A call for better understanding of social media in surveillance and management of noncommunicable diseases.

Using social media for health purposes has attracted much attention over the past decade. Given the challenges of population ageing and changes in national health profile and disease patterns following the epidemiologic transition, researchers and policy-makers should pay attention to the potential of social media in chronic disease surveillance, management and support. This commentary overviews the evidence base for this inquiry and outlines the key challenges to research laying ahead. The authors provide concrete suggestions and recommendations for developing a research agenda to guide future investigation and action on this topic.

Modifiable risk factors for multidrug-resistant Gram-negative infection in critically ill burn patients: a systematic review and meta-analysis.

BACKGROUND: We conducted a systematic review and meta-analysis to identify potentially modifiable risk factors for multidrug-resistant Gram-negative colonization or infection in critically ill burn patients. METHODS: A systematic search was conducted of PubMed, Embase, CINAHL, Web of Science and Central (Cochrane). Risk factors including antibiotic use and hospital interventions were summarized in a random-effects meta-analysis. Risk of publication bias was assessed using the Grading of Recommendations Assessment, Development and Evaluation method and funnel plots. RESULTS: A total of 11 studies met the inclusion criteria. We identified several potentially modifiable risk factors and were able to grade their importance based on effect size. Related to prior antibiotic exposure, extended-spectrum cephalosporins (pooled odds ratio (OR) 7.00, 95% confidence interval (CI) 2.77-17.67), carbapenems (pooled OR 6.65, 95% CI 3.49-12.69), anti-pseudomonal penicillins (pooled OR 4.23, 95% CI 1.23-14.61) and aminoglycosides (pooled OR 4.20, 95% CI 2.10-8.39) were most significant. Related to hospital intervention, urinary catheters (pooled OR 11.76, 95% CI 5.03-27.51), arterial catheters (pooled OR 8.99, 95% CI 3.84-21.04), mechanical ventilation (pooled OR 5.49, 95% CI 2.59-11.63), central venous catheters (pooled OR 4.26, 95% CI 1.03-17.59), transfusion or blood product administration (pooled OR 4.19, 95% CI 1.48-11.89) and hydrotherapy (pooled OR 3.29, 95% CI 1.64-6.63) were most significant. CONCLUSION: Prior exposure to extended-spectrum cephalosporins and carbapenems, as well as the use of urinary catheters and arterial catheters pose the greatest threat for infection or colonization with multidrug-resistant Gram-negative organisms in the critically ill burn patient population.

Monitoring Pertussis Infections Using Internet Search Queries.

This study aims to assess the utility of internet search query analysis in pertussis surveillance. This study uses an empirical time series model based on internet search metrics to detect the pertussis incidence in Australia. Our research demonstrates a clear seasonal pattern of both pertussis infections and Google Trends (GT) with specific search terms in time series seasonal decomposition analysis. The cross-correlation function showed significant correlations between GT and pertussis incidences in Australia and each state at the lag of 0 and 1 months, with the variation of correlations between 0.17 and 0.76 (p < 0.05). A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed to track pertussis epidemics pattern using GT data. Reflected values for this model were generally consistent with the observed values. The inclusion of GT metrics improved detective performance of the model (ß = 0.058, p < 0.001). The validation analysis indicated that the overall agreement was 81% (sensitivity: 77% and specificity: 83%). This study demonstrates the feasibility of using internet search metrics for the detection of pertussis epidemics in real-time, which can be considered as a pre-requisite for constructing early warning systems for pertussis surveillance using internet search metrics.

Effect of Weather Variability on Seasonal Influenza Among Different Age Groups in Queensland, Australia: A Bayesian Spatiotemporal Analysis.

Background: The effects of weather variability on seasonal influenza among different age groups remain unclear. The comparative study aims to explore the differences in the associations between weather variability and seasonal influenza, and growth rates of seasonal influenza epidemics among different age groups in Queensland, Australia. Methods: Three Bayesian spatiotemporal conditional autoregressive models were fitted at the postal area level to quantify the relationships between seasonal influenza and monthly minimum temperature (MIT), monthly vapor pressure, school calendar pattern, and Index of Relative Socio-Economic Advantage and Disadvantage for 3 age groups (<15, 15-64, and ≥65 years). Results: The results showed that the expected decrease in monthly influenza cases was 19.3% (95% credible interval [CI], 14.7%-23.4%), 16.3% (95% CI, 13.6%-19.0%), and 8.5% (95% CI, 1.5%-15.0%) for a 1°C increase in monthly MIT at <15, 15-64, and ≥65 years of age, respectively, while the average increase in the monthly influenza cases was 14.6% (95% CI, 9.0%-21.0%), 12.1% (95% CI, 8.8%-16.1%), and 9.2% (95% CI, 1.4%-16.9%) for a 1-hPa increase in vapor pressure. Conclusions: Weather variability appears to be more influential on seasonal influenza transmission in younger (0-14) age groups. The growth rates of influenza at postal area level were relatively small for older (≥65) age groups in Queensland, Australia.

