Social stress drives the multi-wave dynamics of COVID-19 outbreaks

Substance use trajectories among urban college students: associations with symptoms of stress, anxiety, and depression before and during COVID-19

This study explored substance use trajectories and associations with mental health among an ethnically/racially diverse college student sample before and during the COVID-19 pandemic. We combined repeated cross-sections and panel data from a total of 3,247 college students assessed with an online survey in 2018, 2019, and in three waves in 2020. We estimated trends in substance use and their relation to mental health over the survey waves using generalized estimating equations (GEE). Our results revealed notable associations between 30-day substance use patterns, time of assessment, and psychological well-being. These findings suggest a complex interplay between access to substances, context of substance use, and mental health. We discuss several initiatives that have been launched in response to the results of this ongoing study. Such initiatives may serve as examples for expanding similar efforts to other commuter colleges to prevent further increases in mental health problems and risky substance use.

Chaos in Opinion-Driven Disease Dynamics

During the COVID-19 pandemic, it became evident that the effectiveness of applying intervention measures is significantly influenced by societal acceptance, which, in turn, is affected by the processes of opinion formation. This article explores one among the many possibilities of coupled opinion-epidemic systems. The findings reveal either intricate periodic patterns or chaotic dynamics, leading to substantial fluctuations in opinion distribution and, consequently, significant variations in the total number of infections over time. Interestingly, the model exhibits a protective pattern.

Exploring the Impact and Prevention of Epidemics from a New Perspective: COVID-19 Transmission through Express Boxes

The number of express boxes worldwide exceeded 170 billion in 2021, and, from several regions in China, tested positive. Therefore, it is important to study the transmission of viruses through express boxes. In this paper, we establish a model of express box virus transmission based on comprehensive consideration of environmental factors, such as temperature, disinfection, humidity, virus release intensity, and volume of vehicle, to study the transmission of express box virus, and explore the spatial and geographic spread variation of express box viruses in China. Several important findings emerged from the study, including: (1) Disinfection can prolong the spread of viruses in the express box for ≥21 h; (2) For every 1 °C rise in temperature, the infected time can be prolonged by ≥1.2 h, and for every 10% rise in relative humidity, the virus transmission time can be prolonged by ≥1.32 h; (3) In an environment suitable for virus transmission, when loaded with 1000, 2000, 4000 express boxes, areas where the express delivery time exceeds 22.56, 18, 14.64 h will face the risk of all the boxes in the carriage being infected. These findings could help public health departments prevent the risk of virus transmission from express boxes.

A Fast kNN Algorithm Using Multiple Space-Filling Curves

The paper considers a time-efficient implementation of the k nearest neighbours (kNN) algorithm. A well-known approach for accelerating the kNN algorithm is to utilise dimensionality reduction methods based on the use of space-filling curves. In this paper, we take this approach further and propose an algorithm that employs multiple space-filling curves and is faster (with comparable quality) compared with the kNN algorithm, which uses kd-trees to determine the nearest neighbours. A specific method for constructing multiple Peano curves is outlined, and statements are given about the preservation of object proximity information in the course of dimensionality reduction. An experimental comparison with known kNN implementations using kd-trees was performed using test and real-life data.

In.To. COVID-19 socio-epidemiological co-causality

Social media can forecast disease dynamics, but infoveillance remains focused on infection spread, with little consideration of media content reliability and its relationship to behavior-driven epidemiological outcomes. Sentiment-encoded social media indicators have been poorly developed for expressed text to forecast healthcare pressure and infer population risk-perception patterns. Here we introduce Infodemic Tomography (InTo) as the first web-based interactive infoveillance cybertechnology that forecasts and visualizes spatio-temporal sentiments and healthcare pressure as a function of social media positivity (i.e., Twitter here), considering both epidemic information and potential misinformation. Information spread is measured on volume and retweets, and the Value of Misinformation (VoMi) is introduced as the impact on forecast accuracy where misinformation has the highest dissimilarity in information dynamics. We validated InTo for COVID-19 in New Delhi and Mumbai by inferring distinct socio-epidemiological risk-perception patterns. We forecast weekly hospitalization and cases using ARIMA models and interpolate spatial hospitalization using geostatistical kriging on inferred risk perception curves between tweet positivity and epidemiological outcomes. Geospatial tweet positivity tracks accurately [Formula: see text]60[Formula: see text] of hospitalizations and forecasts hospitalization risk hotspots along risk aversion gradients. VoMi is higher for risk-prone areas and time periods, where misinformation has the highest non-linear predictability, with high incidence and positivity manifesting popularity-seeking social dynamics. Hospitalization gradients, VoMi, effective healthcare pressure and spatial model-data gaps can be used to predict hospitalization fluxes, misinformation, healthcare capacity gaps and surveillance uncertainty. Thus, InTo is a participatory instrument to better prepare and respond to public health crises by extracting and combining salient epidemiological and social surveillance at any desired space-time scale.