Utilizing spatiotemporal analysis of influenza-like illness and rapid tests to focus swine-origin influenza virus intervention

Wilson, J. Gaines; Ballou, Jessica; Yan, Chris; Fisher‐Hoch, Susan P.; Reininger, Belinda M.; Salinas, Jennifer J.; Sánchez, Pablo J.; Salinas, Yvette; Calvillo, Fidel; Lopez, Leonel; deLima, Ionara P.; McCormick, Joseph B.

doi:10.1016/j.healthplace.2010.08.010

Cited by 9 publications

(9 citation statements)

References 31 publications

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“…Such discrepancies are commonly recorded (e.g. [10][11][12][13]) and may be seen as capturing different aspects of the true underlying epidemic. For example, if younger cases are over-represented in a given system and also typically present earlier in the epidemic than older cases, this will be reflected as an earlier epidemic peak compared to other systems.…”

Section: Introductionmentioning

confidence: 99%

Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis

et al. 2014

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Influenza surveillance enables systematic collection of data on spatially and demographically heterogeneous epidemics. Different data collection mechanisms record different aspects of the underlying epidemic with varying bias and noise. We aimed to characterize key differences in weekly incidence data from three influenza surveillance systems in Melbourne, Australia, from 2009 to 2012: laboratory-confirmed influenza notified to the Victorian Department of Health, influenza-like illness (ILI) reported through the Victorian General Practice Sentinel Surveillance scheme, and ILI cases presenting to the Melbourne Medical Deputising Service. Using nonlinear regression, we found that after adjusting for the effects of geographical region and age group, characteristics of the epidemic curve (including season length, timing of peak incidence and constant baseline activity) varied across the systems. We conclude that unmeasured factors endogenous to each surveillance system cause differences in the disease patterns recorded. Future research, particularly data synthesis studies, could benefit from accounting for these differences.

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Section: Introductionmentioning

confidence: 99%

Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis

et al. 2014

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“…For instance, modifications to size of the area under investigation, the number of iterations used to compute the solution, the maximum sizes of spatial and temporal windows, and unit of time and space have all been shown to alter the location of clusters [18], [19], [20]. To date, most previous research into the detection of suicide clusters has only sought to examine clusters at the national level.…”

Section: Introductionmentioning

confidence: 99%

Application of Scan Statistics to Detect Suicide Clusters in Australia

et al. 2013

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BackgroundSuicide clustering occurs when multiple suicide incidents take place in a small area or/and within a short period of time. In spite of the multi-national research attention and particular efforts in preparing guidelines for tackling suicide clusters, the broader picture of epidemiology of suicide clustering remains unclear. This study aimed to develop techniques in using scan statistics to detect clusters, with the detection of suicide clusters in Australia as example.Methods and FindingsScan statistics was applied to detect clusters among suicides occurring between 2004 and 2008. Manipulation of parameter settings and change of area for scan statistics were performed to remedy shortcomings in existing methods. In total, 243 suicides out of 10,176 (2.4%) were identified as belonging to 15 suicide clusters. These clusters were mainly located in the Northern Territory, the northern part of Western Australia, and the northern part of Queensland. Among the 15 clusters, 4 (26.7%) were detected by both national and state cluster detections, 8 (53.3%) were only detected by the state cluster detection, and 3 (20%) were only detected by the national cluster detection.ConclusionsThese findings illustrate that the majority of spatial-temporal clusters of suicide were located in the inland northern areas, with socio-economic deprivation and higher proportions of indigenous people. Discrepancies between national and state/territory cluster detection by scan statistics were due to the contrast of the underlying suicide rates across states/territories. Performing both small-area and large-area analyses, and applying multiple parameter settings may yield the maximum benefits for exploring clusters.

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“…Three sites were involved in the sample collection protocol: Baylor College of Medicine; Centro para Vacunas en Desarrollo-Chile; and the University of Texas School of Public Health in Brownsville, TX (see references [10,11] for descriptions of the Brownsville outbreak), During the study a number of viruses were sequenced and the information made publically available in GenBank by late 2009 (GenBank accession numbers for the Houston isolates are CY051903, CY051926, CY052959, CY053173, CY053366, CY087216, CY087223). Selected samples were deposited at the National Institutes of Health (NIH) Biodefense and Emerging Infections Research Resources Repository (BEI).…”

Section: Discussionmentioning

confidence: 99%

Rapid research response to the 2009 A(H1N1)pdm09 influenza pandemic (Revised)

Keitel

Piedra

Atmar³

et al. 2013

BMC Res Notes

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BackgroundWhen novel influenza viruses cause human infections, it is critical to characterize the illnesses, viruses, and immune responses to infection in order to develop diagnostics, treatments, and vaccines. The objective of the study was to collect samples from patients with suspected or confirmed A(H1N1)pdm09 infections that could be made available to the scientific community. Respiratory secretions, sera and peripheral blood mononuclear cells (PBMCs) were collected sequentially (when possible) from patients presenting with suspected or previously confirmed A(H1N1)pdm09 infections. Clinical manifestations and illness outcomes were assessed. Respiratory secretions were tested for the presence of A(H1N1)pdm09 virus by means of isolation in tissue culture and real time RT-PCR. Sera were tested for the presence and level of HAI and neutralizing antibodies against the A(H1N1)pdm09 virus.Findings and conclusionsThirty patients with confirmed A(H1N1)pdm09 infection were enrolled at Baylor College of Medicine (BCM). Clinical manifestations of illness were consistent with typical influenza. Twenty-eight of 30 had virological confirmation of illness; all recovered fully. Most patients had serum antibody responses or high levels of antibody in convalescent samples. Virus-positive samples were sent to J. Craig Venter Institute for sequencing and sequences were deposited in GenBank. Large volumes of sera collected from 2 convalescent adults were used to standardize antibody assays; aliquots of these sera are available from the repository. Aliquots of serum, PBMCs and stool collected from BCM subjects and subjects enrolled at other study sites are available for use by the scientific community, upon request.

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Utilizing spatiotemporal analysis of influenza-like illness and rapid tests to focus swine-origin influenza virus intervention

Cited by 9 publications

References 31 publications

Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis

Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis

Application of Scan Statistics to Detect Suicide Clusters in Australia

Rapid research response to the 2009 A(H1N1)pdm09 influenza pandemic (Revised)

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