Gut microbiota disturbance during helminth infection: can it affect cognition and behaviour of children?

BACKGROUND: Bidirectional signalling between the brain and the gastrointestinal tract is regulated at neural, hormonal, and immunological levels. Recent studies have shown that helminth infections can alter the normal gut microbiota. Studies have also shown that the gut microbiota is instrumental in the normal development, maturation and function of the brain. The pathophysiological pathways by which helminth infections contribute to altered cognitive function remain poorly understood. DISCUSSION: We put forward the hypothesis that gastrointestinal infections with parasitic worms, such as helminths, induce an imbalance of the gut-brain axis, which, in turn, can detrimentally manifest in brain development. Factors supporting this hypothesis are: 1) research focusing on intelligence and school performance in school-aged children has shown helminth infections to be associated with cognitive impairment, 2) disturbances in gut microbiota have been shown to be associated with important cognitive developmental effects, and 3) helminth infections have been shown to alter the gut microbiota structure. Evidence on the complex interactions between extrinsic (parasite) and intrinsic (host-derived) factors has been synthesised and discussed. While evidence in favour of the helminth-gut microbiota-central nervous system hypothesis is circumstantial, it would be unwise to rule it out as a possible mechanism by which gastrointestinal helminth infections induce childhood cognitive morbidity. Further empirical studies are necessary to test an indirect effect of helminth infections on the modulation of mood and behaviour through its effects on the gut microbiota.

Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases.

Effective disease surveillance is critical to the functioning of health systems. Traditional approaches are, however, limited in their ability to deliver timely information. Internet-based surveillance systems are a promising approach that may circumvent many of the limitations of traditional health surveillance systems and provide more intelligence on cases of infection, including cases from those that do not use the healthcare system. Infectious disease surveillance systems built on Internet search metrics have been shown to produce accurate estimates of disease weeks before traditional systems and are an economically attractive approach to surveillance; they are, however, also prone to error under certain circumstances. This study sought to explore previously unmodeled diseases by investigating the link between Google Trends search metrics and Australian weekly notification data. We propose using four alternative disease modelling strategies based on linear models that studied the length of the training period used for model construction, determined the most appropriate lag for search metrics, used wavelet transformation for denoising data and enabled the identification of key search queries for each disease. Out of the twenty-four diseases assessed with Australian data, our nowcasting results highlighted promise for two diseases of international concern, Ross River virus and pneumococcal disease.

Using Baidu Search Index to Predict Dengue Outbreak in China.

This study identified the possible threshold to predict dengue fever (DF) outbreaks using Baidu Search Index (BSI). Time-series classification and regression tree models based on BSI were used to develop a predictive model for DF outbreak in Guangzhou and Zhongshan, China. In the regression tree models, the mean autochthonous DF incidence rate increased approximately 30-fold in Guangzhou when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 382. When the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 91.8, there was approximately 9-fold increase of the mean autochthonous DF incidence rate in Zhongshan. In the classification tree models, the results showed that when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 99.3, there was 89.28% chance of DF outbreak in Guangzhou, while, in Zhongshan, when the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 68.1, the chance of DF outbreak rose up to 100%. The study indicated that less cost internet-based surveillance systems can be the valuable complement to traditional DF surveillance in China.

Porphyromonas loveana sp. nov., isolated from the oral cavity of Australian marsupials.

