81 research outputs found
Impact of data accuracy on the evaluation of COVID-19 mitigation policies
Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number R(t), a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of R(t) could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.Peer ReviewedPostprint (published version
The Scaling of Human Contacts in Reaction-Diffusion Processes on Heterogeneous Metapopulation Networks
We present new empirical evidence, based on millions of interactions on
Twitter, confirming that human contacts scale with population sizes. We
integrate such observations into a reaction-diffusion metapopulation framework
providing an analytical expression for the global invasion threshold of a
contagion process. Remarkably, the scaling of human contacts is found to
facilitate the spreading dynamics. Our results show that the scaling properties
of human interactions can significantly affect dynamical processes mediated by
human contacts such as the spread of diseases, and ideas
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The scaling of human contacts and epidemic processes in metapopulation networks
We study the dynamics of reaction-diffusion processes on heterogeneous metapopulation networks where interaction rates scale with subpopulation sizes. We first present new empirical evidence, based on the analysis of the interactions of 13 million users on Twitter, that supports the scaling of human interactions with population size with an exponent γ ranging between 1.11 and 1.21, as observed in recent studies based on mobile phone data. We then integrate such observations into a reaction- diffusion metapopulation framework. We provide an explicit analytical expression for the global invasion threshold which sets a critical value of the diffusion rate below which a contagion process is not able to spread to a macroscopic fraction of the system. In particular, we consider the Susceptible-Infectious-Recovered epidemic model. Interestingly, the scaling of human contacts is found to facilitate the spreading dynamics. This behavior is enhanced by increasing heterogeneities in the mobility flows coupling the subpopulations. Our results show that the scaling properties of human interactions can significantly affect dynamical processes mediated by human contacts such as the spread of diseases, ideas and behaviors
Wearable Proximity Sensors for Monitoring a Mass Casualty Incident Exercise: Feasibility Study
Forecasting seasonal influenza fusing digital indicators and a mechanistic disease model
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 from our framework 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 not considering 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 Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic
After the emergence of the H1N1 influenza in 2009, some countries responded with
travel-related controls during the early stage of the outbreak in an attempt to
contain or slow down its international spread. These controls along with
self-imposed travel limitations contributed to a decline of about 40% in
international air traffic to/from Mexico following the international alert.
However, no containment was achieved by such restrictions and the virus was able
to reach pandemic proportions in a short time. When gauging the value and
efficacy of mobility and travel restrictions it is crucial to rely on epidemic
models that integrate the wide range of features characterizing human mobility
and the many options available to public health organizations for responding to
a pandemic. Here we present a comprehensive computational and theoretical study
of the role of travel restrictions in halting and delaying pandemics by using a
model that explicitly integrates air travel and short-range mobility data with
high-resolution demographic data across the world and that is validated by the
accumulation of data from the 2009 H1N1 pandemic. We explore alternative
scenarios for the 2009 H1N1 pandemic by assessing the potential impact of
mobility restrictions that vary with respect to their magnitude and their
position in the pandemic timeline. We provide a quantitative discussion of the
delay obtained by different mobility restrictions and the likelihood of
containing outbreaks of infectious diseases at their source, confirming the
limited value and feasibility of international travel restrictions. These
results are rationalized in the theoretical framework characterizing the
invasion dynamics of the epidemics at the metapopulation level
Social data mining and seasonal influenza forecasts: The FluOutlook platform
FluOutlook is an online platform where multiple data sources are integrated to initialize and train a portfolio of epidemic models for influenza forecast. During the 2014/15 season, the system has been used to provide real-time forecasts for 7 countries in North America and Europe
Wearable proximity sensors for monitoring a mass casualty incident exercise: a feasibility study
Over the past several decades, naturally occurring and man-made mass casualty
incidents (MCI) have increased in frequency and number, worldwide. To test the
impact of such event on medical resources, simulations can provide a safe,
controlled setting while replicating the chaotic environment typical of an
actual disaster. A standardised method to collect and analyse data from mass
casualty exercises is needed, in order to assess preparedness and performance
of the healthcare staff involved. We report on the use of wearable proximity
sensors to measure proximity events during a MCI simulation. We investigated
the interactions between medical staff and patients, to evaluate the time
dedicated by the medical staff with respect to the severity of the injury of
the victims depending on the roles. We estimated the presence of the patients
in the different spaces of the field hospital, in order to study the patients'
flow. Data were obtained and collected through the deployment of wearable
proximity sensors during a mass casualty incident functional exercise. The
scenario included two areas: the accident site and the Advanced Medical Post
(AMP), and the exercise lasted 3 hours. A total of 238 participants simulating
medical staff and victims were involved. Each participant wore a proximity
sensor and 30 fixed devices were placed in the field hospital. The contact
networks show a heterogeneous distribution of the cumulative time spent in
proximity by participants. We obtained contact matrices based on cumulative
time spent in proximity between victims and the rescuers. Our results showed
that the time spent in proximity by the healthcare teams with the victims is
related to the severity of the patient's injury. The analysis of patients' flow
showed that the presence of patients in the rooms of the hospital is consistent
with triage code and diagnosis, and no obvious bottlenecks were found
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