dataesr/scientific-paragraphs-categorization huggingface dataset:
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 mobile phones and census sources 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, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns.
Such direction of research has gained great popularity, leading to the discovery of universal characteristics of individual mobility patterns, and the possibility to study mobility in space and at timescales that were unreachable before  [21] [22] [23] [24] [25] [26]. Such increasing volumes of finely resolved human mobility data, thanks to the near ubiquity of mobile phones, also offered an opportunity to contrast the huge deficit of quantitative data on individual mobility from underdeveloped regions. They were indeed used to shed light on malaria diffusion and identify hotspot areas  [24, 26, 27] , to monitor human displacements in case of natural disasters  [25, 28]  and to study disease containment strategies in Ivory Coast  [29]. Despite the variety of modeling approaches and data sources, the impact of using different proxies for human commuting in epidemic models for rapidly disseminated infections is still poorly understood.
Each approach or source of data clearly has its own intrinsic strengths and weaknesses, related to accuracy and availability of the dataset. More specifically, mobility models require some assumptions or input data for calibration and fit to the real commuting behavior. The gravity model requires full knowledge of mobility data for its parameter fitting and can be extended to other regions where data is not available in case of empirical evidence pointing to "universal" commuting behavior at a given resolution scale, i. e. well described by the same set of parameter values  [14] , or by making assumptions on generalizability. The radiation model requires population distribution values and the total commuter flows out of a given region, a quantity that may not be easily accessible at the desired level of resolution or with sufficient coverage.
While mobile phone data can provide mobility information at a high granularity level, they are also characterized by a number of issues that may hinder their use. Phone data are inevitably affected by biases related to the population sampling: coverage is usually not homogenous across space and it depends on the market share of the operator providing the data. Phone ownership and usage may differ across social groups, gender or age classes depending on the country under study  [30, 31] , and access to users' metadata to evaluate the representativeness of the sample is limited by privacy concerns  [32]. Given the recent availability of these data, the impact of such biases on mobility estimates is still poorly understood. Recent studies have assessed the effects of using gravity models in mathematical epidemic models  [12, 33] , however similar works on the use of data-saving options like the radiation models or of alternative strategies like mobile phone activity data for epidemic applications are still missing.
The aim of this paper is therefore to assess the adequacy of two specific proxies -mobile phone data and the radiation model -to reproduce commuter movement data for the modeling of the spatial spread of influenza-like-illness (ILI) epidemics in a set of European countries. We first compare the commuting networks extracted from the official census surveys of three European countries (Portugal, Spain and France) to the corresponding proxy networks extracted from three high-resolution datasets tracking the daily movements of millions of mobile phone users in each country. More specifically, we examine through a detailed statistical analysis the ability of mobile phone data to match the empirical commuting patterns reported by census surveys at different geographic scales. We then examine whether the observed discrepancies between the datasets affect the results of epidemic simulations.
Ethics statement The study relied on billing datasets that were previously recorded by a mobile provider as required by law and billing purposes, and not for the purposes of this project. To safeguard personal privacy, individual phone numbers were anonymized by the operator before leaving storage facilities, in agreement to national regulations on data treatment and privacy issues, and they were identified with a security ID (hash code). The research was reviewed and approved by the MIT's Institutional Review Board (IRB). As part of the IRB review, authors, who handled the data, and the PI participated in ethics training sessions at the outset of the study.
Census surveys used in the present study are not homogeneous in terms of collection date and geographic resolution at which data is collected. They represent however the most accurate and reliable description of commuter movements that is available for the countries under study. Commuting data for Portugal is extracted from the database of the National Institute of Statistics  [5]  and refers to the 2001 National Census Survey. The survey is nationwide and data is collected at the level of freguesia (namely, parish) that is the smallest local administrative unit of Portugal. For the purpose of making a comparison between census and mobile phones data feasible, we need to coarse-grain the commuting data on larger spatial scales corresponding to higher administrative levels of the country. This ensures the establishment of a resolution level that is common to both approaches and that allows the coarse-graining of each dataset maintaining the quality of the data.
