Back

COVID-19 pandemic: A Hill type mathematical model predicts the US death number and the reopening date

Aboelkassem, Y.

2020-04-17 epidemiology
10.1101/2020.04.12.20062893 medRxiv
Show abstract

A mathematical model that can be used to estimate the total number of cases and deaths due to COVID-19 pandemic is presented in this study. The parameters and the associated uncertainty in the model are optimized and quantified using various reported data sets reported from different countries. The results suggest that, by the mid of June or early July 2020, the outbreak will strongly decay and the US will have about 800K confirmed cases and less than 50K deaths. Significance StatementThis study presents a mathematical model that can be used to estimate the total number of cases and deaths in the US due to COVID-19. The model forecasting about < 800K cases and < 50K total numbers of deaths in the United States. The results suggest that late May or early June, 2020 is probably a good time to end the shutdown order and reopen the country for the daily routine business.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 9%
19.1%
Similar papers in this journal
2
Infectious Disease Modelling
○ 54 papers in training set
Top 0.1%
8.1%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 9%
6.9%
Similar papers in this journal
4
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.1%
5.7%
Similar papers in this journal
5
Computer Methods in Biomechanics and Biomedical Engineering
○ 10 papers in training set
Top 0.1%
3.3%
Similar papers in this journal
6
Mathematical Biosciences
○ 49 papers in training set
Top 0.3%
3.3%
7
Frontiers in Public Health
○ 148 papers in training set
Top 2%
3.3%
8
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.3%
2.8%
50% of probability mass above
9
PLOS Computational Biology
● 1863 papers in training set
Top 11%
2.8%
10
BMC Public Health
○ 158 papers in training set
Top 2%
2.5%
11
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.1%
2.5%
12
Disaster Medicine and Public Health Preparedness
◐ 16 papers in training set
Top 0.1%
2.2%
13
BMC Infectious Diseases
○ 133 papers in training set
Top 2%
2.2%
14
Quantitative Biology
○ 12 papers in training set
Top 0.1%
1.8%
15
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.5%
1.8%
16
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 0.8%
1.7%
17
Royal Society Open Science
● 214 papers in training set
Top 3%
1.7%
18
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 3%
1.5%
19
Nonlinear Dynamics
○ 10 papers in training set
Top 0.2%
1.5%
20
JMIRx Med
◐ 32 papers in training set
Top 1%
1.2%
21
Mathematics
○ 11 papers in training set
Top 0.2%
1.0%
22
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 6%
0.9%
23
PeerJ
◐ 308 papers in training set
Top 10%
0.9%
24
Physical Biology
○ 46 papers in training set
Top 0.8%
0.9%
25
Biomechanics and Modeling in Mechanobiology
○ 29 papers in training set
Top 0.4%
0.9%
26
Epidemics
○ 116 papers in training set
Top 2%
0.6%
27
Biology
○ 45 papers in training set
Top 1%
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.