COVID-19 in Ethiopia: A Regional Geographical Analysis and Examination of Complex Interactions between a Pandemic, Geopolitics, Domestic War, Drought, and Poverty
Bibliographic record
Abstract
COVID-19 arrived in Ethiopia - a least developed country – amidst internal instabilities including a civil war, drought, and a contentious national election, all taking place concurrently with international pressures linked to disagreements with Egypt and Sudan over the construction of the Great Ethiopian Renaissance Dam (GERD) along the Ethio-Sudanese border. External challenges also came in the form of US and European Union’s pressures on Ethiopia, a close trading partner of China, over perceived threats posed by growing influences of Russia and China in the Horn of Africa and Red Sea regions. External pressures emboldened internal grumblings resulting in an all-out domestic war. Not much is known about the combined or isolated impacts of these events on Ethiopia’s COVID-19 response and on the spread of the disease. To fill this knowledge gap, a descriptive analysis is conducted for the July 27, 2020 through May 9th, 2022 period using data obtained from the Ministry of Health websites. In a possible sign of the impacts of the stated influences, the capital city Addis Ababa, home to just four percent of the country’s estimated population of 120 million, accounted for roughly two-thirds of the 470,760 COVID-19 cases, and of the 7,510 deaths, as testing and death reporting outside of the capital was constrained by security concerns. A peak daily count of 5,185 cases and a case-positivity ratio of 39 percent were reached on December 28, 2021. It is uncertain to what extent the low national and regional counts of cases and deaths are the results of prevention or demographic invulnerability as opposed to limited testing and underreporting of cases and deaths outside of Addis Ababa.  Ethiopia needs to put in place rigorous systems of data generation through testing, as well as pipelines for its dissemination, both in normal times and during unforeseen exigencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".