Editorial: Interactions between COVID-19 and malaria
Bibliographic record
Abstract
Interactions between COVID-19 and malariaAs the first wave of SARS-CoV-2 infections spread across the globe there was a noticeable paucity of cases reported from countries in sub-Saharan Africa (SSA).Many theories were proposed to explain the epidemiology of COVID-19 in this region.One relates to population demographics; there is a clear positive correlation between age and risk of infection (1) suggesting the younger age distribution in many countries in SSA could account for fewer cases.Likewise, infection is generally less severe in younger people (2) and the mild symptoms may influence care-seeking behaviours.The case numbers may also reflect limited capacity for SARS-CoV-2 diagnosis.This was particularly relevant prior to the availability of rapid diagnostic tests when diagnosis relied heavily on molecular testing.Together, the population structure and limited diagnostic capacity could contribute to a gross underreporting of cases.An alternative and compelling theory is that the severity and/or risk of infection with SARS-CoV-2 is modulated by malaria.At the same time there were major concerns about the potential impact of COVID-19 and the public health responses to the pandemic upon routine health service delivery, including malaria programmes, and the potential mis-diagnosis of malaria fevers as COVID-19 by providers and communities, who may be concerned about seeking services (3).The six articles within the Research Topic 'Interactions between COVID-19 and malaria' address these ideas by considering the potential biological interactions between the two pathogens and more broadly, the public health impact of the pandemic on malaria research and control.Over 95% of malaria cases occur in SSA with 229 million new cases reported in the year prior to the emergence of SARS-CoV-2 (4).Given this intense force of infection, malaria is considered among the strongest drivers of human evolution and African populations exhibit specific genetic traits that favour resistance to this parasite.One of the most striking examples is selection for the Duffy-null genotype that is strongly associated with resistance to infection by Plasmodium vivax (5).This gene encodes the DARC receptor on red blood cells (RBCs), and merozoites from this species rely primarily on binding to DARC to initiate invasion of RBCs.Plasmodium can also interact biologically with other pathogens and typically the parasite exacerbates disease, as observed for example during co-infection with HIV (6).In their review article, Konozy et al. explore the biological links between Plasmodium and SARS-CoV-2, discuss the evidence that host genetic variation could alter susceptibility to infection and draw parallels between the immunological responses evoked Frontiers in Immunology frontiersin.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.021 | 0.020 |
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".