Background Child mortality is a key indicator of the performance of the health system of a nation. Impressive progress in the reduction of under-five mortality has been made in Ethiopia. However, still there are some regions where the under-five mortality rates are high. Southern Nations, Nationalities, and Peoples’ Region (SNNPR) is among those regions in Ethiopia with high under-five mortality rates. This study aimed to identify the determinant factors of under-five mortality in SNNPR. Methods Data used for the study were drawn from the 2016 EDHS. A total of 1277 under-five children were included in the study. A multivariable logistic regression model was fitted to identify determinant factors associated with under-five mortality. Results Children with second or third birth order (OR = 1.316, 95% CI: (1.097, 2.343)), fourth or fifth birth order (OR = 1.934, 95% CI: (1.678, 3.822)), sixth or above birth order (OR = 3.980, 95% CI: (2.352, 6.734)) were significantly associated with increased risk of under-five mortality as compared to those with first birth order. Increased risk of under-five mortality was also significantly associated with a family size of five or more (OR = 3.397, 95% CI: (1.702, 6.782)) as compared to the family size of less than five; smaller size at birth (OR = 1.714, 95% CI: (1.120, 2.623)) as compared to larger size at birth; multiple births (OR = 1.472, 95% CI: (1.289, 2.746)) as compared to singletons. On the other hand, female children (OR = 0.552, 95% CI: (0.327, 0.932)), children born at health institutions (OR = 0.449, 95% CI: (0.228, 0.681)) and children who were breastfed (OR = 0.657, 95% CI: (0.393, 0.864)) were significantly associated with decreased risk of under-five mortality as compared to male children, those born at home and those who were not breastfed respectively. Conclusions Sex of a child, birth order, size of a child at birth, place of delivery, birth type, breastfeeding status, and family size were significant factors associated with under-five mortality in SNNPR, Ethiopia. Thus, planning and implementing relevant strategies that focus on those identified determinant factors of under-five mortality is required for the improvement of child survival in SNNPR, Ethiopia.

In this study, predictive models are proposed to accurately estimate the confirmed cases and deaths due to of Corona virus 2019 (COVID-19) in Africa. The study proposed the predictive models to determine the spatial and temporal pattern of COVID 19 in Africa. The result of the study has shown that the spatial and temporal pattern of the pandemic is varying across in the study area. The result has shown that cubic model is best outperforming compared to the other six families of exponentials ( . The adopted cubic algorithm is more robust in predicting the confirmed cases and deaths due to COVID 19. The cubic algorithm is more superior to the state of the art of the works based on the world health organization data. This also entails the best way to mitigate the expansion of COVID 19 is through persistent and strict self-isolation. This pandemic will sustain to grow up, and peak to the highest for which a strong care and public health interventions practically implemented. It is highly recommended for Africans must go beyond theory preparations implementations practically through the public interventions.

Background: Children face the highest risk of dying in their first month of life. Ethiopia is one of the sub-Saharan countries with highest newborn deaths. Afar and Somalia regions in Ethiopia are among the regions with high death rates of newborn children. This study aimed to analyse and identify determinants of neonatal mortality in Afar and Somalia regions, Ethiopia.  Methods: This study used 2016 Ethiopian Demographic and Health Survey data for the analysis. The multivariable logistic regression model was used to identify the significant determinants of neonatal mortality. Adjusted odds ratio with a 95% confidence interval and p-value < 0.05 in the multivariable logistic regression model was reported to declare the statistical significance and strength of association between neonatal mortality and determinants. Results: A total of 2567 newborn children were included in this study. Mortality rate among newborns in the first month was 41 per 1000 live births in Afar and Somalia regions. Health facility delivery (AOR: 0.634; 95% CI: 0.409–0.982), being female (AOR: 0.206; 95% CI: 0.073–0.528), multiple births (AOR: 3.958; 95% CI: 2.293–11.208), small size at birth (AOR: 1.208; 95% CI: 1.003–1.728), secondary and above educational level of mothers (AOR: 0.484; 95% CI: 0.294–0.797) were statistically significant determinants neonatal mortality. Conclusions: In this study, sex of child, place of delivery, birth type, size at birth, mother’s educational level were found to be statistically significant determinants of neonatal death in Afar and Somalia regions, Ethiopia. Mothers with no education should be given health education and institutional delivery should be encouraged to improve the survival of the neonates in Afar and Somalia regions, Ethiopia.

No abstract

The aims of this study was to predict COVID-19 new cases using multiple linear regression model based on May to June 2020 data in Ethiopia. The COVID-19 cases data was collected from the Ethiopia Ministry of Health Organization Facebook page. Pearson’s correlation analysis and linear regression model were used in the study. And, the COVID-19 new cases was positively correlated with the number of days, daily laboratory tests, new cases of males, new cases of females, new cases from Addis Ababa city, and new cases from foreign natives. In the multiple linear regression model, COVID-19 new cases was significantly predicted by the number of days at 5%, the number of daily laboratory tests at 10%, and the number of new cases from Addis Ababa city at 1% levels of significance. Then, the researchers recommended that Ethiopian Government, Ministry of Health, and Addis Ababa city administrative should give more awareness and protections for societies, and they should open again more COVID-19 laboratory testing centers. And, this study will help the government and doctors in preparing their plans for the next times.