Back

Quantifying the Regional Disproportionality of COVID-19 Spread

Sasaki, K.; Ikeda, Y.; Nakano, T.

2024-04-07 public and global health
10.1101/2024.04.05.24305413 medRxiv
Show abstract

BackgroundThe COVID-19 pandemic has caused serious health problems and has had major economic and social consequences worldwide. Understanding how infectious diseases spread can help mitigating the social and economic impact. ObjectiveThe study focuses to capture the degrees of disproportionality in prevalence rates of infectious disease across different regions over time. MethodsWe analyze the numbers of daily COVID-19 confirmed cases in the United States collected by Johns Hopkins University over 1100 days since the first reported case in January 2020 in order to assess quantitatively the disproportionality of the confirmed cases using the Theil index, a measure of imbalance used in economics. Results: Our results reveal a dynamic pattern of interregional disproportionality in the confirmed cases by monitoring variations in regional contributions to the Theil index as the pandemic progresses. ConclusionsThe combined monitoring of this indicator and the confirmed cases is crucial for understanding regional differences in infectious diseases and for effective planning of response and resource allocation.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 6%
26.2%
Similar papers in this journal
2
Frontiers in Public Health
○ 148 papers in training set
Top 0.1%
10.9%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 12%
6.2%
Similar papers in this journal
4
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.1%
6.2%
Similar papers in this journal
5
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 0.8%
5.4%
Similar papers in this journal
50% of probability mass above
6
JMIRx Med
◐ 32 papers in training set
Top 0.2%
4.3%
7
Applied Sciences
○ 25 papers in training set
Top 0.1%
3.2%
8
Infectious Disease Modelling
○ 54 papers in training set
Top 0.6%
2.7%
9
BMC Infectious Diseases
○ 133 papers in training set
Top 1%
2.6%
10
Vaccines
○ 198 papers in training set
Top 1%
2.4%
11
Journal of Medical Internet Research
◐ 87 papers in training set
Top 1%
2.0%
12
Epidemiology and Infection
◐ 89 papers in training set
Top 1%
1.7%
13
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.5%
1.7%
14
PLOS Computational Biology
● 1863 papers in training set
Top 15%
1.7%
15
Heliyon
○ 152 papers in training set
Top 4%
1.4%
16
Cureus
○ 68 papers in training set
Top 3%
1.3%
17
Infectious Diseases of Poverty
○ 11 papers in training set
Top 0.2%
1.1%
18
BMC Public Health
○ 158 papers in training set
Top 4%
1.1%
19
PLOS Global Public Health
● 344 papers in training set
Top 7%
1.0%
20
COVID
○ 14 papers in training set
Top 0.5%
0.8%
21
Expert Systems with Applications
○ 11 papers in training set
Top 0.5%
0.6%
22
Disaster Medicine and Public Health Preparedness
◐ 16 papers in training set
Top 0.8%
0.6%
23
PeerJ
◐ 308 papers in training set
Top 13%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.