COVID-19 epidemic in Spain in the first wave: Estimation of the epidemic curve inferred from seroprevalence data and simulation of scenarios based on SEIR model

被引:0
|
作者
Prado-Galbarro, Francisco-Javier [1 ]
Cruz-Cruz, Copytzy [1 ]
Gamino-Arroyo, Ana-Estela [2 ]
Sanchez-Piedra, Carlos [3 ]
机构
[1] Metropolitan Autonomous Univ, Orphan Drug Lab, Biol Syst Dept, Mexico City, DF, Mexico
[2] Hosp Infantil Mexico Dr Federico Gomez, Mexico City, DF, Mexico
[3] Hlth Technol Assessment Agcy Carlos III Inst Hlth, Res Unit, Madrid, Spain
关键词
public health; COVID-19; epidemiology; health policy; CHINA;
D O I
暂无
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
The COVID-19 pandemic represents one of the most severe challenges in the recent history of public health. The aim of this study is to estimate the transmission rate parameter (beta) and to predict the epidemic progression in Spain. We integrated data from Our World in Data. Our model considered a mean time from infection to death to be 24 days and the results of the seroprevalence survey in Spain. We calculated beta using a SEIR model estimated by least squares. We also used a SEIR model to evaluate four scenarios: 1) model 1: no containment measures, 2) model 2: containment measures from the beginning of the epidemic, 3) model 3: mild measures since the 20th day, 4) model 4: strict containment measures since the 20th day. The estimated beta parameter was 1.087. We calculated 41,210,330 infected people and 725,302 deaths in model 1; 165,036 infected people and 2,905 deaths in model 2; 4,640,400 infected people and 81,671 deaths in model 3; and, 62.012 infected people and 1,091 deaths in model 4. Peak of the epidemic varied from 69th day in model 1 to 216th day in model 4. Containment measures prevented a scenario with a significant increase in deaths and infected people. Our findings showed that, by stricter interventions such as quarantine and isolation could lead to reduce the potential peak number of COVID-19 cases and delay the time of peak infection.
引用
收藏
页码:59 / 72
页数:14
相关论文
共 50 条
  • [41] Enhanced surveillance of COVID-19 in Scotland: population-based seroprevalence surveillance for SARS-CoV-2 during the first wave of the epidemic
    Dickson, E.
    Palmateer, N. E.
    Murray, J.
    Robertson, C.
    Waugh, C.
    Wallace, L. A.
    Mathie, L.
    Heatlie, K.
    Mavin, S.
    Gousias, P.
    Von Wissman, B.
    Goldberg, D. J.
    McAuley, A.
    PUBLIC HEALTH, 2021, 190 : 132 - 134
  • [42] Analysis of the second wave of COVID-19 in India based on SEIR model
    Gopal, R.
    Chandrasekar, V. K.
    Lakshmanan, M.
    EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 2022, 231 (18-20) : 3453 - 3460
  • [43] Using the SEIR model to constrain the role of contaminated fomites in spreading an epidemic: An application to COVID-19 in the UK
    Meiksin, Avery
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2022, 19 (04) : 3564 - 3590
  • [44] Modeling the epidemic dynamics of COVID-19: Agent-based approach including molecular dynamics simulation and SEIR type methods
    Aghaei, Fatemeh
    Lohrasebi, Amir
    INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING, 2021, 12 (06)
  • [45] Novel model prediction time-to-event analysis: data validation and estimation of 200 million cases in the global COVID-19 epidemic
    Rezania, Ali
    Ghorbani, Elaheh
    Hassanian-Moghaddam, Davood
    Faeghi, Farnaz
    Hassanian-Moghaddam, Hossein
    BMJ OPEN, 2023, 13 (01):
  • [46] Dynamical characteristics of the COVID-19 epidemic: Estimation from cases in Colombia
    Diaz, Hernando
    Espana, Guido
    Castaneda, Nelson
    Rodriguez, Laura
    De la Hoz-Restrepo, Fernando
    INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES, 2021, 105 : 26 - 31
  • [47] Extended SEIQR type model for COVID-19 epidemic and data analysis
    Sharma, Swarnali
    Volpert, Vitaly
    Banerjee, Malay
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2020, 17 (06) : 7562 - 7604
  • [48] Prediction of COVID-19 Infected Population for Indian States through a State Interaction Network-based SEIR Epidemic Model
    Mallick, Piklu
    Bhowmick, Sourav
    Panja, Surajit
    IFAC PAPERSONLINE, 2022, 55 (01): : 691 - 696
  • [49] A Continuous Markov-Chain Model for the Simulation of COVID-19 Epidemic Dynamics
    Xu, Zhaobin
    Zhang, Hongmei
    Huang, Zuyi
    BIOLOGY-BASEL, 2022, 11 (02):
  • [50] Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model
    Gupta, Koyel Datta
    Dwivedi, Rinky
    Sharma, Deepak Kumar
    OPEN COMPUTER SCIENCE, 2022, 12 (01) : 27 - 36