Sasaki Kenji, Ikeda Yoichi, Nakano Takashi
Center for Infectious Disease Education and Research, Osaka University, Co-creation BLDG. D88-1, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan, 81 50-5604-3730.
Research Center for Nuclear Physics, Osaka University, Ibaraki, Osaka, Japan.
JMIR Form Res. 2025 Jan 3;9:e59230. doi: 10.2196/59230.
The COVID-19 pandemic has caused serious health, economic, and social consequences worldwide. Understanding how infectious diseases spread can help mitigate these impacts. The Theil index, a measure of inequality rooted in information theory, is useful for identifying geographic disproportionality in COVID-19 incidence across regions.
This study focused on capturing the degrees of regional disproportionality in incidence rates of infectious diseases over time. Using the Theil index, we aim to assess regional disproportionality in the spread of COVID-19 and detect epicenters where the number of infected individuals was disproportionately concentrated.
To quantify the degree of disproportionality in the incidence rates, we applied the Theil index to the publicly available data of daily confirmed COVID-19 cases in the United States over a 1100-day period. This index measures relative disproportionality by comparing daily regional case distributions with population proportions, thereby identifying regions where infections are disproportionately concentrated.
Our analysis revealed a dynamic pattern of regional disproportionality in the confirmed cases by monitoring variations in regional contributions to the Theil index as the pandemic progressed. Over time, the index reflected a transition from localized outbreaks to widespread transmission, with high values corresponding to concentrated cases in some regions. We also found that the peaks in the Theil index often preceded surges in confirmed cases, suggesting its potential utility as an early warning signal.
This study demonstrated that the Theil index is one of the effective indices for quantifying regional disproportionality in COVID-19 incidence rates. Although the Theil index alone cannot fully capture all aspects of pandemic dynamics, it serves as a valuable tool when used alongside other indicators such as infection and hospitalization rates. This approach allows policy makers to monitor regional disproportionality efficiently, offering insights for early intervention and targeted resource allocation.
新冠疫情在全球范围内造成了严重的健康、经济和社会后果。了解传染病的传播方式有助于减轻这些影响。泰尔指数是一种基于信息理论的不平等度量指标，可用于识别不同地区新冠发病率的地理不均衡情况。
本研究聚焦于捕捉传染病发病率随时间变化的区域不均衡程度。我们旨在利用泰尔指数评估新冠疫情传播中的区域不均衡情况，并检测感染人数过度集中的疫情中心。
为了量化发病率的不均衡程度，我们将泰尔指数应用于美国1100天内每日新冠确诊病例的公开数据。该指数通过比较每日区域病例分布与人口比例来衡量相对不均衡程度，从而确定感染过度集中的地区。
通过监测疫情发展过程中各地区对泰尔指数贡献的变化，我们的分析揭示了确诊病例区域不均衡的动态模式。随着时间推移，该指数反映了从局部爆发到广泛传播的转变，高值对应于某些地区的集中病例。我们还发现，泰尔指数的峰值往往先于确诊病例的激增，这表明其作为早期预警信号的潜在效用。
本研究表明，泰尔指数是量化新冠发病率区域不均衡的有效指标之一。尽管仅靠泰尔指数无法完全捕捉疫情动态的所有方面，但与感染率和住院率等其他指标一起使用时，它是一个有价值的工具。这种方法使政策制定者能够有效地监测区域不均衡情况，为早期干预和有针对性的资源分配提供见解。
