Sustained reductions in life-threatening invasive bacterial diseases during the first two years of the COVID-19 pandemic: analyses of prospective surveillance data from 30 countries participating in the IRIS Consortium
Bibliographic record
Abstract
Summary Background The Invasive Respiratory Infection Surveillance (IRIS) Consortium was established to assess the impact of the COVID-19 pandemic on invasive diseases caused by Streptococcus pneumoniae, Haemophilus influenzae, Neisseria meningitidis and Streptococcus agalactiae . Here we analyse the incidence and distribution of disease during the first two years of the pandemic. Methods Laboratories in 30 countries/territories representing five continents submitted case data from 2018-2021 to private projects within databases in PubMLST. The impact of COVID-19 containment measures on the overall number of cases was analysed, and changes in disease distributions by patient age and serotype/group were examined. Interrupted time series analyses quantified the impact of pandemic response measures and their relaxation on disease rates, and autoregressive integrated moving average models estimated effect sizes and forecasted counterfactual trends by hemisphere. Findings Overall, 116,841 cases were analysed: 76,481 (2018-2019, pre-pandemic) plus 40,360 (2020-2021, pandemic). During the pandemic there was a significant reduction in the risk of disease caused by S pneumoniae (risk ratio: 0.47; 95% confidence interval: 0.40-0.55), H influenzae (0.51; 0.40-0.66) and N meningitidis (0.26; 0.21-0.31), whereas no significant changes were observed for the non-respiratory-transmitted pathogen S agalactiae (1.02; 0.75-1.40). No major changes in the distribution of cases were observed when stratified by patient age or serotype/group. An estimated 36,289 (17,145-55,434) cases of invasive bacterial disease were averted during the first two years of the pandemic among IRIS participating countries/territories. Interpretation COVID-19 containment measures were associated with a sustained decrease in the incidence of invasive disease caused by S pneumoniae, H influenzae and N meningitidis during the first two years of the pandemic, but cases began to increase in some countries as pandemic restrictions were lifted. Research in context Evidence before this study Early in the COVID-19 pandemic the IRIS Consortium reported a significant reduction in invasive disease due to respiratory-transmitted bacterial pathogens, which was associated with the implementation of COVID-19 stringency measures and changes in human social behaviour. All 26 countries/territories participating in IRIS at the time experienced a significant reduction in infections between January and May 2020, compared with the previous two years. In particular, S pneumoniae infections decreased by 68% at four weeks after COVID-19 containment measures were imposed, and by 82% at eight weeks. Added value of this study These new data from the expanded IRIS Consortium across 30 countries/territories demonstrated a sustained reduction in invasive disease throughout the first two years of the COVID-19 pandemic. Using time series modelling, we estimated that over 36,000 cases of invasive bacterial disease were averted in 2020-2021 among the countries participating in IRIS; however, minor increases in disease in the latter half of 2021 require close monitoring to understand the nature of re-emerging cases. Implications of all the available evidence Future epidemics and pandemics will occur, and we need to understand not only the pathogen that is directly responsible for the pandemic, but also that population-level responses to an epidemic or pandemic more broadly affect overall human health and other microbes. IRIS provides evidence for the effects of such public health responses on severe invasive bacterial infections across many countries. Moreover, these IRIS data provide a better understanding of microbial transmission, will inform vaccine development and implementation, and can contribute to healthcare service planning and provision of policies.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".