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)

Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

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.

Usefulness and applicability of infectious disease control measures in air travel: A review

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 vaccine effectiveness assessment through sentinel virological data in three postpandemic seasons

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.

Passing crisis and emergency risk communications: The effects of communication channel, information type, and repetition

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.

An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method

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.

Industry opinion on the likely routes of introduction of highly pathogenic porcine reproductive and respiratory syndrome into Australia from south-east Asia

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.

Automatic identification of web-based risk markers for health events

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 in the United States: Summary of case reports from two national surveillance systems, 2000-2012

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.

Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda

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.

Epidemiology of Cholera in the Philippines

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.

Soft computing based epidemical crisis prediction

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.

A mathematical approach to gauging influence by identifying shifts in the emotions of social media users

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.

Transmission dynamics and optimal control of measles epidemics

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.

Spatio-temporal epidemiology of hand, foot and mouth disease in Liaocheng City, North China

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.

Triggering interventions for influenza: The ALERT algorithm

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.

Characterizing the Transmission Dynamics and Control of Ebola Virus Disease

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.

29 April 2013

April Literature Review: Data Visualizations

ISDS’s Research Committee hosted another great Literature Review this month!

The ISDS Literature Review calls take place bi-monthly and are an opportunity to discuss journal articles related to biosurveillance. Recently, the Research Committee decided that, in order to generate more focused discussion, the Literature Review calls would be centered around topics of interest to the community. These topics are decided on by the Research Committee about two months prior to a Literature Review. The group’s Zotero library is then searched for keywords relating to the topic. Finally, these articles are sent to the Research Committee. This month the Literature Review was focused on data visualization.

Call Highlights:

Three articles were summarized and discussed on the April Literature Review Call:

Chui KKH, Wenger JB, Cohen SA, Naumova EN. Visual Analytics for Epidemiologists: Understanding the Interactions Between Age, Time, and Disease with Multi-Panel Graphs. PLoS ONE. 2011;6(2):e14683. In this article, the authors propose the use of a visual analytics tool called multi-panel graphs to facilitate the interaction among age, temporal trends and disease. The authors provide examples of using this technique for influenza, salmonella, and asthma from CMS, Massachusetts DPH and the Children’s hospital of Wisconsin.

While, the call participants found this to be an interesting presentation of data, there was interest in assessing reactions among public health practitioners (i.e., ‘Would practioners find this visualization useful?’).

Hafen RP, Anderson DE, Cleveland WS, et al. Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts. BMC Medical Informatics and Decision Making. 2009;9(1):21. Here the authors present a statistical outbreak detection method along with visualizations of data decomposition introducing a method they call STL, for “seasonal-trend decomposition procedure based on loess smoothing.” The authors compared the STL-derived detector to EARS methods C1, C2, and C3 and to a regression detector based on general linear modeling (GLM) for small, medium, and large-scale Emergency Department (ED) time series for detection of artificial lognormal signals at 3 levels. The STL method outperformed the EARS methods for in each situation. It outperformed the GLM method significantly for the smaller signals and slightly for the larger ones.

Again, there was general interest in this method from a theoretical viewpoint, but the question of practical utility to public health practitioners was raised. Essentially, would practitioner be interested in this standardized technique? If so, it may be worth developing into a standardized tool.

Ji X, Chun S, Geller J. Epidemic Outbreak and Spread Detection System Based on Twitter Data. In: He J, Liu X, Krupinski E, Xu G, eds. Health Information Science.Vol 7231. Lecture Notes in Computer Science. Springer Berlin / Heidelberg; 2012:152–163. This study discusses the design and utility of the New Jersey Institute of Technology’s Epidemics Outbreak and Spread Detection System (EOSDS). EOSDS utilizes publicly available information from Twitter to generate three different visualizations of the space and time dimensions of a spreading epidemic: static map, distribution map, and filter map. As an example of the utility of this system they considered a time period during which there was a severe outbreak of listeria in North America and compared the EOSDS visualizations (collecting a test dataset by specifying the keyword “listeria,” and monitoring the period from “09-26-2011” to “09-28-2011”) and CDC official reports – a gold standard. They reported that all three visualizations showed good correlation with CDC reports and concluded that EOSDS may be used as an effective early warning system.

