Livestock trade network: potential for disease transmission and implications for risk-based surveillance on the island of Mayotte

The island of Mayotte is a department of France, an outermost region of the European Union located in the Indian Ocean between Madagascar and the coast of Eastern Africa. Due to its close connection to the African mainland and neighbouring islands, the island is under constant threat of introduction of infectious diseases of both human and animal origin. Here, using social network analysis and mathematical modelling, we assessed potential implications of livestock movements between communes in Mayotte for risk-based surveillance. Our analyses showed that communes in the central region of Mayotte acted as a hub in the livestock movement network. The majority of livestock movements occurred between communes in the central region and from communes in the central region to those in the outer region. Also, communes in the central region were more likely to be infected earlier than those in the outer region when the spread of an exotic infectious disease was simulated on the livestock movement network. The findings of this study, therefore, suggest that communes in the central region would play a major role in the spread of infectious diseases via livestock movements, which needs to be considered in the design of risk-based surveillance systems in Mayotte

A Network-Based Analysis of International Refugee Migration Patterns Using GERGMs

Understanding determinants of migration is central to anticipating and mitigating the adverse effects of large-scale human displacement. Traditional migration models quantify the influence of different factors on migration but fail to consider the interdependent nature of human displacement. In contrast, network models inherently take into account interdependencies in data, making them ideal for modeling relational phenomena such as migration. In this study, we apply one such model, a Generalized Exponential Random Graph Model (GERGM), to two different weighted-edge networks of international refugee migration from 2015, centered around Syria and the Democratic Republic of Congo (DRC), respectively. The GERGM quantifies the influence of various factors on out-migration and in-migration within the networks, allowing us to determine which push and pull factors are largely at play. Our results indicate that both push factors and pull factors drive migration within the DRC network, while migration within the Syria network is predominately driven by push factors. We suspect the reason for this difference may lie in that the conflict in Syria is relatively recent, in contrast to the conflict in the DRC, which has been ongoing for almost two decades, allowing for the establishment of systematic migration channels, migration networks, and resettlement, all which are related to pull factors, throughout the years