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SARS-COV-2 outbreak and control in Kenya - Mathematical model analysis.

Rachel Waema Mbogo1, Titus Okello Orwa1

  • 1Institute of Mathematical Sciences, Strathmore University, Box 59857 00200, Nairobi, Kenya.

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Summary

Aggressive mass testing and individual behavior change are crucial for controlling the COVID-19 epidemic in Kenya. These interventions significantly impact transmission dynamics and epidemic trajectory, even with recovery rates.

Keywords:
Basic reproduction numberCOVID-19Compartmental modelMass testingSEIHCRD-ModelSimulationsSocial distancing

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • The COVID-19 pandemic significantly impacted Kenya starting March 2020.
  • By December 2020, Africa recorded over 2.8 million cases and 67,000 deaths.
  • Understanding early transmission and control effectiveness is vital for preventing sustained outbreaks.

Purpose of the Study:

  • To estimate COVID-19 transmission dynamics in Kenya using a mathematical model.
  • To evaluate the impact of mass testing and behavioral changes on COVID-19 spread.
  • To assess factors driving infection using the basic reproduction number (R0).

Main Methods:

  • A SEIHCRD mathematical transmission model was employed.
  • Kenyan COVID-19 case data was used for model calibration.
  • The next-generation matrix approach was used to calculate the basic reproduction number (R0).

Main Results:

  • The SEIHCRD model effectively captured the COVID-19 outbreak's course in Kenya.
  • Mass testing and individual behavioral changes were identified as key factors in controlling the epidemic.
  • Sustained high infection rates are likely without aggressive testing and behavioral interventions.

Conclusions:

  • Mathematical modeling provides insights into COVID-19 transmission trends.
  • Effective mass testing and self-initiated behavioral changes are critical for COVID-19 epidemic control in Kenya.
  • These findings have significant implications for public health management and prevention policies.