Tracking the spatial diffusion of influenza and norovirus using telehealth data: a spatiotemporal analysis of syndromic data

Novel Approach for Identification of Basic and Effective Reproduction Numbers Illustrated with COVID-19

This paper presents a novel numerical technique for the identification of effective and basic reproduction numbers, Re and R0, for long-term epidemics, using an inverse problem approach. The method is based on the direct integration of the SIR (Susceptible-Infectious-Removed) system of ordinary differential equations and the least-squares method. Simulations were conducted using official COVID-19 data for the United States and Canada, and for the states of Georgia, Texas, and Louisiana, for a period of two years and ten months. The results demonstrate the applicability of the method in simulating the dynamics of the epidemic and reveal an interesting relationship between the number of currently infectious individuals and the effective reproduction number, which is a useful tool for predicting the epidemic dynamics. For all conducted experiments, the results show that the local maximum (and minimum) values of the time-dependent effective reproduction number occur approximately three weeks before the local maximum (and minimum) values of the number of currently infectious individuals. This work provides a novel and efficient approach for the identification of time-dependent epidemics parameters.

Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study

BACKGROUND

Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control.

OBJECTIVE

This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England.

METHODS

We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region.

RESULTS

Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including "flu symptoms," "norovirus in pregnancy," and norovirus activity in specific years, such as "norovirus 2016." Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources.

CONCLUSIONS

Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information-seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual's conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies.

The Rise in Norovirus-Related Acute Gastroenteritis During the Fight Against the COVID-19 Pandemic in Southern China

Background: There has been a significant decline in the morbidity of almost all infectious diseases during the COVID-19 pandemic. However, while the incidence of norovirus-related acute gastroenteritis declined in Guangzhou, China during the initial period of the pandemic, incidence increased significantly once the new school year began in September 2020.

Methods: Norovirus-related acute gastroenteritis clusters and outbreaks were assessed in Guangzhou from 2015 to 2020. Medians and interquartile ranges were compared between groups using the Mann–Whitney U-test, and attack rates were calculated.

Results: While 78,579 cases of infectious diarrhea were reported from 2015 to 2019, with an average of 15,716 cases per year, only 12,065 cases of infectious diarrhea were reported in 2020. The numbers of sporadic cases and outbreaks reported from January to August 2020 were lower than the average numbers reported during the same time period each year from 2015 to 2019 but began to increase in September 2020. The number of cases in each reported cluster ranged from 10 to 70 in 2020, with a total of 1,280 cases and an average attack rate of 5.85%. The median number of reported cases, the cumulative number of cases, and the attack rate were higher than the average number reported each year from 2015 to 2019. The intervention time in 2020 was also higher than the average intervention time reported during 2015–2019. The main norovirus genotypes circulating in Guangzhou during 2015–2020 included genogroup 2 type 2 (GII.2) (n = 79, 26.69%), GII.17 (n = 36, 12.16%), GII.3 (n = 27, 9.12%), GII.6 (n = 8, 2.7%), GII.4 Sydney_2012 (n = 7, 2.36%), and GII.4 (n = 6, 2.03%).

Conclusions: Our findings illustrate the importance of maintaining epidemiological surveillance for viral gastroenteritis during the COVID-19 pandemic. Local disease prevention and control institutions need to devote sufficient human resources to control norovirus clusters.

Follow-Up of Coronavirus Infected Patients Using Telemedicine in a Referral Pulmonary Center

BACKGROUND

Telemedicine is considered an innovative approach for management and follow up of communicable diseases, when person to person contact has the risk of disease dissemination, such as the situation being experienced with corona virus infection. The aim of this study was to evaluate the role of telemedicine in patient follow-up and patient compliance in different communication methods.

MATERIALS AND METHODS

All patients discharged from a referral pulmonary hospital dedicated to coronavirus infected patients were given instructions on follow-up of symptoms. One group received messages via short message system regarding the severity of their symptoms. For the other group a mobile application was specially designed for tracking their well-being on a daily basis. Severity of symptoms and course of disease were monitored in each group for a two-month period.

RESULTS

A total 1091 patients with mean age of 53.96± 17.95 years were enrolled in the study. In the first group 406 (60.14%) messages were successfully sent, from which 150 (36.94%) patients replied. Also, 243(35%) patients contacted us by making phone calls. Of the total patients in the second group, 153(64%) patients started using the mobile application. Chief complaint of patients was mainly cough, shortness of breath, fatigue, and myalgia. Deep vein thrombosis, hyperglycemia, post kidney transplant patient and bloody diarrhea were among the reported cases.

