Epidemiological Trends of Malaria in Five Years and under Children of Nsanje District in Malawi, 2015–2019

Theodore Gondwe, Yongi Yang, Simeon Yosefe, Maisa Kasanga, Griffin Mulula, Mphatso Prince Luwemba, Annie Jere, Victor Daka and Tobela Mudenda
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Theodore Gondwe: Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
Yongi Yang: Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
Simeon Yosefe: Department of Planning and Policy Development, Ministry of Health Malawi, Lilongwe 30377, Malawi
Maisa Kasanga: Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
Griffin Mulula: Department of Education Planning, Ministry of Education Malawi, Lilongwe 328, Malawi
Mphatso Prince Luwemba: Mathematical Department, Chancellor College, University of Malawi, Zomba 280, Malawi
Annie Jere: School of Engineering, Malawi University of Business and Applied Sciences, Blantyre 303, Malawi
Victor Daka: Public Health Department, Michael Chilufya Sata School of Medicine, Copperbelt University, Ndola 21692, Zambia
Tobela Mudenda: Pathology Department, Ndola Teaching Hospital, Ndola 10101, Zambia

IJERPH, 2021, vol. 18, issue 23, 1-12

Abstract: Background: Malaria continues to be a major public health problem in Malawi and the greatest load of mortality and morbidity occurs in children five years and under. However, there is no information yet regarding trends and predictions of malaria incidence in children five years and under at district hospital level, particularly at Nsanje district hospital. Aim: Therefore, this study aimed at investigating the trends of malaria morbidity and mortality in order to design appropriate interventions on the best approach to contain the disease in the near future. Methodology: Trend analysis of malaria morbidity and mortality together with time series analysis using the SARIMA (Seasonal Autoregressive Integrated Moving Average) model was used to predict malaria incidence in Nsanje district. Results: The SARIMA model used malaria cases from 2015 to 2019 and created the best model to forecast the malaria cases in Nsanje from 2020 to 2022. An SARIMA (0, 1, 2) (0,1,1) 12 was suitable for forecasting the incidence of malaria for Nsanje. Conclusion: The mortality and morbidity trend showed that malaria cases were growing at a fluctuating rate at Nsanje district hospital. The relative errors between the actual values and predicted values indicated that the predicted values matched the actual values well. Therefore, the model proved that it was adequate to forecast monthly malaria cases and it had a good fit, hence, was appropriate for this study

Keywords: malaria incidence; time series; SARIMA (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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