Speaker
Description
In recent years, network science predictive methods leveraging artificial intelligence have gained particular prominence in big data analysis. Among the various implementations of these tools, they have proven particularly useful for monitoring and predicting socioeconomic and health-related phenomena, uncovering intriguing and non-intuitive patterns [1]. In this way, a consensus began to prevail within the international community that global challenges can only be effectively addressed by mitigating factors such as poverty, inequality, and severe diseases on a global scale. This perspective underlies the establishment of the United Nations’ 2030 Agenda for Sustainable Development, which sets forth the Sustainable Development Goals (SDGs) [2].
In this work, we explore the association of cancer incidence recorded by WHO in 2022 [3] and a variety of SDGs indicators about the social, economic, and environmental status of UN member states (UNMS). To choose relevant indicators for prediction, we construct a weighted complex network, where nodes are SDG indicators, and links correspond to statistically significant correlations between indicators, calculated from 2022 data recorded across UNMS. Applying the disparity filter [4], we reduce network complexity by preserving significant connections while eliminating statistically weak links. After performing community detection through modularity optimization [5], we select the most representative node in each community. We then develop a regression model using XGBoost to predict cancer incidence rates using as independent variables these selected indicators. Our model is able to explain 60% of the variance in cancer incidence (measured on the test set).
More interestingly, our results show how various social, economic, and environmental factors contribute to explaining cancer incidence. The SHAP analysis in Figure 1 highlights the SDG indicators that most influence the prediction of the WHO All Cancer incidence indicator. Some indicators as ”Proportion of tariff lines applied to imports with zero-tariff (%)” (SDG 10) and ”National Biodiversity Strategy ” (SDG 9) reflect, respectively, the socio-economic status of a country and its strategic actions taken to control invasive species and protect ecosystems. Furthermore, several SHAP indicators are related to water resources (SDG 6). These SDG indicators underscore the importance of both natural and artificial water sources. Specifically, they measure changes in the surface water area of artificial reservoirs, a critical factor in preventing droughts, enhancing water resource management, and improving public sanitation and hygiene conditions.
We are now further analyzing these interesting but not necessarily intuitive results in order to gain a better under- standing of these complex interactions. In future work, we also aim at applying this method to different types of cancers to investigate potential differences in the role of the various social, economic and environmental factors.
References
[1] Chi, Guanghua, et al. ”Microestimates of wealth for all low-and middle-income countries.” Proceedings of the National Academy of Sciences 119.3 (2022): e2113658119.
[2] United Nations Statistics Division. SDG Global Database. Available at: https://unstats.un.org/sdgs/dataportal/ database.
[3] Global Cancer Observatory. Cancer Today. Available at: https://gco.iarc.fr/today/en/dataviz/maps-most-common-sites?
mode=cancer&key=total&types=1&cancers=15.
[4] Serrano, M. A´ngeles, Mari´an Bogun´a, and Alessandro Vespignani. ”Extracting the multiscale backbone of complex weighted networks.” Proceedings of the national academy of sciences 106.16 (2009): 6483-6488.
[5] Traag, Vincent A., Ludo Waltman, and Nees Jan Van Eck. ”From Louvain to Leiden: guaranteeing well-connected communi- ties.” Scientific reports 9.1 (2019): 1-12.
