Clinical Features and Time to Recovery of Admitted COVID-19 Cases at Dilla University Referral Hospital Treatment Center, South Ethiopia

Integrate-and-fire model of disease transmission

We create an epidemiological susceptible-infected-susceptible model of disease transmission using integrate-and-fire nodes on a network, allowing memory of previous interactions and infections. Agents in the network sum infectious matter from their nearest neighbors at every time step, until they exceed their infection threshold, at which point they "fire" and become infected for as long as the recovery time. The model has memory of previous interactions by tracking the amount of infectious matter carried by agents as well as just binary infected or susceptible states, and the model has memory of previous infections by modeling immunity as increasing the infection threshold after recovery. Creating a simulation of the model on networks with a power-law degree distribution and homogeneous agent parameters, we find a single strain version of the model matches well with the England COVID-19 case data, with a root-mean-squared error of 0.014%. A simulation of a multistrain version of the model (where there is cross-strain immunity) matches well with the influenza strain A and strain B case numbers in Canada, with a root-mean-squared error of 0.002% and 0.0012%, respectively, though due to the coupling in the model, both strains peak in phase. Since the dynamics of the model successfully capture real-life transmission dynamics, we test interventions to study their effect on case numbers, with both quarantining and social gathering restrictions lowering the peak. Since the model has memory, the stricter the intervention, the higher the secondary peak when the restriction is removed, showing that interventions change only the shape of the curves and not the overall number infected in the population.

COVID-19 mortality rate and its determinants in Ethiopia: a systematic review and meta-analysis

Background

The COVID-19 mortality rate continues to be high in low-income countries like Ethiopia as the new variant's transmission expands and the countries' limited capacity to combat the disease causes severe outcomes, including deaths. The aim of this study is to determine the magnitude of the COVID-19 mortality rate and its determinants in Ethiopia.

Methods

The main electronic databases searched were PubMed, CINAHL, Google Scholar, and African journals online. The included studies' qualities were assessed independently using the Newcastle-Ottawa scale. The data was extracted in Microsoft Excel spreadsheet format. The pooled effect size and odds ratios with 95% confidence intervals across studies were determined using the random-effects model. I2 is used to estimate the percentage of overall variation across studies due to heterogeneity. Egger's test and funnel plot were used to find the published bias. A subgroup analysis was conducted. The effect of a single study on the overall estimation was determined by sensitivity analysis.

Results

A total of 21 studies with 42,307 study participants were included in the final analysis. The pooled prevalence of COVID-19 mortality was 14.44% (95% CI: 10.35-19.08%), with high significant heterogeneity (I2 = 98.92%, p < 0.001). The risk of mortality from COVID-19 disease was higher for patients with comorbidity (AHR = 1.84, 95% CI: 1.13-2.54) and cardiovascular disease (AHR = 2, 95% CI: 1.09-2.99) than their counterparts without these conditions.

Conclusion

A significant number of COVID-19 patients died in Ethiopia. COVID-19 patients with comorbidities, particularly those with cardiovascular disease, should receive special attention to reduce COVID-19 mortality.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, registration identifier (ID) CRD42020165740.

Time to recovery and determinant factors of COVID-19 patients under treatment in Sidama region, Ethiopia: A retrospective cohort study

Introduction

Evidence from several studies shows that the median recovery time among COVID-19 patients varies in different settings, and why that difference occurs is questionable. The current study was aimed to estimate the time to recovery and to identify determinant factors among COVID-19 patients admitted to treatment centers in the Sidama region, Ethiopia.

Methods

The secondary data was extracted from the Sidama Public Health Institute (SPHI), data management unit. A total of 1038 COVID-19 patients who were under treatment from 1, June 2020, to 30, June 2022, at different treatment centers in the region were included in the study. The Log-Logistic Accelerated Failure Time (AFT) model was employed to estimate the effects size of different covariates on recovery time of COVID-19 patients under treatment.

Results

Among the total number of patients in the study, 704 (67.82 %) were recovered, while 334(32.18 %) were censored. The median recovery time was 14 days (IQR: 10-18 days). Patients who were above 65 years old had nearly 1.17 times more prolonged recovery time as compared to patients who were below 25 years old, (OR = 1.168, p-value = 0.032, CI = 0.013,0.298). The log odds of recovery for patients who were in critical severity status at admission was 1.279 times more decelerated as compared to asymptomatic patients (P-value = 0.005, CI = 0.076, 0.417). The log odds of recovery for patients who had no history of headache was 1.107 times more accelerated as compared to patients who had a history of headache (OR = 1.107, p-value = 0.027, CI = 0.011, 0.192). The log odds of recovery for non-diabetic patients was 1.244 times more accelerated as compared to patients who were diabetic (OR = 1,244, p-value = 0.002, CI = 0.077, 0.360), holding other covariates constant in the model.

Conclusion

Age, critical severity status of infection, having symptoms of infection, having a history of headaches, and being diabetic had statistically significant effects on time to recovery among COVID-19 patients admitted to the treatment centers in the Sidama region.

Neutrophil-to-Lymphocyte Ratio and Cut-off Values as Predictor of Severity and Mortality in COVID-19 Patients in Millennium COVID-19 Care Center, Addis Ababa, Ethiopia

Background

Early identification of patients at high risk of poor clinical outcomes is the key to success in saving the lives of patients with coronavirus disease 2019 (COVID-19). Neutrophil to Lymphocyte Ratio (NLR) is an easily available and cheap surrogate inflammatory marker, its baseline NLR role in African COVID-19 patients remains to be investigated. The objective of the study aimed to evaluate the role of NLR as a predictor of severity and mortality of COVID-19 patients admitted at the Millennium COVID 19 care center in Addis Ababa, Ethiopia.

Methods

A cross-sectional study was conducted on patients with COVID-19 admitted to the Millennium COVID-19 care center from August 1 to October 30, 2021. Receiver Operating Characteristic curve analysis was used to calculate the area under the curve to assess the predictive capacity of NLR on mortality and severity. Multivariable logistic regression analysis was done to identify the association between independent variables and disease outcomes with an Adjusted Odds Ratio (AOR), P-value, and 95% CI for AOR were used for testing significance.

Results

The NLR of 9.47 was identified as the optimal cut-off value for predicting mortality with a sensitivity of 88.7% and a specificity of 95.4% (Area Under the Curve (AUC):0.95, 95% CI 0.92-98; P<0.001) and the NLR of 5.86 was an effective threshold value in predicting the severity of disease with a sensitivity of 92.2% and a specificity of 75% (AUC:0.85, 95% CI 0.800-0.905; P<0.001). In multivariable logistic regression analysis, after adjusting for confounding factors, NLR of more than 9.47 and 5.86 was significantly associated with all-cause of in-hospital mortality (AOR=4.73, 95% CI, 1.19-33.68; P<0.02), and severity of disease (AOR=12.98, 95% CI 3.85-43.80; P=0.001), respectively.

Conclusion

NLR greater than 9.47 and 5.86 effectively predict mortality and severity of the disease, respectively. It provides an objective input for early decision-making in inpatient management especially in resources limited area.