A predictive model for Covid-19 spread - with application to eight US states and how to end the pandemic

被引:22
作者
Khan, Z. S. [1 ]
Van Bussel, F. [1 ]
Hussain, F. [1 ]
机构
[1] Texas Tech Univ, Dept Mech Engn, 2703 7th St,Box 41021, Lubbock, TX 79409 USA
关键词
COVID-19;
D O I
10.1017/S0950268820002423
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
A compartmental model is proposed to predict the coronavirus 2019 (Covid-19) spread. It considers: detected and undetected infected populations, social sequestration, release from sequestration, plus reinfection. This model, consisting of seven coupled equations, has eight coefficients which are evaluated by fitting data for eight US states that make up 43% of the US population. The evolution of Covid-19 is fairly similar among the states: variations in contact and undetected recovery rates remain below 5%; however, variations are larger in recovery rate, death rate, reinfection rate, sequestration adherence and release rate from sequestration. Projections based on the current situation indicate that Covid-19 will become endemic. If lockdowns had been kept in place, the number of deaths would most likely have been significantly lower in states that opened up. Additionally, we predict that decreasing contact rate by 10%, or increasing testing by approximately 15%, or doubling lockdown compliance (from the current similar to 15% to similar to 30%) will eradicate infections in Texas within a year. Extending our fits for all of the US states, we predict about 11 million total infections (including undetected), and 8 million cumulative confirmed cases by 1 November 2020.
引用
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页数:13
相关论文
共 54 条
[21]   Introductions and early spread of SARS-CoV-2 in the New York City area [J].
Gonzalez-Reiche, Ana S. ;
Hernandez, Matthew M. ;
Sullivan, Mitchell J. ;
Ciferri, Brianne ;
Alshammary, Hala ;
Obla, Ajay ;
Fabre, Shelcie ;
Kleiner, Giulio ;
Polanco, Jose ;
Khan, Zenab ;
Alburquerque, Bremy ;
van de Guchte, Adriana ;
Dutta, Jayeeta ;
Francoeur, Nancy ;
Melo, Betsaida Salom ;
Oussenko, Irina ;
Deikus, Gintaras ;
Soto, Juan ;
Sridhar, Shwetha Hara ;
Wang, Ying-Chih ;
Twyman, Kathryn ;
Kasarskis, Andrew ;
Altman, Deena R. ;
Smith, Melissa ;
Sebra, Robert ;
Aberg, Judith ;
Krammer, Florian ;
Garcia-Sastre, Adolfo ;
Luksza, Marta ;
Patel, Gopi ;
Paniz-Mondolfi, Alberto ;
Gitman, Melissa ;
Sordillo, Emilia Mia ;
Simon, Viviana ;
van Bakel, Harm .
SCIENCE, 2020, 369 (6501) :297-+
[22]   Presidential Powers and Response to COVID-19 [J].
Gostin, Lawrence O. ;
Hodge, James G., Jr. ;
Wiley, Lindsay F. .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2020, 323 (16) :1547-1548
[23]   NON-LINEAR OSCILLATIONS IN EPIDEMIC MODELS [J].
HETHCOTE, HW ;
STECH, HW ;
VANDENDRIESSCHE, P .
SIAM JOURNAL ON APPLIED MATHEMATICS, 1981, 40 (01) :1-9
[24]  
Jones J, 2020, REUTERS
[25]  
Kennedy Deanna M., 2020, Journal of Clinical Virology, V128, DOI 10.1016/j.jcv.2020.104440
[26]  
Kessler Glenn, 2020, The Washington Post
[27]   Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period [J].
Kissler, Stephen M. ;
Tedijanto, Christine ;
Goldstein, Edward ;
Grad, Yonatan H. ;
Lipsitch, Marc .
SCIENCE, 2020, 368 (6493) :860-+
[28]  
Kriston L, 2020, B WORLD HLTH ORGAN C, V2020, DOI [10.2471/BLT.20.257386, DOI 10.2471/BLT]
[29]   Global properties of a delayed SIR model with temporary immunity and nonlinear incidence rate [J].
Kyrychko, YN ;
Blyuss, KB .
NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS, 2005, 6 (03) :495-507
[30]  
Lee J., 2020, The New York Times