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Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various “densities” were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conc...
Contexts in source publication
Context 1
... density estimation produces a smooth and continuous surface on which each position in the study area is assigned a density value, regardless of any administrative boundary. Considering the space of the empirical area and the spatial range of COVID-19 cases, the bandwidth of the kernel function was set to 2.5 km and the average density values in each grid were calculated as the epidemic risk values (Figure 3). ...
Context 2
... steps used for the construction of the age-hierarchical Gaussian optimized gravity model were as follows ( Figure 3): ...
Context 3
... density estimation produces a smooth and continuous surface on which each position in the study area is assigned a density value, regardless of any administrative boundary. Considering the space of the empirical area and the spatial range of COVID-19 cases, the bandwidth of the kernel function was set to 2.5 km and the average density values in each grid were calculated as the epidemic risk values ( Figure 3). ...
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Community plays a crucial role in the successful prevention and control of the COVID-19 pandemic in China. However, evaluation of community capability to fight against COVID-19 is rarely reported. The present study provides a first attempt to assess community capability to combat COVID-19 in Shenyang, the capital city of Liaoning province in Northe...
Citations
... neural networks, extreme gradient boosting and random forest regression models [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93]). The feature importance analysis highlighted three interesting results: the geospatial autocorrelation between postal codes was informative for the model to predict the number of COVID-19 cases; demographic characteristics such as number of households in each postal code were not; that the feature importance differed per age group. ...
Source and contact tracing (SCT) is a core public health measure that is used to contain the spread of infectious diseases. It aims to identify a source of infection, and to advise those who have been exposed to this source. Due to the rapid increases in incidence of COVID-19 in the Netherlands, the capacity to conduct a full SCT quickly became insufficient. Therefore, the public health services (PHS) might benefit from a restricted strategy targeted to geographical regions where (predicted) case-to-case transmission is high. In this study, we set out to develop a prediction model for the number of COVID-19 cases per postal code within the Netherlands using geographic and demographic features. The study population consists of individuals residing in one of the participating nine Dutch PHS regions who tested positive for SARS-CoV-2 between 1 June 2020 and 27 February 2021. Using a machine learning random forest regression model, we predicted the top 100 postal codes with the highest number of cases with an accuracy of 49% for the current week, 42% for next week, and 44% for two weeks from present. In addition, the age groups of 20-39 and 40-64 years had a higher prediction accuracy than groups outside these age ranges. The developed model provides a starting point for targeted preventive SCT efforts that incorporate geospatial and demographic characteristics of a neighbourhood. It should nonetheless be noted that during the early stages of the outbreak, the number of available datapoints needed to inform such models are likely insufficient. Given the accuracy and data requirements of the developed model, it is unlikely that this class of models can play a pivotal role in informing policy during the early phases of a future epidemic.
... Among the 17 articles concerned with risk assessment in specific areas, five articles focused on a single province in China, such as Qingdao 17 and Hubei. 18 India followed China as the second most popular specific area to have been studied, but the frequency was just two. ...
... Personal risk was calculated by an equation consisting of COVID-19 transmission risk, public health risk, and socioeconomic risk. Regional risk focused on food productivity and supply chain network in a region [25] A framework was generated to dynamically assess the infection risk on board ships, based on a data-driven approach [28,41] Optimized gravity models and spatiotemporal risk modelling Geographically and temporally weighted regressions (GTWR) were used to build the models, and kernel density estimations (KDE) based on the Gaussian kernel function were used to spatially smooth the epidemic data [17] Multidimensional item response theory, confirmatory factor analyses, and structural equation modelling These techniques were used to construct and assess the quality of the proposed pandemic-risk-perception scale [33] (Continued) ST (seeding time) and DT (doubling time) Model A 2D plane was divided into four quadrants by using the mean ST and mean DT, with ST on the x-axis and DT on the y-axis to construct the model Sensitivity analyses were conducted to verify and validate the model [43] Conceivable mathematical model -Accelerated Phase ...
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.
Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM2.5) due to their low cost, wide coverage, and all-weather capability. Estimation of PM2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons (p < 0.01) and different spatial resolutions (p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM2.5 monitoring stations and historical PM2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions.
