A Vector Field Approach to Estimating Environmental Exposure Using Human Activity Data

COVID-19 Pandemic Risk Assessment: Systematic Review

Background

The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries' pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era.

Objective

We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning.

Methods

This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts.

Results

Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles.

Conclusion

Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.

Spatial variations of the third and fourth COVID-19 waves in Hong Kong: A comparative study using built environment and socio-demographic characteristics

Since the first confirmed case was reported in January 2020, Hong Kong has experienced multiple waves of COVID-19 outbreaks. Recent literature has explored the spatial patterns of disease incidence and their relationships with the built environment and demographic characteristics. Nonetheless, few studies aim at the comparative patterns of different epidemic waves occurring in the same spatial context. This study analyses spatial patterns of the third and fourth COVID-19 epidemic waves and then evaluates the spatial relationship between case incidence and built environment and socio-demographic characteristics. By collecting local-related cases, this study incorporates a two-fold analytical strategy: (1) Using rank-size distribution and log-odd ratio to depict the spatial pattern of COVID-19 incidence rates; (2) through global and local regression models, investigating incidence's associations with the urban built environment and socio-demographic characteristics. The results reveal that the two different epidemic waves have far distinct spatial tendencies to their infection risk factors, reflecting location-specific associations with the built environments and socio-demographics. Collectively, we discover that the third and fourth COVID-19 waves are likely associated with residential context and urban activities, respectively. Practical implications are discussed that would be of interest to policymakers and health professionals.

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.