Measles Metapopulation Modeling using Ideal Flow of Transportation Networks
In developing countries with limited access to medical resources, infectious diseases like measles can develop rapidly within and between communities. Combination of data coming from various sources that report historical disease incidences and transportation infrastructures are valuable sources of knowledge that can assist in public health policies and initiatives surrounding disease surveillance. This study integrates population, disease incidence, and transportation network data into measles modeling. Results show that a hybrid metapopulation modeling approach using ideal flow distribution over mobility networks can yield more accurate models for measles progression. This demonstrates the feasibility of using big data in the monitoring of measles propagation.
Jann Railey Montalan, Maria Regina Justina Estuar, Kardi Teknomo, and Roselle Wednesday Gardon. 2019. Measles Metapopulation Modeling using Ideal Flow of Transportation Networks. In Proceedings of the 2nd International Conference on Software Engineering and Information Management (ICSIM 2019). Association for Computing Machinery, New York, NY, USA, 147–151. DOI:https://doi.org/10.1145/3305160.3305210