Abstract:

Abstract: Emerging pathogens events, from the H1N1 influenza pandemic, to the current Zika invasion of the Americas, represent serious global health threats that necessitate rapid risk assessment and intervention planning. Computational and statistical models provide an invaluable assisting tool in this effort, but their design and calibration face major scientific challenges, not least the accounting for the effect of awareness and human response. In the era of global information, awareness may anticipate the epidemic spread rising in yet unaffected territories and impacting their risk of experience the epidemic. I will discuss this aspect through two examples from the recent Ebola and MERS-CoV epidemics. In the first example, spontaneous reaction to the risk of global dissemination yielded flight cancellations and borders? closure. Through accurate data collection and extensive numerical simulations we showed that the strong modification in the flight network observed delayed by only a few weeks the risk of outbreak propagation in new countries. In the second example, we measured collective and public health awareness through digital proxies (Google Trends, ProMED-mail and Disease Outbreak News of WHO) and we quantified the impact of this component on the management of imported MERS cases in unaffected countries. We found that when attention was high, the time from hospitalization to isolation was reduced from 6 to 2 days, on average, consequently halving the risk of onward transmission following importation. These two examples show how the rise of novel information sources and the large increase in data availability are making possible the accounting for the human factor on epidemic models, increasing the reliability of risk assessment analyses.

Abstract: Poor air quality episodes in high populated cities across the world are getting more and more common, these events are not longer sporadic, instead, they are getting recurrent and put millions of people at risk due to the high concentrations of pollutants on the air that they breath. Most of the impact of those episodes go unnoticed because they imply symptoms like headaches, fever or respiratory problems which do not become important to be diagnosed. However, those symptoms affect our daily life, our performance at work and thus, in turn, they impact our economy and/or society. To understand and quantify that effect, we have analyzed a large database of social media messages (136 Million geolocalized tweets) in Spain and, using natural language processing and machine learning techniques, we have identify 0.8 Million tweets in which users talk about suffering symptoms like ILI, common cold, fever, headache, digestive or respiratory problems and potential treatments (perceived health). We have also collected information from official sources on air quality, pollens and weather. With that information we have constructed weekly time series and studied their interdependence and predicting power. We found first ILI cases can be explained and nowcasted by perceived symptomatological data and second that perceived symptomatological data can be nowcasted from atmospheric factors such as pollutants, pollens and climate data. Our results apply both at the regional and city level at different regions in Spain, suggesting that using this kind of digital health data from users in social media could help councils and governments to construct better air quality monitoring systems that not only consider level of contaminants in the air, but also how those levels impact in real time perceived (and possibly real) health conditions of the population.

Abstract: Gastroenteritis is one of the most common illnesses worldwide. It is characterised by the symptoms of diarrhoea and vomiting. Although most cases of gastroenteritis in high income countries are self-limiting, there is a significant impact on healthcare services and the economy. Determining the burden of gastroenteritis is challenging. Presentation biases mean that public health datasets of gastroenteritis incidence, and of incidence of gastroenteritis causing pathogens, do not give a complete picture of the community burden. We have been exploring the possibility of establishing a comprehensive near real-time picture of the levels of activity of gastroenteritis in the UK by investigating online data for syndromic surveillance of this illness. This includes webpage view statistics and an online community cohort survey; both of which have been extensively demonstrated as suitable for surveillance of other illnesses. Incidence data from these new online sources is compared to more traditional surveillance data from public health departments. This work contributes towards an improved understanding of gastroenteritis burden, which can influence policy decisions regarding the management of this illness.

Abstract: The Flusurvey is an internet-based tool through which real-time surveillance of self-reported influenza like illness (ILI) in the community is undertaken. The Flusurvey collects information on vaccination status as well as the reason(s) given for either getting or not getting vaccinated. (Participants are allowed to provide more than 1 reason). We used these responses to explore the attitudes participants have about the influenza vaccine. 2,901 (34.9%) of Flusurvey participants reported as being vaccinated for the 2015/16 season (5,418 participants (65.1%) were not vaccinated). The majority of those vaccinated were vaccinated at their GP (56.5%), with 21.4% and 18.2% vaccinated at their place of work and pharmacy, respectively. 45.4% of those vaccinated said they were in a risk group and 32.6% said the vaccine was readily available to them and vaccine administration was convenient. Of those not vaccinated; 49.9% felt they did not belong to a risk group, 43.6% were not offered it by their GP, 7% said the vaccine was not free of charge, 6.4% were worried the vaccine was not safe or could cause illness or other adverse events, 7.5% doubted the effectiveness of the vaccine and 2.5% believed the vaccine could cause influenza, Encouragingly, a relatively small percentage of people had negative attitudes about the influenza vaccine (i.e. concerns around safety, side effects or efficacy of vaccine). Our results suggest that increasing vaccine availability and improving convenience of administration (e.g. in the work place) would increase vaccine uptake in the general population.

