The South African government’s COVID-19 response: protecting lives and livelihoods
Bibliographic record
Abstract
This chapter brings together aspects of the impact of COVID-19 on lives and livelihoods in South Africa. As at 15 August 2021, the epidemic is reported to have led to 77 141 deaths and been associated with 229 850 excess deaths. At the same time, the national economy has been severely affected with Gross Domestic Product decline of 7% in 2020/21, job losses exceeding 2 million, and sharp reductions in national revenue. The chapter takes a case study approach to provide an overview of the Government’s budgetary support to the health and income protection responses as well as the modelling that informed these. It also reviews some of the carry-through implications of the economic down-turn on public finances, including health budgets. The authors draw primarily on their experiences and subsequent reflection, with particular focus on the period 1 March 2021 to 28 February 2022. The budget provision for the health response to COVID-19 exceeded R20 billion, which was achieved through additional allocations and reprioritisation. Income protection measures exceeded R100 billion. However, suboptimal attention was given to how prolonged lockdowns would affect businesses, jobs, livelihoods and the economy over the medium and long term, with job losses initially exceeding 2.2 million and 1.4 million by the first quarter of 2021. As these effects fed through to public finances, growth and tax revenue declined substantially, resulting in reductions in virtually all government budgets. Over the 2021 Medium-term Expenditure Framework period, the economic effects of the epidemic and stringent lockdown measures have resulted in the reduction of provincial health budget projections by as much as R76 billion. The chapter emphasises the need to consider both lives and livelihoods in pandemic decision-making, ideally bringing together various dimensions of epidemiological and economic modelling in a multi-criteria decision framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".