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.

Experiences of the physicians in the largest COVID-19 dedicated hospital of Bangladesh about COVID-19 and its aftermath (preprint)

Background: The doctors and the other health care workers are the first-line fighters against COVID-19. This study aims to identify the prevalence, risk factors, clinical severity of COVID-19 infection among the doctors working in the COVID unit. We also analyzed the hospital data for admission and RT-PCR positivity among the physicians. Methods: It was a cross-sectional survey and review of the hospital database. We surveyed from September 2021 to October 2021 and explored the hospital data from march 2020 to September 2021.We included 342 physicians for analysis in the survey. We reviewed hospital data of 1578 total admitted patients and 336 RT-PCR test positive physicians for analyzing the hospital admission rate, the positivity rate for COVID-19 among the physicians and the other patients in the different COVID- 19 surges. Findings: In this study, we demonstrated the physicians sufferings during the pandemic era. We have observed four surges in the hospital admission and RT-PCR for COVID-19 positivity rate among the physicians and the general population. The physicians experienced a similar surge in the hospital admission and positivity rate to the general population. The hospital admission was lower in the fourth surge among the physicians than the general population. The positivity rate was higher in the first, second and third surge among the physicians. In the survey, a total of 146(42%) respondents had COVID-19 infection, and among them, 50(34.2%) had re-detectable positive SARS-CoV-2 infection. Most of them experienced mild (77[52.7%]) to moderate (41[28.1%]) symptoms. Increasing age (OR, 95%CI, p-value; 1.15, 1.05-1.25, 0.002), male sex (OR, 95%CI, p-value; 5.8, 3.2-9.8, <0.001), and diabetes (OR, 95%CI, p-value; 25.6, 2-327.2, 0.01) were the risk factor of having COVID-19. Female sex and diabetes were the risk factors for re-detectable positive SARS-CoV-2 infection. (OR, 95%CI, p-value; 0.24, 0.09-0.67, 0.006; 44, 8.9-218.7, <0.001 respectively). Most respondents suffered for 7-14 days. Total 98(67%) suffered from post-COVID fatigue. Conclusions: The physicians observed four surges in hospital admission and COVID-19 positivity rate. A significant number of the COVID-warrior became positive for SARS-CoV-2, had r e-detectable positive SARS-CoV-2 infection, and suffered in the post-COVID-19 state.

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.