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The real time effective reproductive number for COVID-19 in the United States
Zhang, Y.; Keegan, L. T.; Yuqing, Q.; Samore, M. H.
2020-05-13
epidemiology
10.1101/2020.05.08.20095703
medRxiv
Show abstract
none.
Matching journals
●Non-profit
◐University press
○Commercial
The top 11 journals account for 50% of the predicted probability mass.
1
PLOS ONE
●
5266 papers in training set
Top 9%
19.0%
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- Population density and basic reproductive number of COVID-19 across United States counties 94%
- A Stratified Model to Quantify the Effects of Containment Policies on the Spread of COVID-19 93%
- The basic reproduction number can be accurately estimated within 14 days after societal lockdown: The early stage of the COVID-19 epidemic in Denmark 93%
2
BMC Medicine
○
176 papers in training set
Top 0.4%
5.7%
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3
JMIR Public Health and Surveillance
◐
45 papers in training set
Top 0.1%
5.0%
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- Serial interval, basic reproduction number and prediction of COVID-19 epidemic size in Jodhpur, India 93%
- Testing, Testing: What SARS-CoV-2 testing services do adults in the United States actually want? 91%
- Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan 91%
4
Royal Society Open Science
●
214 papers in training set
Top 0.8%
4.5%
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- School and community reopening during the COVID-19 pandemic: a mathematical modeling study 91%
- Assessing the impact of widespread respirator use in curtailing COVID-19 transmission in the United States 91%
- Modeling COVID-19 outbreaks in United States with distinct testing, lockdown speed and fatigue rates 91%
5
Scientific Reports
○
3612 papers in training set
Top 24%
4.1%
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- Several countries in one: a mathematical modeling analysis for COVID-19 in inner Brazil 93%
- Can a herd immunity strategy become a viable option against COVID-19? A model-based analysis on social acceptability and feasibility 92%
- Uncertainty and Inconsistency of COVID-19 Non-Pharmaceutical Intervention Effects with Multiple Competitive Statistical Models 91%
50% of probability mass above
"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.