While there is still debate about the utility of social media for disease surveillance, the study proposed geocoding and visualization methods that may provide information on health events prior to official reports. To see this system in action, visit the EOSDS website.

To view the full article summaries, please visit the ISDS April 2013 Summaries Wiki.
You may also review the Literature Review archives here.

Written by ISDS Program Manager, Tera Reynolds.

27 July 2012

July 2012 Literature Review Call

ISDS’s Research Committee hosted another great Literature Review yesterday, July 26, 2012!

The ISDS Literature Review calls take place bi-monthly and are an opportunity to discuss new journal articles related to biosurveillance. Behind the scenes of the Literature Reviews, the Research Committee leadership meets weekly to narrow down the results of a Scopus search query (developed by Dr. Katie Suda, University of Tennessee) to the most relevant articles. The use of Scopus has improved the range of articles that we have been able to identify - capturing relevant papers from disciplines that were previously missed. Recently, the Literature Review organizers have taken another giant step towards a more efficient, organized, and user-friendly Literature Review process with the addition of a citation manager - Zotero. Using Zotero has not only increased the efficiency for the organizers, but for Literature Review attendees as well. Zotero allows participants to sort by title or author and to search for keywords using tags. We hope that you find Zotero useful for finding the article(s) that you are interested in!

Call Highlights:
The Research Committee gratefully acknowledges two authors for their participation in the July call: Dr. Andrew Fine, Children’s Hospital Boston, and Dr. Erika Samoff, The University of North Carolina at Chapel Hill, Gillings School of Global Public Health. Both authors provided Literature Review participants with the invaluable opportunity to ask questions and learn more about their research. Brief summaries:

Dr. Andrew Fine and his colleagues wrote the article “Improved Diagnostic Accuracy of Group A Streptococcal Pharyngitis With Use of Real-Time Biosurveillance” published in Annals of Internal Medicine. This article highlighted the authors work in testing the benefit of adjusting the conventional Centor score for group A streptococcal pharyngitis using epidemiological data in the form of the recent local proportion positive (RLPP). They conclude that the number of additional true cases diagnosed and the number of unnecessary antibiotic prescriptions avoided for false diagnoses demonstrates a distinct advantage to using the epidemiological information.

Dr. Samoff and her colleagues authored the article “Integration of syndromic surveillance data into public health practice at state and local levels in north Carolina” published in Public Health Reports. This study sought to describe integration of syndromic surveillance data into daily surveillance practice at local health departments (LHDs). In order to achieve this objective, the authors compared the use of syndromic surveillance data and reportable disease data at the state and local levels. The authors conclude that, while the routine use of syndromic surveillance data by North Carolina state and local public health authorities resulted in meaningful public health action, the syndromic surveillance system is oriented towards sensitivity not efficiency. In the future, systems will need to be oriented towards efficiency in order to successfully incorporate new surveillance data into systems.

Other articles discussed:

Venkatarao E, Patil RR, Prasad D, Anasuya A, Samuel R. Monitoring data quality in syndromic surveillance: Learnings from a resource limited setting. Journal of Global Infectious Diseases. 2012;4(2):120–127.
Cowling BJ, Fang VJ, Nishiura H, et al. Increased risk of noninfluenza respiratory virus infections associated with receipt of inactivated influenza vaccine. Clinical Infectious Diseases. 2012;54(12):1778–1783.
Kohl KS, Arthur RR, O’Connor R, Fernandez J. Assessment of public health events through international health Regulations, United States, 2007-2011. Emerging Infectious Diseases. 2012;18(7):1047–1053.
Ribeiro SHR, Costa MA. Optimal selection of the spatial scan parameters for cluster detection: A simulation study. Spatial and Spatio-temporal Epidemiology. 2012;3(2):107–120.

