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Models of epidemics: when contact repetition and clustering should be included
Timo Smieszek1, Lena Fiebig, Roland W Scholz
1Institute for Environmental Decisions, Natural and Social Science Interface, ETH Zurich, Universitaetsstrasse 22, 8092 Zurich, Switzerland. timo.smieszek@env.ethz.ch
Background:
The spread of infectious disease is determined by biological factors, e.g. the duration of the infectious period, and social factors, e.g. the arrangement of potentially contagious contacts. Repetitiveness and clustering of contacts are known to be relevant factors influencing the transmission of droplet or contact transmitted diseases. However, we do not yet completely know under what conditions repetitiveness and clustering should be included for realistically modelling disease spread.
Methods:
We compare two different types of individual-based models: One assumes random mixing without repetition of contacts, whereas the other assumes that the same contacts repeat day-by-day. The latter exists in two variants, with and without clustering. We systematically test and compare how the total size of an outbreak differs between these model types depending on the key parameters transmission probability, number of contacts per day, duration of the infectious period, different levels of clustering and varying proportions of repetitive contacts.
Results:
The simulation runs under different parameter constellations provide the following results: The difference between both model types is highest for low numbers of contacts per day and low transmission probabilities. The number of contacts and the transmission probability have a higher influence on this difference than the duration of the infectious period. Even when only minor parts of the daily contacts are repetitive and clustered can there be relevant differences compared to a purely random mixing model.
Conclusion:
We show that random mixing models provide acceptable estimates of the total outbreak size if the number of contacts per day is high or if the per-contact transmission probability is high, as seen in typical childhood diseases such as measles. In the case of very short infectious periods, for instance, as in Norovirus, models assuming repeating contacts will also behave similarly as random mixing models. If the number of daily contacts or the transmission probability is low, as assumed for MRSA or Ebola, particular consideration should be given to the actual structure of potentially contagious contacts when designing the model.
Insights
Disease spread models differ based on contact patterns. Repetitive or clustered contacts significantly impact outbreak size, especially with low transmission or contact rates, necessitating careful model selection for diseases like MRSA or Ebola.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- Infectious disease spread is influenced by biological and social factors, including contact patterns.
- Repetitive and clustered contacts are known to affect transmission but their role in realistic disease modeling is not fully understood.
Purpose of the Study:
- To compare individual-based models with random mixing versus those incorporating repetitive and clustered contacts.
- To determine how contact patterns influence outbreak size under varying transmission probabilities and contact rates.
Main Methods:
- Comparison of two individual-based models: random mixing vs. repetitive contacts (with and without clustering).
- Systematic testing of parameters: transmission probability, contacts per day, infectious period duration, clustering levels, and repetitive contact proportions.
Main Results:
- Differences between models are most pronounced with low daily contacts and low transmission probabilities.
- Contact number and transmission probability significantly influence model divergence more than infectious period length.
- Even minor contact repetition/clustering can cause substantial deviations from random mixing models.
Conclusions:
- Random mixing models suffice for high contact/transmission rates (e.g., measles) or very short infectious periods (e.g., Norovirus).
- Models with repetitive contacts are crucial when daily contact numbers or transmission probabilities are low (e.g., MRSA, Ebola).
- Accurate disease modeling requires considering the actual contact structure for specific pathogens and transmission scenarios.
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