Fullerton) compared to those that were not. A separate content analysis of tweets included in chi-square tests was conducted to identify major themes by campus smoking policy status. RESULTS: The percentage of positive sentiment tweets toward tobacco use was higher on campuses without a smoke- or tobacco-free campus policy than on campuses with a smoke- or tobacco-free campus policy (76.7% vs 66.4%, P=.03). Higher positive sentiment on campuses without a smoke- or tobacco-free campus policy may have been driven by general comments about one's own smoking behavior and comments about smoking as a general behavior. Positive sentiment tweets originating from campuses without a smoke- or tobacco-free policy had greater variation in tweet type, which may have also contributed to differences in sentiment among universities. CONCLUSIONS: Our study introduces preliminary data suggesting that campus smoke- and tobacco-free policies are associated with a reduction in positive sentiment toward smoking. However, continued expressions and intentions to smoke and reports of one's own smoking among Twitter users suggest a need for more research to better understand the dynamics between implementation of smoke- and tobacco-free policies and resulting tobacco behavioral sentiment.

The network limits of infectious disease control via occupation-based targeting.

Policymakers commonly employ non-pharmaceutical interventions to reduce the scale and severity of pandemics. Of non-pharmaceutical interventions, physical distancing policies-designed to reduce person-to-person pathogenic spread - have risen to recent prominence. In particular, stay-at-home policies of the sort widely implemented around the globe in response to the COVID-19 pandemic have proven to be markedly effective at slowing pandemic growth. However, such blunt policy instruments, while effective, produce numerous unintended consequences, including potentially dramatic reductions in economic productivity. In this study, we develop methods to investigate the potential to simultaneously contain pandemic spread while also minimizing economic disruptions. We do so by incorporating both occupational and contact network information contained within an urban environment, information that is commonly excluded from typical pandemic control policy design. The results of our methods suggest that large gains in both economic productivity and pandemic control might be had by the incorporation and consideration of simple-to-measure characteristics of the occupational contact network. We find evidence that more sophisticated, and more privacy invasive, measures of this network do not drastically increase performance.

Stay-at-home orders associate with subsequent decreases in COVID-19 cases and fatalities in the United States.

Governments issue "stay-at-home" orders to reduce the spread of contagious diseases, but the magnitude of such orders' effectiveness remains uncertain. In the United States these orders were not coordinated at the national level during the coronavirus disease 2019 (COVID-19) pandemic, which creates an opportunity to use spatial and temporal variation to measure the policies' effect. Here, we combine data on the timing of stay-at-home orders with daily confirmed COVID-19 cases and fatalities at the county level during the first seven weeks of the outbreak in the United States. We estimate the association between stay-at-home orders and alterations in COVID-19 cases and fatalities using a difference-in-differences design that accounts for unmeasured local variation in factors like health systems and demographics and for unmeasured temporal variation in factors like national mitigation actions and access to tests. Compared to counties that did not implement stay-at-home orders, the results show that the orders are associated with a 30.2 percent (11.0 to 45.2) average reduction in weekly incident cases after one week, a 40.0 percent (23.4 to 53.0) reduction after two weeks, and a 48.6 percent (31.1 to 61.7) reduction after three weeks. Stay-at-home orders are also associated with a 59.8 percent (18.3 to 80.2) average reduction in weekly fatalities after three weeks. These results suggest that stay-at-home orders might have reduced confirmed cases by 390,000 (170,000 to 680,000) and fatalities by 41,000 (27,000 to 59,000) within the first three weeks in localities that implemented stay-at-home orders.

Characterizing Self-Reported Tobacco, Vaping, and Marijuana-Related Tweets Geolocated for California College Campuses.

Introduction: College-aged youth are active on social media yet smoking-related social media engagement in these populations has not been thoroughly investigated. We sought to conduct an exploratory infoveillance study focused on geolocated data to characterize smoking-related tweets originating from California 4-year colleges on Twitter. Methods: Tweets from 2015 to 2019 with geospatial coordinates in CA college campuses containing smoking-related keywords were collected from the Twitter API stream and manually annotated for discussions about smoking product type, sentiment, and behavior. Results: Out of all tweets detected with smoking-related behavior, 46.7% related to tobacco use, 50.0% to marijuana, and 7.3% to vaping. Of these tweets, 46.1% reported first-person use or second-hand observation of smoking behavior. Out of 962 tweets with user sentiment, the majority (67.6%) were positive, ranging from 55.0% for California State University, Long Beach to 95.8% for California State University, Los Angeles. Discussion: We detected reporting of first- and second-hand smoking behavior on CA college campuses representing possible violation of campus smoking bans. The majority of tweets expressed positive sentiment about smoking behaviors, though there was appreciable variability between college campuses. This suggests that anti-smoking outreach should be tailored to the unique student populations of these college communities. Conclusion: Among tweets about smoking from California colleges, high levels of positive sentiment suggest that the campus climate may be less receptive to anti-smoking messages or adherence to campus smoking bans. Further research should investigate the degree to which this varies by campuses over time and following implementation of bans including validating using other sources of data.