CONCLUSION

Patient follow-up during epidemics, especially when the disease course is unknown, is an important step in both successful patient management and disease control. This study showed the role of telemedicine for patient follow-up, mostly in detecting special situations. But, in order to be successful patient education and active follow-up are important factors that must be considered.

Novel public health risk assessment process developed to support syndromic surveillance for the 2012 Olympic and Paralympic Games

Background

Syndromic surveillance aims to provide early warning and real time estimates of the extent of incidents; and reassurance about lack of impact of mass gatherings. We describe a novel public health risk assessment process to ensure those leading the response to the 2012 Olympic Games were alerted to unusual activity that was of potential public health importance, and not inundated with multiple statistical 'alarms'.

Methods

Statistical alarms were assessed to identify those which needed to result in 'alerts' as reliably as possible. There was no previously developed method for this. We identified factors that increased our concern about an alarm suggesting that an 'alert' should be made.

Results

Between 2 July and 12 September 2012, 350 674 signals were analysed resulting in 4118 statistical alarms. Using the risk assessment process, 122 'alerts' were communicated to Olympic incident directors.

Conclusions

Use of a novel risk assessment process enabled the interpretation of large number of statistical alarms in a manageable way for the period of a sustained mass gathering. This risk assessment process guided the prioritization and could be readily adapted to other surveillance systems. The process, which is novel to our knowledge, continues as a legacy of the Games.

Identification of environmental determinants for spatio-temporal patterns of norovirus outbreaks in Korea using a geographic information system and binary response models

Although norovirus outbreaks are well-recognized to have strong winter seasonality relevant to low temperature and humidity, the role of artificial human-made features within geographical areas in norovirus outbreaks has rarely been studied. The aim of this study is to assess the natural and human-made environmental factors favoring the occurrence of norovirus outbreaks using nationwide surveillance data. We used a geographic information system and binary response models to examine whether the norovirus outbreaks are spatially patterned and whether these patterns are associated with specific environmental variables including service levels of water supply and sanitation systems and land-use types. The results showed that small-scale low-tech local sewage treatment plants and winter sports areas were statistically significant factors favoring norovirus outbreaks. Compactness of the land development also affected the occurrence of norovirus outbreaks; transportation, water, and forest land-uses were less favored for effective transmission of norovirus, while commercial areas were associated with an increased rate of norovirus outbreaks. We observed associations of norovirus outbreaks with various outcomes of human activities, including discharge of poorly treated sewage, overcrowding of people during winter season, and compactness of land development, which might help prioritize target regions and strategies for the management of norovirus outbreaks.

What is the utility of using syndromic surveillance systems during large subnational infectious gastrointestinal disease outbreaks? An observational study using case studies from the past 5 years in England

Syndromic surveillance systems in England have demonstrated utility in the early identification of seasonal gastrointestinal illness (GI) tracking its spatio-temporal distribution and enabling early public health action. There would be additional public health utility if syndromic surveillance systems could detect or track subnational infectious disease outbreaks. To investigate using syndromic surveillance for this purpose we retrospectively identified eight large GI outbreaks between 2009 and 2014 (four randomly and four purposively sampled). We then examined syndromic surveillance information prospectively collected by the Real-time Syndromic Surveillance team within Public Health England for evidence of possible outbreak-related changes. None of the outbreaks were identified contemporaneously and no alerts were made to relevant public health teams. Retrospectively, two of the outbreaks - which happened at similar times and in proximal geographical locations - demonstrated changes in the local trends of relevant syndromic indicators and exhibited a clustering of statistical alarms, but did not warrant alerting local health protection teams. Our suite of syndromic surveillance systems may be more suited to their original purposes than as means of detecting or monitoring localized, subnational GI outbreaks. This should, however, be considered in the context of this study's limitations; further prospective work is needed to fully explore the use of syndromic surveillance for this purpose. Provided geographical coverage is sufficient, syndromic surveillance systems could be able to provide reassurance of no or minor excess healthcare systems usage during localized GI incidents.

