24 February 2015

Research Committee Selected Articles for the Week of Feb 23, 2015

Articles from February_23_2015

Research Committee Selected Articles for the Week of February_23_2015

  • Eichstaedt J.C., Schwartz H.A., Kern M.L., Park G., Labarthe D.R., Merchant R.M., Jha S., Agrawal M. Psychological Language on Twitter Predicts County-Level Heart Disease Mortality
  • Huizer Y.L., Swaan C.M., Leitmeyer K.C., Timen A. Usefulness and applicability of infectious disease control measures in air travel: A review
  • Torner N., Martinez A., Basile L., Marcos M.A., Anton A., Mosquera M.M., Isanta R., Cabezas C., Jane Influenza vaccine effectiveness assessment through sentinel virological data in three postpandemic seasons
  • Edworthy J., Hellier E., Newbold L., Titchener K. Passing crisis and emergency risk communications: The effects of communication channel, information type, and repetition
  • Sun G., Matsui T., Hakozaki Y., Abe S. An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method
  • Brookes V.J., Hernandez-Jover M., Holyoake P., Ward M.P. Industry opinion on the likely routes of introduction of highly pathogenic porcine reproductive and respiratory syndrome into Australia from south-east Asia
  • Yom-Tov E., Borsa D., Hayward A.C., McKendry R.A., Cox I.J. Automatic identification of web-based risk markers for health events
  • Dahlgren F.S., McQuiston J.H., Massung R.F., Anderson A.D. Q fever in the United States: Summary of case reports from two national surveillance systems, 2000-2012
  • Andrianakis I., Vernon I.R., McCreesh N., McKinley T.J., Oakley J.E., Nsubuga R.N., Goldstein M., Wh Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda
  • Lopez A.L., Macasaet L.Y., Ylade M., Tayag E.A., Ali M. Epidemiology of Cholera in the Philippines
  • Tamir D.E., Rishe N.D., Last M., Kandel A. Soft computing based epidemical crisis prediction
  • Servi L., Elson S.B. A mathematical approach to gauging influence by identifying shifts in the emotions of social media users
  • Pang L., Ruan S., Liu S., Zhao Z., Zhang X. Transmission dynamics and optimal control of measles epidemics
  • Zhang S., Zhao J. Spatio-temporal epidemiology of hand, foot and mouth disease in Liaocheng City, North China
  • Reich N.G., Cummings D.A.T., Lauer S.A., Zorn M., Robinson C., Nyquist A.-C., Price C.S., Simberkoff Triggering interventions for influenza: The ALERT algorithm
  • Chowell G., Nishiura H. Characterizing the Transmission Dynamics and Control of Ebola Virus Disease
  • '

    Mapping Medical Disasters: Ebola Makes Old Lessons, New

    Disaster medicine is characterized by shortages of everything but patients. There are never enough beds, equipment, personnel, or supplies. In the 2014 Ebola epidemic, another scarcity was maps. The need for maps of the affected areas, and the ways the maps were used, serve to emphasize the way maps have always served in both disaster medicine and public health preparedness. Those lessons are reviewed here in the context of the Ebola epidemic. (Disaster Med Public Health Preparedness. 2015;0:1-8)

    Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotionsóespecially angeróemerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.

    Background: Air travel has opened up opportunities for world transportation, but has also increased infectious disease transmission and public health risks. To control disease spread, airlines and governments are able to implement control measures in air travel. This study inventories experiences and applicability of infectious disease control measures. Methods: A literature search was performed in PubMed, including studies between 1990 and 2013. Search terms included air travel terms and intervention terms. Interventions were scored according outcome, required resources, preparation, passenger inconvenience and passenger compliance. Results: Provision of information to travelers, isolation, health monitoring, hygiene measures and vector control reportedly prevent disease spread and are well applicable. Contact tracing can be supportive in controlling disease spread but depend on disease characteristics. Exit and entry screening, quarantine and travel restrictions are unlikely to be very effective in preventing disease spread, while implementation requires extensive resources or travel implications. Conclusions: Control measures should focus on providing information towards travelers, isolation, health monitoring and hygiene measures. Appropriateness of measures depends on disease characteristics, and the required resources. As most studies analyze one type of measure in a particular situation, further research comparing the effectiveness of measures is recommended.

