Global patterns in monthly activity of influenza virus, respiratory syncytial virus, parainfluenza virus, and metapneumovirus: a systematic analysis
Bibliographic record
Abstract
BACKGROUND: Influenza virus, respiratory syncytial virus, parainfluenza virus, and metapneumovirus are the most common viruses associated with acute lower respiratory infections in young children (<5 years) and older people (≥65 years). A global report of the monthly activity of these viruses is needed to inform public health strategies and programmes for their control. METHODS: In this systematic analysis, we compiled data from a systematic literature review of studies published between Jan 1, 2000, and Dec 31, 2017; online datasets; and unpublished research data. Studies were eligible for inclusion if they reported laboratory-confirmed incidence data of human infection of influenza virus, respiratory syncytial virus, parainfluenza virus, or metapneumovirus, or a combination of these, for at least 12 consecutive months (or 52 weeks equivalent); stable testing practice throughout all years reported; virus results among residents in well-defined geographical locations; and aggregated virus results at least on a monthly basis. Data were extracted through a three-stage process, from which we calculated monthly annual average percentage (AAP) as the relative strength of virus activity. We defined duration of epidemics as the minimum number of months to account for 75% of annual positive samples, with each component month defined as an epidemic month. Furthermore, we modelled monthly AAP of influenza virus and respiratory syncytial virus using site-specific temperature and relative humidity for the prediction of local average epidemic months. We also predicted global epidemic months of influenza virus and respiratory syncytial virus on a 5° by 5° grid. The systematic review in this study is registered with PROSPERO, number CRD42018091628. FINDINGS: We initally identified 37 335 eligible studies. Of 21 065 studies remaining after exclusion of duplicates, 1081 full-text articles were assessed for eligibility, of which 185 were identified as eligible. We included 246 sites for influenza virus, 183 sites for respiratory syncytial virus, 83 sites for parainfluenza virus, and 65 sites for metapneumovirus. Influenza virus had clear seasonal epidemics in winter months in most temperate sites but timing of epidemics was more variable and less seasonal with decreasing distance from the equator. Unlike influenza virus, respiratory syncytial virus had clear seasonal epidemics in both temperate and tropical regions, starting in late summer months in the tropics of each hemisphere, reaching most temperate sites in winter months. In most temperate sites, influenza virus epidemics occurred later than respiratory syncytial virus (by 0·3 months [95% CI -0·3 to 0·9]) while no clear temporal order was observed in the tropics. Parainfluenza virus epidemics were found mostly in spring and early summer months in each hemisphere. Metapneumovirus epidemics occurred in late winter and spring in most temperate sites but the timing of epidemics was more diverse in the tropics. Influenza virus epidemics had shorter duration (3·8 months [3·6 to 4·0]) in temperate sites and longer duration (5·2 months [4·9 to 5·5]) in the tropics. Duration of epidemics was similar across all sites for respiratory syncytial virus (4·6 months [4·3 to 4·8]), as it was for metapneumovirus (4·8 months [4·4 to 5·1]). By comparison, parainfluenza virus had longer duration of epidemics (6·3 months [6·0 to 6·7]). Our model had good predictability in the average epidemic months of influenza virus in temperate regions and respiratory syncytial virus in both temperate and tropical regions. Through leave-one-out cross validation, the overall prediction error in the onset of epidemics was within 1 month (influenza virus -0·2 months [-0·6 to 0·1]; respiratory syncytial virus 0·1 months [-0·2 to 0·4]). INTERPRETATION: This study is the first to provide global representations of month-by-month activity of influenza virus, respiratory syncytial virus, parainfluenza virus, and metapneumovirus. Our model is helpful in predicting the local onset month of influenza virus and respiratory syncytial virus epidemics. The seasonality information has important implications for health services planning, the timing of respiratory syncytial virus passive prophylaxis, and the strategy of influenza virus and future respiratory syncytial virus vaccination. FUNDING: European Union Innovative Medicines Initiative Respiratory Syncytial Virus Consortium in Europe (RESCEU).
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".