Ten-year performance of Influenzanet: ILI time series, risks, vaccine effects, and care-seeking behaviour

Recent public health threats have propelled major innovations on infectious disease monitoring, culminating in the development of innovative syndromic surveillance methods. Influenzanet is an internet-based system that monitors influenza-like illness (ILI) in cohorts of self-reporting volunteers in European countries since 2003. We investigate and confirm coherence through the first ten years in comparison with ILI data from the European Influenza Surveillance Network and demonstrate country-specific behaviour of participants with ILI regarding medical care seeking. Using regression analysis, we determine that chronic diseases, being a child, living with children, being female, smoking and pets at home, are all independent predictors of ILI risk, whereas practicing sports and walking or bicycling for locomotion are associated with a small risk reduction. No effect for using public transportation or living alone was found. Furthermore, we determine the vaccine effectiveness for ILI for each season.

Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models

Background

Emerging novel influenza outbreaks have increasingly been a threat to the public and a major concern of public health departments. Real-time data in seamless surveillance systems such as health insurance claims data for influenza-like illnesses (ILI) are ready for analysis, making it highly desirable to develop practical techniques to analyze such readymade data for outbreak detection so that the public can receive timely influenza epidemic warnings. This study proposes a simple and effective approach to analyze area-based health insurance claims data including outpatient and emergency department (ED) visits for early detection of any aberrations of ILI.

Methods

The health insurance claims data during 2004–2009 from a national health insurance research database were used for developing early detection methods. The proposed approach fitted the daily new ILI visits and monitored the Pearson residuals directly for aberration detection. First, negative binomial regression was used for both outpatient and ED visits to adjust for potentially influential factors such as holidays, weekends, seasons, temporal dependence and temperature. Second, if the Pearson residuals exceeded 1.96, aberration signals were issued. The empirical validation of the model was done in 2008 and 2009. In addition, we designed a simulation study to compare the time of outbreak detection, non-detection probability and false alarm rate between the proposed method and modified CUSUM.

Results

The model successfully detected the aberrations of 2009 pandemic (H1N1) influenza virus in northern, central and southern Taiwan. The proposed approach was more sensitive in identifying aberrations in ED visits than those in outpatient visits. Simulation studies demonstrated that the proposed approach could detect the aberrations earlier, and with lower non-detection probability and mean false alarm rate in detecting aberrations compared to modified CUSUM methods.

Conclusions

The proposed simple approach was able to filter out temporal trends, adjust for temperature, and issue warning signals for the first wave of the influenza epidemic in a timely and accurate manner.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-1500-4) contains supplementary material, which is available to authorized users.

Cannabis cultivation in Quebec: between space-time hotspots and coldspots

BACKGROUND

Cannabis cultivation has become increasingly localized, whether soil-based or hydroponic growing methods are used. Characteristics of a given location, such as its climate and the equipment it requires may influence general accessibility or attract different types of offenders based on potential profits. The location of crops, especially hydroponic crops, suggests a certain proximity to the consumer market via semi-urban and urban environments, while making it possible to avoid detection. This article examines the cannabis market through its cultivation.

METHODS

The stability of temporal and spatial clusters of cannabis cultivation, hotspots, and coldspots between 2001 and 2009 in the province of Quebec, Canada, are addressed. Studying the geography of crime is not a new endeavor, but coldspots are rarely documented in drug market research. Using arrests and general population data, as well as Kulldorff's scan statistics, results show that the temporal distribution of cannabis cultivation is highly seasonal for soil-based methods.

RESULTS

Hydroponic production shows adaptation to its soil-based counterpart. Stable patterns are found for both spatial distributions. Hotspots for soil-based cultivation are found near several urban centers and the Ontario border. For hydroponic cannabis cultivation, a new hotspot suggests the emergence of an American demand for Quebec-grown cannabis between 2007 and 2009. Curiously, the region surrounding Montreal, the largest urban center in Quebec, is a recurrent and stable coldspot for both methods of cultivation.

CONCLUSION

For all periods, spatial clusters are stronger for soil-based methods than in the hydroponic context. Temporal differences and spatial similarities between soil-based cultivation and hydroponic cultivation are discussed. The role of the metropolis is also addressed.

Influenza surveillance and incidence in a rural area in China during the 2009/2010 influenza pandemic

BACKGROUND

Most influenza surveillance is based on data from urban sentinel hospitals; little is known about influenza activity in rural communities. We conducted influenza surveillance in a rural region of China with the aim of detecting influenza activity in the 2009/2010 influenza season.

METHODS

The study was conducted from October 2009 to March 2010. Real-time polymerase chain reaction was used to confirm influenza cases. Over-the-counter (OTC) drug sales were daily collected in drugstores and hospitals/clinics. Space-time scan statistics were used to identify clusters of ILI in community. The incidence rate of ILI/influenza was estimated on the basis of the number of ILI/influenza cases detected by the hospitals/clinics.

