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The Effect of Strict State Measures on the Epidemiologic Curve of COVID-19 Infection in the Context of a Developing Country: A Simulation from Jordan

Author

Listed:
  • Khalid A. Kheirallah

    (Department of Public Health, Medical School of Jordan University of Science and Technology, Irbid 22110, Jordan
    Authors equally contributed to this manuscript.)

  • Belal Alsinglawi

    (School of Computer, Data and Mathematical Sciences, Western Sydney University, Rydalmere 2116, NSW, Australia
    Authors equally contributed to this manuscript.)

  • Abdallah Alzoubi

    (Department of Pharmacology, Medical School of Jordan University of Science and Technology, Irbid 22110, Jordan)

  • Motasem N. Saidan

    (Chemical Engineering Department, School of Engineering, The University of Jordan, Amman 11942, Jordan)

  • Omar Mubin

    (School of Computer, Data and Mathematical Sciences, Western Sydney University, Rydalmere 2116, NSW, Australia)

  • Mohammed S. Alorjani

    (Department of Pathology and Microbiology, Medical School of Jordan University of Science and Technology, Irbid 22110, Jordan)

  • Fawaz Mzayek

    (Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, The University of Memphis, Memphis, TN 38152, USA)

Abstract

COVID-19 has posed an unprecedented global public health threat and caused a significant number of severe cases that necessitated long hospitalization and overwhelmed health services in the most affected countries. In response, governments initiated a series of non-pharmaceutical interventions (NPIs) that led to severe economic and social impacts. The effect of these intervention measures on the spread of the COVID-19 pandemic are not well investigated within developing country settings. This study simulated the trajectories of the COVID-19 pandemic curve in Jordan between February and May and assessed the effect of Jordan’s strict NPI measures on the spread of COVID-19. A modified susceptible, exposed, infected, and recovered (SEIR) epidemic model was utilized. The compartments in the proposed model categorized the Jordanian population into six deterministic compartments: suspected, exposed, infectious pre-symptomatic, infectious with mild symptoms, infectious with moderate to severe symptoms, and recovered. The GLEAMviz client simulator was used to run the simulation model. Epidemic curves were plotted for estimated COVID-19 cases in the simulation model, and compared against the reported cases. The simulation model estimated the highest number of total daily new COVID-19 cases, in the pre-symptomatic compartmental state, to be 65 cases, with an epidemic curve growing to its peak in 49 days and terminating in a duration of 83 days, and a total simulated cumulative case count of 1048 cases. The curve representing the number of actual reported cases in Jordan showed a good pattern compatibility to that in the mild and moderate to severe compartmental states. The reproduction number under the NPIs was reduced from 5.6 to less than one. NPIs in Jordan seem to be effective in controlling the COVID-19 epidemic and reducing the reproduction rate. Early strict intervention measures showed evidence of containing and suppressing the disease.

Suggested Citation

  • Khalid A. Kheirallah & Belal Alsinglawi & Abdallah Alzoubi & Motasem N. Saidan & Omar Mubin & Mohammed S. Alorjani & Fawaz Mzayek, 2020. "The Effect of Strict State Measures on the Epidemiologic Curve of COVID-19 Infection in the Context of a Developing Country: A Simulation from Jordan," IJERPH, MDPI, vol. 17(18), pages 1-11, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6530-:d:410502
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    References listed on IDEAS

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    1. P. Castorina & A. Iorio & D. Lanteri, 2020. "Data analysis on Coronavirus spreading by macroscopic growth laws," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(07), pages 1-12, July.
    2. Mohammed Al Zobbi & Belal Alsinglawi & Omar Mubin & Fady Alnajjar, 2020. "Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic," IJERPH, MDPI, vol. 17(15), pages 1-9, August.
    3. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
    4. Joe Hilton & Matt J Keeling, 2020. "Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-10, July.
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    Cited by:

    1. Xue Zhang & Mildred E. Warner, 2020. "COVID-19 Policy Differences across US States: Shutdowns, Reopening, and Mask Mandates," IJERPH, MDPI, vol. 17(24), pages 1-17, December.
    2. Khalid A. Kheirallah & Mohammed Al-Nusair & Shahed Aljabeiti & Nadir Sheikali & Abdallah Alzoubi & Jomana W. Alsulaiman & Abdel-Hameed Al-Mistarehi & Hamed Alzoubi & Ayman Ahmad Bani Mousa & Mohammed , 2022. "Jordan’s Pandemic Influenza Preparedness (PIP): A Reflection on COVID-19 Response," IJERPH, MDPI, vol. 19(12), pages 1-16, June.

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