Failure to balance social contact matrices can bias models of infectious disease transmission
Bibliographic record
Abstract
ABSTRACT Spread of transmissible diseases is dependent on contact patterns in a population (i.e. who contacts whom). Therefore, many epidemic models incorporate contact patterns within a population through contact matrices. Social contact survey data are commonly used to generate contact matrices; however, the resulting matrices are often imbalanced, such that the total number of contacts reported by group A with group B do not match those reported by group B with group A. While the importance of balancing contact matrices has been acknowledged, how these imbalances affect modelled projections (e.g., peak infection incidence, impact of public health measures) has yet to be quantified. Here, we explored how imbalanced contact matrices from age-stratified populations (<15, 15+) may bias transmission dynamics of infectious diseases. First, we compared the basic reproduction number of an infectious disease when using imbalanced versus balanced contact matrices from 177 demographic settings. Then, we constructed a susceptible exposed infected recovered transmission model of SARS-CoV-2 and compared the influence of imbalanced matrices on infection dynamics in three demographic settings. Finally, we compared the impact of age-specific vaccination strategies when modelled with imbalanced versus balanced matrices. Models with imbalanced matrices consistently underestimated the basic reproduction number, had delayed timing of peak infection incidence, and underestimated the magnitude of peak infection incidence. Imbalanced matrices also influenced cumulative infections observed per age group, and the projected impact of age-specific vaccination strategies. For example, when vaccine was prioritized to individuals <15 in a context where individuals 15+ underestimated their contacts with <15, imbalanced models underestimated cumulative infections averted among 15+ by 24.4%. We conclude stratified transmission models that do not consider reciprocity of contacts can generate biased projections of epidemic trajectory and impact of targeted public health interventions. Therefore, modellers should ensure and report on balancing of their contact matrices for stratified transmission models. AUTHOR SUMMARY Transmissible diseases such as COVID-19 spread according to who contacts whom. Therefore, mathematical transmission models – used to project epidemics of infectious diseases and assess the impact of public health interventions – require estimates of who contacts whom (also referred to as a contact matrix). Contact matrices are commonly generated using contact surveys, but this data is often imbalanced, where the total number of contacts reported by group A with group B does not match those reported by group B with group A. Although these imbalances have been acknowledged as an issue, the influence of imbalanced matrices on modelled projections (e.g. peak incidence, impact of public health interventions) has not been explored. Using a theoretical model of COVID-19 with two age groups (<15 and 15+), we show models with imbalanced matrices had biased epidemic projections. Models with imbalanced matrices underestimated the initial spread of COVID-19 (i.e. the basic reproduction number), had later time to peak COVID-19 incidence and smaller peak COVID-19 incidence. Imbalanced matrices also influenced cumulative infections observed per age group, and the estimated impact of an age-specific vaccination strategy. Given imbalanced contact matrices can reshape transmission dynamics and model projections, modellers should ensure and report on balancing of contact matrices.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".