    Influenza vaccination aims at reducing the incidence of serious disease, complications and death among those with the most risk of severe influenza disease. Influenza vaccine effectiveness (VE) through sentinel surveillance data from the PIDIRAC program (Daily Acute Respiratory Infection Surveillance of Catalonia) during 2010-2011, 2011-2012, and 2012-2013 influenza seasons, with three different predominant circulating influenza virus (IV) types [A(H1N1)pdm09, A(H3N2) and B, respectively] was assessed. The total number of sentinel samples with known vaccination background collected during the study period was 3173, 14.7% of which had received the corresponding seasonal influenza vaccine. 1117 samples (35.2%) were positive for IV. A retrospective negative case control design was used to assess vaccine effectiveness (VE) for the entire period and for each epidemic influenza season. An overall VE of 58.1% (95% CI:46.8-67) was obtained. Differences in VE according to epidemic season were observed, being highest for the 2012-2013 season with predominance of IV type B (69.7% ;95% CI:51.5-81) and for the 2010-2011 season, with predominance of the A(H1N1) pdm09 influenza virus strain (67.2% ;95%CI:49.5-78.8) and lowest for the 2011-2012 season with A(H3N2) subtype predominance (34.2% ;95%CI:4.5-54.6). Influenza vaccination prevents a substantial number of influenza-associated illnesses. Although vaccines with increased effectiveness are needed and the search for a universal vaccine that is not subject to genetic modifications might increase VE, nowadays only the efforts to increase vaccination rates of high-risk population and healthcare personnel let reduce the burden of influenza and its complications.

    Three experiments explore several factors which influence information transmission when warning messages are passed from person to person. In Experiment 1, messages were passed down chains of participants using five different modes of communication. Written communication channels resulted in more accurate message transmission than verbal. In addition, some elements of the message endured further down the chain than others. Experiment 2 largely replicated these effects and also demonstrated that simple repetition of a message eliminated differences between written and spoken communication. In a final field experiment, chains of participants passed information however they wanted to, with the proviso that half of the chains could not use telephones. Here, the lack of ability to use a telephone did not affect accuracy, but did slow down the speed of transmission from the recipient of the message to the last person in the chain. Implications of the findings for crisis and emergency risk communication are discussed.

    Objectives: To classify higher-risk influenza patients within 10s, we developed an infectious disease and fever screening radar system. Methods: The system screens infected patients based on vital signs, i.e., respiration rate measured by a radar, heart rate by a finger-tip photo-reflector, and facial temperature by a thermography. The system segregates subjects into higher-risk influenza (HR-I) group, lower-risk influenza (LR-I) group, and non-influenza (Non-I) group using a neural network and fuzzy clustering method (FCM). We conducted influenza screening for 35 seasonal influenza patients and 48 normal control subjects at the Japan Self-Defense Force Central Hospital. Pulse oximetry oxygen saturation (SpO2) was measured as a reference. Results: The system classified 17 subjects into HR-I group, 26 into LR-I group, and 40 into Non-I group. Ten out of the 17 HR-I subjects indicated SpO2 <96%, whereas only two out of the 26 LR-I subjects showed SpO2 <96%. The chi-squared test revealed a significant difference in the ratio of subjects showed SpO2 <96% between HR-I and LR-I group (p<0.001). There were zero and nine normal control subjects in HR-I and LR-I groups, respectively, and there was one influenza patient in Non-I group. Conclusions: The combination of neural network and FCM achieved efficient detection of higher-risk influenza patients who indicated SpO2 96% within 10s.

