MATHEMATICAL MODEL OF LASSA FEVER TRANSMISSION DYNAMICS IN PREVALENT COMMUNITIES IN NIGERIA: THE CASE STUDY OF ONDO STATE
DOI:
https://doi.org/10.33003/fjs-2024-0805-2493Keywords:
Global health, SARS-CoV-2,transmission dynamics, Disease Free Equilibrium, Ordinary Differential EquationAbstract
With the current waive of global health problems and resurgence of many disease around the world. Cholera, Yellow fever,SARS-CoV-2, Monkey pox and Lassa fever resurgence in some West African countries, with Ondo State recording highest number of Lassa fever case in Nigeria. Prompting Nigeria Centre for
Disease Control (NCDC), Ondo State Primary Health (OSPH) expert and researchers begin ways to reduce transmission dynamics of Lassa Fever Disease (LFD). In this research, we developed and investigated using System of Ordinary Differential Equation (ODE) mathematical model of Lassa fever disease transmission dynamics, verifying positivity of system of equation as well as feasible region of the model. However, the Disease Free Equilibrium (DFE) of the model is computed and analysed with basic reproduction number $R_0$ of the model, showing the global stability of the DFE. Furthermore, we determined using model-fitting parameters the condition to attain stability. Finally, numerical simulations shows reduction in transmission with effective pest control measure.
References
Abubakar, U., Abubakar, A., Sulaiman, A., Ringim, H. I., Salisu, I. A., Osi, A. A., James, I., Sani, A. M., & Haruna, I. S. (2023). APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR PREDICTING HYPERTENSION STATUS AND INDICATORS IN HADEJIA METROPOLITAN. FUDMA JOURNAL OF SCIENCES, 7(1), 284–289. https://doi.org/10.33003/fjs-2023-0701-2052
Albanese, J. (2014). Organized crime in our times. Routledge.
Bekesiene, S., Smaliukiene, R., & Vaicaitiene, R. (2021). Using artificial neural networks in predicting the level of stress among military conscripts. Mathematics, 9(6). https://doi.org/10.3390/math9060626
Brody, R. G., Kern, S., & Ogunade, K. (2022). An insider's look at the rise of Nigerian 419 scams. Journal of Financial Crime, 29(1), 202-214.
Chambliss, W. J. (2011). Crime and criminal behavior (Vol. 1). Sage.
Classen, A., & Scarborough, C. (Eds.). (2012). Crime and punishment in the Middle Ages and Early Modern Age: Mental-historical investigations of basic human problems and social responses (Vol. 11). Walter de Gruyter.
Dikko A, OSI AA. (2014). Discriminate analysis as an aid to the Classification and prediction of safety across State of Nigeria. International journal of statistics and application , 4(3):153-160.
Farmer, L. (2022). The ‘market’in criminal law theory. The Modern Law Review, 85(2), 435-460.
Forbes, A. D. (1995). Classification-algorithm evaluation: Five performance measures based onconfusion matrices. Journal of Clinical Monitoring, 11, 189-206.
Hoffmann, J. L., & Stuntz, W. J. (2021). Defining Crimes. Aspen Publishing.
Hooton E. (1939). the American criminals. cambridge: Harvad university press.
Kennedy, J. (2021). Crimes as public wrongs. Legal Theory, 27(4), 253-284.
Lewis, R. J. (2000, May). An introduction to classification and regression tree (CART) analysis. In Annual meeting of the society for academic emergency medicine in San Francisco, California (Vol. 14). San Francisco, CA, USA: Department of Emergency Medicine Harbor-UCLA Medical Center Torrance.
Logan, W. A., & Ferguson, A. G. (2016). Policing criminal justice data. Minn. L. Rev., 101, 541.
Lucic, B., Batista, J., Bojovic, V., Lovric, M., Krzic, A. S., Beslo, D., & Nadramija, D. (2019). Estimation of Random Accuracy and its Use in Validation of Predictive Quality of Classification Models within Predictive Challenges. Croatica Chemica Acta, 92(3), 1I-1I.
MacDonald, Z. (2002). Official crime statistics: their use and interpretation. The Economic Journal, 112(477), F85-F106.
Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883.
Sandri, M., & Zuccolotto, P. (2010). Analysis and correction of bias in total decrease in node impurity measures for tree-based algorithms. Statistics and Computing, 20, 393-407.
Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130.
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