Mengxi Zhang

Bio: Mengxi Zhang is an academic researcher from Ball State University. The author has contributed to research in topics: Medicine & Public health. The author has an hindex of 6, co-authored 22 publications receiving 106 citations. Previous affiliations of Mengxi Zhang include Tulane University.

Human Mobility Data in the COVID-19 Pandemic: Characteristics, Applications, and Challenges

Abstract: The COVID-19 pandemic poses unprecedented challenges around the world. Many studies have applied mobility data to explore spatiotemporal trends over time, investigate associations with other variables, and predict or simulate the spread of COVID-19. Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks. We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective. We identified three major sources of mobility data: public transit systems, mobile operators, and mobile phone applications. Four approaches have been commonly used to estimate human mobility: public transit-based flow, social activity patterns, index-based mobility data, and social media-derived mobility data. We compared mobility datasets' characteristics by assessing data privacy, quality, space-time coverage, high-performance data storage and processing, and accessibility. We also present challenges and future directions of using mobility data. This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective.

Abstract: Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective: The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. Methods: We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. Results: An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

Early-life exposure to severe famine and subsequent risk of depressive symptoms in late adulthood: the China Health and Retirement Longitudinal Study.

Abstract: The Chinese Great Famine caused widespread starvation in 1959–1961. Its long-term association with depressive symptoms has not been studied. To estimate the burden of depressive symptoms and the association of famine exposure with depressive symptoms. The China Health and Retirement Longitudinal Study is a nationwide representative survey of 17 708 Chinese adults aged ≥45. Propensity score matching and modified Poisson regression were used to evaluate the association between self-reported famine exposure in early life and depressive symptoms among the overall participants. Such associations were also assessed by developmental stage using modified Poisson regression and logistic regression. The prevalence of depressive symptoms was 26.2% (95% CI 25.1–27.3%) in 2011. As defined by loss of family members because of starvation, 11.6% (95% CI 10.1–13.1%) of this population experienced severe famine. When compared with participants who did not experience starvation, those who had experienced severe famine during fetal, mid-childhood, young-teenage and early-adulthood stages had 1.87 (95% CI 1.36–2.55), 1.54 (95% CI 1.23–1.94), 1.47 (95% CI 1.09–2.00) and 1.77 (95% CI 1.42–2.21) times higher odds of having depressive symptoms in late adulthood, respectively. The first two trimesters of pregnancy were a critical time window during the fetal stage when severe famine had a stronger association with depressive symptoms. Famine during infant, toddler, preschool or teenage stages was not associated with depressive symptoms. Overall, famine contributed to 13.6% of the depressive symptom burden in this population. The Chinese Great Famine contributed substantially to the burden of depressive symptoms in China. None.

COVID-19 and Immigrant Essential Workers: Bhutanese and Burmese Refugees in the United States.

Abstract: ObjectivesImmigrants are believed to be at high risk of infection with severe acute respiratory syndrome coronavirus 2, the virus that causes coronavirus disease 2019 (COVID-19). A leading suspecte...

Obesity: Epidemiology, Pathophysiology, and Therapeutics.

Abstract: Obesity is a complex multifactorial disease that accumulated excess body fat leads to negative effects on health. Obesity continues to accelerate resulting in an unprecedented epidemic that shows no significant signs of slowing down any time soon. Raised body mass index (BMI) is a risk factor for noncommunicable diseases such as diabetes, cardiovascular diseases, and musculoskeletal disorders, resulting in dramatic decrease of life quality and expectancy. The main cause of obesity is long-term energy imbalance between consumed calories and expended calories. Here, we explore the biological mechanisms of obesity with the aim of providing actionable treatment strategies to achieve a healthy body weight from nature to nurture. This review summarizes the global trends in obesity with a special focus on the pathogenesis of obesity from genetic factors to epigenetic factors, from social environmental factors to microenvironment factors. Against this background, we discuss several possible intervention strategies to minimize BMI.

Association Between Sleep Duration and Cognitive Decline.

Abstract: Importance An association between sleep duration and the trajectory of cognitive decline has not been conclusively demonstrated. Objective To investigate the association between sleep duration and cognitive decline by a pooled analysis of 2 nationally representative aging cohorts. Design, Setting, and Participants A pooled cohort study using data from waves 4 to 8 (2008-2009 to 2016-2017) in the English Longitudinal Study of Ageing and waves 1 to 3 (2011 to 2015) in the China Health and Retirement Longitudinal Study in a population-based setting. Participants were 2 randomly enrolled cohorts comprising 28 756 individuals living in England who were 50 years or older and those living in China who were 45 years or older. Exposure Self-reported sleep duration per night according to face-to-face interviews. Main Outcomes and Measures Global cognitivezscores were calculated according to immediate and delayed recall test, an animal fluency test, the serial sevens test, an intersecting pentagon copying test, and a date orientation test. Results Data were analyzed from 20 065 participants, including 9254 from the English Longitudinal Study of Ageing (mean [SD] age, 64.6 [9.8] years; 55.9% [5174 of 9254] women; median follow-up duration, 8 [interquartile range, 6-8] years) and 10 811 from the China Health and Retirement Longitudinal Study (mean [SD] age, 57.8 [9.0] years; 50.2% [5425 of 10 811] men; median follow-up duration, 4 [interquartile range, 4-4] years). During 100 000 person-years of follow-up, global cognitivezscores in individuals with 4 hours or less (pooled β = −0.022; 95% CI, −0.035 to −0.009 SD per year;P = .001) and 10 hours or more (pooled β = −0.033; 95% CI, −0.054 to −0.011 SD per year;P = .003) of sleep per night declined faster than in the reference group (7 hours per night) after adjusting for a number of covariates. An inverted U-shaped association between sleep duration and global cognitive decline was also observed. Conclusions and Relevance In this pooled cohort study, an inverted U-shaped association between sleep duration and global cognitive decline was found, indicating that cognitive function should be monitored in individuals with insufficient (≤4 hours per night) or excessive (≥10 hours per night) sleep duration. Future studies are needed to examine the mechanisms of the association between sleep duration and cognitive decline.

Limits of predictability in human mobility

Abstract: A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.