Early and Subsequent Epidemic Characteristics of COVID-19 and Their Impact on the Epidemic Size in Ethiopia

Clinical profiles and mortality predictors of hospitalized patients with COVID-19 in Ethiopia

BACKGROUND

Studying the characteristics of hospitalized Coronavirus Disease 2019 (COVID-19) patients is vital for understanding the disease and preparing for future outbreaks. The aim of this study was to analyze and describe the clinical profiles and factors associated with mortality among COVID-19 patients admitted to Jimma Medical Center COVID-19 Treatment Center (JMC CTC) in Ethiopia.

METHODS

All confirmed COVID-19 patients admitted to JMC CTC between 17 April 2020 and 05 March 2022 were included in this study. Socio-demographic data, clinical information, and outcome variables were collected retrospectively from medical records and COVID-19 database at the hospital. Bivariable and multivariable analyses were performed to determine factors associated with COVID-19 severity and mortality. A P-value < 0.05 was considered statistically significant.

RESULTS

A total of 542 confirmed COVID-19 patients were admitted to JMC CTC, of which 322 (59.4%) were male. Their median age was 48 years (IQR 32-64). About 51% (n = 277) of them had severe COVID-19 upon admission. Patients with hypertension [AOR: 2.8 (95% CI: 1.02-7.7, p = 0.046)], diabetes [AOR: 8.8 (95% CI: 1.2-17.3, p = 0.039)], and underlying respiratory diseases [AOR: 18.8 (95% CI: 2.06-71.51, p = 0.009)] were more likely to present with severe COVID-19 cases. Overall, 129 (23.8%) died in the hospital. Death rate was higher among patients admitted with severe disease [AHR = 5.5 (3.07-9.9) p < 0.001)] and those with comorbidities such as hypertension [AHR = 3.5 (2.28-5.41), p < 0.001], underlying respiratory disease [AHR = 3.4 (1.97-5.94), p < 0.001], cardiovascular disease (CVDs) [AHR = 2.8 (1.73-4.55), p < 0.001], and kidney diseases [AHR = 3.7 (2.3-5.96), p < 0.001].

CONCLUSION

About half of COVID-19 cases admitted to the hospital had severe disease upon admission. Comorbidities such as hypertension, diabetes, and respiratory diseases were linked to severe illness. COVID-19 admissions were associated with high inpatient mortality, particularly among those with severe disease and comorbidities.

SARS-CoV-2 variant typing using real-time reverse transcription-polymerase chain reaction-based assays in Addis Ababa, Ethiopia

Objectives

This study aimed to determine the SARS-CoV-2 variants in the first four COVID-19 waves using polymerase chain reaction (PCR)-based variant detection in Addis Ababa, Ethiopia.

Methods

A cross-sectional study was conducted using repository nasopharyngeal samples stored at the Ethiopian Public Health Institute COVID-19 testing laboratory. Stored positive samples were randomly selected from the first four waves based on their sample collection date. A total of 641 nasopharyngeal samples were selected and re-tested for SARS-CoV-2. RNA was extracted using nucleic acid purification instrument. Then, SARS-CoV-2 detection was carried out using 10 μl RNA and 20 μl reverse transcription-PCR fluorescent mix. Cycle threshold values <38 were considered positive.

Results

A total of 374 samples qualified for B.1.617 Lineage and six spike gene mutation variant typing kits. The variant typing kits identified 267 (71.4%) from the total qualifying samples. Alpha, Beta, Delta, and Omicron were dominantly identified variants from waves I, II, III, and IV, respectively. From the total identified positive study samples, 243 of 267 (91%) of variants identified from samples had cycle threshold values <30.

Conclusions

The study data demonstrated that reverse transcription-PCR-based variant typing can provide additional screening opportunities where sequencing opportunity is inaccessible. The assays could be implemented in laboratories performing SARS-CoV-2 molecular testing.

The Inverted U-Shaped Relationship Between Socio-Economic Status and Infections During the COVID-19 Pandemic

Although the World Health Organization has declared that the COVID-19 pandemic no longer qualifies as a global public health emergency, it still needs to explore the response of society to the COVID-19 pandemic. Socio-economic status (SES) was proven to be linearly associated with the COVID-19 pandemic, although this relationship may be more complex due to regional differences. In the study, we analyzed and revealed the effects and mechanisms of SES on infections among low, lower-middle, upper-middle and high SES group (LSG, LMSG, UMSG, and HSG, respectively). The results showed that the relationship between SES and infections was inverted U-shaped, especially in the first three phases. In Phase I, UMSG had the highest number of infections, with an average of 238.31/1M people (95%CI: 135.47-341.15/1M people). In Phases II and III, infections decreased insignificantly with increasing SES (r = -0.01, p = 0.92; r = -0.11, p = 0.22) and the highest number of infections were found in the LMSG. In Phase IV, SES was positively related to the number of infections (r = 0.54, p < 0.001). Furthermore, the nonlinear impact of multiple factors related to SES on the infections explains the complex relationships between SES and infections. SES affected infections mainly through medical resources, demographics and vaccination, and differed across the SES groups. Particularly, demographics could exert an impact on population mobility, subsequently influencing infections in LMSG, with an indirect effect of 0.01 (p < 0.05) in Phase II. This study argues for greater attention to countries with middle SES and the need for future targeted measures to cope with infectious diseases.

A standardised differential privacy framework for epidemiological modeling with mobile phone data

During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework.

Molecular Epidemiology and Diversity of SARS-CoV-2 in Ethiopia, 2020–2022

Ethiopia is the second most populous country in Africa and the sixth most affected by COVID-19 on the continent. Despite having experienced five infection waves, >499,000 cases, and ~7500 COVID-19-related deaths as of January 2023, there is still no detailed genomic epidemiological report on the introduction and spread of SARS-CoV-2 in Ethiopia. In this study, we reconstructed and elucidated the COVID-19 epidemic dynamics. Specifically, we investigated the introduction, local transmission, ongoing evolution, and spread of SARS-CoV-2 during the first four infection waves using 353 high-quality near-whole genomes sampled in Ethiopia. Our results show that whereas viral introductions seeded the first wave, subsequent waves were seeded by local transmission. The B.1.480 lineage emerged in the first wave and notably remained in circulation even after the emergence of the Alpha variant. The B.1.480 was outcompeted by the Delta variant. Notably, Ethiopia’s lack of local sequencing capacity was further limited by sporadic, uneven, and insufficient sampling that limited the incorporation of genomic epidemiology in the epidemic public health response in Ethiopia. These results highlight Ethiopia’s role in SARS-CoV-2 dissemination and the urgent need for balanced, near-real-time genomic sequencing.