Be-CoDiS

Be-CoDiS: a mathematical model to predict the risk of human diseases spread between countries -- validation and application to the 2014--2015 ebola virus disease epidemic. Ebola virus disease is a lethal human and primate disease that currently requires a particular attention from the international health authorities due to important outbreaks in some Western African countries and isolated cases in the UK, the USA and Spain. Regarding the emergency of this situation, there is a need for the development of decision tools, such as mathematical models, to assist the authorities to focus their efforts in important factors to eradicate Ebola. In this work, we propose a novel deterministic spatial-temporal model, called Between-Countries Disease Spread (Be-CoDiS), to study the evolution of human diseases within and between countries. The main interesting characteristics of Be-CoDiS are the consideration of the movement of people between countries, the control measure effects and the use of time-dependent coefficients adapted to each country. First, we focus on the mathematical formulation of each component of the model and explain how its parameters and inputs are obtained. Then, in order to validate our approach, we consider two numerical experiments regarding the 2014-2015 Ebola epidemic. The first one studies the ability of the model in predicting the EVD evolution between countries starting from the index cases in Guinea in December 2013. The second one consists of forecasting the evolution of the epidemic by using some recent data. The results obtained with Be-CoDiS are compared to real data and other model outputs found in the literature. Finally, a brief parameter sensitivity analysis is done. A free MATLAB version of Be-CoDiS is available at: url{http://www.mat.ucm.es/momat/software.htm}.


References in zbMATH (referenced in 20 articles , 1 standard article )

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  1. Dautel, Kimberly A.; Agyingi, Ephraim O.: Modeling the impact of educational campaign on the transmission dynamics of Ebola (2021)
  2. Maji, Chandan: Impact of media-induced fear on the control of COVID-19 outbreak: a mathematical study (2021)
  3. Ouedraogo, Harouna; Ouedraogo, Dramane; Ibrango, Idrissa; Guiro, Aboudramane: A study of stability of SEIHR model of infectious disease transmission (2021)
  4. Rajan, R. Sundara; Kumar, K. Jagadeesh; Shantrinal, A. Arul; Rajalaxmi, T. M.; Rajasingh, Indra; Balasubramanian, Krishnan: Biochemical and phylogenetic networks. I: Hypertrees and corona products (2021)
  5. Ramos, A. M.; Ferrández, M. R.; Vela-Pérez, M.; Kubik, A. B.; Ivorra, B.: A simple but complex enough (\theta)-SIR type model to be used with COVID-19 real data. Application to the case of Italy (2021)
  6. Ramos, A. M.; Vela-Pérez, M.; Ferrández, M. R.; Kubik, A. B.; Ivorra, B.: Modeling the impact of SARS-CoV-2 variants and vaccines on the spread of COVID-19 (2021)
  7. Tyagi, Swati; Gupta, Shaifu; Abbas, Syed; Das, Krishna Pada; Riadh, Baazaoui: Analysis of infectious disease transmission and prediction through SEIQR epidemic model (2021)
  8. El Alami Laaroussi, Adil; Rachik, Mostafa: On the regional control of a reaction-diffusion system SIR (2020)
  9. Ivorra, Benjamin; Ngom, Diène; Ramos, Angel M.: Stability and sensitivity analysis of the epidemiological model Be-CoDiS predicting the spread of human diseases between countries (2020)
  10. Ivorra, B.; Ferrández, M. R.; Vela-Pérez, Maria; Ramos, A. M.: Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China (2020)
  11. Rafiq, Muhammad; Ahmad, Waheed; Abbas, Mujahid; Baleanu, Dumitru: A reliable and competitive mathematical analysis of Ebola epidemic model (2020)
  12. Mhlanga, A.: Dynamical analysis and control strategies in modelling Ebola virus disease (2019)
  13. Berge, T.; Chapwanya, M.; Lubuma, J. M.-S.; Terefe, Y. A.: A mathematical model for Ebola epidemic with self-protection measures (2018)
  14. Berge, Tsanou; Bowong, Samuel; Lubuma, Jean; Manyombe, Martin Luther Mann: Modeling ebola virus disease transmissions with reservoir in a complex virus life ecology (2018)
  15. Yamazaki, Kazuo: Threshold dynamics of reaction-diffusion partial differential equations model of Ebola virus disease (2018)
  16. Berge, T.; Lubuma, J. M.-S.; Moremedi, G. M.; Morris, N.; Kondera-Shava, R.: A simple mathematical model for Ebola in Africa (2017)
  17. D’Silva, Jeremy P.; Eisenberg, Marisa C.: Modeling spatial invasion of Ebola in West Africa (2017)
  18. Tsanou, Berge; Bowong, Samuel; Lubuma, Jean; Mbang, Joseph: Assessing the impact of the environmental contamination on the transmission of Ebola virus disease (EVD) (2017)
  19. Mangiarotti, Sylvain; Peyre, Marisa; Huc, Mireille: A chaotic model for the epidemic of Ebola virus disease in West Africa (2013--2016) (2016)
  20. Ivorra, Benjamin; Ngom, Diène; Ramos, Ángel M.: Be-CoDiS: a mathematical model to predict the risk of human diseases spread between countries -- validation and application to the 2014--2015 ebola virus disease epidemic (2015)