Emerging infectious diseases: prediction and detection.

Emerging infectious diseases (EIDs), including West Nile virus, severe acute respiratory syndrome (SARS) and Lyme disease, have had a direct effect within Canada, while many more EIDs such as Zika, chikungunya and Ebola are a threat to Canadians while travelling. Over 75% of EIDs affecting humans are, or were originally, zoonoses (infectious diseases transmitted from animals to humans). There are two main ways by which infectious diseases can emerge: by changes in their geographical ranges and by adaptive emergence, a genetic change in a microorganism that results in it becoming capable of invading a new niche, often by jumping to a new host species such as humans. Diseases can appear to emerge simply because we become capable of detecting and diagnosing them. Management of EID events is a key role of public health globally and a considerable challenge for clinical care. Increasingly, emphasis is being placed on predicting EID occurrence to "get ahead of the curve" - that is, allowing health systems to be poised to respond to them, and public health to be ready to prevent them. Predictive models estimate where and when EIDs may occur and the levels of risk they pose. Evaluation of the internal and external drivers that trigger emergence events is increasingly considered in predicting EID events. Currently, global changes are driving increasing occurrence of EIDs, but our capacity to prevent and deal with them is also increasing. Web-based scanning and analysis methods are increasingly allowing us to detect EID outbreaks, modern genomics and bioinformatics are increasing our ability to identify their genetic and geographical origins, while developments in geomatics and earth observation will enable more real-time tracking of outbreaks. EIDs will, however, remain a key, global public health challenge in a globalized world where demographic, climatic, and other environmental changes are altering the interactions between hosts and pathogen in ways that increase spillover from animals to humans and global spread.

Big Data and the Global Public Health Intelligence Network (GPHIN).

BACKGROUND: Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events. OBJECTIVE: To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN. FINDINGS: Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN's capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development. CONCLUSION: The GPHIN's early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN's capability for timely detection and early warning.

Multidimensional point transform for public health practice.

BACKGROUND: With increases in spatial information and enabling technologies, location-privacy concerns have been on the rise. A commonly proposed solution in public health involves random perturbation, however consideration for individual dimensions (attributes) has been weak. OBJECTIVES: The current study proposes a multidimensional point transform (MPT) that integrates the spatial dimension with other dimensions of interest to comprehensively anonymise data. METHODS: The MPT relies on the availability of a base population, a subset patient dataset, and shared dimensions of interest. Perturbation distance and anonymity thresholds are defined, as are allowable dimensional perturbations. A preliminary implementation is presented using sex, age and location as the three dimensions of interest, with a maximum perturbation distance of 1 kilometre and an anonymity threshold of 20%. A synthesised New York county population is used for testing with 1000 iterations for each of 25, 50, 100, 200 and 400 patient dataset sizes. RESULTS: The MPT consistently yielded a mean perturbation distance of 46 metres with no sex or age perturbation required. Displacement of the spatial mean decreased with patient dataset size and averaged 5.6 metres overall. CONCLUSIONS: The MPT presents a flexible, customisable and adaptive algorithm for perturbing datasets for public health, allowing tweaking and optimisation of the trade-offs for different datasets and purposes. It is not, however, a substitute for secure and ethical conduct, and a public health framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information is required. The MPT presents an important component of such a framework.

Model of frequent, recurrent, and spontaneous seizures in the intact mouse hippocampus.

This study presents a model of chronic, recurrent, spontaneous seizures in the intact isolated hippocampal preparation from mice aged P8-P25. Field activity from the CA1 pyramidal cell layer was recorded and recurrent, spontaneous seizure-like events (SLEs) were observed in the presence of low Mg2+ (0.25 mM) artificial cerebrospinal fluid (ACSF). Hippocampi also showed interictal epileptiform discharges (IEDs) of 0.9-4.2 Hz occurring between seizures. No age-specific differences were found in SLE occurrence (2 SLEs per 10 min, on average), duration, and corresponding frequencies. After long exposure to low Mg2+ ACSF (>3 h), SLEs were completely reversible within minutes with the application of normal (2 mM Mg2+) ACSF. The AMPA antagonist, CNQX, blocked all epileptiform activity, whereas the NMDA antagonist, APV, did not. The gamma-aminobutyric acid (GABA)A antagonist, bicuculline, attenuated and fragmented SLEs, implicating interneurons in SLE generation. The L-type Ca2+ blocker, nifedipine, enhanced epileptiform activity. Analysis of dual site recordings along the septotemporal hippocampus demonstrated that epileptiform activity began first in the temporal pole of the hippocampus, as illustrated by disconnection experiments. Once an SLE had been established, however, the septal hippocampus was sometimes seen to lead the epileptiform activity. The whole hippocampus with intact local circuitry, treated with low Mg2+, provides a realistic model of recurrent spontaneous seizures, which may be used, in normal and genetically modified mice, to study the dynamics of seizures and seizure evolution, as well as the mechanisms of action of anti-epileptic drugs and other therapeutic modalities.

Low platelet count in a 22q11 deletion syndrome subtype of schizophrenia.

BACKGROUND: 22q11 Deletion Syndrome (22qDS) is a genetic syndrome associated with various physical features and schizophrenia. Some reports have identified thrombocytopenia (platelet count < 150 x 10(9)/l) in individuals with 22qDS, especially children. We investigated whether adults with 22qDS and schizophrenia (22qDS-SZ) have lower platelet counts than other patients with schizophrenia (SZ). METHOD: Complete blood counts (CBC) were recorded from medical records for 18 22qDS-SZ and 60 SZ subjects. Five CBCs per subject were randomly selected and used to calculate a within-subject mean for analyses. RESULTS: 22qDS-SZ subjects had significantly lower mean platelet counts than comparison SZ subjects (142.2 x 10(9)/l versus 282.5 x 10(9)/l, t = -11.5, p < 0.0001). Ten 22qDS-SZ (55%) and no comparison subjects had thrombocytopenia. CONCLUSIONS: These results suggest that thrombocytopenia may be a common feature of 22qDS and that low platelet counts may comprise a readily available screening criterion to help identify this genetic syndrome among adults with schizophrenia.