To view all of the article summaries, please visit the ISDS July 2012 Summaries Wiki.

You may also review the Literature Review archives here.

Written by Tera Reynolds, MPH, ISDS Program Manager.

07 June 2012

May 2012 Literature Review Summary

ISDS Research Committee

May 31, 2012

Between the bi-monthly Literature Review calls, the Research Committee leadership is hard at work sifting through articles on a weekly basis, and identifying those that would be of interest to the committee. The leadership’s efforts leading up to the May call were not in vain – they resulted in very stimulating conversation.

One article discussed on the call is titled Social and News Media Enable Estimation of Epidemiological Patterns Early in the 2010 Haitian Cholera Outbreak (American Journal of Tropical Medicine and Hygiene); it assessed the correlation of volume of cholera-related HealthMap reports, Tweets, and government cholera cases reported in the first 100 days of the 2010 Haitian cholera outbreak. The Research Committee gratefully welcomed first author, Dr. Rumi Chunara of the Children’s Hospital Informatics Program, Children’s Hospital Boston, to the Literature Review call to answer questions about the publication. Dr. Chunara’s presence resulted in all participants gaining a deeper understanding of the research and its implications.

Other articles that were discussed:

Durham DP, Casman EA. Incorporating individual health-protective decisions into disease transmission models: a mathematical framework. Journal of The Royal Society Interface. 2012;9(68):562–570.
Li J, Davies GJ, Kendall G, et al. Evidence and belief in regulatory decisions – Incorporating expected utility into decision modelling. Expert Systems with Applications. 2012;39(10):8604–8610.
Halvorson EE, Peters TR, Snively BM, Poehling KA. Potential impact of accelerating the primary dose of rotavirus vaccine in infants. Vaccine. 2012;30(17):2738–2741.
Wu WY, Hripczak G, Lurio J, et al. Impact of Integrating Public Health Clinical Decision Support Alerts Into Electronic Health Records on Testing for Gastrointestinal Illness. J Public Health Manag Pract. 2012 May-Jun; 18(3):224-7.

Full article summaries written by Research Committee members are available here.

In addition to the article summaries and discussions, the Award for Outstanding Research Articles in Biosurveillance was announced! This award is being offered in order to recognize disease surveillance scientists and professionals for contributions to their fields of research. Nominations for the awards are currently being accepted (Deadline: June 25th). Nominate today.

12 April 2012

March 2012 Literature Review Update

ISDS Research Committee

March 30, 2012

The Literature Review bi-monthly calls are a great opportunity to engage with other biosurveillance researchers and practitioners about new journal articles highlighting developments in the field. Literature Review participants are at all career levels, from some of the top professionals in the disease surveillance to those just starting out. It is a great way to stay up on the latest in biosurveillance research and practice and for participants to learn from one another. All levels of participation are welcome – from summarizing an article to joining in on the discussions to just listening.

We had an exciting March call!

Dr. Katie Suda, Associate Professor and Director, University of Tennessee, presented an update on the new Literature Review process. The previous process included creating pubcrawler and Google Scholar searches with the results sent to an ISDS Research Committee Gmail account. However, we had received feedback from members that we were missing relevant literature. Over the course of several weeks, Dr. Suda, along with the Literature Review leadership, developed search strings in PubMed, Embase, and Scopus, consisting of over 100 terms suggested by members from different content areas. Based on the results, we decided that the Scopus search is the most comprehensive – includes articles indexed in both Medline and Embase (compared to pubcrawler which only includes articles indexed in Medline). This updated process dramatically increases the efficiency and range of articles captured. In addition, Dr. Suda has graciously offered to continue to help us to refine our search string as necessary.

