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Background: Disease morbidity, mortality and speed of spread vary substantially spatially. These have important implications for effective planning and targeting intervention strategies. The purpose of this study was to model the spatial dependence of fever prevalence and suspected malaria cases among children in Ethiopia. Methods: Data were obtained from 2011 EDHS collected for 144 districts at SNNP and Oromia Regional States. Explanatory spatial data analysis and spatial lag and error models were applied. Results:The results showed that the spatial lag model better fitted to the data. Prevalence rate of each of the events in a district was shown to be affected by that of its neighbors status. It was revealed that altitude, access to piped water, proportion of children under five, vaccination Original Research Articlecoverage, child wasting core, proportion of children born below average size and toilet availability were significant risk factors of fever rate. Moreover, altitude, proportion of children born below average, vaccination overage, stunting score, wasting score, proportion of children under five, mother education, and access to mass media were found to have significant effects on the rate of suspected malaria cases. Conclusion: There is spatial dependency for both variables -childhood fever prevalence and suspected malaria cases. The hot spot areas are at the center of each region. Several risk factors need attention. Interventions to mitigate occurrence of malaria infection among children would take in to account the nature of spatial variability and the identified risk factors.

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Background: Laboratory biomarkers are amongst the best imperative predictors of disease outcomes in hospital-admitted COVID-19 patients. Although data is available in this regard at a global level, there is a paucity of information in Ethiopia. Thus, this study aimed to assess the laboratory biomarkers association with death among COVID-19 patients in Ethiopia. Methods: A health facility-based longitudinal study was conducted from 2020 to 2022 among RT-PCR-confirmed COVID-19 patients admitted and on treatment follow-up at COVID-19 treatment hospitals in Addis Ababa. A robust Poisson regression model was fitted to assess the association between demographic, clinical, and laboratory factors and death. Significance was determined at p<0.05, and variables with p < 0.15 in bivariate analyses were included in the final multivariable models. Incidence rate ratio (IRR) with a 95% confidence interval (CI) was used to describe associations. Results: Of the 2357 COVID-19 patients, 248 (10.5%) died. The median age of participants was 59 (IQR= 45- 70) years, and the majority (64.9%) of them were male. Lower median RBC was observed among those who died at 4.58 (4.06-5.07) as compared to those who survived at 4.69 (4.23-5.12) whereas high median (IQR) WBC was a predictor of mortality with 11.2 (7.7-15.9). After adjusting for confounders, death was associated with age >74 years having adjusted incidence rate ratio [aIRR (95%CI): 2.46 (1.40-4.34)], and critical clinical situations [aIRR (95% CI): 4.04 (2.18-7.52)]. Conclusion: Our results demonstrate that abnormal liver function tests, abnormal white blood cells, age of the patients, and clinical status of the patients during admission are associated with unfavorable outcomes of COVID-19. Hence, timely monitoring of these laboratory results at the earliest phase of the disease was highly commendable.