INVESTIGATING THE RELATIONSHIP BETWEEN METEOLOROGICAL VARIABLES AND MALARIA CASES IN LOWERSHIRE: CASE STUDY OF MASANDUKO AREA IN NGABU, CHIKWAWA, SOUTHERN MALAWI.

Malaria is one of the major biological hazards and remains a significant public health problem in Malawi, causing a substantial number of deaths annually. The disease continues to pose a greater health burden than many other emerging diseases, including Coronavirus Disease 2019 (COVID-19). However, there is limited understanding of the relationship between climatic variables, particularly temperature and rainfall, and malaria incidence, especially in rural areas. Previous studies have indicated that rural populations in Malawi are more exposed to mosquito bites and, consequently, have a higher risk of malaria infection compared with populations in urban areas. Therefore, this study aimed to investigate the influence of climatic factors on malaria incidence in the Lower Shire, specifically targeting the Masanduko area in Traditional Authority (TA) Ngabu.

Methods: Secondary data on malaria surveillance, comprising 136,291 recorded cases, were extracted from the Chikwawa Health Information System for the period 2010–2020. Climatic data, including rainfall and temperature records for the same period, were obtained from the Ngabu Weather Station through the Department of Meteorological Services in Blantyre. Microsoft Excel Version 16 was used to plot graphs and assess trends in climatic variables and malaria cases. Linear correlation and regression analyses were subsequently conducted using SPSS Version 25 to determine the relationship between malaria incidence and climatic variables and to develop a predictive model for malaria outbreaks based on rainfall and temperature.

Results: The study found that malaria cases were recorded throughout the year in the study area. Although there was insufficient evidence of significant climate variability during the study period, relatively small increases in average minimum and maximum temperatures and rainfall were associated with malaria incidence in areas surrounding Masanduko. Maximum temperature was the only climatic parameter that showed a significant correlation with malaria incidence during both wet and dry seasons. Furthermore, maximum temperature demonstrated a strong relationship with malaria cases when analysed with a one-month lag (r = 0.836, p = 0.003). Maximum temperature was therefore used to develop a predictive model that explained approximately 40% of the variation in malaria cases in the Masanduko area, with malaria incidence in a given month being associated with maximum temperature recorded in the previous month (R2 = 0.405, β = 758.514, 95% CI = 14,683.048–55,923.475). The model may help predict the number of malaria cases expected in an upcoming month based on current and previous weather conditions, particularly maximum temperature. This could provide an early indication of the potential severity of malaria outbreaks and assist community members, health practitioners, government officials, and non-governmental organisations in planning and implementing appropriate malaria prevention and control measures.