RESULTS

A total of 434 ILI cases (3.88% of all consultations) were reported; 64.71% of these cases were influenza A (H1N1) pdm09. The estimated incidence rate of ILI and influenza were 5.19/100 and 0.40/100, respectively. The numbers of ILI cases and OTC drug purchases in the previous 7 days were strongly correlated (Spearman rank correlation coefficient [r] = 0.620, P = 0.001). Four ILI outbreaks were detected by space-time permutation analysis.

CONCLUSIONS

This rural community surveillance detected influenza A (H1N1) pdm09 activity and outbreaks in the 2009/2010 influenza season and enabled estimation of the incidence rate of influenza. It also provides a scientific data for public health measures.

Predictive performance of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden

Syndromic data sources have been sought to improve the timely detection of increased influenza transmission. This study set out to examine the prospective performance of telenursing chief complaints in predicting influenza activity. Data from two influenza seasons (2007/08 and 2008/09) were collected in a Swedish county (population 427,000) to retrospectively determine which grouping of telenursing chief complaints had the largest correlation with influenza case rates. This grouping was prospectively evaluated in the three subsequent seasons. The best performing telenursing complaint grouping in the retrospective algorithm calibration was fever (child, adult) and syncope (r=0.66; p<0.001). In the prospective evaluation, the performance of 14-day predictions was acceptable for the part of the evaluation period including the 2009 influenza pandemic (area under the curve (AUC)=0.84; positive predictive value (PPV)=0.58), while it was strong (AUC=0.89; PPV=0.93) for the remaining evaluation period including only influenza winter seasons. We recommend the use of telenursing complaints for predicting winter influenza seasons. The method requires adjustments when used during pandemics.

Young people's use of NHS Direct: a national study of symptoms and outcome of calls for children aged 0-15

OBJECTIVES

National Health Service (NHS) Direct provides 24/7 expert telephone-based healthcare information and advice to the public in England. However, limited research has explored the reasons to why calls are made on behalf of young people, as such this study aimed to examine call rate (CR) patterns in younger people to enable a better understanding of the needs of this population in England.

SETTING

NHS Direct, England, UK.

PARTICIPANTS AND METHODS

CRs (expressed as calls/100 persons/annum) were calculated for all calls (N=358 503) made to NHS Direct by, or on behalf of, children aged 0-15 during the combined four '1-month' periods within a year (July 2010, October 2010, January 2011 and April 2011). χ² Analysis was used to determine the differences between symptom, outcome and date/time of call.

RESULTS

For infants aged <1, highest CRs were found for 'crying' for male (n=14, 440, CR=13.61) and female (n=13 654, CR=13.46) babies, which is used as a universal assessment applied to all babies. High CRs were also found for symptoms relating to 'skin/hair/nails' and 'colds/flu/sickness' for all age groups, whereby NHS Direct was able to support patients to self-manage and provide health information for these symptoms for 59.7% and 51.4% of all cases, respectively. Variations in CRs were found for time and age, with highest peaks found for children aged 4-15 in the 15:00-23:00 period and in children aged <1 in the 7:00-15:00 period.

CONCLUSIONS

This is the first study to examine the symptoms and outcome of calls made to NHS Direct for and on behalf of young children. The findings revealed how NHS Direct has supported a range of symptoms through the provision of health information and self-care support which provides important information about service planning and support for similar telephone-based services.

Blood donation clusters in Québec, Canada (2003-2008): spatial variations according to sex and age

BACKGROUND AND OBJECTIVES

The detection of spatial clusters of blood donation rate is an important issue, especially for targeting spatial units with significantly low rates, where it could be possible to increase the numbers of donors. The objective of this study is to detect spatial clusters of high or low blood donation rate in Québec according to sex and age of the donors.

MATERIALS AND METHODS

Blood donation data were obtained from Héma-Québec over a period of 5 years. We aggregated these data for each of 101 municipalités regionales de comté (i.e. counties) for men, women and four age groups. To detect spatial high/low donation rate areas, we used the Kulldorff's scan statistics. Kappa coefficient was used to assess discordance between clusters obtained for the different groups (18-29, 30-39, 40-49, 50-59, 60-69 years old). T-test analyses were conducted to identify significant associations between spatial clusters and socio-economic variables.

