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Published on: August 22, 2018
Superspreading of airborne pathogens in a heterogeneous world
Julius B Kirkegaard1, Joachim Mathiesen2, Kim Sneppen2
1Niels Bohr Institute, University of Copenhagen, 2100, Copenhagen, Denmark. julius.kirkegaard@nbi.ku.dk.
Abstract:
Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20% infecting more than 80%, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.
Insights
Superspreading events in epidemics are often due to random chance and social factors, not just inherent biological traits. Our model shows how social gatherings and disease infectiousness duration influence epidemic spread and mitigation effectiveness.
Area of Science:
- Epidemiology
- Mathematical Biology
- Public Health
Background:
- Epidemics frequently feature superspreading events, where a few individuals infect many others.
- Distinguishing between inherent superspreader traits and random chance in transmission is crucial for understanding disease dynamics.
Purpose of the Study:
- To develop an analytically solvable model for airborne disease spread in heterogeneous social spaces.
- To establish a baseline for comparing epidemic data and understanding superspreading phenomena.
Main Methods:
- Explicitly modeling social events and airborne pathogen transmission to capture simultaneous infections.
- Analyzing epidemic spreading statistics in socio-spatial heterogeneous environments.
- Quantifying social gathering sizes using power-law distributions based on real-world data.
Main Results:
- Short infectiousness duration combined with socio-spatial heterogeneity can lead to extreme superspreading statistics (e.g., 20% infecting >80%).
- Social gathering size distributions, approximated by power laws, significantly influence transmission patterns.
- Banning large gatherings is an effective mitigation strategy, independent of disease infectiousness duration but highly dependent on social heterogeneity.
Conclusions:
- Random chance and social structures, particularly large gatherings, play a significant role in superspreading events.
- Socio-spatial heterogeneity is a key factor in determining the extent of superspreading and the effectiveness of interventions.
- Mathematical modeling provides valuable insights into epidemic dynamics and informs public health strategies.
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