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

Abstract

Background: Given the dynamism and heterogeneity of COVID-19 transmission patterns, determining the most effective yet timely strategies for specific regions remains a severe challenge for public health decision-makers.

Methods: In this work, we proposed a spatiotemporal connectivity analysis method for discovering transmission patterns across geographic locations and age-groups throughout different COVID-19 outbreak phases. First, we constructed the transmission networks of the confirmed cases during different phases by considering the spatiotemporal connectivity of any two cases. Then, for each case and those cases immediately pointed from it, we characterized the corresponding cross-district/population transmission pattern by counting their district-to-district and age-to-age occurrences. By summating the cross-district/population transmission patterns of all cases during a given period, we obtained the aggregated cross-district and cross-population transmission patterns.

Findings: We conducted a region-wide comprehensive retrospective study in Hong Kong based on the complete data report of COVID-19 cases, covering all 18 districts between January 23, 2020, and January 8, 2021 (https://data.gov.hk/en-data/dataset/hk-dh-chpsebcddr-novel-infectious-agent). The spatiotemporal connectivity analysis clearly unveiled the quantitative differences among various outbreak waves in their transmission scales, durations, and patterns. Moreover, for the statistically similar waves, their cross-district/population transmission patterns could be quite different (e.g., the cross-district transmission of the fourth wave was more diverse than that of the third wave, while the transmission over age-groups of the fourth wave was more concentrated than that of the third wave). At an overall level, super-spreader individuals (highly connected cases in the transmission networks) were usually concentrated in only a few districts (2 out of 18 in our study) or age-groups (3 out of 11 in our study).

Interpretation: With the discovered cross-district or cross-population transmission patterns, all of the waves of COVID-19 outbreaks in Hong Kong can be systematically scrutinized. Among all districts, quite a few (e.g., the Yau Tsim Mong district) were instrumental in spreading the virus throughout the pandemic. Aside from being exceptionally densely populated, these districts were also social-economic centers. With a variety of situated public venues, such as restaurants and singing/dancing clubs, these districts played host to all kinds of social gathering events, thereby providing opportunities for widespread and rapid transmission of the virus. Thus, these districts should be given the highest priority when deploying district-specific social distancing or intervention strategies, such as lockdown and stringent mandatory coronavirus testing for identifying and obstructing the chain of transmission. We also observed that most of the reported cases and the highly connected cases were middle-aged and elderly people (40- to 69-year-olds). People in these age-groups were active in various public places and social activities, and thus had high chances of being infected by or infecting others.

Funding: General research fund of the Hong Kong research grants council.

Keywords: COVID-19; Cross-district and Cross-population Transmission Patterns; Retrospective Study in Hong Kong; Spatiotemporal Connectivity Analysis; Transmission Networks.

Fig. 1

The transmission networks of the major waves of COVID-19 outbreaks in Hong Kong between January 23, 2020, and January 8, 2021. (A) The number of daily new cases of COVID-19 in Hong Kong from January 2020 to January 2021. (B) The transmission networks (constructed based on the proposed spatiotemporal connectivity analysis method) of the second, third, and fourth waves of COVID-19 outbreaks in Hong Kong. The transmission networks were visualized using Gephi 0.9.2 . Gephi is an open-source and multiplatform software distributed under the dual license CDDL 1.0 and GNU General Public License v3. Note: Hong Kong's first wave of COVID-19 outbreaks between late January and mid-February 2020 was also shown in (A) but not emphasized in this study. Retrospectively, the first wave of outbreaks was much less significant in terms of the scale and duration of COVID-19 infections than the other waves. Therefore, we focused on the second, third, and fourth waves.

Fig. 2

The detailed transmission network of the fourth wave of COVID-19 outbreaks in Hong Kong, visualized using Gephi 0.9.2 . Nodes (cases) with different degrees of connectivity were shown in different sizes. Different clusters were shown in different colors. A representative node that connected two major clusters and the longest transmission path over the transmission network were also highlighted.

Fig. 3

The cross-district and cross-population transmission patterns throughout different outbreak phases. (A) The number of daily new cases of COVID-19 in Hong Kong from January 2020 to January 2021. (B) The heat maps of the cross-district transmission patterns of the second, third, and fourth waves of outbreaks, where D1 to D18 denote the indices of Hong Kong's 18 districts. More details were provided in Table S1. (C) The heat maps of the cross-population transmission patterns of the second, third, and fourth waves of outbreaks, where A1 to A11 denote the indices of the population's 11 age-groups. More details were provided in Table S2.