    Objective: To assess industry expert opinion on the likely occurrence of entry and exposure routes relevant to a potential incursion of highly pathogenic (HP) porcine reproductive and respiratory syndrome (PRRS) virus from south-east Asia to Australia. Design: Expert opinion elicitation of pig-industry stakeholders using a face-to-face questionnaire. Results: Pig industry experts identified exposure routes involving the disposal of food waste to have the highest probability of occurrence. They were also concerned about the exposure of commercial pigs to humans acting as fomites of PRRS virus, and to feed and additives imported from south-east Asia. They did not consistently agree on the probability of occurrence of entry routes. Conclusion: This study demonstrated that the expert elicitation technique was useful in rapidly assessing opinion from a large group of pig industry experts regarding exposure of pigs in Australia to HP-PRRS virus. The results of this survey were used to direct a risk assessment of an incursion of HP-PRRS.

    Background: The escalating cost of global health care is driving the development of new technologies to identify early indicators of an individual's risk of disease. Traditionally, epidemiologists have identified such risk factors using medical databases and lengthy clinical studies but these are often limited in size and cost and can fail to take full account of diseases where there are social stigmas or to identify transient acute risk factors. Objective: Here we report that Web search engine queries coupled with information on Wikipedia access patterns can be used to infer health events associated with an individual user and automatically generate Web-based risk markers for some of the common medical conditions worldwide, from cardiovascular disease to sexually transmitted infections and mental health conditions, as well as pregnancy. Methods: Using anonymized datasets, we present methods to first distinguish individuals likely to have experienced specific health events, and classify them into distinct categories. We then use the self-controlled case series method to find the incidence of health events in risk periods directly following a user's search for a query category, and compare to the incidence during other periods for the same individuals. Results: Searches for pet stores were risk markers for allergy. We also identified some possible new risk markers; for example: searching for fast food and theme restaurants was associated with a transient increase in risk of myocardial infarction, suggesting this exposure goes beyond a long-term risk factor but may also act as an acute trigger of myocardial infarction. Dating and adult content websites were risk markers for sexually transmitted infections, such as human immunodeficiency virus (HIV). Conclusions: Web-based methods provide a powerful, low-cost approach to automatically identify risk factors, and support more timely and personalized public health efforts to bring human and economic benefits.

    Q fever is a worldwide zoonosis historically associated with exposure to infected livestock. This study summarizes cases of Q fever, a notifiable disease in the United States, reported to the Centers for Disease Control and Prevention through two national surveillance systems with onset during 2000-2012. The overall incidence rate during this time was 0.38 cases per million persons per year. The reported case fatality rate was 2.0%, and the reported hospitalization rate was 62%. Most cases (61%) did not report exposure to cattle, goats, or sheep, suggesting that clinicians should consider Q fever even in the absence of livestock exposure. The prevalence of drinking raw milk among reported cases of Q fever (8.4%) was more than twice the national prevalence for the practice. Passive surveillance systems for Q fever are likely impacted by underreporting and underdiagnosis because of the nonspecific presentation of Q fever.

    Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology. We present a novel method that has the potential to improve the calibration of complex infectious disease models (hereafter called simulators). We present this in the form of a tutorial and a case study where we history match a dynamic, event-driven, individual-based stochastic HIV simulator, using extensive demographic, behavioural and epidemiological data available from Uganda. The tutorial describes history matching and emulation. History matching is an iterative procedure that reduces the simulator's input space by identifying and discarding areas that are unlikely to provide a good match to the empirical data. History matching relies on the computational efficiency of a Bayesian representation of the simulator, known as an emulator. Emulators mimic the simulator's behaviour, but are often several orders of magnitude faster to evaluate. In the case study, we use a 22 input simulator, fitting its 18 outputs simultaneously. After 9 iterations of history matching, a non-implausible region of the simulator input space was identified that was (Formula presented.) times smaller than the original input space. Simulator evaluations made within this region were found to have a 65% probability of fitting all 18 outputs. History matching and emulation are useful additions to the toolbox of infectious disease modellers. Further research is required to explicitly address the stochastic nature of the simulator as well as to account for correlations between outputs.

