extfieldsofstudy: Biology Medicine

text:
Integrating molecular epidemiology and social network analysis to study infectious diseases: Towards a socio-molecular era for public health

The number of public health applications for molecular epidemiology and social network analysis has increased rapidlysincetheimprovementincomputational capacities and thedevelopment of new sequencing techniques. Currently,molecularepidemiology methodsareusedinavariety ofsettings:frominfectiousdiseasesurveillance systems to the description of disease transmission pathways. The latter are of great epidemiological importance as they let us describe how a virus spreads in a community, make predictions for the further epidemic develop- ments,andplanpreventiveinterventions.Socialnetworkmethodsareusedtounderstandhowinfectionsspread through communities and what the risk factors for this are, as well as in improved contact tracing and message- dissemination interventions. Research is needed on how to combine molecular and social network data as both include essential, but not fully suf ﬁ cient information on infection transmission pathways. The main differences between the two data sources are that, ﬁ rstly, social network data include uninfected individuals unlike the mo-leculardata sampledonly frominfectednetwork members.Thus, socialnetwork data include moredetailed pic- ture of a network and can improve inferences made from molecular data. Secondly, network data refer to the current state and interactions within the social network, while molecular data refer to the time points when transmissions happened, which might have happened years before the sampling date. As of today, there have beenattemptstocombineandcomparethedataobtainedfromthetwosources.Eventhoughthereisnoconsen- sus on whether and how social and genetic data complement each other, this research might signi ﬁ cantly improve our understanding of how viruses spread through communities.

Molecular epidemiology of infectious diseases aims to harness molecular (nucleotide or amino acid) sequences to study the ecology and dynamics of pathogens (Foxman and Riley, 2001). With the recent advances in nucleotide sequencing (i.e. high throughput sequencing technologies) which allow faster and more affordable sequencing of pathogens (Grada and Weinbrecht, 2013), vast amounts of genetic data can be produced faster, cheaper and more efficiently than ever. This data-driven revolution has generated expectations with respect to more effective use of molecular sequences for scientific and public health purposes. Even though techniques have been developed to use these new forms and structures of data in research on spread, distribution, treatment and prevention of infectious disease epidemics (Kuhnert et al., 2011;Hartfield et al., 2014), it is still unclear what can be inferred by means of next generation sequencing (NGS, refers to multiple current sequencing techniques) data and, most importantly, how can we exploit them as much as possible.
Here we will review theoretical developments and applications of using molecular sequences to study the spread of infectious diseases and more particularly human viruses. We first use a "frequently asked questions" approach: we answer questions that we have heard during discussions with researchers not directly related to the field of molecular epidemiology. Then we focus on the emerging field of integrating social network data with molecular sequences, as we believe we are entering an exciting new era of socio-molecular epidemiology.
1. Epidemics on a macro scale: inferring the statistics of epidemic spread 1.1. Can we use molecular sequences to estimate traditional epidemiological parameters such as the basic reproductive number?
Genomic and epidemiological data can be used to estimate infectious disease spread parameters as reliably as using mathematical models or detailed epidemiological contact-tracing information. Parameters of interest usually include R 0 , the basic reproductive number, which can be thought of as the number of secondary infections Infection, Genetics and Evolution xxx (2016)  attributed to one infected individual in a completely susceptible population, and the generation time, which is the time that it takes one infected person to transmit a virus to another person (Anderson and May, 1991). Estimating R 0 is crucial for the prognosis of an epidemic and for developing strategies to stop the epidemic spread; knowing the generation time is important as it tells us when in the course of epidemic interventions can be more effective to prevent transmissions. Using genetic data R 0 has been estimated for different viruses, including multiple types and subtypes of Hepatitis C (HCV) (Pybus et al., 2001;Magiorkinis et al., 2013), HIV (Magiorkinis et al., 2014), and Influenza A (Fraser et al., 2009). By assuming a range of durations of the infectivity periods and different proportions of transmitters in a population, we were able to estimate the generation time of HCV in different populations in Greece by combining genetic and surveillance data (Magiorkinis et al., 2013). During the 2009 Influenza outbreak and the 2014 Ebola outbreak researchers have shown that epidemiological parameters inferred through molecular data are similar to those coming from the count-based epidemiological studies (Fraser et al., 2009;Alizon et al., 2014), suggesting that molecular data are reliable for epidemiological parameters estimations.
1.2. Can we use molecular sequences to monitor/infer the spatiotemporal spread of an epidemic within a population?
Firstly, there are multiple examples where genomic data have been used to improve epidemiological surveillance. For instance, public health efforts to control influenza outbreaks were strengthened by developing a genomic surveillance system that allows monitoring the temporal trends in virus mutations and planning preventive efforts (including vaccine design) for the following years (Russell et al., 2008). Surveillance systems like that have become more affordable/available with the appearance of NGS data. Retrospectively, data on the air transportation network and influenza A surveillance were used to show that, as expected, the spread of influenza H3N2 can be explained by air passenger flows (Lemey et al., 2014). Another example of the use of phylogenetics to enhance infectious disease surveillance is an approach used to define the clustering of HCV infections. Researchers from Canada used genetic data sequentially collected from people who inject drugs (PWID) to define the intra-host genetic distance (Olmstead et al., 2015). They then classified "recent transmission clusters" if the between-hosts genetic distance fell within the intra-host distance intervals. This approach allows monitoring small viral infections outbreaks within the PWID group, which, to the extent to which it can be done in real time, can assist in transmission-prevention, particularly since for some viruses large proportions of transmissions occur soon after infection, regardless of the risk group (Magiorkinis et al., 2013;Brenner et al., 2007;Powers et al., 2011).
Further, molecular sequences have been increasingly used to reconstruct population dynamics in time. The term phylodynamics has been used to describe combination of methods that are based on epidemiologic and phylogenetic techniques for this purpose (Grenfell et al., 2004). Most of the time the phylodynamics approach is used for rapidly evolving pathogens (usually RNA viruses), as these tend to measurably evolve within the host on a similar time scale as they spread between hosts (Kuhnert et al., 2011;Magiorkinis et al., 2013). The phylodynamics methods rely on the hypothesis of the molecular clock which posits that the evolutionary rate of nucleotide sequences can be described by mathematical models, the simplest form being the strict molecular clock with a constant evolutionary rate (Kimura, 1968). Phylodynamics has been extensively used to reconstruct the transmission dynamics of multiple viruses in deeper or more recent time, globally or within specific regions (Magiorkinis et al., 2013;Alizon et al., 2014;Yebra et al., 2015;Zehender et al., 2015).
To study spatial viral disease distribution, phylogeography superimposes geographical information about the molecular sequences on the phylogenetic tree to provide inferences about the spread of the organisms that we are interested in. Phylogeographic methods can be used not only to describe how infectious diseases spread over particular territories, but also to hypothesize what factors (political, socio-economical, and/or ecological) initiated these particular dissemination trends. These methods have been applied within countries and globally to study viruses such as HCV (Pybus et al., 2007;Magiorkinis et al., 2009), HIV (Paraskevis et al., 2009;Angelis et al., 2015;Faria et al., 2014), and Influenza A (Pollett et al., 2015;Alkhamis et al., 2015).
Finally, epidemiological and genetic data can be combined to reconstruct most probable transmission pathways on a community level. For example, the probabilities of the spread of infection between farms during an avian flu outbreak in Netherlands were estimated by taking weighted averages over the set of possible transmission trees (Ypma et al., 2012). The authors concluded that their method provides a more accurate estimate of the transmission pathway than methods based on solely genetic or epidemiological data.
2. Epidemics on the micro scale: reconstructing the details of transmission networks 2.1. Can we use molecular sequences to infer transmission pathways during infectious disease outbreaks?
Phylogenetic trees reconstructed from genetic sequences contain valuable information about the evolutionary history of the viral strain that can be used to infer possible scenarios of viral infection spread during infectious disease outbreaks. This information is very valuable as it could help to make a prognosis about the further spread of the disease as well as develop control measures in similar epidemiological settings. On a community level a depiction of transmission networks can be estimated by means of phylogeography (Famulare and Hu, 2015). To resolve transmission pathways on an individual level epidemiological contact tracing data are usually used during infectious disease outbreaks. Compared to the contact tracing data that heavily rely on the quality of provided by respondents information and are often subject to self-report bias, genetic data has the advantage of containing unbiased biological information. However, sequencing viral strains quickly as an outbreak develops is challenging for multiple reasons, including unspecific/absence of disease symptoms and/or timely sequencing of the viral strains. Thus, molecular data have been mostly used in a retrospective manner to investigate such infectious disease outbreaks as a nosocomial HCV outbreak in Italy (Spada et al., 2004) and Spain (Gonzalez-Candelas et al., 2013), or on a larger scale for influenza (Jombart et al., 2011). Given that now molecular data can be produced faster and at a lower cost than previously, their use in real-life outbreak investigations becomes more attractive and feasible for some diseases. This has a special promise since portable sequencing technologies such as MinION become available and allow pathogen sequencing in the field (Laver et al., 2015). Recently, genomic data were used in Ebola outbreak investigations to prove that the virus that seeded the outbreak in Guinea in 2014 emerged from Zaire ebolavirus lineage (Dudas and Rambaut, 2014). Similarly, molecular epidemiology methods helped to describe how HIV spread in a community of PWID in several recent outbreaks, including those in Athens, Greece, and Bucharest, Romania (Paraskevis et al., 2015), and in Indiana, USA (Galang et al., 2015;personal communication).

