Simulations of the spread of COVID-19 and control policies in Tunisia

Abstract

Background: On March 11, 2020, the WHO announced that the COVID-19 outbreak had become pandemic, indicating that it was au- tonomous on several continents. Tunisia’s targeted containment and screen- ing strategy aligns with the WHO’s initial guidelines. This method is now showing its limitations. Mass screening in some countries shows that asymptomatic patients play an important role in spreading the virus through the population.
Objective: Our goals are first to assess Tunisia’s COVID-19 control policies, and then understand the effect of various detection, quarantine and confinement strategies and the rule of asymptomatic patients on the spread of the virus in the Tunisian population.
Methods: We develop and analyze a mathematical and epidemiologi- cal models for COVID- 19 in Tunisia. The data come from the Tunisian Health Commission dataset. Results: We calibrate different parameters of the model based on the Tunisian data, we calculate the expression of the basic reproduction num- ber R0 as a function of the model parameters and, finally, we carry out simulations of interventions and compare different strategies for suppress- ing and controlling the epidemic.
Conclusions: We show that Tunisia’s control policies are effective in screening infected and asymptomatic persons.

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References

Anderson, R. M.; May, R. M. Infectious Diseases of Humans: Dynamics and Control. Oxford Science Publications, 1992.

Diekmann, O.; Heesterbeek, J.A.P.; Metz, J. A. J. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. Journal of Mathematical Biology 1990, 28 (4), 365–382. DOI: https://doi.org/10.1007/BF00178324

Liu, Z.; Magal, P.; Seydi, O.; Webb, G. Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions. Biology 2020, 9, 50. DOI: https://doi.org/10.3390/biology9030050

Miles, P. R. pymcmcstat: A Python Package for Bayesian Inference Using Delayed Rejection Adaptive Metropolis. Journal of Open Source Software, 2019 4 (38) 147. DOI: https://doi.org/10.21105/joss.01417

Vabret, N.; Britton, G. J.; Gruber, C.; Hegde, S.; Kim, J.; Levantovsky, R.; Samstein, R. M. (2020). Immunology of COVID-19: current state of the science. Immunity. 2020 Jun 16;52(6):910-941. doi: 10.1016/j.immuni.2020.05.002. DOI: https://doi.org/10.1016/j.immuni.2020.05.002

Vespignani A.; Tian, H., Dye, C.; Lloyd-Smith, J. O.; Eggo, R. M.; Shrestha, M.; Leung, G. M.. Modelling COVID-19. Nature Reviews Physics, 2020; 2, 279281. DOI: https://doi.org/10.1038/s42254-020-0178-4

World Health Organization. Operational Planning Guidelines To Support Country Preparedness and Response, 2020(May), 17.

Published
2021-01-29
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Original Articles
Keywords:
Mathematical modeling; Tunisian Data; SIRD model; Health policies; COVID-19.
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How to Cite
Ben Miled, S., & Kebir, A. (2021). Simulations of the spread of COVID-19 and control policies in Tunisia. Journal of Public Health in Africa. https://doi.org/10.4081/jphia.2021.1420