A Mathematical Model Investigating the Impact of Campaign Program on the Pattern of Transmission of Anemia in Pregnant Women
DOI:
https://doi.org/10.3126/kuset.v18i1.67497Keywords:
Anemia, Campaign program, Pregnant women, SAT model, FoetusAbstract
Anemia, a pervasive global health issue, is on the rise across the globe, impacting both developing and wealthy nations. Anemia, a hematological condition, can manifest at any point in life and is prevalent among pregnant individuals. The worldwide incidence of anemia among women of reproductive age is around 29.4%, with over 40% of pregnant women being affected. Anemia is a widespread issue that significantly contributes to illness and death, particularly in regions where malaria is prevalent. Anemia during pregnancy has a substantial effect on both the health of the fetus and the mother. Anemia has been identified as the cause of 20% of maternal fatalities in Africa. Iron deficiency is the primary cause of anemia. This research presents a comprehensive model that describes the dynamics of anemia in pregnant women and their fetus. The population is categorized into three classes: susceptible, impacted, and treated. A control factor that varies or changes over time, namely a campaign program, is being investigated. The model exhibits an equilibrium point and the stability of such points is assessed. Furthermore, the sensitivity analysis of the equilibrium point is conducted to identify the crucial parameters. Numerical simulations are conducted to observe the dynamic behavior of the model. Evidence demonstrates that the marketing program is successful in reducing the advancement of diseases. The implementation of an early accelerated campaign program greatly reduces the number of pregnant patients, as well as the yearly miscarriages and fatalities. However, discontinuing the applied treatment may reverse this positive outcome and increase the burden. Findings also suggest that the implementation of control measures helps to minimize the prevalence of anemia but may not eliminate the condition.
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