RESULTS

The results indicate the presence of several geographical areas with high or low blood donation rates for each group. The size, the location and the socio-demographic profiles of low/high clusters vary according to sex and age categories.

CONCLUSION

The Kulldorff's scan statistics are an efficient tool to assess the blood donation performance across a country or even a specific region over a period of several years. In terms of strategic planning and monitoring, it can be used as a fully operational tool to target areas with significantly low rates (for all donors or specific demographic groups) in future blood donation campaigns.

Comparative spatial dynamics of Japanese encephalitis and acute encephalitis syndrome in Nepal

Japanese Encephalitis (JE) is a vector-borne disease of major importance in Asia. Recent increases in cases have spawned the development of more stringent JE surveillance. Due to the difficulty of making a clinical diagnosis, increased tracking of common symptoms associated with JE-generally classified as the umbrella term, acute encephalitis syndrome (AES) has been developed in many countries. In Nepal, there is some debate as to what AES cases are, and how JE risk factors relate to AES risk. Three parts of this analysis included investigating the temporal pattern of cases, examining the age and vaccination status patterns among AES surveillance data, and then focusing on spatial patterns of risk factors. AES and JE cases from 2007-2011 reported at a district level (n = 75) were examined in relation to landscape risk factors. Landscape pattern indices were used to quantify landscape patterns associated with JE risk. The relative spatial distribution of landscape risk factors were compared using geographically weighted regression. Pattern indices describing the amount of irrigated land edge density and the degree of landscape mixing for irrigated areas were positively associated with JE and AES, while fragmented forest measured by the number of forest patches were negatively associated with AES and JE. For both JE and AES, the local GWR models outperformed global models, indicating spatial heterogeneity in risks. Temporally, the patterns of JE and AES risk were almost identical; suggesting the relative higher caseload of AES compared to JE could provide a valuable early-warning signal for JE surveillance and reduce diagnostic testing costs. Overall, the landscape variables associated with a high degree of landscape mixing and small scale irrigated agriculture were positively linked to JE and AES risk, highlighting the importance of integrating land management policies, disease prevention strategies and promoting healthy sustainable livelihoods in both rural and urban-fringe developing areas.

Local implementation of a syndromic influenza surveillance system using emergency department data in Santander, Spain

BACKGROUND

We assessed the local implementation of syndromic surveillance (SyS) as part of the European project 'System for Information on, Detection and Analysis of Risks and Threats to Health' in Santander, Spain.

METHODS

We applied a cumulative sum algorithm on emergency department (ED) chief complaints for influenza-like illness in the seasons 2010-11 and 2011-12. We fine tuned the algorithm using a receiver operating characteristic analysis to identify the optimal trade-off of sensitivity and specificity and defined alert criteria. We assessed the timeliness of the SyS system to detect the onset of the influenza season.

RESULTS

The ED data correlated with the sentinel data. With the best algorithm settings we achieved 70/63% sensitivity and 89/95% specificity for 2010-11/2011-12. At least 2 consecutive days of signals defined an alert. In 2010-11 the SyS system alerted 1 week before the sentinel system and in 2011-12 in the same week. The data from the ED is available on a daily basis providing an advantage in timeliness compared with the weekly sentinel data.

CONCLUSIONS

ED-based SyS in Santander complements sentinel influenza surveillance by providing timely information. Local fine tuning and definition of alert criteria are recommended to enhance validity.

Inaccuracy, uncertainty and the space-time permutation scan statistic

The space-time permutation scan statistic (STPSS) is designed to identify hot (and cool) spots of space-time interaction within patterns of spatio-temporal events. While the method has been adopted widely in practice, there has been little consideration of the effect inaccurate and/or incomplete input data may have on its results. Given the pervasiveness of inaccuracy, uncertainty and incompleteness within spatio-temporal datasets and the popularity of the method, this issue warrants further investigation. Here, a series of simulation experiments using both synthetic and real-world data are carried out to better understand how deficiencies in the spatial and temporal accuracy as well as the completeness of the input data may affect results of the STPSS. The findings, while specific to the parameters employed here, reveal a surprising robustness of the method's results in the face of these deficiencies. As expected, the experiments illustrate that greater degradation of input data quality leads to greater variability in the results. Additionally, they show that weaker signals of space-time interaction are those most affected by the introduced deficiencies. However, in stark contrast to previous investigations into the impact of these input data problems on global tests of space-time interaction, this local metric is revealed to be only minimally affected by the degree of inaccuracy and incompleteness introduced in these experiments.