Parishes are indeed very heterogeneous in terms of surface area (from 100m 2 to 100km 2 ) and are not structured in a hierarchical form with respect to tower cells as the latter can be smaller than a parish or emcompass a full parish. We thus consider: (i) the Portuguese concelhos (roughly corresponding to municipalities and typically including tens of freguesias) and (ii) the distritos (districts), the largest administrative unit of Portugal. We exclude from our analysis all municipalities located on the islands. Commuting data for Spain is extracted from the database of the National Institute of Statistics [6] and refers to the national workforce survey for the year 2005. The survey is conducted over a population sample and data is provided at the geographical level of provinces. We project the data to the full population of the country and restrict our analysis to continental Spain only.
Commuting data for France is extracted from the database of the French National Institute of Statistics and Economic Studies [4] and refers to the 2007 National Census Survey. The survey is nationwide and data is collected at the level of communes, the smallest local administrative unit of France. Similarly to the case of Portugal, we coarse-grain the original network on two higher administrative levels, corresponding to: (i) the French arrondissements (districts) and (ii) the French departments. For consistency, we exclude from our analysis all the overseas regions and territories of France. In the following we indicate with the census flux of commuters from the administrative unit to the administrative unit. In Section 1 of Text S1 we report additional details about the sources and the definitions of the census data.
Commuting networks extracted from mobile phones records Mobile phone commuting networks are extracted from three high-resolution datasets, based on mobile phone's billing information of a large sample of anonymized users in each country under study (2006 data for Portugal, 2007 for Spain and France), and already used in previous works  [34] [35] [36] [37]. The data provides information about the time of usage of the mobile phone and the coordinates of the corresponding mobile phone tower handling the communication. The data allows us to identify the set of locations visited by each user (georeferenced in terms of tower cells) and to rank them according to the total number of calls placed by a user from each of them. Only users with more than 100 calls are included in the study, to enable the estimation of the individual's commuting mobility pattern.
We coarse-grain the mobile phones commuting network from the tower cell scale to the country's administrative subdivisions for comparison with the census data (see Section 1. 3 in Text S1 for additional details). Once defined on the same geography, the two datasets also need to refer to the same population. The census dataset represents the benchmark, as it comprises the entire population of a country (commuters and non-commuters at a given scale) and its mobility features, whereas the commuting data obtained from the mobile phone dataset is affected by the sampling bias corresponding to the operator's coverage and to the selection of the subset available for the analysis (it only therefore represents a fraction of the total population) and by the algorithm used to identify commuting-like movements.
We explored the geographic coverage of the mobile phone dataset for the three countries (see the Analyses subsection for the corresponding methodology adopted). With no additional information on the subset of individuals included in the mobile phone datasets, we opt for a basic normalization approach that simply rescales the populations of the mobile phone networks at the administrative unit level by the population sampling ratio , where is the resident population of region tracked by the mobile phone dataset and is the resident population of region according to the official census. More sophisticated choices can be made to account for the sampling biases in a more accurate way, as discussed in the Discussion section, however they would require additional information that may not be easily available for a large set of countries.
Our baseline choice for the basic normalization is motivated by imposing minimal requests on additional metadata that may be needed to correctly calibrate the dataset. With the chosen normalization, the total population assigned to each node of the network (including commuters and non-commuters) is equal in the two systems, whereas the relative fraction of commuters may be different in the two cases. As a sensitivity analysis, and for further comparison with the radiation model (see following subsection), we also consider a refined normalization that assumes the same knowledge required by the radiation model -namely, the total number of commuters per administrative unit. Once normalized to the census population of each given region, this amounts to assume that the mobile phone commuting network has the same number of commuters per region as in the census dataset, the same total population per region, and therefore also the same ratio of commuters vs.
Comparison between mobile phone commuting networks and census commuting networks We compare the structural and fluxes properties of the commuting networks extracted from census surveys with those of the networks extracted from mobile phones records, in order to test the quality of mobile phones data as a proxy for commuting at national level. We analyze the topology of the networks obtained from the two sources of data and extract the intersection and its associated travel fluxes. We perform different statistical tests (Spearman's rank correlation coefficient, Lin coefficient, and Wilcoxon test) on the correlation between commuting flows connecting any pair of nodes in each dataset, and between the total numbers of commuters per node in each dataset. We also check for non-trivial correlations between the discrepancies found in the two datasets and nodes' populations and distances between connected vertices.
