Quantifying the Regional Disproportionality of COVID-19 Spread: Modeling Study
Abstract BackgroundThe 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 i...
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JMIR Publications
2025-01-01
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| Series: | JMIR Formative Research |
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| author | Kenji Sasaki Yoichi Ikeda Takashi Nakano |
| author_facet | Kenji Sasaki Yoichi Ikeda Takashi Nakano |
| author_sort | Kenji Sasaki |
| collection | DOAJ |
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Abstract
BackgroundThe 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.
ObjectiveThis 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.
MethodsTo 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.
ResultsOur 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.
ConclusionsThis 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. |
| format | Article |
| id | doaj-art-e176b7f7f4a846cdb1e276512729a96f |
| institution | Kabale University |
| issn | 2561-326X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj-art-e176b7f7f4a846cdb1e276512729a96f2025-01-10T21:17:10ZengJMIR PublicationsJMIR Formative Research2561-326X2025-01-019e59230e5923010.2196/59230Quantifying the Regional Disproportionality of COVID-19 Spread: Modeling StudyKenji Sasakihttp://orcid.org/0000-0001-6124-5827Yoichi Ikedahttp://orcid.org/0000-0002-2235-1464Takashi Nakanohttp://orcid.org/0000-0003-3157-5328 Abstract BackgroundThe 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. ObjectiveThis 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. MethodsTo 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. ResultsOur 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. ConclusionsThis 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.https://formative.jmir.org/2025/1/e59230 |
| spellingShingle | Kenji Sasaki Yoichi Ikeda Takashi Nakano Quantifying the Regional Disproportionality of COVID-19 Spread: Modeling Study JMIR Formative Research |
| title | Quantifying the Regional Disproportionality of COVID-19 Spread: Modeling Study |
| title_full | Quantifying the Regional Disproportionality of COVID-19 Spread: Modeling Study |
| title_fullStr | Quantifying the Regional Disproportionality of COVID-19 Spread: Modeling Study |
| title_full_unstemmed | Quantifying the Regional Disproportionality of COVID-19 Spread: Modeling Study |
| title_short | Quantifying the Regional Disproportionality of COVID-19 Spread: Modeling Study |
| title_sort | quantifying the regional disproportionality of covid 19 spread modeling study |
| url | https://formative.jmir.org/2025/1/e59230 |
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