One Literature Review contributor chose to summarize Assessing the Continuum of Event-Based Biosurveillance Through an Operational Lens by Corley et al., which offered a characterization framework consisting of eight attribute families meant to accommodate the entire continuum of event-based biosurveillance. The Research Committee proudly welcomed Dr. Courtney Corley, Knowledge Discovery and Informatics Group, Pacific Northwest National Laboratory (PNNL), on the Literature Review call. He summarized his article and answered participants' questions. Dr. Corley’s presence enhanced the discussion of his research, resulting in a very lively and interactive conversation. The Research Committee would like to thank Dr. Corley for his time and valuable contribution.

Other articles that were discussed included:

· The Effect of School Dismissal on Rates of Influenza-Like Illness in New York City Schools During the Spring 2009 Novel H1N1 Outbreak by Dr. Joseph R. Egger, SciMetrika, et al., which describes an unusual opportunity to test the effect of school closure on ILI incidence in a situation among schools known to be affected by an epidemic.

· Using Poison Center Data for National Public Health Surveillance for Chemical and Poison Exposure and Associated Illness by Amy F. Wolkin, Centers for Disease Control and Prevention, et al., describing the National Poison Data System and its uses and integration with public health surveillance for situational awareness, identification of early markers of chemical events, and chemical and poising exposures and associated illnesses.

· A novel experience in the use of control charts for the detection of nosocomial infection outbreaks by Isabel Cristina Gomes, Minas Gerais Federal University, Brazil, et al., which evaluates the use of control charts to monitor nosocomial infection rates in a Brazilian university hospital.

· Outbreaks of virulent diarrhoeagenic Escherichia coli – are we in control? by Dirk Werber, Robert Koch Institute, Germany, et al., which highlights how the combination of cutting edge laboratory methods and common sense food tracing was applied to fight – what the authors call – an ever-moving target, the constantly evolving diarrhoeagenic E. coli.

The next Literature Review call will be in late May (the exact date will be announced soon).

To view the full summaries from the March call, please click here.

If you would like to be added to the Literature Review e-mail list or have comments/questions, please e-mail Tera Reynolds, ISDS Program Manager.

18 January 2011

January Literature Review: Spatial Scanning Tips and Tricks for Practical Outbreak Detection

Date: January 27th, 2011; 12:00-1:30 pm EST

For its January 2011 Literature Review, the ISDS Research Committee is pleased to present Daniel Neill, PhD, Assistant Professor of Information Systems at Carnegie Mellon University, who will be speaking about his recent publication, "An empirical comparison of spatial scan statistics for outbreak detection" in the International Journal of Health Geographics.

After conducting the regularly scheduled review of articles during the first 30 minutes of the call, Dr. Neill's talk will begin promptly at 12:30 pm EST.

Register for the webinar.

19 October 2010

October Literature Review: Absolute Humidity and Influenza Transmission Dynamics

On Tuesday, October 26th, the Literature Review subcommittee is hosting its bi-monthly Literature Review, along with a special presentation by scientist Jeffery Shaman of the College of Oceanic and Atmospheric Sciences at Oregon State University. The meeting will begin at 12:00 pm EST with a short summary of recent publications, followed by Shaman's presentation at 12:30 pm EST.

Shaman will discuss his groundbreaking work investigating the role of absolute humidity on influenza transmission dynamics, including his recent paper "Absolute Humidity and the Seasonal Onset of Influenza in the Continental United States," published in PLoS Biology. His recent work on this topic have fundamentally changed the way we understand influenza seasonality -- and they may well alter how we practice disease surveillance in the future. Be sure not to miss this opportunity!

24 August 2010

Upcoming Webinar: August Literature Review and Presentation on Seasonal Synchronization of Influenza

The ISDS Research Committee will be hosting its bi-monthly Literature Review this Thursday, August 26th at 12:00 pm EST.

In addition to the regularly scheduled review session, there will be a presentation by Elena Naumova, PhD, on her recent publication entitled "Seasonal Synchronization of Influenza in the United States Older Adult Population." Dr. Naumova is a professor in the Department of Public Health and Community Medicine and the Director of the Tufts Initiative for the Forecasting and Modeling of Infectious Diseases at Tufts University School of Medicine.

