Spatio-temporal evolution and influencing mechanism of the COVID-19 epidemic in Shandong province, China

Spatial-temporal analysis and spatial drivers of hepatitis-related deaths in 183 countries, 2000-2019

Hepatitis is the seventh leading cause of mortality worldwide and is the only communicable disease where mortality is increasing, yet the long-term spatial-temporal variation at global scale and its possible causes, i.e., drivers, remain unknown. Firstly, this study employed the measure of spatial autocorrelation, Moran's I, and the measure of local spatial cluster, Getis-Ord Gi*, to characterize the spatial variation of mortality due to hepatitis in 183 countries globally for years 2000, 2010, 2015 and 2019. Then, a novel spatial statistical method, named the Geographical Detector, was utilized to investigate eight possible influencing factors, i.e., risk factors, of the spatial-temporal variation of mortality due to hepatitis. The results showed significant disparities of hepatitis-related mortality rates among countries. Hot spots, representing locations with higher mortality rates, were consistently observed in Africa, East Asia, and Southeast Asia, while the cold spots, representing locations with lower mortality rates, were predominantly found in Europe and the Americas. Potential spatial drivers of hepatitis mortality, identified by geographical detector, include "health expenditure", "universal health coverage", and "per capita income". However, "hepatitis B immunization" and "total population" were not identified as significant spatial drivers for hepatitis mortality The findings highlighted the critical role of socioeconomic factors in the variations in hepatitis mortality, and pointed out relative importance of increasing health expenditure, per capita income, and improve universal health coverage on alleviating global hepatitis-related mortality.

Application of neighborhood-scale wastewater-based epidemiology in low COVID-19 incidence situations

Wastewater-based epidemiology (WBE), an emerging approach for community-wide COVID-19 surveillance, was primarily characterized at large sewersheds such as wastewater treatment plants serving a large population. Although informed public health measures can be better implemented for a small population, WBE for neighborhood-scale sewersheds is less studied and not fully understood. This study applied WBE to seven neighborhood-scale sewersheds (average population of 1471) from January to November 2021. Community testing data showed an average of 0.004 % incidence rate in these sewersheds (97 % of monitoring periods reported two or fewer daily infections). In 92 % of sewage samples, SARS-CoV-2 N gene fragments were below the limit of quantification. We statistically determined 10^-2.6 as the threshold of the SARS-CoV-2 N gene concentration normalized to pepper mild mottle virus (N/PMMOV) to alert high COVID-19 incidence rate in the studied sewershed. This threshold of N/PMMOV identified neighborhood-scale outbreaks (COVID-19 incidence rate higher than 0.2 %) with 82 % sensitivity and 51 % specificity. Importantly, neighborhood-scale WBE can discern local outbreaks that would not otherwise be identified by city-scale WBE. Our findings suggest that neighborhood-scale WBE is an effective community-wide disease surveillance tool when COVID-19 incidence is maintained at a low level.

Spatial-Temporal Patterns of Population Aging in Rural China

(1) Background: Population aging has been accelerating in China since the 1990s, and the number of people over 65 reached 190 million in 2020. However, the spatial distribution of the aged is not homogeneous; in rural areas, the aged population accounted for 17.72% of the total population, whereas in urban areas, it accounted for 11.11%, which is 6.61 p.p. less. Therefore, this study aims to examine the spatial heterogeneity and influencing factors of population aging in rural China from 2000 to 2020. (2) Methods: First, Getis-Ord Gi* was used to analyze the spatial clustering of the aged population in rural China. Then, standard deviational ellipse was used to characterize the temporal trend of the spatial clustering of population aging in rural China. Finally, potential influencing factors that could have contributed to the spatial-temporal patterns were analyzed using a novel spatial statistical package "Geographical Detector". (3) Results: (a). Aging in rural populations increased and occurred throughout China from 2000 to 2020. (b). The spatial patterns of aging in China are roughly divided by the Hu Line, which is the population density boundary of China. (c). The mean center of the aged population tended to orient around a northeast-to-southwest major axis over the past 20 years, contrary to the internal migration pattern that flows from north to south. (d). The population age structure, longevity rate, and fertility rate were the predominant factors of aging in rural areas. (4) Conclusions: As the aged population is rapidly increasing in rural areas in China in a spatially heterogeneous fashion, governments and private sectors need to collaborate to alleviate the problem.

Prevalence and associated influential factors of mental health problems among Chinese college students during different stages of COVID-19 pandemic: A systematic review

This systematic review aims to take China as an example to determine the prevalence of mental health problems and associated influential factors of college students in different stages of the COVID-19 pandemic and provide a reference for effective intervention in the future. A systematic search was conducted on PubMed, Web of Science, Scopus, Science Direct, and Google scholar. A total of 30 articles were included. 1,477,923 Chinese college students were surveyed. In the early stage, the prevalence rates of depression, anxiety, stress, and PTSD ranged from 9.0% to 65.2%, 6.88%-41.1%, 8.53%-67.05%, and 2.7%-30.8%, respectively. Major risk factors were being a female, a medical student, isolation or quarantine, having family members or friends infected with COVID-19, and challenges of online learning. During the normalization stage, depression, anxiety, and insomnia prevalence rates ranged from 8.7% to 50.2%, 4.2%-34.6%, and 6.1%-35.0%, respectively. The main risk factors were self-quarantined after school reopening, regular taking temperature, and wearing face masks. The prevalence rates of mental health problems and associated influential factors unveiled in both stages showed that the students' mental health status was greatly affected. Therefore, a combination of efforts from the government, universities, and families or communities is highly needed to alleviate the mental health sufferings of students.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Background

Currently, the knowledge of associations among newly recovered cases (NR), newly healed cases (NH), newly confirmed cases (NC), and newly dead cases (ND) can help to monitor, evaluate, predict, control, and curb the spreading of coronavirus disease 2019 (COVID-19). This study aimed to explore the panel associations of ND, NH, and NR with NC.

Methods

Data from China Data Lab in Harvard Dataverse with China (January 15, 2020 to January 14, 2021), the United States of America (the USA, January 21, 2020 to April 5, 2021), and the World (January 22, 2020 to March 20, 2021) had been analyzed. The main variables included in the present analysis were ND, NH, NR, and NC. Pooled regression, stacked within-transformed linear regression, quantile regression for panel data, random-effects negative binomial regression, and random-effects Poisson regression were conducted to reflect the associations of ND, NH, and NR with NC. Event study analyses were performed to explore how the key events influenced NC.

Results

Descriptive analyses showed that mean value of ND/NC ratio regarding China was more than those regarding the USA and the World. The results from tentative analysis reported the significant relationships among ND, NH, NR, and NC regarding China, the USA, and the World. Panel regressions confirmed associations of ND, NH, and NR with NC regarding China, the USA, and the World. Panel event study showed that key events influenced NC regarding USA and the World more greatly than that regarding China.

Conclusion

The findings in this study confirmed the panel associations of ND, NH, and NR with NC in the three datasets. The efficiencies of various control strategies of COVID-19 pandemic across the globe were compared by the regression outcomes. Future direction of research work could explore the influencing mechanisms of the panel associations.