Comparison of the metapopulation epidemic outcomes obtained integrating different mobility sources In all realizations and for each subpopulation, we keep track of the following epidemic observables. The temporal information about the epidemic spreading is encoded in the arrival time ( ) of the infection at each subpopulation. The arrival time is defined as the first day an infected individual is recorded (either as a worker or as a resident) in a location with no previously notified cases. The probability distribution of the arrival time and its average value are evaluated for every location. In addition, we discount systematic anticipation/delay effects by subtracting the average arrival time difference obtained from the arrival times of all nodes when two different mobility datasets are used (e.
Statistical comparison of commuting networks Commuting networks obtained from census data and mobile phone activity data share the same number of nodes at all hierarchies considered in all countries, given that all administrative units were covered by both datasets, however variations are observed in the number of commuting links (Table  1 ). The set of links common in both datasets in the Portugal case at the municipality level account for about 60% of the total links of each network and include more than 96% of the total travel flux of both We further analyze whether the observed discrepancies between the weights in the mobile phone networks and the census networks show any dependency on the variables that characterize the underlying spatial and social structure, namely the Euclidean distance between the connected nodes (calculated from the coordinates of the administrative unit's centroid), the population of the origin node and the population of the destination node (Figure  3 ).
The overestimation of the magnitude of commuting fluxes in the mobile phone dataset does not show a significant dependence on the population sizes. Fluxes are instead found to be more similar when they connect units at shorter distances with respect to longer distances across the countries. Such variation disappears if we consider the topological distance defined by a neighbor joining approach (see Section 3. 4 in Text S1). Spatial aggregation into larger administrative units does not alter this overall picture but weakens the effect observed on distance (see details in Section 2. 1 in Text S1). If we refine the normalization of the mobile phone networks by taking into account the total number of commuters in each administrative unit, the agreement with the census dataset improves in the sideby-side weight comparison on every link (see Section 3 in Text S1).
Epidemic simulations We examine whether the observed non-negligible discrepancies in the commuting fluxes of the two datasets are also significant from an epidemic modeling perspective, altering substantially the outcome of disease spreading scenarios. We compare scenarios obtained from stochastic metapopulation models equally defined and initialized, except for the mobility data they integrate (see Methods). In addition to the census commuting network and the mobile phone commuting network, we also consider the synthetic commuting network generated with the radiation model. By discounting a posteriori the average anticipation of the model built on mobile phone data, which is trivially due to the overestimation of the census commuting fluxes, we find a very good correlation between arrival times for the models built on the census network and on the mobile phone network, with most of the points lying close to the identity line (Lin concordance correlation coefficient ranging from 0.
77 to 0. 88, panels c, f and i of Figure  4 ). If we consider the refined normalization, anticipation effects produced with the mobile phone data are preserved but reduced in magnitude (Figure  5 ). Epidemic peak times are also affected by the different distributions of commuting flows in the two networks (see Section 2. 2 in Text S1). As soon as the disease reaches most of the nodes, the epidemic model integrating the mobile phone network displays a more homogeneous behavior, with epidemic peaks that follow very shortly after each other in all the subpopulations, while peak times in the census networks span a wider time frame. On coarser spatial scales (Portuguese districts, French departments), we obtain a higher similarity between simulated results with proxies vs. census (see Section 2.
If mobility models often require aggregated input data from national statistics on movement habits  [18]  or the full mobility census database  [19]  for the fitting procedure, mobile phone data may be thought as an ideal alternative candidate for a proxy of human movements in absence of (complete and/or high-resolution) mobility data from official sources  [24, 26, 27]. In order to systematically test this hypothesis exploiting the full resolution of both the proxy data and the official census data for commuting, we have compared these two datasets in three European countries and performed a rigorous assessment of the adequacy of proxy commuting patternsextracted from mobile phone data or synthetically modeled -to reproduce the spatiotemporal spread of an emerging ILI infection. Mobility data from mobile phones is able to well capture the fluxes of the commuting patterns of the countries under study, reproducing the large fluctuations in the travel flows observed in the census networks.
In all countries the intersection between the two networks includes the vast majority of the commuting flows and the correlation measured on links' traffic and nodes' total fluxes of incoming or outgoing commuters is high (though not statistically equivalent). This suggests that mobile phone data can be used as a surrogate tracking the commuting patterns of a given country, identifying the relative importance of its mobility connections in terms of flows' magnitude, with a resolution that is equivalent to the one adopted by official census surveys or higher. This is a particularly relevant result for datapoor situations, where census data may not be available and official statistics may not be enough to correctly inform a mobility model. Discrepancies are however found, especially in the overestimation of commuting flows per link and in the larger variations observed for weaker flows and longer distances, that appear to be responsible for the differences observed in the simulated epidemics.
