Uncovering transmission patterns of COVID-19 outbreaks: A region-wide comprehensive retrospective study in Hong Kong

Liu, Yang; Gu, Zhonglei; Liu, Jiming

doi:10.1016/j.eclinm.2021.100929

Cited by 23 publications

(16 citation statements)

References 15 publications

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“…First, we considered the dataset from Hong Kong ( figure 4 a (i)). A previous study [ 35 ] reconstructed the transmission network of cases in that region (between 23 January 2020 and 8 January 2021; although in principle a similar analysis could be conducted at a smaller spatial scale for shorter time periods, as would likely be most useful for early real-time estimation of R t ), inferring the ‘outdegree’ of imported and local cases. Based on the aggregated data shown in table 1 of that study, the mean outdegree was 0.74 for imported cases and 3.68 for local cases, which corresponds to a value of ε = 0.2.…”

Section: Resultsmentioning

confidence: 99%

“…Incidence data were obtained for the time period from 23 January to 24 March 2020 [34]. Cases were classified as imported if they were listed as ‘imported case, confirmed’ in the Hong Kong Department of Health COVID-19 database (see [35] for further details). All other cases were classified as local cases.Hainan Province, China (figure 4 a (ii)).…”

Section: Methodsmentioning

confidence: 99%

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Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number

Creswell

Augustin

Bouros

et al. 2022

Phil. Trans. R. Soc. A.

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During infectious disease outbreaks, inference of summary statistics characterizing transmission is essential for planning interventions. An important metric is the time-dependent reproduction number ( R t ), which represents the expected number of secondary cases generated by each infected individual over the course of their infectious period. The value of R t varies during an outbreak due to factors such as varying population immunity and changes to interventions, including those that affect individuals' contact networks. While it is possible to estimate a single population-wide R t , this may belie differences in transmission between subgroups within the population. Here, we explore the effects of this heterogeneity on R t estimates. Specifically, we consider two groups of infected hosts: those infected outside the local population (imported cases), and those infected locally (local cases). We use a Bayesian approach to estimate R t , made available for others to use via an online tool, that accounts for differences in the onwards transmission risk from individuals in these groups. Using COVID-19 data from different regions worldwide, we show that different assumptions about the relative transmission risk between imported and local cases affect R t estimates significantly, with implications for interventions. This highlights the need to collect data during outbreaks describing heterogeneities in transmission between different infected hosts, and to account for these heterogeneities in methods used to estimate R t . This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number

Creswell

Augustin

Bouros

et al. 2022

Phil. Trans. R. Soc. A.

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“…In Hong Kong or mainland China, we found no peer-reviewed publications specifically on “pandemic fatigue” as a self-reported subjective feeling, although usage of the term by the mass media has become widespread and popular. Moreover, the fourth wave outbreak in Hong Kong (from November 2020 to March 2021) has been attributed to pandemic fatigue, with lapses in social distancing and mask-wearing resulting in singing and dancing clusters with over 730 confirmed cases in November 2020 20 , 21 .…”

Section: Introductionmentioning

confidence: 99%

COVID-19 pandemic fatigue and its sociodemographic and psycho-behavioral correlates: a population-based cross-sectional study in Hong Kong

