Understanding Human-COVID-19 Dynamics using Geospatial Big Data: A Systematic Literature Review (preprint)

The COVID-19 pandemic has changed human life. To mitigate the pandemic's impacts, different regions implemented various policies to contain COVID-19 and residents showed diverse responses. These human responses in turn shaped the uneven spatial-temporal spread of COVID-19. Consequently, the human-pandemic interaction is complex, dynamic, and interconnected. Delineating the reciprocal effects between human society and the pandemic is imperative for mitigating risks from future epidemics. Geospatial big data acquired through mobile applications and sensor networks have facilitated near-real-time tracking and assessment of human responses to the pandemic, enabling a surge in researching human-pandemic interactions. However, these investigations involve inconsistent data sources, human activity indicators, relationship detection models, and analysis methods, leading to a fragmented understanding of human-pandemic dynamics. To assess the current state of human-pandemic interactions research, we conducted a synthesis study based on 67 selected publications between March 2020 and January 2023. We extracted key information from each article across six categories, e.g., research area and time, data, methodological framework, and results and conclusions. Results reveal that regression models were predominant in relationship detection, featured in 67.16% of papers. Only two papers employed spatial-temporal models, notably underrepresented in the existing literature. Studies examining the effects of policies and human mobility on the pandemic's health impacts were the most prevalent, each comprising 12 articles (17.91%). Only 3 papers (4.48%) delved into bidirectional interactions between human responses and the COVID-19 spread. These findings shed light on the need for future research to spatially and temporally model the long-term, bidirectional causal relationships within human-pandemic systems.

Statistical Machine Learning Meets High-Dimensional Spatiotemporal Challenges -- A Case Study of COVID-19 Modeling (preprint)

Diverse non-pharmacological interventions (NPIs), serving as the primary approach for COVID-19 control prior to pharmaceutical interventions, showed heterogeneous spatiotemporal effects on pandemic management. Investigating the dynamic compounding impacts of NPIs on pandemic spread is imperative. However, the challenges posed by data availability of high-dimensional human behaviors and the complexity of modeling changing and interrelated factors are substantial. To address these challenges, this study analyzed social media data, COVID-19 case rates, Apple mobility data, and the stringency of stay-at-home policies in the United States throughout the year 2020, aiming to (1) uncover the spatiotemporal variations in NPIs during the COVID-19 pandemic utilizing geospatial big data; (2) develop a statistical machine learning model that incorporates spatiotemporal dependencies and temporal lag effects for the detection of relationships; (3) dissect the impacts of NPIs on the pandemic across space and time. Three indices were computed based on Twitter (currently known as X) data: the Negative and Positive Sentiments Adjusted by Demographics (N-SAD and P-SAD) and the Ratio Adjusted by Demographics (RAD), representing negative sentiment, positive sentiment, and public awareness of COVID-19, respectively. The Multivariate Bayesian Structural Time Series Time Lagged model (MBSTS-TL) was proposed to investigate the effects of NPIs, accounting for spatial dependencies and temporal lag effects. The developed MBSTS-TL model exhibited a high degree of accuracy. Determinants of COVID-19 health impacts transitioned from an emphasis on human mobility during the initial outbreak period to a combination of human mobility and stay-at-home policies during the rapid spread phase, and ultimately to the compound of human mobility, stay-at-home policies, and public awareness of COVID-19.

Revealing the Global Linguistic and Geographical Disparities of Public Awareness to Covid-19 Outbreak through Social Media (preprint)

The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts. This study analyzed the global Twitter data from January 1st to June 30th, 2020, seeking to answer two research questions. What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media? Can the changing pandemic awareness predict the Covid-19 outbreak? We established a Twitter data mining framework calculating the Ratio index to quantify and track the awareness. The lag correlations between awareness and health impacts were examined at global and country levels. Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh. Asian countries showed more significant disparities in awareness than European countries, and awareness in the eastern part of Europe was higher than in central Europe. Finally, the Ratio index could accurately predict global mortality rate, global case fatality ratio, and country-level mortality rate, with 21-30, 35-42, and 17 leading days, respectively. This study yields timely insights into social media use in understanding human behaviors for public health research.

Serum calcium as a biomarker of clinical severity and prognosis in patients with coronavirus disease 2019: a retrospective cross-sectional study (preprint)

Background: To investigate the correlations between serum calcium and clinical severity and outcomes in patients with coronavirus disease 2019 (COVID-19).Methods: In this clinical retrospective study, the levels of serum calcium, hormone levels and clinical laboratory parameters of admission were recorded. The clinical severity and outcome variables were also recorded.Results: From February 10 to February 28 2020, 241 patients were enrolled in this study. Of these patients, 180 (74.7%) had hypocalcemia on admission. The median serum calcium levels were 2.12 (IQR, 2.04-2.20) mmol/L, median parathyroid hormone (PTH) levels were 55.27 (IQR, 42.73-73.15) pg/mL, median 25-hydroxy-vitamin D (VD) levels were 10.20 (IQR, 8.20-12.65) ng/mL. The serum calcium levels were significantly positive correlated with VD levels (P =0.004), whereas negative correlated with PTH levels (P = 0.048). Patients with lower serum calcium levels (especially ≤2.0 mmol/L) had worse clinical parameters, higher incidence of organ injury septic shock and higher 28-day mortality. The areas under the receiver operating characteristic curves of multiple organ dysfunction syndrome, septic shock, and 28-day mortality were 0.923 (P <0.001), 0.905 (P =0.001), and 0.929 (P <0.001), respectively. The overall mortality of COVID-19 was 4.1% (10/241), whereas the mortality of critical patients was up to 40.0% (10/25). Conclusions: Serum calcium was associated with clinical severity and prognosis of patients with COVID-19. Hypocalcemia may be associated with imbalanced VD and PTH.