Cost-effective modeling of the transmission dynamics of malaria: a case study in Bangladesh

Rahman, Azizur, and Kuddus, Md Abdul (2020) Cost-effective modeling of the transmission dynamics of malaria: a case study in Bangladesh. Communications in Statistics Case Studies Data Analysis and Applications, 6 (2). pp. 270-286.

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Each year infectious disease such as malaria causes severe population loss in both the developing and developed countries. Modeling of malaria is considered one of the effective ways to understand and mitigate this problem. This article develops a reliable quantitative modeling technique to support the design and characterization of the malaria disease in Bangladesh. Results reveal that the effects of some influencing factors such as service factors (i.e., lack of budgets in government agencies, antiviral medicine, and poor quality of service facilities), disease related factors (i.e., nutrition status and existing illness), environmental factors (i.e., urbanization and crowdedness), and sociological factors (i.e., education and religious believes) are significant. In particular, social factor education showed significant multidimensional impacts to the occurrence of the disease and its mitigation. A poor educational status leads to a range of impacts through (i) lack of awareness in the causes of malaria, severity of health effect and how and where to access the treatment services, (ii) refusal of vaccination, and (iii) unfamiliarity with good health and nutritional facts contributing to nutrition status. The study also provides the prediction of new cases in malaria until 2025 using the developed model and recommends, control strategies of malaria.

Item ID: 67213
Item Type: Article (Research - C1)
ISSN: 2373-7484
Keywords: Bangladesh, confidence interval, malaria, prediction, reproduction number, SEIRS model
Copyright Information: © 2020 Taylor & Francis Group, LLC
Date Deposited: 21 May 2021 05:48
FoR Codes: 42 HEALTH SCIENCES > 4206 Public health > 420699 Public health not elsewhere classified @ 50%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320211 Infectious diseases @ 50%
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