Suggested by: Christian Neuwirth (Z_GIS – Spatial Simulation)
Short description:
In addition to the basic reproduction number, R0, the overdispersion parameter, k, plays a crucial role in characterizing the spread of infectious diseases. Estimates for COVID-19 indicate that the dispersion parameter k is approximately 0.1, suggesting that 80% of transmissions have been caused by only 10% of infectious individuals [1].
Observed overdispersion can arise from various factors. For instance, the same pathogen may exhibit different behaviors across individuals, e.g. the infectious period is better represented as a distribution rather than a fixed constant [2].
Additionally, observed overdispersion in disease transmission may stem from overdispersion in social contact networks. For example, a French social contact survey caried out by [3] demonstrated that a small number of individuals account for a disproportionately large share of overall social contacts, while many individuals have few or no social interactions.
Modeling experiments indicate that outbreaks within such social networks tend to be particularly explosive (Fig. 1).
Figure 1. The blue curves represent simulated outbreaks in empirical social networks exhibiting overdispersion, while the red curves depict outbreaks in networks where every individual has an equal number of social contacts. The basic reproduction numbers are as follows: R0=1.8 (A), R0=2.5 (B), R0=3.1 (C), and R0=3.7 (D).
Hypothesis: It is hypothesized that overdispersion in social contact networks is influenced by the physical structures of space, such as transportation infrastructure and other elements of the built environment. For instance, recent investigations showed that hierarchical cities are more vulnerable to the rapid spread of infectious diseases than decentralized cities [4]. In other words, overdispersion in physical structures translates into overdispersion in social contact networks, which in turn leads to overdispersion in disease transmission and explosive outbreaks.
The aim of this thesis is to quantify the vulnerability of locations to epidemic outbreaks by analyzing their structural properties.
Method: (1) Quantify the overdispersion parameter k in physical infrastructures using data from OpenStreetMap, MobilityDatabase GTFS Feeds or open air travel network data (with the appropriate scale to be determined), (2) Run network simulations in a SIR-model (model is available) using the empirical parameter k as an input, (3) Compare epidemic doubling time in the simulation with empirical COVID-19 excess mortality doubling time at selected sites using a ranking scale approach.
Start: ASAP
Prerequisites/qualifications:
Interest in spatial simulation and scripting (NetLogo, Python, R or GAMA)
Please contact Christian Neuwirth in case of interest: christian.neuwirth@plus.ac.at
References:
- K. Sneppen, B. F. Nielsen, R. J. Taylor, and L. Simonsen, “Overdispersion in COVID-19 increases the effectiveness of limiting nonrepetitive contacts for transmission control,” Proceedings of the National Academy of Sciences, vol. 118, no. 14, p. e2016623118, 2021.
- A. L. Lloyd, “Destabilization of epidemic models with the inclusion of realistic distributions of infectious periods,” Proceedings of the Royal Society of London. Series B: Biological Sciences, vol. 268, no. 1470, pp. 985–993, 2001.
- G. Béraud et al., “The French connection: the first large population-based contact survey in France relevant for the spread of infectious diseases,” PloS one, vol. 10, no. 7, p. e0133203, 2015.
- J. Aguilar et al., “Impact of urban structure on infectious disease spreading,” Scientific reports, vol. 12, no. 1, p. 3816, 2022.
- O. Wegehaupt, A. Endo, and A. Vassall, “Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature,” BMC Public Health, vol. 23, no. 1, p. 1003, 2023.
