COVID-19 Pandemic Situation in Kenya: A Data Driven SEIR Model
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Abstract
Background: COVID-19 disease has persisted since it was declared a global pandemic by the World health organization (WHO) in February 2020. Kenya had notified a total of 203,213 cases, reported 3,931 deaths by July 26, 2021. Currently, Kenya is experiencing the fourth wave of the pandemic that is driven by the delta variant which has become dominant in the country. Non-pharmaceutical and vaccination interventions have been ongoing in the country. With the emergence of new variants, it’s challenging to know the future of the pandemic and its effects on the healthcare systems, vaccination plans, public readiness, and social behavior. To attain realistic predictions, data-driven modeling approaches are paramount. The goal of this study was to model COVID-19 cases in Kenya using the already available data to estimate parameters
Methods: An SEIR compartmental model was developed to predict the daily new cases, severe, critically ill, and death cases of COVID-19. The model had 8 compartments containing sub-populations of: Susceptible, Exposed, Symptomatic Infectious, Asymptomatic Infectious, Hospitalized, Intensive Care Unit, Deaths and Recovered. The model equations were then solved to obtain the number of cases that would be infected on a daily basis beginning March 14th to July 2021.
Results: The results demonstrated evidence of three peaks, first in mid July 2020, second in mid-October 2020 and third in early March of 2021. The number of daily cases in wave 1 was 1128 then increased to 1344 in the second wave and finally a decline was observed in wave 3 with 1057 number of daily of infections. The number of severely, critical and deaths followed a similar pattern. This therefore means that with the absence of herd immunity in the general human population, relaxation of the mitigation measures will eventually result to progression of COVID-19 cases.
Conclusion: Increased vigilance on the COVID-19 curve is indispensable. Continued interventions such as testing, social distancing measures, vaccination, and health facilities preparedness are imperative to ensure that new infections are isolated in real-time. Use of real time data to estimate the pandemic trends is a more realistic way of informing new and appropriate interventions amidst the ongoing pandemic.
Article Details
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References
2. Guo YR, Cao QD, Hong ZS, et.al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak–an update on the status. Military Medical Research. Dec 2020;7(1):1-10.
3. World Health Organization. Epidemic-Prone and Pandemic-Prone Acute Respiratory Diseases: Infection Prevention and Control in Health-Care Facilities. WHO. Indonesia Partner in Development. 2008;53(2):8-25.
4. Leung K, Wu JT, Liu D, Leung GM. First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. The Lancet. Apr 25 2020;395(10233):1382-1393.
5. Jewell NP, Lewnard JA, Jewell BL. Predictive mathematical models of the COVID-19 pandemic: underlying principles and value of projections. JAMA : the journal of the American Medical Association. May 19 2020;323(19):1893-1894.
6. Thompson RN. Epidemiological models are important tools for guiding COVID-19 interventions. BMC medicine. Dec 2020; 18:1-4.
7. Zareie B, Roshani A, Mansournia MA, et.al. A model for COVID-19 prediction in Iran based on China parameters. MedRxiv. Jan 1 2020.
8. Santosh KC. COVID-19 prediction models and unexploited data. Journal of medical systems. Sep 2020;44(9):1-4.
9. Saez M, Tobias A, Varga D, et.al. Effectiveness of the measures to flatten the epidemic curve of COVID-19. The case of Spain. Science of the Total Environment. Jul 20 2020; 727:138761.
10. Courtemanche CJ, Garuccio J, Le A, et.al. Did Social-Distancing Measures in Kentucky Help to Flatten the COVID-19 Curve?
11. Feng Z, Damon-Feng H, Zhao H. Sustaining social distancing policies to prevent a dangerous second peak of covid-19 outbreak. medRxiv. 1 Jan 2020.
12. Mwalili S, Kimathi M, Ojiambo V, et.al. SEIR model for COVID-19 dynamics incorporating the environment and social distancing. BMC Research Notes. Dec 2020;13(1):1-5.
13. Kiarie J, Mwalili S, Mbogo R. COVID-19 Flattening Curve," A Recipe for Second Wave of Infections in Kenya''.
14. Ranjan R. Temporal dynamics of COVID-19 outbreak and future projections: a data-driven approach. Transactions of the Indian National Academy of Engineering. Jun 2020;5:109-15.
15. Kuhl E. Data-driven modeling of COVID-19—Lessons learned. Extreme Mechanics Letters. Aug 2020 14:100921.
16. Ghamizi S, Rwemalika R, Cordy M, et.al. Data-driven simulation and optimization for covid-19 exit strategies. InProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Aug 23 2020 (pp. 3434-3442).
17. Pereira IG, Guerin JM, Silva Júnior AG, Garcia GS, Piscitelli P, Miani A, Distante C, Gonçalves LM. Forecasting Covid-19 dynamics in Brazil: a data driven approach. International Journal of Environmental Research and Public Health. Jan 2020;17(14):5115.
18. Liu X, Zheng X, Balachandran B. COVID-19: data-driven dynamics, statistical and distributed delay models, and observations. Nonlinear Dynamics. Aug 2020;101(3):1527-1543.
19. Chintalapudi N, Battineni G, Amenta F. COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach. Journal of Microbiology, Immunology and Infection. Jun 1 2020;53(3):396-403.