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

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.Author summaryHuman mobility plays a key role in the spread of many infectious diseases. Integrating this variable into spatial epidemic models can provide valuable insights for epidemic preparedness and response. Yet, there are numerous limitations and pitfalls often driven by data scarcity, especially in developing countries. To improve our understanding of the potential benefits of different human mobility data for outbreak prediction, in this work we focused on the aggregated mobility patterns derived from Call Detail Records (CDRs) data in comparison to more traditional data, including census data and mathematical mobility models. Using the 2015-2016 Zika virus (ZIKV) outbreak in Colombia as a case study, we employed a stochastic metapopulation model for vector-borne disease to simulate the ZIKV spread at the sub-national level in Colombia and assess the performance of each mobility network in capturing the ZIKV outbreak both nationally and sub-nationally. We found evidence that the population movements derived from aggregated CDRs data better capture the mobility and mixing patterns relevant to predict the local spread of ZIKV infections.