Identification and characterization of tweets related to the 2015 Indiana HIV outbreak: A retrospective infoveillance study.

INTRODUCTION: From late 2014 through 2015, Scott County, Indiana faced an HIV outbreak triggered by opioid abuse and transition to injection drug use. Investigating the origins, risk factors, and responses related to this outbreak is critical to inform future surveillance, interventions, and policymaking. In response, this retrospective infoveillance study identifies and characterizes user-generated messages related to opioid abuse, heroin injection drug use, and HIV status using natural language processing (NLP) among Twitter users in Indiana during the period of this HIV outbreak. MATERIALS AND METHODS: Our study consisted of two phases: data collection and processing, and data analysis. We collected Indiana geolocated tweets from the public Twitter API using Amazon Web Services EC2 instances filtered for geocoded messages in the immediate pre and post period of the outbreak. In the data analysis phase we applied an unsupervised machine learning approach using NLP called the Biterm Topic Model (BTM) to identify tweets related to opioid, heroin/injection, and HIV behavior and then examined these messages for HIV risk-related topics that could be associated with the outbreak. RESULTS: More than 10 million geocoded tweets occurring in Indiana during the immediate pre and post period of the outbreak were collected for analysis. Using BTM, we identified 1350 tweets thought to be relevant to the outbreak and then confirmed 358 tweets using human annotation. The most prevalent themes identified were tweets related to self-reported abuse of illicit and prescription drugs, opioid use disorder, self-reported HIV status, and public sentiment regarding the outbreak. Geospatial analysis found that these messages clustered in population dense areas outside of the outbreak, including Indianapolis and neighboring Clark County. DISCUSSION: This infoveillance study characterized the social media conversations of communities in Indiana in the pre and post period of the 2015 HIV outbreak. Behavioral themes detected reflect discussion about risk factors related to HIV transmission stemming from opioid and heroin abuse for priority populations, and also help identify community attitudes that could have motivated or detracted the use of HIV prevention methods, along with helping identify factors that can impede access to prevention services. CONCLUSIONS: Infoveillance approaches, such as the analysis conducted in this study, represent a possibly strategy to detect "signal" of the emergence of risk factors associated with an outbreak though may be limited in their scope and generalizability. Our results, in conjunction with other forms of public health surveillance, can leverage the growing ubiquity of social media platforms to better detect opioid-related HIV risk knowledge, attitudes and behavior, as well as inform future prevention efforts.

Characterising communities impacted by the 2015 Indiana HIV outbreak: A big data analysis of social media messages associated with HIV and substance abuse.

INTRODUCTION AND AIMS: Infoveillance approaches (i.e. surveillance methods using online content) that leverage big data can provide new insights about infectious disease outbreaks and substance use disorder topics. We assessed social media messages about HIV, opioid use and injection drug use in order to understand how unstructured data can prepare public health practitioners for response to future outbreaks. DESIGN AND METHODS: We conducted an retrospective analysis of Twitter messages during the 2015 HIV Indiana outbreak using machine learning, statistical and geospatial analysis to examine the transition between opioid prescription drug abuse to heroin injection use and finally HIV transmission risk, and to test possible associations with disease burden and demographic variables in Indiana and Marion County. Tweets from October 2014 to June 2015 were compared to disease burden at the county level for Indiana, and classification of census blocks by presence of relevant messages was done at the census block level for Marion County. Marion County was used as it exhibited the highest total count of Tweets. RESULTS: 257 messages about substance abuse and HIV were significantly related to HIV rates (P < 0.001) and opioid-related hospitalisations (P = 0.037). Using 157 characteristics from the American Community Survey, a linear classifier was computed with an appreciable correlation (r = 0.49) to risk-related social media messages from Marion County. DISCUSSION AND CONCLUSIONS: Communities appear to communicate online in response to disease burden. Classification produced an accurate equation to model census block risk based on census data, allowing for high-dimensional estimation of risk for blocks with sparse populations.

Using remarkability to define coastal flooding thresholds.

