Abstract:
Infectious diseases continue to be one of the biggest global public health concerns, despite major progress in microbiological research. For the past decades, more than 50 emerging or re-emerging infectious diseases have occurred all over the world. In Africa each year, almost half of the countries experience an emerging or re-emerging infectious disease. Researchers have been using Social Network Analysis techniques to model
the spread of infectious diseases in veterinary medicine, and now various researchers are concentrating on how the overall state and structure of the spread of infection are affected by social networks formed by humans. A series of recent studies have indicated that many networks are revealed to obey the power-law degree distribution. This study aims at leveraging an anonymized snapshot of all active Facebook users and their friendship networks to measure the intensity of connectedness between locations (countries) in Africa, to better understand the structure of the spread of potentially infectious diseases and the effect of social ties on death rate due to the spread of communicable diseases.
This study adopts a quantitative analytical approach by applying three main methods of analysis: social network analysis, hypothesis testing on the distributions of network data, and assessing the effect of controlling crucial nodes in the community by fitting a simple one-parameter regression function (dependent variable is the proportion of death due to infectious diseases). The resulting Social Connectedness Index (SCI) network data in Africa is composed of 50 nodes and 2,450 edges. The estimated network parameters
were: proportion of all potential connections between vertices (6%), 3.98 average length of the shortest paths between all combinations of vertices in the network, and the longest path length between any pair of vertices was 5. Results from the initial estimation revealed that the social network structure formed by the Facebook social connectedness index follows power-law distribution and is a scale-free network subset of small-world
networks. Our results cast a light on how countries' social connectedness might affect the spread of infectious diseases and subsequently increase the death rate due to these diseases. This aspect of the research only covered a basic set of tools for drawing inferences from complex network
data. Therefore, future research should be conducted in more realistic network models for how networks
change over time and affect the spread of infectious diseases.
