![]() ![]() This approach involves the development of computer simulation models that portray processes of accumulation and feedbacks and that may be tested systematically to find effective policies for overcoming policy resistance https. The System Dynamics (SD) modeling methodology is well suited to address the dynamic complexity that characterizes many public health issues. Models provide concise quantitative descriptions of complicated, non-linear processes, and a method for relating the process of infection in individuals to the incidence of infection or disease in a population over time https. falciparum.ĭuring the process of understanding the epidemiology of malaria and other infectious diseases, mathematical models have historically played an important role. vivax are characterized by relapses of malaria arising from the persistent liver stage of the parasite (hypnozoites) https, which results in a different model structure than the case of P. For example, infections by the variant, P. The analysis would be similar, though not the same, for other malaria parasites. Due to its impact worldwide, we have chosen to focus our analysis on the transmission of P. ![]() Only in Africa, where most of the malaria is concentrated, over 95% of episodes are caused by P. falciparum is the most prevalent and dangerous malaria parasite. Malaria in humans is caused by 5 Plasmodium parasites: Plasmodium falciparum, P. System Dynamics articles, Malaria Model articles, Integrated Vector Management (IVM) articles, Entomological Inoculation Rate (EIR) articles Article Details 1. System Dynamics, Malaria Model, Integrated Vector Management (IVM), Entomological Inoculation Rate (EIR) To the extent possible, the validity of the model under these assumptions has been analyzed by way of mathematic equations. Such model has been developed based on a number of simplifying assumptions. Given that the model includes the principle mechanisms of malaria transmission, it acts as a foundation for simulations that represent the dynamics between humans and mosquitoes. The model is generic in nature and may be parameterized to portray a wide variety of locations, different malaria parasites, vector species, and to cater for seasonality. Based on the obtained formulae from the human and mosquito sectors, we are able to eliminate three degrees of freedom, allowing us to calculate the temporal steady state relationship between Plasmodium falciparum prevalence in humans and mosquitoes. In this sense, the paper offers a new way to obtain the most representative malaria indicators derived from stock-and-flow diagrams that encompass the causal relationships that exist between the attributes of such a system. the structure that governs the dynamics of the disease. The model has been built following the System Dynamics methodology, explicitly representing the variables, the feedbacks and the nonlinearities, i.e. In addition, we document the dynamics of malaria, illustrating the impact of control strategies and how the bites per mosquito have a larger effect on transmission intensity than the mosquito mortality, the ratio of mosquitoes to humans, or the transmission efficiency. The present paper explores a simple dynamic model from which we review the classic formulae in malaria epidemiology that relate entomological and epidemiological variables to malaria transmission. ![]()
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