Spatial diffusion of influenza outbreak-related climate factors in Chiang Mai Province, Thailand

Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.

Novel measurement of spreading pattern of influenza epidemic by using weighted standard distance method: retrospective spatial statistical study of influenza, Japan, 1999-2009

BACKGROUND

Annual influenza epidemics occur worldwide resulting in considerable morbidity and mortality. Spreading pattern of influenza is not well understood because it is often hampered by the quality of surveillance data that limits the reliability of analysis. In Japan, influenza is reported on a weekly basis from 5,000 hospitals and clinics nationwide under the scheme of the National Infectious Disease Surveillance. The collected data are available to the public as weekly reports which were summarized into number of patient visits per hospital or clinic in each of the 47 prefectures. From this surveillance data, we analyzed the spatial spreading patterns of influenza epidemics using weekly weighted standard distance (WSD) from the 1999/2000 through 2008/2009 influenza seasons in Japan. WSD is a single numerical value representing the spatial compactness of influenza outbreak, which is small in case of clustered distribution and large in case of dispersed distribution.

RESULTS

We demonstrated that the weekly WSD value or the measure of spatial compactness of the distribution of reported influenza cases, decreased to its lowest value before each epidemic peak in nine out of ten seasons analyzed. The duration between the lowest WSD week and the peak week of influenza cases ranged from minus one week to twenty weeks. The duration showed significant negative association with the proportion of influenza A/H3N2 cases in early phase of each outbreak (correlation coefficient was -0.75, P = 0.012) and significant positive association with the proportion of influenza B cases in the early phase (correlation coefficient was 0.64, P = 0.045), but positively correlated with the proportion of influenza A/H1N1 strain cases (statistically not significant). It is assumed that the lowest WSD values just before influenza peaks are due to local outbreak which results in small standard distance values. As influenza cases disperse nationwide and an epidemic reaches its peak, WSD value changed to be a progressively increasing.

CONCLUSIONS

The spatial distribution of nationwide influenza outbreak was measured by using a novel WSD method. We showed that spreading rate varied by type and subtypes of influenza virus using WSD as a spatial indicator. This study is the first to show a relationship between influenza epidemic trend by type/subtype and spatial distribution of influenza nationwide in Japan.

Increased emergency department chief complaints of fever identified the influenza (H1N1) pandemic before outpatient symptom surveillance

OBJECTIVE

To determine whether a sentinel clinic network or an emergency department (ED) was more timely in identifying the 2009 influenza A (H1N1) pandemic.

METHODS

All reasons for presenting to the adult regional medical ED were coded online by admission secretaries, without the aid of medical personnel. Increased influenza activity defined by weekly chief complaints of fever was compared with activity defined by the Israel Center for Disease Control (viral surveillance as well as a large sentinel clinic network).

RESULTS

Influenza activity during the pandemic increased in the ED 2 weeks before outpatient sentinel clinics. During the pandemic, maximal ED activity was much higher than in previous seasons. Maximal activity during the past 5 years correlated with the timeliness of the chief complaint of fever in identifying the onset of epidemics.

CONCLUSION

Chief complaint of fever in the ED can be a sensitive marker of increased influenza activity and might replace the use of sentinel clinics.

Use of spatiotemporal analysis of laboratory submission data to identify potential outbreaks of new or emerging diseases in cattle in Great Britain

BACKGROUND

New and emerging diseases of livestock may impact animal welfare, trade and public health. Early detection of outbreaks can reduce the impact of these diseases by triggering control measures that limit the number of cases that occur. The aim of this study was to investigate whether prospective spatiotemporal methods could be used to identify outbreaks of new and emerging diseases in scanning surveillance data. SaTScan was used to identify clusters of unusually high levels of submissions where a diagnosis could not be reached (DNR) using different probability models and baselines. The clusters detected were subjected to a further selection process to reduce the number of false positives and a more detailed epidemiological analysis to ascertain whether they were likely to represent real outbreaks.

RESULTS

187,925 submissions of clinical material from cattle were made to the Regional Laboratory of the Veterinary Laboratories Agency (VLA) between 2002 and 2007, and the results were stored on the VLA FarmFile database. 16,925 of these were classified as DNRs and included in the analyses. Variation in the number and proportion of DNRs was found between syndromes and regions, so a spatiotemporal analysis for each DNR syndrome was done. Six clusters were identified using the Bernoulli model after applying selection criteria (e.g. size of cluster). The further epidemiological analysis revealed that one of the systemic clusters could plausibly have been due to Johne's disease. The remainder were either due to misclassification or not consistent with a single diagnosis.