Can we use phylogenetic trees to infer directionality and timing of transmission events?
Unfortunately, reconstructing phylogenetic trees does not allow us to answer the burning question of "Who infected whom?" (i.e. define the direction of the infection), but only informs us if the two sequences evolved from the same genetic strain. If two sequences are clustered together on a phylogenetic tree, we can say that they have an ancestor in common. Further assigning sampling dates to sequences and implementing molecular clock analyses provides us with an estimate of the timing of the putative transmission events (Leigh Brown et al., 2011). However, these estimates are a subject to bias, because there is a discrepancy between the timing of phylogenetic tree branching that refers to the moment when a viral strain evolved into two and the timing of in-between hosts transmission events. The strain that is transmitted might have evolved within the virus-donor long before the transmission event happened ( Fig. 1) (Ypma et al., 2013;Romero-Severson et al., 2014), and, thus, the branching on the tree can overestimate the time of transmission events. This issue can partially be resolved if we can estimate the time of transmission events otherwise. For example, if we sample multiple quasispecies' sequences from each patient (e.g. with NGS, single genome amplification, or cloning), then by running a molecular clock analysis we can estimate the time to most recent common ancestor (TMRCA) of sequences within a patient. This TMRCA can be then superimposed on the phylogenetic tree constructed from sequences obtained from multiple patients.
2.3. In which epidemiological settings are phylogenetic trees more useful for transmission pathways reconstruction?
Inferring transmission networks from phylogenetic trees can be problematic for many epidemiological settings. For example, in densely sampled outbreaks of infectious agents phylogenetic trees are likely to have low confidence support. This is especially true when the timescale of the transmission events is comparably fast relative to the evolution of the pathogens, which makes it difficult to infer transmission events based on these phylogenetic trees. Recently, a Bayesian model that takes into account within-host genetic diversity attempted to resolve this issue (Didelot et al., 2014). Even though transmission pathways inferred from genetic data were more ambiguous than those inferred from detailed epidemiological data, the model could still reconstruct some parts of the transmission network, including correctly defining the source of infection in a hypothetical population. An alternative Bayesian model, which also accounts for the within-host virus evolution, but takes into account the non-random host population structure of the epidemic (instead of assuming population panmixis), was suggested for similar settings where dense sampling is available (Hall et al., 2015). It suggests that in a densely sampled outbreak, a well-resolved phylogenetic tree contains the transmission pathway and by sampling different sub-sets of the tree and calculating its posterior probability it is possible to reconstruct a reliable transmission network.
The limitation of the later approach is that all of the cases have to be sampled, which is unrealistic for some viral infections, particularly those where a large proportion of cases are unlikely to be diagnosed (like influenza, when many people will be never referred to a hospital) or those that can be asymptomatic for a long time, such as HIV and HCV.

