Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia

Abstract

Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne diseases. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson’s r = 0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility networks in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed networks, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data captures a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.

Call detail record aggregation methodology impacts infectious disease models informed by human mobility

“…However, there has been significant research interest in how different choices of mobility data, or different mobility models can impact the progression of an epidemic. This has included the comparison of empirical and modelled movement networks [ 19 , 25 ], varying mobility model constructions [ 20 , 26 , 27 ], and varying sources of empirical mobility data [ 2 , 16 , 28 ]. A key task for human mobility researchers is to understand areas of stability in epidemiological models informed by human mobility, where choices of mobility data or mobility model construction influence epidemiological models in predictable ways.…”

Modeling the Regional Distribution of International Travelers in Spain to Estimate Imported Cases of Dengue and Malaria: Statistical Inference and Validation Study

“…These are vectors for malaria, dengue, yellow fever, West Nile virus, Zika, and chikungunya, and thus, they pose a significant public health risk that needs adequate preparedness [8][9][10]. Air travel plays a central role in the diffusion of most of these diseases, allowing their spread through imported cases at nonendemic locations [11][12][13] with vector presence, and it must be incorporated in decision-support systems to achieve operational preparedness and risk prediction [14][15][16]. Reliable descriptions and predictions of migration flows have been proven to be valuable tools for the design of more effective public health policies [17][18][19][20].…”

Detecting behavioural bias in GPS location data collected by mobile applications

“…Human mobility data derived from mobile phones are increasingly used to measure economic activity (1,2), predict the spread of disease (3)(4)(5)(6), forecast travel demand (7,8), measure responses to natural disasters (9,10), and understand human social dynamics (11,12). There are a number of sources of mobility data used in these applications, ranging from Call Detail Record Data, which estimates mobility based on mobile phone connections to nearby cell towers, to GPS data, which is collected by GPS sensors in smartphone devices.…”

“…Human mobility data derived from mobile phones are increasingly used to measure economic activity (1,2), predict the spread of disease (3)(4)(5)(6), forecast travel demand (7,8), measure responses to natural disasters (9,10), and understand human social dynamics (11,12). The use of mobile phone mobility data accelerated during the COVID-19 pandemic as a result of accessible mobility indices produced by major technology companies such as Google (13)(14)(15)(16).…”