Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China

Zhou, Shuli; Zhou, Suhong; Zheng, Zhong; Lu, Junwen; Song, Tie

doi:10.1016/j.apgeog.2022.102702

Cited by 16 publications

(9 citation statements)

References 41 publications

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“…Similarly, when epidemic prevention and control measures are relaxed, high-risk areas will also expand significantly, especially in the early stages of the epidemic. However, the vast majority of previous modeling studies have focused on the national, state, and regional levels [ 40 ]. The strength of this study is that building an onset risk prediction model at the urban-community level can facilitate accurate and differentiated public health responses at the finer spatial scale [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, the vast majority of previous modeling studies have focused on the national, state, and regional levels [ 40 ]. The strength of this study is that building an onset risk prediction model at the urban-community level can facilitate accurate and differentiated public health responses at the finer spatial scale [ 40 ]. This allows the vast majority of areas in the city to maintain normal social and economic activities while maintaining strict prevention and control measures in a very small number of areas.…”

Section: Discussionmentioning

confidence: 99%

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A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis

Tong

Shi

Zhang

et al. 2023

JMIR Public Health Surveill

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Background Following the recent COVID-19 pandemic, returning to normalcy has become the primary goal of global cities. The key for returning to normalcy is to avoid affecting social and economic activities while supporting precise epidemic control. Estimation models for the spatiotemporal spread of the epidemic at the refined scale of cities that support precise epidemic control are limited. For most of 2021, Hong Kong has remained at the top of the “global normalcy index” because of its effective responses. The urban-community-scale spatiotemporal onset risk prediction model of COVID-19 symptom has been used to assist in the precise epidemic control of Hong Kong. Objective Based on the spatiotemporal prediction models of COVID-19 symptom onset risk, the aim of this study was to develop a spatiotemporal solution to assist in precise prevention and control for returning to normalcy. Methods Over the years 2020 and 2021, a spatiotemporal solution was proposed and applied to support the epidemic control in Hong Kong. An enhanced urban-community-scale geographic model was proposed to predict the risk of COVID-19 symptom onset by quantifying the impact of the transmission of SARS-CoV-2 variants, vaccination, and the imported case risk. The generated prediction results could be then applied to establish the onset risk predictions over the following days, the identification of high–onset-risk communities, the effectiveness analysis of response measures implemented, and the effectiveness simulation of upcoming response measures. The applications could be integrated into a web-based platform to assist the antiepidemic work. Results Daily predicted onset risk in 291 tertiary planning units (TPUs) of Hong Kong from January 18, 2020, to April 22, 2021, was obtained from the enhanced prediction model. The prediction accuracy in the following 7 days was over 80%. The prediction results were used to effectively assist the epidemic control of Hong Kong in the following application examples: identified communities within high–onset-risk always only accounted for 2%-25% in multiple epidemiological scenarios; effective COVID-19 response measures, such as prohibiting public gatherings of more than 4 people were found to reduce the onset risk by 16%-46%; through the effect simulation of the new compulsory testing measure, the onset risk was found to be reduced by more than 80% in 42 (14.43%) TPUs and by more than 60% in 96 (32.99%) TPUs. Conclusions In summary, this solution can support sustainable and targeted pandemic responses for returning to normalcy. Faced with the situation that may coexist with SARS-CoV-2, this study can not only assist global cities in responding to the future epidemics effectively but also help to restore social and economic activities and people’s normal lives.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis

Tong

Shi

Zhang

et al. 2023

JMIR Public Health Surveill

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“…Most of the studies on the risk assessment of public health emergencies are based on people who live in cities, including simulations of people's behavior [22], the determination of population movement patterns based on mobile phone data [23], and assessments of the public's ability to cope with risk during public health emergencies from a psychological perspective [24]. At the same time, the application of new technologies has also played an essential role in risk assessment; establishing the Smart City Risk Assessment System will provide technical support for accurate risk assessments in cities [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Regional Health Resource Carrying Capacity and Security in Public Health Emergencies Based on the COVID-19 Outbreak

Huang

2023

IJERPH

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The Omicron variant of COVID-19, which emerged at the end of 2021, has caused a new wave of infections around the world and is causing a new wave of the crisis due to the extreme variability of the pathogen. In response to public health emergencies such as SARS and COVID-19, the first task is to identify the vulnerabilities of regional health systems and perform a comprehensive assessment of the region’s resilience. In this paper, we take the carrying capacity of medical resources as the focus; evaluate the medical, human, and financial resources of various regions; and construct an epidemic safety index based on the actual situation or future trend of the epidemic outbreak to evaluate and predict the risk level of each region in response to the epidemic. The study firstly evaluates the epidemic safety index for each province and city in China and 150 countries around the world, using the first wave of the COVID-19 epidemic in 2020 and the Omicron variant virus in 2022 as the background, respectively, and justifies the index through the actual performance in terms of epidemic prevention and control, based on which the epidemic safety index for 150 countries in the next year is predicted. The conclusions show that Europe, the Americas, and parts of Asia will face a significant risk of epidemic shocks in the coming period and that countries need to formulate policies in response to the actual situation of the epidemic.

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“…Many studies have focused on respiratory diseases such as SARS-CoV-2, and have considered contacts between people as a primary factor for measuring or controlling transmission (Choi & Hohl, 2021;Esposito et al, 2021;Ford et al, 2006;Thunström et al, 2020;Yao et al, 2021;Zhang et al, 2020;Zhou et al, 2022). The number of contacts or transmission rates have been estimated through statistical inference from surveys, other papers, or infected population data (Abdulkareem et al, 2018;Kuo & Wen, 2022;Luo, 2016;Peng et al, 2020;Zhu et al, 2020), by establishing a dynamic contact network (Duncan et al, 2012;Eubank et al, 2004;Hernández-Flores et al, 2020;Milne et al, 2008), pixel parameters from geostatistical population maps (Touloupou et al, 2020) or statistical approaches (Deeth & Deardon, 2016;Fumanelli et al, 2012;Mei et al, 2015), agent-based models (ABMs) simulation (Kai et al, 2020;Mao, 2014;Perez & Dragicevic, 2009), or by making assumptions (Xiong & Yan, 2020) to predict infection patterns for large areas, such as cities and countries.…”

Section: Introductionmentioning

confidence: 99%

Investigating factors in indoor transmission of respiratory disease through agent‐based modeling

Choi,

Hohl

2023

Transactions in GIS

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The transmission of respiratory diseases such as COVID‐19 is exacerbated in densely populated urban areas and crowded indoor settings. Despite the majority of transmissions occurring in such settings, controlling viral spread through individual‐level contacts indoors remains challenging. Experimental studies have investigated the transmission patterns of respiratory behaviors such as coughing or sneezing in controlled spatial environments. However, the effects of dynamic movement and spatial structures have been ignored, making it difficult to apply findings to urban policy and planning. To address this gap, we developed agent‐based simulations to investigate individual virus inhalation patterns across multiple scenarios in a symmetrical and formulaic indoor space. We conducted sensitivity analysis using regression emulator models to identify significant factors for viral transmission. Our results indicate positive associations with viral transmission in descending order of: (1) stay time; (2) encounter frequency; and (3) initial infected population; while negative associations are: (4) mask wearing; (5) distance to infected people; (6) nearest infected people's mask wearing; and (7) distance to entrance. We also found that narrow passages between obstacles increase virus transmission from breathing. Furthermore, we conducted a case study to investigate the potential of reducing the amount of individually inhaled virus by controlling behaviors and spatial environments. Our findings suggest that mask wearing and reduced stay time can substantially reduce transmission risk, while a large number of contacts and high grouping time result in the growth of the infected population at a certain threshold. These results provide guidance for decision makers to formulate guidelines for curbing the spread of respiratory diseases in indoor spaces.

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Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China

Cited by 16 publications

References 41 publications

A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis

A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis

Assessment of Regional Health Resource Carrying Capacity and Security in Public Health Emergencies Based on the COVID-19 Outbreak

Investigating factors in indoor transmission of respiratory disease through agent‐based modeling

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