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Background South Africa is the focus of the current epidemic caused by Omicron. Understanding the spatiotemporal spread of Omicron in South Africa and how to control it is crucial to global countries. Methods To explore the spatiotemporal spread of Omicron in 9 provinces in South Africa, a province-level geographic prediction model of COVID-19 symptom onset risk, is proposed. Results It has been found that i) The spatiotemporal spread was relatively slow during the first stage and following the emergence of Omicron in Gauteng. The spatial spread of Omicron accelerated after it had become the dominant variant, and continued to spread from Gauteng to the neighboring provinces and main transport nodes. ii) Compared with current Alert Levels 1–4 in all provinces, the imposition of lockdown in the high-onset-risk Gauteng together with the Alert Level 1 in other 8 provinces, was found to more effectively control the spread of Omicron in South Africa. Moreover, it can reduce the spread of the Omicron epidemic in the provinces where main international airports are located to other parts of the world. iii) Due to declining vaccine efficiency over time, even when the daily vaccination rates in each province increased by 10 times, the daily overall onset risk was only reduced by 0.34%–7.86%. Conclusions Our study has provided a comprehensive investigation concerning the spatiotemporal dynamics of Omicron and hence provided scientific findings to enable a contribution which will assist in controlling the spatiotemporal spread of Omicron by integrating the prevention measures and vaccination.

Inferring individual's home country from geotagged footprints is widely applied in human mobility research. Previous studies mainly used simple empirical methods that are based on intuitive hypothetical assumptions. Because the exact relationships between users' home countries and geotagged footprints haven't be quantitatively revealed, empirical methods based on human intuitions and past experiences are used for rough approximation. In this study, we propose a machine-learning approach for the task of home country detection, by formulating the task as a query-ranking problem and using a machine-learned ranking model for problem solving. The used model is a Multiple Additive Regression Trees framework that aims to rank regions in specific orders and the region ranked first is designated as the home country. Our approach is data-driven and can adaptively learn the unknown function from input (geotagged footprints) to output (user's home country), thus alleviating the bias introduced by previous empirical methods. We conduct experiments with real-world datasets, and results demonstrate that our approach achieves better performance than previous empirical methods. The model's parameter sensitivity is also investigated, and results show that user's origin may be a factor affecting the approach's performance and that our approach achieves robust good performance with various parameter settings.

This article presents a new differential evolution (DE) algorithm for mining optimized statistically significant fuzzy association rules that are abundant in number and high in rule interestingness measure (RIM) values, with strict control over the risk of spurious rules. The risk control over spurious rules, as the most distinctive feature of the proposed DE compared with existing evolutionary algorithms (EAs) for association rule mining (ARM), is realized via two new statistically sound significance tests on the rules. The two tests, in the experimentwise and generationwise adjustment approach, can respectively limit the familywise error rate (the probability that any spurious rules occur in the ARM result) and percentage of spurious rules upon the user specified level. Experiments on variously sized data show that the proposed DE can keep the risk of spurious rules well below the user specified level, which is beyond the ability of existing EA-based ARM. The new method also carries forward the advantages of EA-based ARM and distinctive merits of DE in optimizing the rules: it can obtain several times as many rules and as high RIM values as conventional non-evolutionary ARM, and even more informative rules and better RIM values than genetic-algorithm-based ARM. Case studies on hotel room price determinants and wildfire risk factors demonstrate the practical usefulness of the proposed DE.