Department of Mathematics, University of Hawai'i at Manoa Department of Mathematics, Honolulu, Hawai'i, United States of America.
Hawai'i Data Science Institute, University of Hawai'i at Manoa, Honolulu, Hawai'i, United States of America.
PLoS One. 2022 May 18;17(5):e0263866. doi: 10.1371/journal.pone.0263866. eCollection 2022.
It is critical to capture data and modeling from the COVID-19 pandemic to understand as much as possible and prepare for future epidemics and possible pandemics. The Hawaiian Islands provide a unique opportunity to study heterogeneity and demographics in a controlled environment due to the geographically closed borders and mostly uniform pandemic-induced governmental controls and restrictions.
The goal of the paper is to quantify the differences and similarities in the spread of COVID-19 among different Hawaiian islands as well as several other archipelago and islands, which could potentially help us better understand the effect of differences in social behavior and various mitigation measures. The approach should be robust with respect to the unavoidable differences in time, as the arrival of the virus and promptness of mitigation measures may vary significantly among the chosen locations. At the same time, the comparison should be able to capture differences in the overall pandemic experience.
We examine available data on the daily cases, positivity rates, mobility, and employ a compartmentalized model fitted to the daily cases to develop appropriate comparison approaches. In particular, we focus on merge trees for the daily cases, normalized positivity rates, and baseline transmission rates of the models.
We observe noticeable differences among different Hawaiian counties and interesting similarities between some Hawaiian counties and other geographic locations. The results suggest that mitigation measures should be more localized, that is, targeting the county level rather than the state level if the counties are reasonably insulated from one another. We also notice that the spread of the disease is very sensitive to unexpected events and certain changes in mitigation measures.
Despite being a part of the same archipelago and having similar protocols for mitigation measures, different Hawaiian counties exhibit quantifiably different dynamics of the spread of the disease. One potential explanation is that not sufficiently targeted mitigation measures are incapable of handling unexpected, localized outbreak events. At a larger-scale view of the general spread of the disease on the Hawaiian island counties, we find very interesting similarities between individual Hawaiian islands and other archipelago and islands.
从 COVID-19 大流行中获取数据和建模对于尽可能了解并为未来的疫情和可能的大流行做好准备至关重要。由于地理位置上的封闭边界以及大流行期间政府的控制和限制基本统一，夏威夷群岛为在受控环境中研究异质性和人口统计学特征提供了独特的机会。
本文的目的是量化 COVID-19 在不同夏威夷岛屿以及其他一些群岛和岛屿之间传播的差异和相似之处，这可能有助于我们更好地了解社会行为差异和各种缓解措施的影响。由于所选地点的病毒到达时间和缓解措施的及时性可能存在显著差异，因此该方法应具有稳健性。同时，该比较应能够捕捉到总体大流行经验的差异。
我们检查了有关每日病例、阳性率、流动性的可用数据，并采用适用于每日病例的分组模型来开发适当的比较方法。特别是，我们关注每日病例的合并树、标准化阳性率以及模型的基线传播率。
我们观察到不同夏威夷县之间存在明显差异，并且一些夏威夷县与其他地理位置之间存在有趣的相似之处。结果表明，如果各县之间相对隔离，那么缓解措施应该更加本地化，即针对县级而不是州级。我们还注意到，疾病的传播对意外事件和缓解措施的某些变化非常敏感。
尽管属于同一群岛并且具有相似的缓解措施协议，但不同的夏威夷县表现出可量化的疾病传播动态差异。一个潜在的解释是，目标不够明确的缓解措施无法应对意外的、局部爆发的事件。在夏威夷岛县的疾病传播总体规模上，我们发现个别夏威夷岛屿与其他群岛和岛屿之间存在非常有趣的相似之处。