Social network approach in infectious disease epidemiology
Many pathogens including viruses like HIV and HCV spread non-randomly through networks of closely connected people who engage together in injecting or sexual practices. Consequently, social network analysis has been extensively used as an approach in viral infectious disease epidemiology to recruit participants, monitor and predict behavior patterns, and model further and past disease spread. Initially, the network approach was used a lot to recruit participants into surveys and behavioural studies. Since the mid 1980s epidemiologists faced the problem of obtaining a probability sample of vulnerable to HIV groups, such as those of PWID or men who have sex with men (MSM). Random sampling assumes that every individual and every possible sub-sample within a population has an equal probability to be sampled, which is impossible to define for PWID or MSM, because we don't have a full list of individuals that belong to these groups. Since risky sexual and injecting behaviors are also often stigmatized, contacting PWID or MSM was more difficult than representatives of a general population. Researchers started designing studies based on "snowball" sampling to recruit and study these hard to reach populations (Morris, 2004). One popular sampling strategy is respondent driven sampling, which allows respondents to recruit their peers, but also lets researchers adjust the obtained results to generalize to the whole population (Heckathorn, 1997).
Researchers have integrated social network methods into classical infectious disease epidemiology to study risk factors that enable viral infectious diseases to spread not only at an individual, but at the network level. One of the first network studies, where MSM in California were asked about their sexual partners, found clusters of MSM with AIDS diagnosis who shared sexual partners (Auerbach et al., 1984). This study was of a great importance as it presented epidemiological evidence that AIDS is caused by an infectious agent. Later early network studies on HIV were conducted among drug users in New York (Neaigus et al., 1994;Friedman et al., 1997), female sex workers and PWID in Colorado (Rothenberg et al., 1998), and MSM in California. These studies discovered that the risk to acquire HIV is not only associated with individuals' behaviors; the network position and the behaviors of peers (sexual/ injecting partners) play an important role as well (Christley et al., 2005). The network structure might facilitate or limit the spread of viral infections (as well as safe behavior messages) within groups. As a consequence, the social network approach became an important epidemiological tool in the prevention and treatment of viral infectious diseases (Latkin et al., 2013).
Data from real-life social network studies are widely used in mathematical modeling to accurately describe epidemic spread and help to define aims for prevention efforts. The important role of an underlying non-panmictic population structure in epidemic prognosis has been shown for HIV outbreaks a long time ago (Gupta et al., 1989). Populations with assortative mixing of individuals are more likely to experience a rapid epidemic growth early on, while outbreaks in populations with disassortative mixing are more likely to grow into larger epidemics. Later the so-called "firewall" effect was introduced which in theory can be observed when the HIV long-term infected individuals "protect" susceptible individuals from getting in contact with highly infectious acutely infected individuals, inducing saturation at a lower prevalence than the one predicted by a panmictic model (Friedman et al., 2000;Khan et al., 2013;Dombrowski et al., 2013). Improvements in computational capacities have facilitated such advanced epidemiological modeling that takes more complicated population network structures into account (Danon et al., 2011). 4. Integrating social and molecular data: the potential of the sociomolecular approach Similarly to phylogenetic trees, social network data from people who share some risky behaviors include information about possible transmission routes. However, using only network information to resolve transmission pathways is not straightforward: firstly, self-reported behavioural data can contain biased information (especially with regard to socially undesirable/illegal activities); secondly, network studies can almost never recruit all the network members, resulting in incomplete network information. Finally, behavioural data do not necessarily provide evidence about the transmission history of an infectious agent, rather contain information about the shared risks, which might not have led to an infection. For example, sharing syringes/injecting material or practicing unprotected sex with multiple partners can tell about the risks that an individual has undertaken, but cannot be conclusive about the putative source or the date of viral transmission(s). Consequently, combining genetic and social network data into a complex socio-molecular approach might improve the way we infer transmission pathways and reduce the limitations of each other.
How can the two kinds of data contribute to analysing each of them? First, recognizing the network structure of a population can help advance the phylodynamics methodology itself. Many phylodynamics methods are based on the coalescence model that falsely assumes that host populations are panmictic-that is, that every host has an equal probability to contact and transmit the viral infection agent to another individual, which is not the case in real life. On the contrary, the network population structure of hosts implies preferential mixing by the number of partners (i.e., "highly active people tend to have highly active partners") and often within social groups (like race/ethnicities) or across sexual groups (men with women more than men with men or women with women) (Goodreau, 2006). Consequently, it leads to heterogeneity in the number of secondary infections, i.e. some people transmit to more people than others, giving birth to more new infections (Goodreau, 2006). Models have been developed to estimate the effect of this heterogeneity on the phylogenies. Some researchers have found that the distribution of the number of secondary infections in a population have an effect on reconstructed pathogen phylogenies (Robinson et al., 2013). Heterogeneity in the number of secondary infections results in phylogenies with more clusters of a smaller size and shorter mean branch lengths compared to phylogenies reconstructed from a population with homogeneity in the number of secondary infections. Introducing this kind of more complex phylodynamics models that account for the network structure of a population may allow more accurate estimates of transmission chains.
In practice, social network information can sometimes be applied to resolving ambiguous or equally plausible transmission pathways reconstructed from phylogenetic trees if genetic and social data come from the same individuals. One of the main differences between the social and the genetic data is that the social network data may theoretically include an overall contact network (as in Fig. 2.1), including non-infected individuals, even individuals who were never recruited (but reported to be part of the network by other members). Knowing additional information about other members of the network, whose viral genetic information was not sampled for some reason, can help to choose one out of multiple plausible transmission pathways inferred from a phylogenetic tree (Fig. 3). For example, for many infectious agents, spontaneous clearance is part of the natural history of the disease. This makes it difficult to rely on phylogenies in an attempt to reconstruct transmission pathways: there are individuals who might have transmitted the pathogen in the past, but at the time of sampling have successfully cleared the infection (Brewer et al., 2006). In this case, viral strains cannot be retrieved for sequencing and phylogenetic analyses, but the epidemiological data from disease-free network members can include self-reported information about their previous disease status. Even more, an antibody positive test might indicate that a person used to be infected, while the type (e.g. IgM or IgG) and specificity (e.g. avidity test) of the antibodies, might provide us with information about the recent or non-recent nature of a transmission. Thus, connections that lead to transmissions might not be captured in the phylogenetic tree, but social network information and epidemiological data might help to fill in the gaps on a hypothetical transmission pathway.
Social network information in theory can bring many insights onto how to interpret the phylogenetic trees, although the methods to do this have yet to be developed. Supposedly, knowing the network position of sampled individuals can help to estimate how reliable are the transmission pathways estimated from the phylogenetic trees. As previously said, in most settings, it is too optimistic to think that all of the network members can be recruited (or specimens from all the infected collected, either), and incomplete sampling can bias the analyses. Luckily, in social network studies respondents are usually asked about other members of the network. Theoretically, this information about other members of the network can help to place additional nodes at the transmission networks estimated from phylogenetic trees helping to resolve ambiguous transmission patterns. Further, hypothetically, sampling individuals who have more central position in a network allows estimating phylogenetic trees that are more likely to contain a real transmission pathway ( Fig. 2.1, 2.2). At the same time, sampling individuals with less central position in a network might result in multiple phylogenetic trees, thus, making an attempt to reconstruct the true path of the virus spread more challenging.
Also, social network data and phylogenetic trees usually refer to different time points and using the estimated timing of the transmission events can provide insight on dynamic changes in the structure of a transmission network. Contact data collected in social network studies describe the most recent connections among individuals. Most social network studies have only addressed a short period of time, because relationships among individuals change rapidly and, additionally, recall bias is higher with respect to older connections (Bell et al., 2007). Phylogenetic trees on the contrary can infer past events that happened when the viruses evolved within infected individuals. For chronic viral infections these events might have occurred many years ago. Thus, combining the two data sources can provide complementary insights about changes in social structure of the population of hosts and, consequently, transmission networks.