    Despite being a cholera-endemic country, data on cholera in the Philippines remain sparse. Knowing the areas where cholera is known to occur and the factors that lead to its occurrence will assist in planning preventive measures and disaster mitigation.We identified 12 articles from ProMED and none from PubMed that reported on cholera in the Philippines from 2008 to 2013. Data from ProMed and surveillance revealed 42,071 suspected and confirmed cholera cases reported from 2008 to 2013, among which only 5,006 were confirmed. 38 (47%) of 81 provinces and metropolitan regions reported at least one confirmed case of cholera and 32 (40%) reported at least one suspected case. The overall case fatality ratio in sentinel sites was 0.62%, but was 2% in outbreaks. All age groups were affected. Using both confirmed and suspected cholera cases, the average annual incidence in 2010ñ2013 was 9.1 per 100,000 population. Poor access to improved sanitation was consistently associated with higher cholera incidence. Paradoxically, access to improved water sources was associated with higher cholera incidence using both suspected and confirmed cholera data sources. This finding may have been due to the breakdown in the infrastructure and non-chlorination of water supplies, emphasizing the need to maintain public water systems.Using sentinel surveillance data, PubMed and ProMED searches covering information from 2008ñ2013 and event-based surveillance reports from 2010ñ2013, we assessed the epidemiology of cholera in the Philippines. Using spatial log regression, we assessed the role of water, sanitation and population density on the incidence of cholera.Our findings confirm that cholera affects a large proportion of the provinces in the country. Identifying areas most at risk for cholera will support the development and implementation of policies to minimize the morbidity and mortality due to this disease.

    Epidemical crisis prediction is one of the most challenging examples of decision making with uncertain information. As in many other types of crises, epidemic outbreaks may pose various degrees of surprise as well as various degrees of ìderivativesî of the surprise (i.e., the speed and acceleration of the surprise). Often, crises such as epidemic outbreaks are accompanied by a secondary set of crises, which might pose a more challenging prediction problem. One of the unique features of epidemic crises is the amount of fuzzy data related to the outbreak that spreads through numerous communication channels, including media and social networks. Hence, the key for improving epidemic crises prediction capabilities is in employing sound techniques for data collection, information processing, and decision making under uncertainty and exploiting the modalities and media of the spread of the fuzzy information related to the outbreak. Fuzzy logic-based techniques are some of the most promising approaches for crisis management. Furthermore, complex fuzzy graphs can be used to formalize the techniques and methods used for the datamining. Another advantage of the fuzzy-based approach is that it enables keeping account of events with perceived low possibility of occurrence via low fuzzy membership/truth-values and updating these values as information is accumulated or changed. In this chapter we introduce several soft computing based methods and tools for epidemic crises prediction. In addition to classical fuzzy techniques, the use of complex fuzzy graphs as well as incremental fuzzy clustering in the context of complex and high order fuzzy logic system is presented.

    Although an extensive research literature on influence exists in fields like social psychology and communications, the advent of social media opens up new questions regarding how to define and measure influence online. In this paper, we present a new definition of influence that is tailored uniquely for online contexts and an associated methodology for gauging influence. According to our definition, influence entails the capacity to shift the patterns of emotion levels expressed by social media users. The source of influence may be the content of a user's message or the context of the relationship between exchanging users. Regardless of the source, measuring influence requires first identifying shifts in the patterns of emotion levels expressed by users and then studying the extent that these shifts can be associated with a user. This paper presents a new quantitative approach that combines the use of a text analysis program with a mathematical algorithm to derive trends in levels of emotions expressed in social media and, more importantly, detect breakpoints when those trends changed abruptly. First steps have also been taken to predict future trends in expressions (as well as quantify their accuracy). These methods constitute a new approach to quantifying influence in social media that focuses on detecting the impact of influence (e.g., shifts in levels of emotions expressed) as opposed to focusing on the dynamics of simple social media counts, e.g., retweets, followers, or likes.

