A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data

Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a realistic infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals' spatial behaviour and its relationship with the risk of infectious diseases' contagion. In particular, we show that CDRs-based indicators of individuals' spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing containment strategies to support decision-making during country-level pandemics

Analyzing the Spread of Chagas Disease with Mobile Phone Data

We use mobile phone records for the analysis of mobility patterns and the detection of possible risk zones of Chagas disease in two Latin American countries. We show that geolocalized call records are rich in social and individual information, which can be used to infer whether an individual has lived in an endemic area. We present two case studies, in Argentina and in Mexico, using data provided by mobile phone companies from each country. The risk maps that we generate can be used by health campaign managers to target specific areas and allocate resources more effectively.Comment: 6 pages, 6 figure

On the use of human mobility proxy for the modeling of epidemics

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control policies, but may be hindered by incomplete data in some regions of the world. Here we explore the opportunity of using proxy data or models for individual mobility to describe commuting movements and predict the diffusion of infectious disease. We consider three European countries and the corresponding commuting networks at different resolution scales obtained from official census surveys, from proxy data for human mobility extracted from mobile phone call records, and from the radiation model calibrated with census data. Metapopulation models defined on the three countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data well capture the empirical commuting patterns, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from both sources of data - mobile phones and census - are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, however preserving the order of infection of newly infected locations. Match in the epidemic invasion pattern is sensitive to initial conditions: the radiation model shows higher accuracy with respect to mobile phone data when the seed is central in the network, while the mobile phone proxy performs better for epidemics seeded in peripheral locations. Results suggest that different proxies can be used to approximate commuting patterns across different resolution scales in spatial epidemic simulations, in light of the desired accuracy in the epidemic outcome under study.Comment: Accepted fro publication in PLOS Computational Biology. Abstract shortened to fit Arxiv limits. 35 pages, 6 figure

Stochasticity in pandemic spread over the World Airline Network explained by local flight connections

Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of early stage variation can be explained by appropriately characterizing the local topology surrounding the debut location of an outbreak. We measure for each airport the expected force of infection (AEF) which a pandemic originating at that airport would generate. We observe, for a subset of world airports, the minimum transmission rate at which a disease becomes pandemically competent at each airport. We also observe, for a larger subset, the time until a pandemically competent outbreak achieves pandemic status given its debut location. Observations are generated using a highly sophisticated metapopulation reaction-diffusion simulator under a disease model known to well replicate the 2009 influenza pandemic. The robustness of the AEF measure to model misspecification is examined by degrading the network model. AEF powerfully explains pandemic risk, showing correlation of 0.90 to the transmission level needed to give a disease pandemic competence, and correlation of 0.85 to the delay until an outbreak becomes a pandemic. The AEF is robust to model misspecification. For 97% of airports, removing 15% of airports from the model changes their AEF metric by less than 1%. Appropriately summarizing the size, shape, and diversity of an airport's local neighborhood in the WAN accurately explains much of the macro-level stochasticity in pandemic outcomes.Comment: article text: 6 pages, 5 figures, 28 reference