Time series decomposition of malaria patterns revealed existence of seasonality of malaria across the years (2011 – 2017) in all the altitude zones (Figure 3). The number of cases of malaria declined from 2011 to least number of cases towards 2017 (Figure 3). There was statistical significant difference (p<0.05) in the number malaria cases per 1000 individuals across the three altitude zones (lower, mid and higher altitude) in each of the years (2011-2017) except the years 2013 and 2017 (Table 1 and Figure 2).
The cases of malaria per 1000 in high, mid and lower altitude were 49 (SD = 40), 67 (SD = 55) and 84 (SD = 96) respectively. Malaria cases revealed a normal curve-shaped trend over each year in the three areas (lower, middle and higher altitude areas) (Figure 3). Also the months of June revealed highest numbers of malaria cases (94, SD = 73; 103, SD = 73 and 128, SD = 134 in high, mid and lower altitudes respectively) over the years (2011 to 2017). The months of January (41, SD = 29; 45, SD = 41 and 52, SD = 67 in high, mid and lower altitudes respectively) and December (28, SD = 23; 29, SD = 21 and 38, SD = 23 in high, mid and lower altitudes respectively) had the least number of malaria cases.
Spatial variation of malaria (Figure 4) revealed higher number of cases of malaria in the lower altitude areas of Kween district. Higher and mid-altitude areas of the district had relatively lower number of malaria cases (49, SD = 40 and 67, SD = 55 respectively), while lower altitude areas had the highest (84, SD = 96) number of malaria cases. The trends however declined from 2011 to 2017 in all the altitudinal zones (Figure 4).
Mann-Kendal trend test revealed a Sen’s slope of -29.0 and -10.9 (CI = 0.95) for malaria cases in the periods of March to September and October to February respectively in the higher altitude areas of Kween district. It also revealed a drastic decline of malaria cases over the seven year period (from 2011 to 2017) with Sen’s value of -21.5 (CI = 0.95). In the middle altitude areas, the Sen’s slope were -44.8, -56.0 and -29 annually, March to September, and October to February respectively (CI = 0.95). In the lower altitude, the Sen’s values were -87.8, -120.7 and -41.9 annually, March to September and October to February respectively (CI = 0.95).
Malaria cases, rainfall and temperature interaction in different altitude zones
The mean temperatures in higher, mid and lower altitude areas of Kween district between 2011 and 2017 were 15.7ᴼC (SD = 2.8), 18.4ᴼC (SD = 1.3) and 21.4ᴼC (SD = 1.8), respectively. Higher altitude areas experienced a very low positive correlation (0.47) between precipitation and number of malaria cases (Table 2 and Figure 5). Similarly, there was a very low negative correlation (-0.46) between temperature and number of malaria cases. Annually, there was a high positive correlation (0.79) between number of malaria cases and precipitation. The relationship between temperature and malaria cases showed a negatively high correlation (-0.84).
For mid-altitude there was a very low positive correlation (0.47) between precipitation and malaria cases (Table 2 and Figure 5). Similarly, there was a low negative correlation (-0.64) between temperature and malaria cases. Also annually, there was a low positive correlation (0.59) between precipitation and malaria cases. Meanwhile, temperature and malaria cases showed a negatively low correlation (-0.45).
Lower altitude reflected a positively high correlation (0.72) between precipitation and malaria cases (Table 2 and Figure 5). Similarly, there was a negatively high correlation (-0.78) between temperature and malaria cases. Annually, there was a positive moderate correlation (0.70) between malaria cases and precipitation. Meanwhile, temperature and malaria trends showed a negatively high correlation (-0.83).
Forecasting of malaria patterns
Forecasts for all the three altitudinal zones revealed malaria cases to continue to decrease if the conditions were kept constant and/or intervention efforts are strengthened (Figure 6). However, relaxation of the malaria control interventions would greatly allow for more increased number of cases of malaria (Figure 6).