Vaccination is vital for preventing disease spread, as demonstrated by its role played in recent outbreaks such as measles, COVID-19, and the 2014 West Africa Ebola crisis. Classical ring vaccination-targeting individuals near infected cases to form protective clusters-has become of interest due to its effectiveness, yet it is strongly influenced by the quality of contact tracing and availability of medical resources. Here, we model the efficiency of a family of ring vaccination-inspired strategies that address these limiting factors and disentangle them from the structure of the contact patterns. In particular, we evaluate scenarios that consider a vaccination radius r, used to vaccinate nodes in the network up to r contacts away (block vaccination) or exactly r contacts away (ring vaccination) from nodes of interest. Each one of these is tested under two further scenarios: the preventive one, where individuals are vaccinated before the epidemic takes place, and the containment one, where vaccination occurs during an outbreak to limit disease spread. They are tested in synthetic networks, where we find that in the preventive scenario, ring outperforms block vaccination, reducing the size of the epidemic and, in some cases, even preventing it from happening. On the other hand, in the containment scenario, we find that both strategies perform slightly similarly in reducing the impact of the diseases but block vaccination using fewer resources. As a case study, the proposed strategies are used to create epidemiological risk maps by employing the spatial position of olive trees in the Salento region in Italy, which recently suffered the impact of the bacterium Xylella fastidiosa.

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.

In the global move toward urbanization, making sure the people remaining in rural areas are not left behind in terms of development and policy considerations is a priority for governments worldwide. However, it is increasingly challenging to track important statistics concerning this sparse, geographically dispersed population, resulting in a lack of reliable, up-to-date data. In this study, we examine the usefulness of the Facebook Advertising platform, which offers a digital “census” of over two billions of its users, in measuring potential rural-urban inequalities. We focus on Italy, a country where about 30% of the population lives in rural areas. First, we show that the population statistics that Facebook produces suffer from instability across time and incomplete coverage of sparsely populated municipalities. To overcome such limitation, we propose an alternative methodology for estimating Facebook Ads audiences that nearly triples the coverage of the rural municipalities from 19% to 55% and makes feasible fine-grained sub-population analysis. Using official national census data, we evaluate our approach and confirm known significant urban-rural divides in terms of educational attainment and income. Extending the analysis to Facebook-specific user “interests” and behaviors, we provide further insights on the divide, for instance, finding that rural areas show a higher interest in gambling. Notably, we find that the most predictive features of income in rural areas differ from those for urban centres, suggesting researchers need to consider a broader range of attributes when examining rural wellbeing. The findings of this study illustrate the necessity of improving existing tools and methodologies to include under-represented populations in digital demographic studies – the failure to do so could result in misleading observations, conclusions, and most importantly, policies.

In recent years, many studies have drawn attention to the important role of collective awareness and human behaviour during epidemic outbreaks. A number of modelling efforts have investigated the interaction between the disease transmission dynamics and human behaviour change mediated by news coverage and by information spreading in the population. Yet, given the scarcity of data on public awareness during an epidemic, few studies have relied on empirical data. Here, we use fine-grained, geo-referenced data from three online sources-Wikipedia, the GDELT Project and the Internet Archive-to quantify population-scale information seeking about the 2016 Zika virus epidemic in the U.S., explicitly linking such behavioural signal to epidemiological data. Geo-localized Wikipedia pageview data reveal that visiting patterns of Zika-related pages in Wikipedia were highly synchronized across the United States and largely explained by exposure to national television broadcast. Contrary to the assumption of some theoretical epidemic models, news volume and Wikipedia visiting patterns were not significantly correlated with the magnitude or the extent of the epidemic. Attention to Zika, in terms of Zika-related Wikipedia pageviews, was high at the beginning of the outbreak, when public health agencies raised an international alert and triggered media coverage, but subsequently exhibited an activity profile that suggests nonlinear dependencies and memory effects in the relation between information seeking, media pressure, and disease dynamics. This calls for a new and more general modelling framework to describe the interaction between media exposure, public awareness and disease dynamics during epidemic outbreaks.

We provide an overview of the DSAA 2018 Data Science for Social Good special session, its aims and contributions.

Traditional surveillance of seasonal influenza is generally affected by reporting lags of at least one week and by continuous revisions of the numbers initially released. As a consequence, influenza forecasts are often limited by the time required to collect new and accurate data. On the other hand, the availability of novel data streams for disease detection can help in overcoming these issues by capturing an additional surveillance signal that can be used to complement data collected by public health agencies. In this study, we investigate how combining both traditional and participatory Web-based surveillance data can provide accurate predictions for seasonal influenza in real-time fashion. To this aim, we use two data sources available in Italy from two different monitoring systems: traditional surveillance data based on sentinel doctors reports and digital surveillance data deriving from a participatory system that monitors the influenza activity through Internet-based surveys. We integrate such digital component in a linear autoregressive exogenous (ARX) model based on traditional surveillance data and evaluate its predictive ability over the course of four influenza seasons in Italy, from 2012-2013 to 2015-2016, for each of the four weekly time horizons. Our results show that by using data extracted from a Web-based participatory surveillance system, which are usually available one week in advance with respect to traditional surveillance, it is possible to obtain accurate weekly predictions of influenza activity at national level up to four weeks in advance. Compared to a model that is only based on data from sentinel doctors, our approach significantly improves real-time forecasts of influenza activity, by increasing the Pearson's correlation up to 30% and by reducing the Mean Absolute Error up to 43% for the four weekly time horizons.

The availability of novel digital data streams that can be used as proxy for monitoring infectious disease incidence is ushering in a new era for real-time forecast approaches to disease spreading. Here, we propose the first seasonal influenza forecast framework based on a stochastic, spatially structured mechanistic model (individual level microsimulation) initialized with geo-localized microblogging data. The framework provides for more than 600 census areas in the United States, Italy and Spain, the initial conditions for a stochastic epidemic computational model that generates an ensemble of forecasts for the main indicators of the epidemic season: peak time and intensity. We evaluate the forecasts accuracy and reliability by comparing the results with the data from the official influenza surveillance systems in the US, Italy and Spain in the seasons 2014/15 and 2015/16. In all countries studied, the proposed framework provides reliable results with leads of up to 6 weeks that became more stable and accurate with progression of the season. The results for the United States have been generated in real-time in the context of the Centers for Disease Control and Prevention ``Forecasting the Influenza Season Challenge''. A characteristic feature of the mechanistic modeling approach is in the explicit estimate of key epidemiological parameters relevant for public health decision-making that cannot be achieved with statistical models that do not consider the disease dynamic. Furthermore, the presented framework allows the fusion of multiple data streams in the initialization stage and can be enriched with census, weather and socioeconomic data.

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, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, 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. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study.