Mining and Validating Social Media Data for COVID-19-Related Human Behaviors Between January and July 2020: Infodemiology Study.

BACKGROUND: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. OBJECTIVE: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. METHODS: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. RESULTS: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to -0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. CONCLUSIONS: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

Estimating influenza incidence using search query deceptiveness and generalized ridge regression.

Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity. Timely knowledge of the outbreak state is valuable for managing an effective response. The current state of the art is to gather this knowledge using in-person patient contact. While accurate, this is time-consuming and expensive. This has motivated inquiry into new approaches using internet activity traces, based on the theory that lay observations of health status lead to informative features in internet data. These approaches risk being deceived by activity traces having a coincidental, rather than informative, relationship to disease incidence; to our knowledge, this risk has not yet been quantitatively explored. We evaluated both simulated and real activity traces of varying deceptiveness for influenza incidence estimation using linear regression. We found that deceptiveness knowledge does reduce error in such estimates, that it may help automatically-selected features perform as well or better than features that require human curation, and that a semantic distance measure derived from the Wikipedia article category tree serves as a useful proxy for deceptiveness. This suggests that disease incidence estimation models should incorporate not only data about how internet features map to incidence but also additional data to estimate feature deceptiveness. By doing so, we may gain one more step along the path to accurate, reliable disease incidence estimation using internet data. This capability would improve public health by decreasing the cost and increasing the timeliness of such estimates.

Exploring Climate-Disease Connections in Geopolitical Versus Ecological Regions: The Case of West Nile Virus in the United States.

Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector-borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large-scale analyses from geopolitical frameworks to more ecologically meaningful regions.

Multi-dimensional resilience: A quantitative exploration of disease outcomes and economic, political, and social resilience to the COVID-19 pandemic in six countries.

The COVID-19 pandemic has highlighted a need for better understanding of countries' vulnerability and resilience to not only pandemics but also disasters, climate change, and other systemic shocks. A comprehensive characterization of vulnerability can inform efforts to improve infrastructure and guide disaster response in the future. In this paper, we propose a data-driven framework for studying countries' vulnerability and resilience to incident disasters across multiple dimensions of society. To illustrate this methodology, we leverage the rich data landscape surrounding the COVID-19 pandemic to characterize observed resilience for several countries (USA, Brazil, India, Sweden, New Zealand, and Israel) as measured by pandemic impacts across a variety of social, economic, and political domains. We also assess how observed responses and outcomes (i.e., resilience) of the COVID-19 pandemic are associated with pre-pandemic characteristics or vulnerabilities, including (1) prior risk for adverse pandemic outcomes due to population density and age and (2) the systems in place prior to the pandemic that may impact the ability to respond to the crisis, including health infrastructure and economic capacity. Our work demonstrates the importance of viewing vulnerability and resilience in a multi-dimensional way, where a country's resources and outcomes related to vulnerability and resilience can differ dramatically across economic, political, and social domains. This work also highlights key gaps in our current understanding about vulnerability and resilience and a need for data-driven, context-specific assessments of disaster vulnerability in the future.

Scoping review of Culex mosquito life history trait heterogeneity in response to temperature.

BACKGROUND: Mosquitoes in the genus Culex are primary vectors in the US for West Nile virus (WNV) and other arboviruses. Climatic drivers such as temperature have differential effects on species-specific changes in mosquito range, distribution, and abundance, posing challenges for population modeling, disease forecasting, and subsequent public health decisions. Understanding these differences in underlying biological dynamics is crucial in the face of climate change. METHODS: We collected empirical data on thermal response for immature development rate, egg viability, oviposition, survival to adulthood, and adult lifespan for Culex pipiens, Cx. quinquefasciatus, Cx. tarsalis, and Cx. restuans from existing literature according to the PRISMA scoping review guidelines. RESULTS: We observed linear relationships with temperature for development rate and lifespan, and nonlinear relationships for survival and egg viability, with underlying variation between species. Optimal ranges and critical minima and maxima also appeared varied. To illustrate how model output can change with experimental input data from individual Culex species, we applied a modified equation for temperature-dependent mosquito type reproduction number for endemic spread of WNV among mosquitoes and observed different effects. CONCLUSIONS: Current models often input theoretical parameters estimated from a single vector species; we show the need to implement the real-world heterogeneity in thermal response between species and present a useful data resource for researchers working toward that goal.

Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks.

The popularization of biobanks provides an unprecedented amount of genetic and phenotypic information that can be used to research the relationship between genetics and human health. Despite the opportunities these datasets provide, they also pose many problems associated with computational time and costs, data size and transfer, and privacy and security. The publishing of summary statistics from these biobanks, and the use of them in a variety of downstream statistical analyses, alleviates many of these logistical problems. However, major questions remain about how to use summary statistics in all but the simplest downstream applications. Here, we present a novel approach to utilize basic summary statistics (estimates from single marker regressions on single phenotypes) to evaluate more complex phenotypes using multivariate methods. In particular, we present a covariate-adjusted method for conducting principal component analysis (PCA) utilizing only biobank summary statistics. We validate exact formulas for this method, as well as provide a framework of estimation when specific summary statistics are not available, through simulation. We apply our method to a real data set of fatty acid and genomic data.

Treating childhood depression over videoconferencing.

Effective cognitive-behavioral treatments for childhood depression have developed over the last decade, but many families face barriers to such care. Telemedicine increases access to psychological interventions by linking the child and the clinician using videoconferencing (VC). The current study evaluated an 8-week, cognitive-behavioral therapy (CBT) intervention for childhood depression either face-to-face (F2F) or over VC. The telemedicine setup included two PC-based PictureTel systems at 128 kilibits per second (kbps). Success was defined by (1) decreasing depressive symptoms at similar rates in both the VC group and the F2F group and (2) demonstrating the feasibility of a randomized controlled trial in telemental health. Children were assessed for childhood depression using the mood section of the Schedule for Affective Disorders and Schizophrenia for School Age Children-Present Episode (K-SADS-P). Twenty-eight children were randomized to either F2F or VC treatment. The participants completed the K-SADS-P and the Children's Depression Inventory (CDI) at pre- and post-treatment. The CBT treatment across the two conditions was effective. The overall response rate based on post-evaluation with the K-SADS-P was 82%. For the CDI total score, both the Time and the Group by Time effects were significant (p < 0.05). The interaction effect reflected a faster rate of decline in the CDI total score for the VC group. The study serves as a model for building on past research to implement a randomized controlled trial. This information provides persuasive research data concerning treatment effectiveness for clinicians, families, and funders.

A community-based intervention for siblings and parents of children with chronic illness or disability: the ISEE study.

OBJECTIVE: Siblings of children with chronic illness or disability have been reported to have a 1.6 to 2.0 risk for behavioral and mental health problems. Our objective was to examine the effects of an intervention for siblings (age 7-15 years) of children with chronic illness or disability. METHOD: A randomized, three-group repeated-measures design was used: full intervention (n=79), partial intervention (n=71), and a waiting list control group (n=102). Outcomes were sibling knowledge about illness, behavior problems, social support, self-esteem, attitude, and mood measured over four postintervention periods. Covariates were family cohesion, maternal mood, socioeconomic status, and well sibling age. The full intervention included structured teaching and psychosocial sessions at a 5-day residential summer camp. The partial intervention included camp only. Treatment effects were estimated by using generalized estimating equation panel analyses. RESULTS: The full treatment group showed significant improvements on all six outcomes over most periods, the partial treatment group on three outcomes, and the control group on two outcomes. Improvements in outcomes ranged from 5% to 25% increases over baseline measures. CONCLUSIONS: A dose-response relationship to intervention was found. Treatment gains were sustained over a period of 12 months.

Interrelationships among variables affecting well siblings and mothers in families of children with a chronic illness or disability.

A structural equation model (SEM) examined interrelationships among psychosocial variables known to affect the health and development of well siblings and parents when a child with a chronic illness or disability is a member of the family. Using dyads of 252 well children and parents, socioeconomic status (SES) and family cohesion were associated with the parent-reported behavior of the well sibling. SES also influenced the mood of the mother that in turn influenced family cohesion. The well sibling's knowledge about the illness of the brother or sister, attitude toward the illness, mood, self-esteem, and feelings of social support were interrelated and related to the behavior of the well sibling. The SEM suggests that interventions may be directed at several points in these interactions including boosting knowledge levels of the well sibling, improving family cohesion, and assuring adequate "income" support to the family through income transfers or in-kind services.