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.

Background Since most of the global population needs to be vaccinated to reduce COVID-19 transmission and mortality, a shortage of COVID-19 vaccine supply is inevitable. We propose a spatial and dynamic vaccine allocation solution to assist in the allocation of limited vaccines to people who need them most. Methods We developed a weighted kernel density estimation (WKDE) model to predict daily COVID-19 symptom onset risk in 291 Tertiary Planning Units in Hong Kong from 18 January 2020 to 22 December 2020. Data of 5,409 COVID-19 onset cases were used. We then obtained spatial distributions of accumulated onset risk under three epidemic scenarios, and computed the vaccine demands to form the vaccine allocation plan. We also compared the vaccine demand under different real-time effective reproductive number (Rt) levels. Results The estimated vaccine usages in three epidemiologic scenarios are 30.86% - 45.78% of the Hong Kong population, which is within the total vaccine availability limit. In the sporadic cases or clusters of onset cases scenario, when 6.26% of the total population with travel history to high-risk areas can be vaccinated, the COVID-19 transmission between higher- and lower-risk areas can be reduced. Furthermore, if the current Rt is increased to double, the vaccine usages needed will be increased by more than 7%. Conclusions The proposed solution can be used to dynamically allocate limited vaccines in different epidemic scenarios, thereby enabling more effective protection. The increased vaccine usages associated with increased Rt indicates the necessity to maintain appropriate control measures even with vaccines available.

It is important to forecast the risk of COVID-19 symptom onset and thereby evaluate how effectively the city lockdown measure could reduce this risk. This study is a first comprehensive, high-resolution investigation of spatiotemporal heterogeneities on the effect of the Wuhan lockdown on the risk of COVID-19 symptom onset in all 347 Chinese cities. An extended Weight Kernel Density Estimation model was developed to predict the COVID-19 onset risk under two scenarios (i.e., with and without the Wuhan lockdown). The Wuhan lockdown, compared with the scenario without lockdown implementation, in general, delayed the arrival of the COVID-19 onset risk peak for 1–2 days and lowered risk peak values among all cities. The decrease of the onset risk attributed to the lockdown was more than 8% in over 40% of Chinese cities, and up to 21.3% in some cities. Lockdown was the most effective in areas with medium risk before lockdown.

The SARS-CoV-2 lineage B.1.1.7 (Alpha) has spread to 114 countries around the world since it was first detected in the UK in September 2020 (P. Wang, Nair, et al., 2021). Some studies have shown that B.1.1.7 is not only 43%-90% (95% CI 38-130) more transmissible than preexisting variants (Jewell, 2021;Walensky et al., 2021) but also causes more severe illness (Davies et al., 2021;Patone et al., 2021). With the accelerated vaccination, the world is gradually entering a regular prevention and control stage (Pham et al., 2021) with some regions preparing to reopen to the world (Chang et al., 2021;Zhao et al., 2021). In order to assist these regions to effectively prevent the emergence of new variants in the reopening process, it became crucial to explore the nonpharmaceutical intervention (NPI) measures that affect the emergence and spread of SARS-CoV-2 variants such as B.1.1.7 and thereby, first learn in general how to effectively control the spatiotemporal spread of SARS-CoV-2 variants. Taiwan, one of the regions where B.1.1.7 appeared during the reopening process (Shonchoy et al., 2021;Tan, 2021;Yu et al., 2021), is chosen regarding the exploration of the spatial dynamics of the B.1.1.7 spread and investigation with the aim of exploring relevant data and thereby finding the potential means to effectively control the spatiotemporal spread of B.1.1.7, potentially by the integration of vaccination and NPI measures.

Background Motivated by the need for precise epidemic control and epidemic-resilient urban design, this study aims to reveal the joint and interactive associations between urban socioeconomic, density, connectivity, and functionality characteristics and the COVID-19 spread within a high-density city. Many studies have been made on the associations between urban characteristics and the COVID-19 spread, but there is a scarcity of such studies in the intra-city scale and as regards complex joint and interactive associations by using advanced machine learning approaches. Methods Differential-evolution-based association rule mining was used to investigate the joint and interactive associations between the urban characteristics and the spatiotemporal distribution of COVID-19 confirmed cases, at the neighborhood scale in Hong Kong. The associations were comparatively studied for the distribution of the cases in four waves of COVID-19 transmission: before Jun 2020 (wave 1 and 2), Jul–Oct 2020 (wave 3), and Nov 2020–Feb 2021 (wave 4), and for local and imported confirmed cases. Results The first two waves of COVID-19 were found mainly characterized by higher-socioeconomic-status (SES) imported cases. The third-wave outbreak concentrated in densely populated and usually lower-SES neighborhoods, showing a high risk of within-neighborhood virus transmissions jointly contributed by high density and unfavorable SES. Starting with a super-spread which considerably involved high-SES population, the fourth-wave outbreak showed a stronger link to cross-neighborhood transmissions driven by urban functionality. Then the outbreak diffused to lower-SES neighborhoods and interactively aggravated the within-neighborhood pandemic transmissions. Association was also found between a higher SES and a slightly longer waiting period (i.e., the period from symptom onset to diagnosis of symptomatic cases), which further indicated the potential contribution of higher-SES population to the pandemic transmission. Conclusions The results of this study may provide references to developing precise anti-pandemic measures for specific neighborhoods and virus transmission routes. The study also highlights the essentiality of reliving co-locating overcrowdedness and unfavorable SES for developing epidemic-resilient compact cities, and the higher obligation of higher-SES population to conform anti-pandemic policies.