Abstract: Big Data offer nowadays the capability of creating a digital nervous system of oursociety, enabling the measurement, monitoring and prediction of various phenomena inquasi real time. But with that, comes the need of more timely forecast, in other wordsnowcast of changes and events in nearly real-time as well. The goal of nowcasting is toestimate up-to-date values for a time series whose actual observations are available onlywith a delay. Choi and Varian introduced the term nowcasting to advocate the tendencyof web searches to correlate with various indicators, which may reveal helpful for shortterm prediction. In the field of epidemiology, it was showed in various works that searchdata from Google Flu Trends, could help predict the incidence of influenza-like illnesses(ILI). But as Lazer and al. notice, in February 2013, Google Flu Trends predicted morethan double the proportion of doctor visits for ILI than the Center for Disease Control.In this work we are studying the flu time series, of cases from 2004/05 to2014/2015 flu season, from physicians and pediatricians from all over Italy. We are interestedto examine whether is possible to use retail market data as a proxy for flu prediction.Our dataset consists of economic transactions collected by COOP, a system of Italian consumers?cooperatives which operates the largest supermarket chain in Italy. The wholedataset contains retail market data in a time window that goes from January 1st, 2007 toApril, 27th 2014. First, we identified the products that have adoption trend similar to theflu trend with the help of an 1-nearest neighbor classifier that uses dynamic time warpingas the distance measure between time series. Based on these products, we identified thecustomers that buy them during the flu-peak, since those individuals would have higherpossibility to be either infected or close to an infected individual. We extracted their mostfrequent baskets during the peak using the Apriori algorithm, an algorithm for frequentitem set mining and association rule learning over transactional databases, and we usethose baskets-sentinels as control set for the following year flu peak. Monitoring the behaviorof these baskets-sentinels we are able to detect patterns similar to the ones of theprevious year?s flu peak, and as a result obtain an alarm for the appearance of the flu.Many lines of research remain open for future work, such as studying whether theretail market data can manage to predict the flu peak even in particular cases such as theyear 2009 non-seasonal H1N1 influenza (flu) pandemic that peaked in October and thendeclined quickly to below baseline levels by January.

Abstract: Background: The potential health threats associated to Zika virus infections (ZIKV) have alarmed the global community. Unusual incidences of microcephaly and Guillain-Barr? syndrome have attracted attention towards a disease that was for decades considered relatively harmless. At present, health authorities worldwide are urged to implement policies to prevent further spreading, in spite of only having scarce and uncertain knowledge on the epidemiology and potential solutions. Complex problems such as infectious diseases are often addressed by simulation techniques. However, vector-borne diseases such as ZIKV present three major challenges that these approaches find difficult to tackle. First, the worldwide distribution of tropical and sub-tropical regions with suitable conditions for ZIKV?s vectors call for a global scope of the study and the use of global data. Second, human travel is the main dissemination mode. Therefore, policy decisions support needs to cope with the high connectivity of regions. Third, the emergence of ZIKV is driven by geographically specific environmental and socio-economic factors. Thus, an appropriate representation requires georeferenced information. Objective: The aim of this research was to explore ways to overcome the limitations of simulation methods conventionally used to study epidemics. More specifically, we aimed at designing robust response strategies to prevent the potential spread of Zika virus infections to regions worldwide. Approach: To achieve this goal, we applied a data-driven simulation approach that integrates different modelling and simulation methods and adopts concepts from other disciplines. We divided the world according to their global administrative divisions and by adopting an object-oriented approach replicated our core model structure describing the epidemiological process in each region. The spread of the disease across regions occurs along two distinct network structures. First, an undirected and symmetric adjacency matrix. Second, model-based predictions of global air passenger flows represented in a directed matrix . Data: The input data was collected from diverse publicly available sources including geospatial data from maps, conventional databases provided by government agencies and open-access modelling outcomes of reported research. We aggregated multiple layers of high resolution raster datasets to administrative divisions by geo-processing zonal statistics and combined this with data on air passenger flows from 3416 airports across the world. After pre-processing steps for data alignment, the data was used as input for our simulation model. Since viewing data on maps enhances users? understanding, we projected back our simulation outcomes on maps. In this way users can interactively plot different plausible scenarios over time and explore their consequences. Sensitivity analysis and policy exploration were conducted on the higher levels of aggregation. Simulation methods: The model components of each region combine two modeling methods taking advantage of their respective strength. Our approach, loosely based on Bobashev et al. (2007), starts with an agent-based model. After, reaching a threshold number of infectious individuals, it switches to equation-based. Next steps: By collecting and managing data at global scale, coping with regions increasingly connected by air travel, and integrating georeferenced environmental and social information, our approach promises to deliver breakthroughs in model based policy analysis. The validity of the presented approach and results of policy exploration will be shown in future work.