Using GPS-enabled mobile phones to evaluate the associations between human mobility changes and the onset of influenza illness

The effect of particulate matter 2.5 on seasonal influenza transmission in 1,330 counties, China: A Bayesian spatial analysis based on Köppen Geiger climate zones classifications

Previous research has linked seasonal influenza transmission with particulate matters (PM2.5). However, the effect of PM2.5 on seasonal influenza transmission varied by region. This study aims explore how PM2.5 influenced seasonal influenza transmission in the elderly across 1330 counties in two Köppen Geiger climate zones in China, incorporating the socio-economic factors to enhance climate-driven early warning systems (EWS) for influenza. Data included weekly 2015-2019 influenza cases in those aged >65 from China's national influenza surveillance system for 1330 counties in two Köppen Geiger climate zones: Temperate, Hot Summer with Dry Winter (Cwa) and No Dry Season (Cfa). PM2.5 data from 2015 to 2019 were sourced from Copernicus Atmosphere Monitoring Services. Additional data on floating population, population density and Gross Domestic Product (GDP) per capita were collected from pertinent departments. A Bayesian spatial autoregressive model assessed the association of PM2.5 and influenza transmission after adjustment of socio-economic factors. Our research results showed PM2.5 (per 1 μg/m³ increase) was linked to increased influenza transmission in the Cwa zone during winter season (Relative Risk (RR) = 1.023, 95% Credible Interval (CI):1.008-1.040) but not in the Cfa winter (RR = 1.003, 95% CI: 0.992-1.015). Floating population significantly enhanced transmission in both zones (highest RR = 1.362, 95% CI:1.181-1.583), while GDP per capita growth was associated with reduced transmission risk (highest RR = 0.619, 95% CI: 0.445-0.861). The study identifies PM2.5 as a significant factor influencing influenza transmission in the elderly, with effects varying by climate zone, suggesting the need to incorporate PM2.5 and socio-economic factors into seasonal influenza EWS.

A brief review of methods for determining time-activity patterns in California

Air pollution exposure has been found to be linked with numerous adverse human health effects. Because both air pollution concentrations and the location of human individuals change spatiotemporally, understanding the time-activity patterns (TAPs) is of utmost importance for the mitigation of adverse exposures and to improve the accuracy of air pollution and health analyses. "Time-activity patterns" outlined here broadly refer to the spatiotemporal positions of individuals. In this review paper, we briefly review past efforts on collecting individual TAP information for air pollution and health studies, with a specific focus on California efforts. We also critically summarize emerging technologies and approaches for collecting TAP data. Specifically, we critically reviewed five types of emerging TAP data sources, including call detail record, social media location data, Google Location History, iPhone Significant Location, and crowd-sourced location data. This review provides a comprehensive summary and critique of different methods to collect TAP information and offers recommendations for use in retrospective air pollution and health studies.Implications: In this review paper, we provide a comprehensive overview of approaches for collecting time-activity pattern (TAP) data from individuals, a crucial component in understanding human behavior and its implications across various fields such as urban planning, environmental science, and, particularly, public health in relation to air pollution exposures.Furthermore, our paper introduces and critically evaluates several emerging methods for TAP data collection. These novel approaches, including but not limited to Google Location History, iPhone Significant Locations, and crowdsourced smartphone location data, offer unprecedented granularity in tracking human activities. By showcasing these methodologies and their often not well-recognized weaknesses, we highlight both the potential and limitations of these tools to advance our understanding of human behavior patterns, especially in terms of how individuals interact with their environments. This discussion not only showcases the originality of our work but also sets the stage for future research directions that can benefit from these innovative data collection strategies.

Human mobility and the infectious disease transmission: A systematic review

Recent decades have witnessed several infectious disease outbreaks, including the coronavirus disease (COVID-19) pandemic, which had catastrophic impacts on societies around the globe. At the same time, the twenty-first century has experienced an unprecedented era of technological development and demographic changes: exploding population growth, increased airline flights, and increased rural-to-urban migration, with an estimated 281 million international migrants worldwide in 2020, despite COVID-19 movement restrictions. In this review, we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease outbreaks. This article covers eight infectious diseases, ranging from respiratory illnesses to sexually transmitted and vector-borne diseases. The review revealed a strong association between human mobility and infectious disease spread, particularly strong for respiratory illnesses like COVID-19 and Influenza. Despite significant research into the relationship between infectious diseases and human mobility, four knowledge gaps were identified based on reviewed literature in this study: 1) although some studies have used big data in investigating infectious diseases, the efforts are limited (with the exception of COVID-19 disease), 2) while some research has explored the use of multiple data sources, there has been limited focus on fully integrating these data into comprehensive analyses, 3) limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks, and 4) lack of standardization in the methodology for measuring the impacts of human mobility on infectious disease spread. By tackling the recognized knowledge gaps and adopting holistic, interdisciplinary methods, forthcoming research has the potential to substantially enhance our comprehension of the intricate interplay between human mobility and infectious diseases.

Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces

Urban dwellers are exposed to communicable diseases, such as influenza, in various urban spaces. Current disease models are able to predict health outcomes at the individual scale but are mostly validated at coarse scales due to the lack of fine-scaled ground truth data. Further, a large number of transmission-driving factors have been considered in these models. Because of the lack of individual-scaled validations, the effectiveness of factors at their intended scale is not substantiated. These gaps significantly undermine the efficacy of the models in assessing the vulnerability of individuals, communities, and urban society. The objectives of this study are twofold. First, we aim to model and, most importantly, validate influenza-like illness (ILI) symptoms at the individual scale based on four sets of transmission-driving factors pertinent to home-work space, service space, ambient environment, and demographics. The effort is supported by an ensemble approach. For the second objective, we investigate the effectiveness of the factor sets through an impact analysis. The validation accuracy reaches 73.2-95.1%. The validation substantiates the effectiveness of factors pertinent to urban spaces and unveils the underlying mechanism that connects urban spaces and population health. With more fine-scaled health data becoming available, the findings of this study may see increasing value in informing policies that improve population health and urban livability.