CONCLUSIONS

Our analyses have demonstrated that spatiotemporal methods can be used to detect clusters of new or emerging diseases, identify clusters of known diseases that may not have been diagnosed and identify misclassification in the data, and highlighted the impact of data quality on the ability to detect outbreaks. Spatiotemporal methods should be used alongside current temporal methods for analysis of scanning surveillance data. These statistical analyses should be followed by further investigation of possible outbreaks to determine whether cases have common features suggesting that these are likely to represent real outbreaks, or whether issues with the collection or processing of information have resulted in false positives.

Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data

Timely detection of clusters of localized influenza activity in excess of background seasonal levels could improve situational awareness for public health officials and health systems. However, no single data type may capture influenza activity with optimal sensitivity, specificity, and timeliness, and it is unknown which data types could be most useful for surveillance. We compared the performance of 10 types of electronic clinical data for timely detection of influenza clusters throughout the 2007/08 influenza season in northern California. Kaiser Permanente Northern California generated zip code-specific daily episode counts for: influenza-like illness (ILI) diagnoses in ambulatory care (AC) and emergency departments (ED), both with and without regard to fever; hospital admissions and discharges for pneumonia and influenza; antiviral drugs dispensed (Rx); influenza laboratory tests ordered (Tests); and tests positive for influenza type A (FluA) and type B (FluB). Four credible events of localized excess illness were identified. Prospective surveillance was mimicked within each data stream using a space-time permutation scan statistic, analyzing only data available as of each day, to evaluate the ability and timeliness to detect the credible events. AC without fever and Tests signaled during all four events and, along with Rx, had the most timely signals. FluA had less timely signals. ED, hospitalizations, and FluB did not signal reliably. When fever was included in the ILI definition, signals were either delayed or missed. Although limited to one health plan, location, and year, these results can inform the choice of data streams for public health surveillance of influenza.

Norovirus as a foodborne disease hazard

Norovirus (NoV) is the most common cause of infectious gastroenteritis in the world. Gastroenteritis caused by bacterial and parasitic pathogens is commonly linked to food sources, but the link between NoV and contaminated foods has been more difficult to establish. Even when epidemiological information indicates that an outbreak originated with food, the presence of NoV in the suspect product may not be confirmed. If food is found to contain a common strain of NoV that circulates widely in the community, it is not possible to use strain typing to link the contamination to patient cases. Although food is certainly implicated in NoV spread, there are additional person-to-person and fomite transmission routes that have been shown to be important. NoV has an extremely low infectious dose, is stable in the environment, and resists disinfection. Cell culture methods are not available, so viability cannot be determined. Finally, many NoV outbreaks originate with when an infected food handler contaminates ready-to-eat food, which can be interpreted as foodborne or person-to-person transmission. This review will discuss both the physical characteristics of NoVs and the available epidemiological information with particular reference to the role of foods in NoV transmission.

Innovation in observation: a vision for early outbreak detection

The emergence of new infections and resurgence of old onesFhealth threats stemming from environmental contamination or purposeful acts of bioterrorismFcall for a worldwide effort in improving early outbreak detection, with the goal of ameliorating current and future risks. In some cases, the problem of outbreak detection is logistically straightforward and mathematically easy: a single case of a disease of great concern can constitute an outbreak. However, for the vast majority of maladies, a simple analytical solution does not exist. Furthermore, each step in developing reliable, sensitive, effective surveillance systems demonstrates enormous complexities in the transmission, manifestation, detection, and control of emerging health threats. In this communication, we explore potential future innovations in early outbreak detection systems that can overcome the pitfalls of current surveillance. We believe that modern advances in assembling data, techniques for collating and processing information, and technology that enables integrated analysis will facilitate a new paradigm in outbreak definition and detection. We anticipate that moving forward in this direction will provide the highly desired sensitivity and specificity in early detection required to meet the emerging challenges of global disease surveillance.

Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?

Background

Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.

Methods and Findings

Using retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999–2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population). Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters. We evaluated how many LRI-clusters were detected in 1999–2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations. We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999–2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact. For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).

Conclusions

To our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period. The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.

