How disease risk awareness modulates transmission: coupling infectious disease models with behavioural dynamics
Bibliographic record
Abstract
Abstract Epidemiological models often assume that individuals do not change their behavior or that those aspects are implicitly incorporated in parameters in the models. Typically these assumption is included in the contact rate between infectious and susceptible individuals. For example models incorporate time variable contact rates to account for the effect of behavior or other interventions than in general terms reduce transmission. However, adaptive behaviors are expected to emerge and to play an important role in the transmission dynamics across populations. Here, we propose a theoretical framework to couple transmission dynamics with behavioral dynamics due to infection awareness. We first model the dynamics of social behavior by using a game theory framework. Then we coupled the model with an epidemiological model that captures the disease dynamics by assuming that individuals are more aware of that epidemiological state (i.e. fraction of infected individuals) and reduces their contacts. Our results from a mechanistic modeling framework show that as individuals increase their awareness the steady-state value of the final fraction of infected individuals in a susceptible-infected-susceptible (SIS) model decreases. We also extend our results to a spatial framework, incorporating a spatially-defined theoretical contact network (social network) and we made the awareness parameter dependent on a global or local contact structure. Our results show that even when individuals increase their awareness of the disease, the spatial structure itself defines the steady state solution of the system, in which more connected networks (networks with random or constant degree distributions) results in a population with no change in their behavior. Our work then shows that explicitly incorporating dynamics about the behavioral response dynamics might significantly change the predicted course of the epidemic and therefore highlights the importance of accounting for this source of variation in the epidemiological models. Author summary We present a theoretical framework for coupling traditional epidemiological models with a behavioral dynamical model in the form of a game-theoretical setting. Here, individual payoffs are assumed to be coupled with the force of infection (FOI) and the transmission probability, which is proportional to the individuals behavior. Our approach studies the temporal dynamics of a mechanistic epidemiological model (SIS) coupled with a prisoners dilemma framework, then we extended the results to an SIS model implemented on a network (social network) using three types of networks: Scale-free, Watts-Strogatz or small world and grid networks. Our results show that behavior can change the final fraction of infected individuals and the fraction of cooperators or individuals who voluntarily take actions to reduce their transmission in the system. In addition, when the dynamics were studied on a contact network we found that the topology of this network plays an essential role in controlling individuals behavior. Specifically, our results show that as the network gets more connected (i.e. degree distribution is random or uniform (Watts-Strogatz or grid networks respectively) disease spread is faster and therefore individuals are not obligated to cooperate. However, when the dynamics are studied in a scale free contact network, as degree distribution follows a power-law, we show that similarly as the mechanistic ODEs model individuals cooperate so their transmission probability is reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".