    Based on the mechanism and characteristics of measles transmission, we propose a susceptible-exposed-infectious-recovered (SEIR) measles epidemic model with vaccination and investigate the effect of vaccination in controlling the spread of measles. We obtain two critical threshold values, ?c1 and ?c2, of the vaccine coverage ratio. Measles will be extinct when the vaccination ratio ? > ?c1, endemic when ?c2 < ? < ?c1, and outbreak periodically when ? < ?c2. In addition, we apply the optimal control theory to obtain an optimal vaccination strategy ?? (t) and give some numerical simulations for those theoretical findings. Finally, we use our model to simulate the data of measles cases in the U.S. from 1951 to 1962 and design a control strategy.

    Hand, foot and mouth disease (HFMD) has posed a notable threat to public health and become a public health priority in China. This study was based on the reported cases of HFMD between 2007 and 2011. A total of 34,176 HFMD cases were geo?coded at town level (n=134). Firstly, a descriptive analysis was conducted to evaluate the epidemic characteristics of HFMD. Then, the Kulldorff scan statistic based on a discrete Poisson model was used to detect spatial?temporal clusters. Spatial distribution of HFMD in Liaocheng City, China from 2007 to 2011 was mapped at town level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of HFMD was non?random and clustered with a significant Moran's I value every year. The local Moran's I Z?score detected three significant spatial clusters for high incidence of HFMD. The space?time analysis identified one most likely cluster and twenty?five secondary clusters for high incidence of HFMD. We demonstrate evidence of the existence of statistically significant HFMD clusters in Liaocheng City. Our results provide better guidance for formulating regional prevention and control strategies.

    Background.Early, accurate predictions of the onset of influenza season enable targeted implementation of control efforts. Our objective was to develop a tool to assist public health practitioners, researchers, and clinicians in defining the community-level onset of seasonal influenza epidemics. Methods.Using recent surveillance data on virologically confirmed infections of influenza, we developed the Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm, a method to identify the period of highest seasonal influenza activity. We used data from 2 large hospitals that serve Baltimore, Maryland and Denver, Colorado, and the surrounding geographic areas. The data used by ALERT are routinely collected surveillance data: weekly case counts of laboratory-confirmed influenza A virus. The main outcome is the percentage of prospective seasonal influenza cases identified by the ALERT algorithm. Results.When ALERT thresholds designed to capture 90% of all cases were applied prospectively to the 2011-2012 and 2012-2013 influenza seasons in both hospitals, 71%-91% of all reported cases fell within the ALERT period. Conclusions.The ALERT algorithm provides a simple, robust, and accurate metric for determining the onset of elevated influenza activity at the community level. This new algorithm provides valuable information that can impact infection prevention recommendations, public health practice, and healthcare delivery.

    Carefully calibrated transmission models have the potential to guide public health officials on the nature and scale of the interventions required to control epidemics. In the context of the ongoing Ebola virus disease (EVD) epidemic in Liberia, Drake and colleagues, in this issue of PLOS Biology, employed an elegant modeling approach to capture the distributions of the number of secondary cases that arise in the community and health care settings in the context of changing population behaviors and increasing hospital capacity. Their findings underscore the role of increasing the rate of safe burials and the fractions of infectious individuals who seek hospitalization together with hospital capacity to achieve epidemic control. However, further modeling efforts of EVD transmission and control in West Africa should utilize the spatial-temporal patterns of spread in the region by incorporating spatial heterogeneity in the transmission process. Detailed datasets are urgently needed to characterize temporal changes in population behaviors, contact networks at different spatial scales, population mobility patterns, adherence to infection control measures in hospital settings, and hospitalization and reporting rates.

    1 comment: