| Author: | Tong, Chengzhuo |
| Title: | Prediction and analysis of COVID-19 symptom onset risk at multi-spatial scales for effective epidemic control |
| Advisors: | Shi, Wenzhong (LSGI) |
| Degree: | Ph.D. |
| Year: | 2022 |
| Subject: | Communicable diseases -- Prevention Communicable diseases -- Control Spatial analysis (Statistics) COVID-19 (Disease) Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Land Surveying and Geo-Informatics |
| Pages: | xii, 181 pages : color illustrations |
| Language: | English |
| Abstract: | The full death toll associated with the Coronavirus disease 2019 (COVID-19) pandemic between 2020 and 2021 was approximately 14.9 million. Moreover, the COVID-19 pandemic has also had a severe impact both on people's normal lives and also the global economic development. The World Health Organization (WHO) has called for a new approach based on those assessment of epidemic risk that directly serves the epidemic control of public health sector agencies. Therefore, from the early epidemic stage of 2020, the study has attempted to produce a continuous improvement regarding the original weighted kernel density estimation (WKDE) model to predict the risk of COVID-19 symptom onset at multi-spatial scales and hence assist the effective epidemic control. Firstly, this study uses the mobility data between cities to improve and form an inter-city-level WKDE model. Based on the prediction results, the Wuhan lockdown effects in 347 cities of China during the early epidemic stage were analyzed. The Wuhan lockdown 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. Secondly, the extended WKDE model was further used to allocate the limited vaccines spatially and dynamic in urban-community-level regarding epidemic control in the early stage. The real-time effective reproduction number (Rt) was used to enhance the new urban-community level model. A case study in Hong Kong indicated that the estimated vaccine usage (30.86-45.78%) under three epidemiologic scenarios was within the total vaccine availability limit. Vaccine usage would need to be increased by more than 7% if the current rate of viral spread was doubled. Thirdly, based on gene sequencing results, the proposed urban-district-level WKDE model was enhanced to analyze the entire dynamic spatiotemporal process of B.1.1.7 spread in 274 districts of Taiwan during the reopening. B.1.1.7 had significant spatiotemporal heterogeneity with human mobility playing an important role in the transmission of B.1.1.7. Imposing lockdown in the high-onset-risk districts and increasing the vaccination rates, could possibly more effectively control the spatiotemporal spread of B.1.1.7. Fourth, by incorporating the respiratory pathogens surveillance reports, the wastewater testing, and other testing data, the WKDE model was further enhanced to predict the onset risk to analyze the early spread of Omicron in 9 provinces of South Africa. After becoming the dominant variant, Omicron spread quickly from the epidemic center to the neighboring provinces and main nodes in the transport network. Taking Alert Level 5 in the high onset risk province could possibly more effectively control the spatiotemporal spread of Omicron. Increased current vaccination seems to be limited regarding the reduction of the symptomatic infection by Omicron. Overall, it is felt that the spatiotemporal prediction methods of COVID-19 symptom onset risk proposed in this thesis can enhance traditional disease surveillance and improve the ability of public health agencies to conduct the risk assessment and decision-making, and as a possible consequence can ensure better preparations for future epidemics and pandemic responses. |
| Rights: | All rights reserved |
| Access: | open access |
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