An obligatory anaerobic, Gram-stain-negative coccobacillus with black-pigmented colonies was isolated from the oral cavity of selected Australian marsupial species. Phenotypic and molecular criteria showed that this bacterium was a distinct species within the genus Porphyromonas, and was closely related to Porphyromonas gingivalis and Porphyromonas gulae. This putative novel species and P. gulae could be differentiated from P. gingivalis by catalase activity. Further characterization by multi-locus enzyme electrophoresis of glutamate dehydrogenase and malate dehydrogenase enzyme mobility and matrix-assisted laser desorption ionization time-of-flight MS showed that this putative novel species could be differentiated phenotypically from P. gingivalis and P. gulae. Definitive identification by 16S rRNA gene sequencing showed that this bacterium belonged to a unique monophyletic lineage, phylogenetically distinct from P. gingivalis (94.9 % similarity) and P. gulae (95.5 %). This also was supported by 16S-23S rRNA intergenic spacer region and glutamate dehydrogenase gene sequencing. A new species epithet, Porphyromonas loveana sp. nov., is proposed for this bacterium, with DSM 28520T (=NCTC 13658T=UQD444T=MRK101T), isolated from a musky rat kangaroo, as the type strain.

Use of big data in the surveillance of veterinary diseases: early detection of tick paralysis in companion animals.

BACKGROUND: Tick paralysis, resultant from envenomation by the scrub-tick Ixodes holocyclus, is a serious threat for small companion animals in the eastern coast of Australia. We hypothesise that surveillance systems that are built on Internet search queries may provide a more timely indication of high-risk periods more effectively than current approaches. METHODS: Monthly tick paralysis notifications in dogs and cats across Australia and the states of Queensland (QLD) and New South Wales (NSW) were retrieved from Disease WatchDog surveillance system for the period 2011-2013. Internet search terms related to tick paralysis in small companion animals were identified using Google Correlate, and corresponding search frequency metrics were downloaded from Google Trends. Spearman's rank correlations and time series cross correlations were performed to assess which Google search terms lead or are synchronous with tick paralysis notifications. RESULTS: Metrics data were available for 24 relevant search terms at national level, 16 for QLD and 18 for NSW, and they were all significantly correlated with tick paralysis notifications (P < 0.05). Among those terms, 70.8, 56.3 and 50 % showed strong Spearman's correlations, at national level, for QLD, and for NSW respectively, and cross correlation analyses identified searches which lead notifications at national or state levels. CONCLUSION: This study demonstrates that Internet search metrics can be used to monitor the occurrence of tick paralysis in companion animals, which would facilitate early detection of high-risk periods for tick paralysis cases. This study constitutes the first application of the rapidly emerging field of Internet-based surveillance to veterinary science.

Heterogeneous and Dynamic Prevalence of Asymptomatic Influenza Virus Infections.

Influenza infection manifests in a wide spectrum of severity, including symptomless pathogen carriers. We conducted a systematic review and meta-analysis of 55 studies to elucidate the proportional representation of these asymptomatic infected persons. We observed extensive heterogeneity among these studies. The prevalence of asymptomatic carriage (total absence of symptoms) ranged from 5.2% to 35.5% and subclinical cases (illness that did not meet the criteria for acute respiratory or influenza-like illness) from 25.4% to 61.8%. Statistical analysis showed that the heterogeneity could not be explained by the type of influenza, the laboratory tests used to detect the virus, the year of the study, or the location of the study. Projections of infection spread and strategies for disease control require that we identify the proportional representation of these insidious spreaders early on in the emergence of new influenza subtypes or strains and track how this rate evolves over time and space.

Co-distribution and co-infection of chikungunya and dengue viruses.

BACKGROUND: Chikungunya and dengue infections are spatio-temporally related. The current review aims to determine the geographic limits of chikungunya, dengue and the principal mosquito vectors for both viruses and to synthesise current epidemiological understanding of their co-distribution. METHODS: Three biomedical databases (PubMed, Scopus and Web of Science) were searched from their inception until May 2015 for studies that reported concurrent detection of chikungunya and dengue viruses in the same patient. Additionally, data from WHO, CDC and Healthmap alerts were extracted to create up-to-date global distribution maps for both dengue and chikungunya. RESULTS: Evidence for chikungunya-dengue co-infection has been found in Angola, Gabon, India, Madagascar, Malaysia, Myanmar, Nigeria, Saint Martin, Singapore, Sri Lanka, Tanzania, Thailand and Yemen; these constitute only 13 out of the 98 countries/territories where both chikungunya and dengue epidemic/endemic transmission have been reported. CONCLUSIONS: Understanding the true extent of chikungunya-dengue co-infection is hampered by current diagnosis largely based on their similar symptoms. Heightened awareness of chikungunya among the public and public health practitioners in the advent of the ongoing outbreak in the Americas can be expected to improve diagnostic rigour. Maps generated from the newly compiled lists of the geographic distribution of both pathogens and vectors represent the current geographical limits of chikungunya and dengue, as well as the countries/territories at risk of future incursion by both viruses. These describe regions of co-endemicity in which lab-based diagnosis of suspected cases is of higher priority.