The first half hour of the call will consist of the regularly scheduled review of notable publications in the last two months, followed by Dr. Naumova's presentation at 12:30 pm EST.

To access the list of recent publications, please contact us.

Register for the webinar here.

28 May 2010

June 2010 Literature Review Presentation

The International Society for Disease Surveillance (ISDS) Research Committee is happy to annouce an online webinar by Anne Presanis, Medical Research Council Biostatistics Unit, Cambridge, UK, presenting their paper "The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis" published in the December 2009 issue of PLoS Medicine.

The presentation will take place during the bi-monthly Literature Review call on Thursday, June 24th, 2010 from 11:00 am to 12:30 pm EST.

Register here.

10 September 2009

September Article Listing for Literature Review

The following list contains the most recent articles collected by the Research Committee's automated search. These articles will be reviewed on the next Literature Review call, taking place on Thursday, October 29th, 2009 at 10:00 am EST.

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van Hal SJ, Foo H, Blyth CC, McPhie K, Armstrong P, Sintchenko V, Dwyer DE.
Influenza outbreak during Sydney World Youth Day 2008: the utility of laboratory testing and case definitions on mass gathering outbreak containment.
PLoS One. 2009 Sep 3;4(9):e6620.

Elbert Y, Burkom HS.
Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data.
Stat Med. 2009 Sep 1

Caudle JM, van Dijk A, Rolland E, Moore KM.
Telehealth Ontario detection of gastrointestinal illness outbreaks.
Can J Public Health. 2009 Jul-Aug;100(4):253-7.

Bayesian Information Fusion Networks for Biosurveillance Applications.
J Am Med Inform Assoc. 2009 Aug 28

Elliot AJ, Powers C, Thornton A, Obi C, Hill C, Simms I, Waight P, Maguire H, Foord D, Povey E, Wreghitt T, Goddard N, Ellis J, Bermingham A, Sebastianpillai P, Lackenby A, Zambon M, Brown D, Smith GE, Gill ON.
Monitoring the emergence of community transmission of influenza A/H1N1 2009 in England: a cross sectional opportunistic survey of self sampled telephone callers to NHS Direct.
BMJ. 2009 Aug 27;339:b3403.

Uscher-Pines L, Farrell CL, Cattani J, Hsieh YH, Moskal MD, Babin SM, Gaydos CA, Rothman RE.
A survey of usage protocols of syndromic surveillance systems by state public health departments in the United States.
J Public Health Manag Pract. 2009 Sep-Oct;15(5):432-8.

Blair PJ, Wierzba TF, Touch S, Vonthanak S, Xu X, Garten RJ, Okomo-Adhiambo MA, Klimov AI, Kasper MR, Putnam SD.
Influenza epidemiology and characterization of influenza viruses in patients seeking treatment for acute fever in Cambodia.
Epidemiol Infect. 2009 Aug 24;:1-11.

Babin SM.
Using syndromic surveillance systems to detect pneumonic plague.
Epidemiol Infect. 2009 Aug 24;:1-8.

20Aug2009
Osnas EE, Heisey DM, Rolley RE, Samuel MD.
Spatial and temporal patterns of chronic wasting disease: fine-scale mapping of a wildlife epidemic in Wisconsin.
Ecol Appl. 2009 Jul;19(5):1311-22.

Wang X, Zeng D, Seale H, Li S, Cheng H, Luan R, He X, Pang X, Dou X, Wang Q.
Comparing Early Outbreak Detection Algorithms Based on Their Optimized Parameter Values.
J Biomed Inform. 2009 Aug 12.

Uphoff H, Geis S, Grüber A, Hauri A.
What will the next influenza season bring about: seasonal influenza or the new A(H1N1)v? An analysis of German influenza surveillance data.
Euro Surveill. 2009 Aug 13;14(32.

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Research Committee Selected Articles for the Week of February_23_2015

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