Current applications that combine multiple data sources
Trying to reconstruct possible transmission chains from phylogenetic data, some researchers have relied on bootstrapping as a way to identify probable ties (Lewis et al., 2008). Leigh Brown et al. used molecular clock analysis of HIV in combination with epidemiologic data obtained from one social network study. The authors used genetic sequences (one per patient) as nodes and links were made if the most recent common ancestor of two nodes went back in time not more than a defined period of time (e.g. less than 3 years) (Leigh Brown et al., 2011). They then compared the distribution of the average number of sexual partners in MSM that they got from the networks constructed from phylogenies to the numbers obtained in surveys. They obtained similar highly right-skewed distributions of the number of links from epidemiological and genetic data.
Several research groups have used additional epidemiological information (patient's risk group) assigned to the viral sequences to study whether transmissions happen within contact networks of particular sub-populations; they found that population mixing in the groups they studied was assortative and there was little bridging between the risk groups (Yebra et al., 2015;de Bruijne et al., 2009;Lunar et al., 2015). Other researchers found, on contrary, that HIV epidemics in one group can be seeded by introductions from other risk groups within the same country. For example, in some European countries, HIV epidemics among heterosexuals were seeded and sustained by transmissions from PWID (Kouyos et al., 2010;Graw et al., 2012). Kouyos et al. were able to identify sexual transmission of HCV in cases of HIV-infected MSM and heterosexuals by combining genetic information and clinical and epidemiological information (patients' risk group and HCV serostatus), improving our knowledge about transmission routes and epidemiology of HIV-HCV co-infection (Kouyos et al., 2014). Revealing such patterns of transmission between groups can assist in designing intervention strategies.
Some attempts to apply the socio-molecular approach to study viral transmissions have compared social and viral genetic distances between individuals. Several studies used HCV spread in PWID as a model system. Such, multiple data sources were combined to look at the association between genetic relatedness of the HCV sequences (within genotype groups) and social distances between PWID in two studies from Melbourne, Australia (Aitken et al., 2004;Sacks-Davis et al., 2012). The authors of both studies found a weak correlation between social geodesic distance (the smallest number of injecting partnerships connecting two nodes) and HCV genetic distance. They have explained this by incomplete sampling, potentially biased self-reported data, long carriage of HCV, and, most importantly, the long injecting history of participants (over 10 years) and long duration of injecting partnerships. The authors suggested that cohorts of recent drug injectors or recently infected individuals might be more appropriate to search for an association between social and genetic distances. This was partly addressed by another study of HCV in PWID in Seattle, Washington. The authors of this research looked at the relationship between social and genetic distances between recently HCV-infected participants (anti-HCV-negative b8 months ago). Initially, the authors found an association between social and genetic distances. However, this seemed to be due to one influential point (one confirmed transmission pair with reported needle sharing and shared common ancestor). When this pair was removed from the phylogenetic tree the association was lost (Brewer et al., 2006). This suggests that even when social and genetic data are collected at the same period of time, there may sometimes be low or no correlation between the two measures.
Looking for similarities between social and genetic patterns, researchers have compared phylogenetic clustering of HCV and social network structures of PWID. The same study from Melbourne that found a weak association between the two distance measures when only individuals and their ties were considered found an association between a social partnership (self-reported injecting in the same place and at the same time) and being in the same phylogenetic cluster (Sacks-Davis et al., 2012). This suggests that including information about venues where people engage in risk behaviors can help to find associations between genetic and social data. Another group of researchers searched for the association between phylogenetic clustering of both HIV and HCV and social partnership (defined as the distance on their recruitment chain) within injecting networks of PWID in Ottawa, Ontario, Canada (Pilon et al., 2011). An interesting finding was that participants co-infected with both infections were not always concordantly clustered within the phylogenies of HIV and HCV. Thus, participants who were clustered on an HIV phylogenetic tree were clustered on an HCV phylogenetic tree only in 50% of the cases. Again, the authors reported that there was a lack of support of social network information from the phylogenetic trees: if two HIV-positive people recruited each other, their specimens never shared a common viral ancestor (for HCV 10% of phylogenetic clustering resulted from recruitment). However, recruitment chain proximity can be a poor proxy for a social partnership/distance.
Although there is no "conventional" way to combine genetic and social network data, several approaches have been suggested up to now. These include network simulations, comparisons between real-life data, informing network data based on the social information and vice versa. Goodreau modelled several social network patterns, consequently simulating HIV spread in those networks and then the viral mutations among infected hosts (Goodreau, 2006). He used the coalescence approach to estimate N e (effective population size which equals the size of an idealized population that shows the same genetic drift as the studied population) under different underlying population structures. He concluded that these estimates for some population structures are similar to those obtained under the assumption of population panmixis. Still, some social network patterns, like those resembling venue-centered networks of female sexual workers, surprisingly produced much higher estimates of N e than the actual population size. Additional research is needed to define if for some risk groups phylodynamic estimates are less reasonable than for the others.
The socio-molecular approach in epidemiology is at its starting point. Many researchers try to find the best way to use both social and molecular data to improve different aspects of infectious disease epidemiology. There are several issues that prevent from wider use of network data. First, the cost of collecting network data is high; secondly, many infectious diseases (e.g. HIV), are associated with stigma that demotivates participants to participate in the studies and/or refer their partners; finally, network surveys often include sensitive questions about sexual and injecting partners, which renders these studies ethically challenging and potentially raises safety issues for the field researchers. However, some main obstacles of previous years, such as computational complexity of the network analysis and expensive and time-consuming sequencing are greatly relaxed, suggesting that a wider use of social and molecular approaches is feasible, and at the same time raising interesting questions. How to relate social data to molecular? What questions can be asked with the two sources that never would have occurred to us with only one source? How reliable are transmission pathways estimated from both sources? Will the combination of the two improve our understanding of how transmissions happen or will they contradict to each other? Can combining the two methods assist in case finding or other interventions? Further research on how the socio-molecular approach can validate data obtained from one of the sources, overcoming limitations, or relaxing assumptions of epidemiological methods will help answering these questions.


extfieldsofstudy:
Mathematics
Computer Science

text:
Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier

Multiview learning problem refers to the problem of learning a classifier from multiple view data. In this data set, each data points is presented by multiple different views. In this paper, we propose a novel method for this problem. This method is based on two assumptions. The first assumption is that each data point has an intact feature vector, and each view is obtained by a linear transformation from the intact vector. The second assumption is that the intact vectors are discriminative, and in the intact space, we have a linear classifier to separate the positive class from the negative class. We define an intact vector for each data point, and a view-conditional transformation matrix for each view, and propose to reconstruct the multiple view feature vectors by the product of the corresponding intact vectors and transformation matrices. Moreover, we also propose a linear classifier in the intact space, and learn it jointly with the intact vectors. The learning problem is modeled by a minimization problem, and the objective function is composed of a Cauchy error estimator-based view-conditional reconstruction term over all data points and views, and a classification error term measured by hinge loss over all the intact vectors of all the data points. Some regularization terms are also imposed to different variables in the objective function. The minimization problem is solve by an iterative algorithm using alternate optimization strategy and gradient descent algorithm. The proposed algorithm shows it advantage in the compression to other multiview learning algorithms on benchmark data sets.

Keywords Multiview learning · Supervised learning · Intact space · Hinge loss 1 Introduction

Background
Multiview learning has been an important in machine learning community [51,42,53,31,54,6,27,28,40,47]. In traditional machine learning problems, we usually assume that a data point has a feature vector to represent its input information. For example, in image recognition problem, we can extract a visual feature vector from an image, using a texture descriptor [36,34,33,52,32,15,49,20]. In this scene, the texture is a view of the image. However, there could be more than one view of an image. Besides the texture view, we can also extract feature vectors from other views, including shape and color. An other example is the problem of classification of scientific articles, and we may extract a feature vector from the main text of the article [30,17,37,25,17,37,22,5,12,24]. However, the main text is just one view the article, and we can also have features from other views, such as abstract, reference list, etc. Multiview learning argues that we should learn from more than one views to present the data and construct a predictor. The motive for multiview learning is that single view based data representation is usually incomplete, and different views can present complementary information for the learning problem. In the problem of multiview learning, the input of a data point is not just one single feature vector of one single view, but multiple feature vectors presenting different views. The target of multiview learning is to learn a predictor to take multiple view feature vectors to predict one single output of a data point. The problem of multiview learning can be classified to two types, supervised multiview learning and unsupervised learning.
-Supervised multiview learning refers to the problem of learning from a data set, where both the multiview input and output are available for each data point [26,21,16]. In this problem, the output is usually a class label, or a continues response. In this case, the learning problem is to build a predictive model from the training data set to predict the output of a input data point, with help the input-output pairs of the training set. -Unsupervised multiview learning refers to the problem of cluster a set of data points, and the multiview inputs of each data point are given [13,41,57]. In this problem, the outputs of the data points are not available.
In this paper, we investigate the problem of supervised multiview learning, and propose a novel algorithm to solve it. The proposed method is based on an assumption that different views of a data point are generated from one single intact feature vector, and the view generation is performed by a linear transformation. We try to recover the intact feature vector for each data point from its multiview feature vectors, with guiding of its corresponding output, i.e., its binary class label.

Relevant works
There are some existing multiview learning methods. We the state-of-the-arts of them as follows.
-Zhang et al. [56] proposed to use local learning (LL) method for the problem of multiview learning problem, and designs a local predictive model for each data point based on the multiview inputs. The local predictive model is learned on the nearest neighbors of a data point. -Sindhwani et al. [39] proposed to use co-training algorithm for multiview learning problems to improve the classification performance of each view (CT). This method is based on multiview regularization, and the agreement and smoothness over both labeled and unlabeled data points. -Quadrianto [38] proposed a multiview learning algorithm to solve the problem of view disagreement (VD), i.e., different views of one single data point do not belong to the same class. This method uses a conditional entropy criterion to find the disagreement among different views, and remove the data points with view disagreement from the training set. -Zhai [55] proposed multiview metric learning method with global consistency and local smoothness (GL) for the multiview learning problem with partially labeled data set. This method simultaneously consider both the global consistency and local smoothness, by assuming that the different views has a shared latent feature space, and imposing global consistency and local structure to the learning procedure. -Chen et al.
[3] proposed a statistical subspace multiview representation method (SS), by leveraging both multiview dependencies and supervision information. This method is based on a subspace Markov network of multiview latent, and assumes that the multiviews and the class labels are conditionally independent. The algorithm is based on the maximization of data likelihood, and the minimization of classification error.

Contributions
In this paper, we propose a novel supervised multiview learning method. This method is based on the assumption of single discriminative intact of different multiview inputs. Under this assumption, although there are different views of one single data point, one single intact feature vector exists for the data point. This intact feature vector is assumed to be discriminative, i.e., it can represents the class information of each data point. Moreover, the feature vector of each view of a data point can be obtained from the intact vector, by performing a linear view-conditional transformation to the intact feature vector. In this way, if we learn the discriminative intact feature vector for each training data point, we can learn a classifier in the intact with the help of the class labels of the training data points. To this end, we proposed a novel method to learn the hidden of the intact feature vector, the view-conditional transformation matrices, and the classifier in the intact space simultaneously.
We define a intact feature vector for each data point, and a transformation matrix for each view. The feature vector of one view of each data point can be reconstructed as the product of its corresponding transformation matrix and intact feature vector. The reconstruction error for each view of each data point is measured by the Cauchy error estimator [18,14]. To learn the optimal intact feature vectors and view-conditional transformation matrices, we propose to minimize the Cauchy errors. Moreover, due to the assumption that the intact feature vectors are discriminative, we also argue that we can design a classifier in the intact space, and the classifier can minimize the classification error. Thus we also propose to learn a linear classifier in the intact space, and use the hinge loss to measure the classification error the training set in the intact space [4,1]. To learn the optimal classifier parameter and the intact feature vectors, we also propose to minimize the hinge loss with regard to both the classifier parameter and the intact feature vectors.
To model the problem, we propose a joint optimization problem for learning of intact vectors, view-conditional transformation matrices, and the classifier parameter vector. The objective function of this problem is composed of two error terms, and three regularization terms. The firs error term is the view reconstruction term measured by Cauchy estimator over all the data points and views. The second error term is the classification error over all the intact feature vectors of all training data points, measured by hinge losses. The three regularization terms are all squared ℓ 2 norm terms over each intact feature vectors, view-conditional matrices, and the classifier parameter vectors. The purpose of impose the squared ℓ 2 norm to these variables are to reduce the complexity of the learned outputs. To minimize the proposed objective function, we adapt an alternate optimization strategy, i.e., when the objective function is minimized with regard to one variable, other variables are fixed. The optimization with regard to each variable is conducted by using gradient descent algorithm.
The contributions of this paper are of three parts: 1. We propose a novel supervised multiview learning framework by simultaneous learning of intact feature vectors, view-conditional transformation matrices, and classifier parameter vector. 2. We build a novel optimization problem for this learning problem, by considering both the view reconstruction problem, and the classifier learning problem. 3. We develop an iterative algorithm to solve this optimization problem based on alternate optimization strategy and gradient descent algorithm.

Paper organization
This paper is organized as follows: In section 2, the proposed method for supervised multiview learning is introduced. In this section, we first model this problem as a minimization problem of a objective function, and then solve it with an iterative algorithm. In section 3, the proposed iterative algorithm is evaluated. We first give an analysis of its sensitivity to parameters, and then compare it to some state-of-the-art algorithms, and finally test the running time performance of the proposed algorithm. In section 4, we give the conclusion of this paper.

Methods
In this section, we introduce the proposed supervised multiview learning method.

Problem modeling
We assume we are dealing with supervised binary classification problem with multiview data. A training data set of n data points is given, The information of each data point is composed of feature vectors of m views, and a binary class label y i . x j i ∈ R dj is the d j -dimensional feature vector of the j-th view of the i-th data point, and y i ∈ {+1, −1} is a the binary class label of the i-th data point. The problem of supervised multiview learning is to learn a predictive model from the training set, which can predict a binary class label from the multiview input of a test data point. We assume there is an intact vector z i ∈ R d for the i-th data point, and its j-th view x j i can be reconstructed by a linear transformation, where W j ∈ R dj×d is the view-conditional linear transformation matrix for the j-th view. Please the view-conditional transformation matrix for the same view of all the data points is the same. By learning both the W j and z i , we can recover the hidden intact vector for the i-th data point, z i , and use it for classification problem. To this end, we propose to minimize the reconstruction error. The reconstruction error is measured by Cauchy error estimator, This error estimator has been shown to be robust, and it also provides a offset. We propose to minimize this error estimator over all data points and all views with regard to both z i , i = 1, · · · , n, and W j , j = 1, · · · , m, Moreover, we also assume that the intact feature vectors of the data points are discriminative, and presents the class information, thus the intact feature vectors can minimize a classification loss function of the data set. We propose to learn the intact feature vector of the i-th data point by jointly learning a liner classifier to predict its class label, y i . The classifier is designed as linear function, The usage of a linear function as the classifier is motive by the work of Fan and Tang This the optimization of this loss function can obtain a large margin classifier.
To learn the optimal classifier and the discriminative intact feature vectors, we propose to minimize the classifier loss measured by the hinge loss function of the classification result over all the training data points, Moreover, to prevent the problem of over-fitting of variables, we propose to minimize the squared ℓ 2 norm of the variables to regularize the learning z i , W j , and ω, Our overall learning problem is obtained by considering both the problems of view-conditional reconstruction in (3), and classifier learning in the intact space in (6), where α is a tradeoff parameter to balance the view-conditional reconstruction terms and the classification error terms, and γ is a tradeoff parameter to balance the view-conditional reconstruction terms and the regularization terms. By optimizing this problem, we can learn intact feature vectors which can present the multiview inputs of the data points, and also is discriminative.

Optimization
To solve the optimization problem in (21), we propose to use the alternate optimization strategy. The optimization is conducted in an iterative algorithm. When one variable is considered, the others are fixed. After one variable is updated, it will be fixed in the next iteration when other variable is updated. In the following subsections, we will discuss how to update each variable.

Updating z i
When we want to update z i , we only consider this single variable, while fix all other variables. Thus we have the following optimization problem, The second term max(0, 1 − y i ω ⊤ z i ) is not a convex function, and it is hard to optimize it directly. Thus we rewrite it as follows, We define a indicator variable, β i , to indicate which of the above cases is true, and rewrite (10) as follows, Please note that β i is also a function of z i , however, we first update it by using z i solved in previous iteration, and then fix it to update z i in current iteration. In this way, (9) is rewritten as where g(z i ) is the objective of this optimization problem. To seek the minimization of g(z i ), we use gradient descent algorithm. This algorithm update z i by descending it to the direction of gradient of g(z i ), where µ is the descent step, and ∇g(z i ) is the gradient function of g(z i ). We set ∇g(z i ) as the partial derivative of g(z i ) with regard to z i , By substituting (15) to (14), we have the final updating rule of z i ,

Updating W j
When we want to optimize W j , we fix all other variables and only consider W j itself. The optimization problem is changed to the follows, where f (W j ) is the objective function of this problem. To solve this problem, we also update W j by using the gradient descent algorithm, where ∇f (W j ) is the gradient function of f (W j ), Substituting (19) to (18), we have the final updating rule of W j ,

Updating ω
When we want to update ω to minimize the objective function of (21), we fix the other variables, and only consider ω. Thus the problem in (21) is transferred to Please note that β i is actually a function of ω. However, similar the strategy to solve z i , we also update it according to ω solved in previous iteration, and fix it to update ω in current iteration. When β i , i = 1, · · · , n are fixed, we update ω to minimize h(ω) by using the gradient descent algorithm, where ∇h(ω) is the gradient function of h(ω), and it is defined as follows, By substituting it to (24), we have the final updating rule for ω,

Iterative algorithm
After we have the updating rules of all the variables, we can design an iterative algorithm for the learning problem. This iterative algorithm has one outer FOR loop, and two inner FOR loops. The outer FOR loop is corresponding to the main iterations. The two inner FOR loops are corresponding to the updating of n intact feature vectors of n data points, and the updating of m viewconditional transformation matrices. The algorithm is given in Algorithm 1. The iteration number T is determined by cross-validation in our experiments.

Experiments
In this section, we will evaluate the proposed algorithm on a few real-world supervised multiview learning problems experimentally.

PASCAL VOC 07 data set
The first data set used in the experiment is the PASCAL VOC 07 data set [19]. In this data set, there are 9,963 images of 20 different object classes. Each image is presented by two different view, which are visual view, and tag view. To extract the feature vector from the visual view of an image, we extract local visual features, SIFT, from the image, and represent the local features as a histogram. To extract the feature vector from the tag view from the image, we use the histogram vector of user tags of the image as the feature vector.

CiteSeer data set
The second data set is the CiteSeer data set [50]. In this data set, there are 3,312 documents of 6 classes. Each document has three views, which are the text view, inbound reference view, and outbound reference view.

HMDB data set
The third data set is the HMDB dataset, which is a video database of human motion recognition problem [23]. In this data set, there are 6,849 video clips of 51 action classes. To present each video clip, we extract 3D Harris corners, and present them by two different types of local features, which are the histogram of oriented gradient (HOG) and histogram of oriented flow (HOF). We further represent each clip by two feature vectors of two views, which are the histograms of HOG and HOF.

Experiment protocols
To conduct the experiments, we split each data set into 10 non-overlapping folds, and use the 10-fold cross-validation to perform the training-testing procedure. Each fold is used as a test set in turn, and the remaining 9 folds are used as the training sets. The proposed algorithm is performed to the training set to obtain the view-conditional transformation matrices, and the classifier parameter. Then the learned view-conditional transformation matrices and the classifier parameter are used to represent and classify the data points in the test set. To handle the multiple class problem, we use the one-vs-all strategy.

Performance measures
To measure the classification performance over the test set, we use the classification accuracy. The classification accuracy is defined as follows, Classif ication accuracy = N umber of correctly classif ied test data points N umber of total test data points .
(29) It is obvious that a better algorithm should be able to obtain a higher classification accuracy.

Experiment results
In this experiment, we first study the sensitivity of the algorithm to the parameters, which are α and γ. To study the performance of the proposed algorithm with different tradeoff parameters, α and β. We perform the algorithm by using the parameters of values 0.1, 1, 10, 100 and 1000, and measured the performance of different parameters. Fig. 1 illustrates the performance on the PASCAL VOC 07 data set with respect to different tradeoff parameter α. The proposed algorithm achieves a stable performance in all the settings of parameter α. In Fig. 2, the performance against different tradeoff parameter β is also shown. From this figure, we can also see that the algorithm is stable tot he changes of value of β. This suggests that MISC is not sensitive to the changes of tradeoff parameters.