Surveillance of febrile patients in a district and evaluation of their spatiotemporal associations: a pilot study

BACKGROUND

Fever is an undifferentiated clinical feature that may enhance the sensitivity of syndromic surveillance systems. By studying the spatiotemporal associations of febrile patients, it may allow early detection of case clustering that indicates imminent threat of infectious disease outbreaks in the community.

METHODS

We captured consecutive emergency department visits that led to hospitalization in a district hospital in Hong Kong during the period of 12 Sep 2005 to 14 Oct 2005. We recorded demographic data, provisional diagnoses, temperature on presentation and residential location for each patient-episode, and geocoded the residential addresses. We applied Geographical Information System technology to study the geographical distribution these cases, and their associations within a 50-m buffer zone spatially. A case cluster was defined by three or more spatially associated febrile patients within each three consecutive days.

RESULTS

One thousand and sixty six patient-episodes were eligible for analysis; 42% of them had fever (>37 degrees C; oral temperature) on presentation. Two hundred and four patient-episodes (19.1%) came from residential care homes for elderly (RCHE). We detected a total of 40 case clusters during the study period. Clustered cases were of older age; 57 (33.3%) were residents of RCHE. We found a median of 3 patients (range: 3 - 8) and time span of 3 days (range: 2 - 8 days) in each cluster. Twenty five clusters had 2 or more patients living in the same building block; 18 of them were from RCHE.

CONCLUSIONS

It is technically feasible to perform surveillance on febrile patients and studying their spatiotemporal associations. The information is potentially useful for early detection of impending infectious disease threats.

Early detection of influenza outbreaks using the DC Department of Health's syndromic surveillance system

Background

Since 2001, the District of Columbia Department of Health has been using an emergency room syndromic surveillance system to identify possible disease outbreaks. Data are received from a number of local hospital emergency rooms and analyzed daily using a variety of statistical detection algorithms. The aims of this paper are to characterize the performance of these statistical detection algorithms in rigorous yet practical terms in order to identify the optimal parameters for each and to compare the ability of two syndrome definition criteria and data from a children's hospital versus vs. other hospitals to determine the onset of seasonal influenza.

Methods

We first used a fine-tuning approach to improve the sensitivity of each algorithm to detecting simulated outbreaks and to identifying previously known outbreaks. Subsequently, using the fine-tuned algorithms, we examined (i) the ability of unspecified infection and respiratory syndrome categories to detect the start of the flu season and (ii) how well data from Children's National Medical Center (CNMC) did versus all the other hospitals when using unspecified infection, respiratory, and both categories together.

Results

Simulation studies using the data showed that over a range of situations, the multivariate CUSUM algorithm performed more effectively than the other algorithms tested. In addition, the parameters that yielded optimal performance varied for each algorithm, especially with the number of cases in the data stream. In terms of detecting the onset of seasonal influenza, only "unspecified infection," especially the counts from CNMC, clearly delineated influenza outbreaks out of the eight available syndromic classifications. In three of five years, CNMC consistently flags earlier (from 2 days up to 2 weeks earlier) than a multivariate analysis of all other DC hospitals.

Conclusions

When practitioners apply statistical detection algorithms to their own data, fine tuning of parameters is necessary to improve overall sensitivity. With fined tuned algorithms, our results suggest that emergency room based syndromic surveillance focusing on unspecified infection cases in children is an effective way to determine the beginning of the influenza outbreak and could serve as a trigger for more intensive surveillance efforts and initiate infection control measures in the community.

Feasibility of telemonitoring for active surveillance of influenza vaccine safety in the primary care setting in the Netherlands

We examined the feasibility of a commercial home telemonitoring system for monitoring adverse events related to vaccination and influenza-like illness (ILI) signs and outcomes in the primary care setting in the Netherlands. A prospective cohort of people eligible for influenza vaccination was monitored daily between mid-October 2007 and mid-March 2008. Adults from five primary care centres were invited to participate. A total of 245 people participated (response rate 75%). Their mean age was 61 years (SD = 15), 50% were female and 60% had a chronic disease. Most (73%) had no problems with installation of the system and 67% finished all sets of monitoring dialogues. The reported incidence of adverse events in the first week after vaccination was 8–38%. The reported incidence rates of ILI symptoms varied and were higher than reference data. A total of 39% of individuals consulted their general practitioner, 7% the hospital emergency department, 6% were hospitalized and 27% used medication. Of those in paid work, one-third reported absence of work due to ILI. Home telemonitoring appears to be feasible for monitoring vaccine adverse events and ILI symptoms and outcomes.