Diseases with a higher transmission potential would enhance this behavior, as with a large value of the peripheral seed can more quickly infect a large fraction of the system in the mobile phone network, than in the census dataset. Such effect is also present in the radiation model that is not able to describe the epidemic behavior better than the mobile phone data when the seeding location is characterized by a small population or degree. Not being able to well capture the mobility coupling between peripheral regions and the rest of the country, the radiation model misses most of the seeding events on long distances even when is large (Figure  6 ). Using a synthetic proxy is therefore not always preferable to data alternatives, and mobile phones appear to be more reliable in matching the spatial epidemic spread starting from peripheral locations.
A clear bias, which is observed consistently across all countries and for all resolution scales considered, is the faster rate of spread of the simulation based on the mobile phone commuting network with respect to the census one. This is clearly induced by the larger commuting flows obtained following the extraction of commuting patterns from mobile phone data using a basic normalization. The effect is stronger for as it is enhanced by the intrinsic large fluctuations characterizing epidemics close to the threshold. In such scenarios, even relatively small differences between networks' topologies can strongly alter the invasion path of the disease, consistently with the results of previous work on the effect of network sampling on simulated outbreaks  [53]. Increasing the value of the reproduction number leads to narrower ranges, because the larger disease transmissibility accelerates the spreading, synchronizing the epidemic behavior at distant locations and, in general, reducing the system's heterogeneity.
Time of arrival of the infection in a given location is better matched by the epidemic model built on the radiation model, though with large fluctuations for small values of. However, it is important to keep in mind that the total number of commuters per administrative unit is an input of the radiation model and no overestimation effects, as the ones resulting from the use of mobile phone data in the basic normalization approach, are possible in the model. If we inform the extraction of commuting patterns from mobile phone data with the same input data of the radiation model, i. e. through the refined normalization, predictions on the time of arrival consistently improve with respect to the basic normalization approach. Fixing the total number of commuters equally in the two datasets is however not enough to obtain an equivalent picture in terms of arrival times, as a considerable anticipation for small values of the transmissibility is still observed.
Jaccard index values display anyway the presence of important differences in the way the epidemic propagates on proxies with respect to census, being only when the outbreak is seeded in Paris. Effects of flows overestimation are visible in the analysis of the epidemic peaks too, but less prominent. The larger number of commuters that travel in the mobile phone networks tends to synchronize the epidemic peak between different subpopulations, leading to shorter overall timespan for all subpopulations to peak in the mobile phone case with respect to census. Differences between the datasets mostly range in a time interval of 2-3 weeks, a time resolution that still allows a meaningful comparison of epidemic results with the average reporting period of standard surveillance systems. In the case of France and Portugal we have also studied multiple hierarchical levels of the administrative units, by aggregating both datasets.
A good agreement in the simulated arrival times is intrinsically related to proxies' calibration and normalization aspects, and observed biases can be reduced by using additional information, such as the knowledge of the total number of commuters in each location. On the other hand, the most probable path of infection from one subpopulation to another appears to be affected by more substantial discrepancies between the different sources of data or synthetic flows that cannot be overcome through a simple normalization. In order to further improve predictions on the path of invasion, we would need to comprehensively understand the causes behind the differences observed in the data analysis. These are inevitably related to the methods used to account for the population sample considered in the mobile phone data and to define the commuting mobility per user.
First, in extracting the commuting behavior of each user from mobile phone data we necessarily have to make assumptions on the identification of home and work locations (in absence of metadata on the user). If we identify these two locations as the two most visited ones  [22] , by definition, we are assuming that place of residence and place of work are two distinct locations, yielding that every mobile phone user is a commuter at the resolution level of the cell phone towers. Once aggregated at a larger scale (i. e. the various administrative units under consideration), we obtain a population made of individuals living and working in the same unit (non-commuters) and of individuals commuting between two different units. While aggregation leads to a certain fraction of non-commuters, the resulting commuting behavior -expressed by the ratio of commuters vs.