Leung

Gong

Sit

et al. 2022

Sci Rep

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Pandemic fatigue is a growing public health concern of the lingering COVID-19 pandemic. Despite its widespread mass media coverage, systematic empirical investigations are scarce. Under the Hong Kong Jockey Club SMART Family-Link Project, we conducted online and telephone surveys amid the pandemic in February to March 2021 to assess self-reported pandemic fatigue (range 0–10) in Hong Kong adults (N = 4726) and its associations with sociodemographic and psycho-behavioral (high vs low to moderate) variables. Data were weighted by sex, age, and education of the general population. Binary logistic regression models yielded adjusted odds ratios (aORs) for high pandemic fatigue (score ≥ 7) for sociodemographic and psycho-behavioral variables. 43.7% reported high pandemic fatigue. It was less common in older people (55–64 years: aOR 0.56, 95% CI 0.39–0.82; 65 + years: 0.33, 0.21–0.52) versus age group 18–24 years, but more common in those with tertiary education (1.36, 1.15–1.62) versus secondary or below. High pandemic fatigue was positively associated with depressive symptoms (aOR 1.83, 95% CI 1.55–2.17), anxiety symptoms (1.87, 1.58–2.20), loneliness (1.75, 1.32–2.31), personal fear of COVID-19 (2.61, 2.12–3.23), family fear of COVID-19 (2.03, 1.67–2.47), and current alcohol use (1.16, 1.00–1.33), but negatively associated with self-rated health (0.79, 0.68–0.92), personal happiness (0.63, 0.55–0.72), personal adversity coping capability (0.71, 0.63–0.81), family adversity coping capability (0.79, 0.69–0.90), family well-being (0.84, 0.73–0.97), family communication quality (0.86, 0.75–0.98), and frequent home exercise (0.82, 0.69–0.96; versus less frequent). We first used a single-item tool to measure COVID-19 pandemic fatigue, showing that it was common and associated with worse mental health, lower levels of personal and family well-being and alcohol use.

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“…In addition, it has been discussed that central government-led methods, such as in South Korea, should be careful in responding to a large population that is geographically spread [ 51 ]. Infectious diseases spread according to the regional hierarchy [ 73 ], which is demonstrated by the fact that COVID-19 spreads from major cities to small cities [ 74 ]. For sustainable management of infectious diseases, this means that local government-centered policies are needed rather than central government-led crisis management responses.…”

Section: Discussionmentioning

confidence: 99%

Exploring spatial distribution of social vulnerability and its relationship with the Coronavirus disease 2019: the Capital region of South Korea

Kim

2022

BMC Public Health

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Background & objective The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to cause death and socioeconomic problems worldwide. This study examined the spatial distribution of social vulnerability to COVID-19 and its relationship with the number of confirmed COVID-19 cases in 2020, focusing on the Capital region of South Korea. Methods A traditional social vulnerability index (SVI), healthy SVI, and the difference of each SVI were constructed in 2015 and 2019. The traditional SVI was constructed across five domains: age, socioeconomic disadvantage, housing, income, and environment. The healthy SVI domains were: prevention, health-related habits, chronic disease, healthcare infrastructure, and mortality. The spatial distribution of the traditional SVI, healthy SVI, and confirmed cases of COVID-19 was explored using ArcGIS 10.5. Pearson correlation was used to identify the relationship between confirmed COVID-19 cases and the two SVIs and their changes between 2015 and 2019. Four multiple linear regression models were used to identify the impact of the changes of the two SVIs on the confirmed COVID-19 cases for the three episodes and total period with control of population using STATA/MP 16.1. Results Confirmed COVID-19 cases were concentrated in a specific area of the Capital region. The traditional SVI was more vulnerable in the outer regions of the Capital region, and some central, western, and eastern areas reflected an increase in vulnerability. Healthy SVI was more vulnerable in the northern part of the Capital region, and increase in vulnerability showed in some central areas above Seoul. By multiple regression with the population controlled, the difference of the traditional SVI between 2015 and 2019 showed a positive relationship with the confirmed COVID-19 cases in all models at a significance level of 0.05, and the 2019 integrated SVI showed a negative relationship with confirmed COVID-19 cases in all models. Conclusions The results of this study showed that the confirmed COVID-19 cases are associated with increased traditional SVI vulnerability between 2015 and 2019 and have a high positive relationship with the spread of COVID-19. Policy efforts are needed to reduce confirmed COVID-19 cases among the vulnerable in regions with relatively increased traditional SVI.

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Uncovering transmission patterns of COVID-19 outbreaks: A region-wide comprehensive retrospective study in Hong Kong

Cited by 23 publications

References 15 publications

Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number

Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number

COVID-19 pandemic fatigue and its sociodemographic and psycho-behavioral correlates: a population-based cross-sectional study in Hong Kong

Exploring spatial distribution of social vulnerability and its relationship with the Coronavirus disease 2019: the Capital region of South Korea

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