Coastal flooding is increasingly common in many areas. However, the degree of inundation and associated disruption depend on local topography as well as the distribution of people, infrastructure and economic activity along the coast. Local measures of flooding that are comparable over large areas are difficult to obtain. Here we use the remarkability of flood events, measured by flood-related posts on social media, to estimate county-specific flood thresholds for shoreline counties along the east coast of the United States. While thresholds in most counties are statistically-indistinguishable from minor flood thresholds of nearby tide gauges, we find evidence that several areas experience noticeable flooding at tide heights lower than existing flood thresholds. These 22 counties include several major cities such as Miami, New York, and Boston, with a total population over 13 million. Our analysis implies that large populations might currently be exposed to nuisance flooding not identified via standard measures.

Risk of a feedback loop between climatic warming and human mobility.

Human behaviours alter-and are altered by-climate. Might the impacts of warming on human behaviours amplify anthropogenic contributions to climate change? Here, we show that warmer temperatures substantially increase transportation use in the USA. To do so, we combine meteorological data with data on vehicle miles travelled (VMT) and public transit trips (PTT) between 2002 and 2018. Moving from freezing temperatures up to 30°C increases VMT by over 10% and amplifies use of public transit by nearly 15%. Temperatures beyond 30°C exert little influence on either outcome. We then examine climate model projections to highlight the possible transportation impacts of future climatic changes. We project that warming over the coming century may add over one trillion cumulative VMT and six billion PTT in the USA alone, presenting the risk of a novel feedback loop in the human-environmental system.

Machine behaviour.

Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.

Rapidly declining remarkability of temperature anomalies may obscure public perception of climate change.

The changing global climate is producing increasingly unusual weather relative to preindustrial conditions. In an absolute sense, these changing conditions constitute direct evidence of anthropogenic climate change. However, human evaluation of weather as either normal or abnormal will also be influenced by a range of factors including expectations, memory limitations, and cognitive biases. Here we show that experience of weather in recent years-rather than longer historical periods-determines the climatic baseline against which current weather is evaluated, potentially obscuring public recognition of anthropogenic climate change. We employ variation in decadal trends in temperature at weekly and county resolution over the continental United States, combined with discussion of the weather drawn from over 2 billion social media posts. These data indicate that the remarkability of particular temperatures changes rapidly with repeated exposure. Using sentiment analysis tools, we provide evidence for a "boiling frog" effect: The declining noteworthiness of historically extreme temperatures is not accompanied by a decline in the negative sentiment that they induce, indicating that social normalization of extreme conditions rather than adaptation is driving these results. Using climate model projections we show that, despite large increases in absolute temperature, anomalies relative to our empirically estimated shifting baseline are small and not clearly distinguishable from zero throughout the 21st century.

Empirical evidence of mental health risks posed by climate change.

Sound mental health-a critical facet of human wellbeing-has the potential to be undermined by climate change. Few large-scale studies have empirically examined this hypothesis. Here, we show that short-term exposure to more extreme weather, multiyear warming, and tropical cyclone exposure each associate with worsened mental health. To do so, we couple meteorological and climatic data with reported mental health difficulties drawn from nearly 2 million randomly sampled US residents between 2002 and 2012. We find that shifting from monthly temperatures between 25 °C and 30 °C to >30 °C increases the probability of mental health difficulties by 0.5% points, that 1°C of 5-year warming associates with a 2% point increase in the prevalence of mental health issues, and that exposure to Hurricane Katrina associates with a 4% point increase in this metric. Our analyses provide added quantitative support for the conclusion that environmental stressors produced by climate change pose threats to human mental health.

Effects of environmental stressors on daily governance.

Human workers ensure the functioning of governments around the world. The efficacy of human workers, in turn, is linked to the climatic conditions they face. Here we show that the same weather that amplifies human health hazards also reduces street-level government workers' oversight of these hazards. To do so, we employ US data from over 70 million regulatory police stops between 2000 and 2017, from over 500,000 fatal vehicular crashes between 2001 and 2015, and from nearly 13 million food safety violations across over 4 million inspections between 2012 and 2016. We find that cold and hot temperatures increase fatal crash risk and incidence of food safety violations while also decreasing police stops and food safety inspections. Added precipitation increases fatal crash risk while also decreasing police stops. We examine downscaled general circulation model output to highlight the possible day-to-day governance impacts of climate change by 2050 and 2099. Future warming may augment regulatory oversight during cooler seasons. During hotter seasons, however, warming may diminish regulatory oversight while simultaneously amplifying the hazards government workers are tasked with overseeing.

Weather impacts expressed sentiment.

We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. We find that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment, even when excluding weather-related posts. We compare the magnitude of our estimates with the effect sizes associated with notable historical events occurring within our data.