Intestinal Epithelial Cell Tyrosine Kinase 2 Transduces IL-22 Signals To Protect from Acute Colitis.

In the intestinal tract, IL-22 activates STAT3 to promote intestinal epithelial cell (IEC) homeostasis and tissue healing. The mechanism has remained obscure, but we demonstrate that IL-22 acts via tyrosine kinase 2 (Tyk2), a member of the Jak family. Using a mouse model for colitis, we show that Tyk2 deficiency is associated with an altered composition of the gut microbiota and exacerbates inflammatory bowel disease. Colitic Tyk2(-/-) mice have less p-STAT3 in colon tissue and their IECs proliferate less efficiently. Tyk2-deficient primary IECs show reduced p-STAT3 in response to IL-22 stimulation, and expression of IL-22-STAT3 target genes is reduced in IECs from healthy and colitic Tyk2(-/-) mice. Experiments with conditional Tyk2(-/-) mice reveal that IEC-specific depletion of Tyk2 aggravates colitis. Disease symptoms can be alleviated by administering high doses of rIL-22-Fc, indicating that Tyk2 deficiency can be rescued via the IL-22 receptor complex. The pivotal function of Tyk2 in IL-22-dependent colitis was confirmed in Citrobacter rodentium-induced disease. Thus, Tyk2 protects against acute colitis in part by amplifying inflammation-induced epithelial IL-22 signaling to STAT3.

Enhancing disease surveillance with novel data streams: challenges and opportunities.

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Modelling parasite aggregation: disentangling statistical and ecological approaches.

The overdispersion in macroparasite infection intensity among host populations is commonly simulated using a constant negative binomial aggregation parameter. We describe an alternative to utilising the negative binomial approach and demonstrate important disparities in intervention efficacy projections that can come about from opting for pattern-fitting models that are not process-explicit. We present model output in the context of the epidemiology and control of soil-transmitted helminths due to the significant public health burden imposed by these parasites, but our methods are applicable to other infections with demonstrable aggregation in parasite numbers among hosts.

Internet-based surveillance systems for monitoring emerging infectious diseases.

Emerging infectious diseases present a complex challenge to public health officials and governments; these challenges have been compounded by rapidly shifting patterns of human behaviour and globalisation. The increase in emerging infectious diseases has led to calls for new technologies and approaches for detection, tracking, reporting, and response. Internet-based surveillance systems offer a novel and developing means of monitoring conditions of public health concern, including emerging infectious diseases. We review studies that have exploited internet use and search trends to monitor two such diseases: influenza and dengue. Internet-based surveillance systems have good congruence with traditional surveillance approaches. Additionally, internet-based approaches are logistically and economically appealing. However, they do not have the capacity to replace traditional surveillance systems; they should not be viewed as an alternative, but rather an extension. Future research should focus on using data generated through internet-based surveillance and response systems to bolster the capacity of traditional surveillance systems for emerging infectious diseases.

Using internet search queries for infectious disease surveillance: screening diseases for suitability.

BACKGROUND: Internet-based surveillance systems provide a novel approach to monitoring infectious diseases. Surveillance systems built on internet data are economically, logistically and epidemiologically appealing and have shown significant promise. The potential for these systems has increased with increased internet availability and shifts in health-related information seeking behaviour. This approach to monitoring infectious diseases has, however, only been applied to single or small groups of select diseases. This study aims to systematically investigate the potential for developing surveillance and early warning systems using internet search data, for a wide range of infectious diseases. METHODS: Official notifications for 64 infectious diseases in Australia were downloaded and correlated with frequencies for 164 internet search terms for the period 2009-13 using Spearman's rank correlations. Time series cross correlations were performed to assess the potential for search terms to be used in construction of early warning systems. RESULTS: Notifications for 17 infectious diseases (26.6%) were found to be significantly correlated with a selected search term. The use of internet metrics as a means of surveillance has not previously been described for 12 (70.6%) of these diseases. The majority of diseases identified were vaccine-preventable, vector-borne or sexually transmissible; cross correlations, however, indicated that vector-borne and vaccine preventable diseases are best suited for development of early warning systems. CONCLUSIONS: The findings of this study suggest that internet-based surveillance systems have broader applicability to monitoring infectious diseases than has previously been recognised. Furthermore, internet-based surveillance systems have a potential role in forecasting emerging infectious disease events, especially for vaccine-preventable and vector-borne diseases.