Comparison to state-of-the-art algorithms
We compare the proposed algorithm to the following methods, multiview learning algorithm using local learning (LL) proposed by Zhang et al. [56], multiview learning algorithm using co-training (CT) proposed by Sindhwani et al. [39], multiview learning algorithm based on view disagreement (VD) proposed by Quadrianto [38], multiview learning algorithm with global consistency and local smoothness (GL) proposed by Zhai [55], and multiview representation method using statistical subspace learning (SS) proposed by Chen et al. The error bars of the classification accuracy of the compared methods over three different data sets are given in Fig. 3, Fig. 3 and Fig. 5. From the figures, we find that the proposed method, MISC, stably outperforms other algorithms at all the data sets. Even on the most difficult data set, HMDB, the proposed method, MISC, also achieves an accuracy as high as about 0.4. The multiple view data are optimally combined by MISC to find the latent intact space and the optimal classifier in the corresponding intact space. The main reason for this is the robust property of the proposed algorithm. This algorithm has the ability to appropriately handle the complementary between multiple views, and learn a discriminative hidden intact space with help of classifier learning.

Conclusions and future works
We propose a novel multiview learning algorithm by learning intact vectors of the training data points and a classifier in the intact space. The intact vectors is assumed to be a hidden but critical vector for each data point, and we can obtain its multiple view feature vectors by view-conditional transformations. Moreover, we also assume that the intact vectors are discriminative, i.e., can be separated by a linear function according to their classes. We propose a novel optimization problem to model both the learning of intact vectors and classifier. An iterative algorithm is developed to solve this problem. This algorithm outperforms other multiview learning algorithms on benchmark data sets, and it also shows its stability over tradeoff parameters. In the future, we will study the potential to use the proposed algorithm for imbalanced data set with multi-view features [9,8,2], and the usage of Bayesian network classifier instead of linear classifier to learn the intact vectors of multi-view data [11,7,10]. Moreover, we will also investigate to use the proposed algorithm to solve the problems of bioinformatics [48,58,29,35], computer vision [46,45], and multi-media data processing [44,43].


Development and Evaluation of a Smartphone Application for Self-estimation of Daily Mental Stress Level

: Heart rate variability (HRV) is known to represent the activity of the autonomic nervous system and is used as a measure for stress levels. Generally, HRV is calculated from the heart rate or the pulse wave using an electrocardiogram (ECG) that is physically-taxing. However, for those with psychological strains and disorders, a handy device that can be used at any time is necessary in order to detect mental illness in its early stages and to prevent it from becoming worse. Therefore, we focused on the idea of using a popular device, the smartphone, and we developed a real-time, HRV-calculating application that uses a photoplethysmogram (PPG) obtained with the smartphone’s camera. The user-friendly interface of the application was designed for use by a wide range of age groups. We conducted two experiments and compared the HRV data obtained by our application with ECG measurements to verify the application’s accuracy. Once a certain level of accuracy was reached, we evaluated the usability of our application among young and elderly users. The results showed that improvements are necessary in terms of the interface, measuring time, and other performance indices.

INTRODUCTION
According to a survey conducted by the Ministry of Health, Labour and Welfare (MHLW, Japan) in 2012, the number of patients with depression, schizophrenia, and anxiety disorders has increased year by year [1]. These mental diseases are frequently caused by psychological strains and burdens. It is thought that self-measurement of daily stress levels will make it possible to detect these mental illness in the early stages.
There are subjective and objective methods for grasping mental stress levels. Subjective methods, such as the questionnaire, are very useful for assimilating psychological transitions in the subject. However, results of these methods are easily influenced by subjects' retention of the evaluation criteria. If we adopt these methods on a daily basis, the results may not be continuous but discrete. In other words, subjective methods are not sufficient for monitoring the daily mental stress.
Whereas, there are objective methods of using indicators such as the heart rate variability (HRV) [2] and the salivary amylase level. These objective methods can estimate mental stress levels more reliably than subjective methods. However, these objective methods are not suitable for daily use because they require special devices, technical skills, and fundamental knowledge. For example, HRV is calculated from the heart rate or the pulse wave using electrocardiogram (ECG) or photoplethysmogram (PPG) that require special measurement devices such as electrocardiographic monitors and sphygmographs. When calculating mental stress levels for the early detection and prevention of mental illness, daily measurements are essential. Furthermore, an objective method with the simple equipment that enables easy measurement is desirable.
As mentioned above, we developed an application that can estimate mental stress levels more easily than the conventional method such as the ECG. For assessing daily levels of mental stress, the objective method is superior to the subjective method but requires special devices. In order to solve this problem, we developed a special-device-free estimation of mental stress levels.
On the other hand, the progress of aging is very rapid in Japan and since 2005 Japan has become the most aged society in the world. The percentage of elderly people (percentage of total population aged 65 and over) in Japan has risen up from 5% in 1950, 10% in 1985, and 20% in 2005, to 25% in 2013, and is expected to rise to 30.3% in 2025 and 39.9% in 2060 [3]. Against these factors, manufacturers in electronics have developed cell phones, smartphones and computers for elderly users [4,5], but there still exists a gap between high-skilled end users and low-skilled end users depending on their demographic traits. One of the marked differences is related to differences within the age group [6]. The usability of the device is significant to the elderly, so the usability of software such as smartphone applications should also be considered through the spread of smartphones.
Therefore, we performed an experiment to verify the application's precision and conducted a user evaluation of the application. The experiment was conducted to compare HRV results from the proposed method with those from the conventionally used ECG. The evaluation focuses particularly on assessing usability of the application among young and elderly users.

THE SMARTPHONE HRV CALCULATION ALGORITHM
The HRV is used as a mental stress indicator in various studies. Generally, it is calculated from the heart rate or the pulse wave using ECG or PPG that requires special measurement devices, technical skills, and knowledge. Figure 1 shows the flow of calculation of HRV in the conventional method using many devices for measuring ECG. It also shows the proposed method to measure the PPG using only a smartphone. Figure 1 presents the measurement method for the electrocardiogram, affixing electrodes to the surface of the human body. Electrodes are connected to the electrocardiograph to measure the potential difference due to heart movements as detected on the surface of the body in the conventional method. The analog data is sent to the PC through an A/D converter. The heart rate fluctuation data obtained from the electrocardiographic wave is sent to the PC and the HRV will be calculated using frequency analysis. We developed an application that can calculate the HRV more easily than the conventional method. The calculation algorithm adopted a white light-emitting diode (LED) light of the smartphone as a light source and smartphone CMOS camera as a photo sensor by applying the pulse wave sensing technology [7]. By means of the low sampling rate of smartphone (33Hz), we determined PPG and corresponding time-series heart beat intervals. Figure 2 shows how to hold the smartphone during the HRV measurement using our application. The first step is to place a fingertip over the lens of the smartphone camera and the LED. The left index finger is preferable. The camera acquires images of blood vessels in the fingertip, and obtains PPG data by measuring the timing of the blood flow. Using the above algorithm, we can calculate HRV from the PPG data using a smartphone.

Method
In order to verify the accuracy of the HRV calculation, we compared the HRV measured by our application with that obtained from an ECG and examined whether there was a correlation between the results of these two methods. We conducted the HRV measurement with ten young people in their 20s (22.1 ± 0.8 years of age). During measurement, the participant was seated in a chair and was instructed to tap a finger on the camera lens and the LED light of the smartphone to obtain stable data. This measurement continued for 150 seconds.
Our data processing program detected the peaks of the PPG. In order to calculate the HRV, the RRI fluctuation wave was interpolated and re-sampled at intervals of 250msec. The power spectrum of the re-sampled RRI was calculated using fast Fourier transform (FFT). By  adopting FFT to obtain HRV, we were able to calculated HRV from 64 seconds RRI. There are many indices related to HRV. For example, the low-frequency index (LF) represents autonomous nervous activity, the high-frequency index (HF) represents parasympathetic nervous activity, and the ratio of LF to HF (LF/HF) represents sympathetic nerve activity. We adopted LF/HF as a comparative index because it has often been used to represent the mental stress level in previous studies. Figure 3 illustrates the R-R interval (RRI) obtained from both methods. According to the verification experiment, we confirmed that the two fluctuation waves were almost the same. The average and minimum correlations among all subjects were r average = 0.90 and r minimum = 0.81, respectively. LF/HF also showed a strong positive correlation (r LF/HF = 0.96) between the two methods ( Figure 4).

USER EVALUATION
We conducted a usability evaluation for the purpose of getting detailed information about the users' subjective impressions and requirements for the application.

Method
Participants included 19 young people in their 20s and 6 elderly people in their 60-70s, all living in Tokyo. The average age of the young people was 21.5 years with a standard deviation (SD) of 1.5 years, and the average age of the elderly users was 69.3 years with an SD of 2.4 years. All 19 young participants had their own smartphones and all 6 elderly people had cell phones.
We defined elderly people as those who are over 65 years of age, based on the World Health Organization (WHO) definition.
First we explained the research it is for the academic purposes and does not have anything to do with the sales or marketing. We then told the participants the expected duration of the experiment and that they were free to quit at anytime during the experiment should they feel uncomfortable with our questions or our treatment of any information gathered.
After obtaining their consent, we explained the evaluation procedure and how to use the application to each participant. Then the participants performed a trial to measure their mental stress level using the application. After the measurement, subjects answered the following questions and commented on the application.
The user evaluation included the following indicators: 1. Demographic characteristics (name, gender, age, experience with using ECG, etc.), 2. Number of errors during the measurement, 3. Whether or not the measurement was successful, and 4. Impressions after using the application.
Only the results of the usability evaluation and the length of measuring time required are described in this paper. Usability and time required for measurement were evaluated with scores from 1 to 5. Along with this evaluation, free-form comments on our application were collected from the subjects, leading to successful extraction of users' requirements.

Results
The results of the evaluations are summarized in Figures 5 and 6. The mean scores on usability evaluations were 3.8 for the young group and 3.7 for the elderly group. Little difference was obtained between the two groups (unpaired t-test, df = 23, t = -0.25, P = n.s.; Fig. 5). The mean scores for the time required for measurement was 4.1 and 3.0 for the young and elderly groups, respectively (unpaired t-test, df = 23, t = -4.06, P < 0.01 Fig. 6). Many subjects in the young group reported that the measurement took too long compared with the reports from the elderly group. Furthermore, suggestions for the application were obtained, such as "Measurement time was too long" (young user) and "It was difficult to hold the smartphone" (elderly user).

DISCUSSION
According to the verification experiment, HRV measurement using our application was found to be valid for assessing mental stress with the same precision as the ECG method.
The application was confirmed to be easy to use, regardless of the age group. The group of young users found the measurement time to be long. All the young group members were using smartphones in their daily lives, hence they are accustomed to using such applications. Because the application does not require any special operation, they reported boredom while staring at the screen for 64 seconds. On the other hand, several members of the group of elderly users who had experience of ECG measurement in hospitals found the measurement time to be short.

CONCLUSION
We have developed an application that can calculate the mental stress level using an objective method and can be used in daily life. We used the smartphone as the measurement equipment because of its convenience. We developed an application that can estimate mental stress levels more easily than the conventional method using an ECG.
We confirmed the accuracy of HRV detected with our application and its usability with a verification experiment and a user evaluation, respectively. The result of the comparative experiment indicated a strong positive correlation between HRV calculated from the ECG and from the smartphone. The results of the user evaluation showed that the proposed application is easy to use regardless of age and prior smartphone experience. Although several young users felt that the measurement time was too long, measurement time cannot be shortened because of possible adverse influence on HRV calculation accuracy.
These results showed that our application can measure mental stress levels with the same accuracy as the conventional method and that our application was appropriate for everyday use. So far, the required measurement time has been the main problem we have faced; this is to be solved by improving the application interface so that users remain interested while taking measurements.   He is developing new medical systems using vital signs, such as heat rate, pulse wave and respiration. His representative work of "UTSUKAMO" is a smart phone application that can provide a method for examining the depression by which whether a user is suffering from the depression or not, can simply be determined by using user's HRV (heart rate variability). He developed the HRV calculation algorithm and designed the interface of "UTSUKAMO" based on the HCD process. He was awarded the best presentation prize at the winter research meeting 2014 from the HCD-Net (Human Centered Design Network).

REFERENCES
Ayako HASHIZUME (Member) Ayako Hashizume received Ph.D. in Kansei Science from Tsukuba University, Japan, in 2011. She is an assistant professor at the Tokyo Metropolitan University. Her research interests include psychology of emotion and Kansei, engineering in relation to HCD (human centered design) and UX (user experience), especially among elderly users. She was awarded the kids design prospective competition 2008 from the Japan Kids Design Association, the best design prize at the design marathon 2009 from the International Association for Universal Design and the research prize 2013 from the Funai Foundation for Information Technology. She is a chief of the special interest group on Kansei of HCD-Net (Human Centered Design Network). She is also a member of Human Interface Society (Japan) and Society of Socio-Informatics (Japan).

Tetsuaki BABA
His research focuses on Interaction Design and Media Art. His works and researches, such as musical instruments, experimental devices, gadgets, educational materials and so on, are located in the area of fusion of product design and media art. He received Ph.D. degrees in art and technology from Kyushu University, Japan, in 2009. He is an associate professor at Tokyo Metropolitan University, Tokyo, Japan. One of his work "Freqtric Drums" is a device that turns users into musical instruments so that users can not only play the instruments but also communicate with the others by touching each other. Through digital technology, Freqtric Drums allows participants to simultaneously enjoy music and skin contact communication and is corrected by art/science centre all over the world as a permanent correction. He is the member of ACM, IPSJ, ADADA, VRSJ.

Takemi MATSUI
Takemi Matsui received the B.S. and M.S. degree from the University of Tsukuba in physics in 1984 and 1986 respectively. He received the Ph.D. degree in medicine in 1995 from Saitama Medical School. He proposes a variety of screening systems, including vital signs based non-contact infection screening system, and depression screening system by autonomic nerves monitoring. He joined National Defense Medical College as a faculty member in 1997, and has been a professor of medical engineering of Tokyo Metropolitan University since 2005.