Infection Rate Monitoring
Infection Rate Monitoring - Related Videos
04:29
Monitoring Neutrophil Recruitment Dynamics and Bacterial Burden at the Infection Site in a Murine Model
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2025
This video demonstrates in vivo imaging to study the neutrophil recruitment dynamics at an infection site in an immunocompromised, transgenic mouse with GFP-expressing neutrophils. It involves injecting luminescent bacteria to cause an infection. This leads to neutrophil recruitment to the infection site, whose tracking is done by imaging.
18:40
Combined In vivo Optical and µCT Imaging to Monitor Infection, Inflammation, and Bone Anatomy in an Orthopaedic Implant Infection in Mice
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Cited by 19 •
2014
Combined optical and μCT imaging in a mouse model of orthopaedic implant infection, utilizing a bioluminescent engineered strain of Staphylococcus aureus, provided the capability to noninvasively and longitudinally monitor the dynamics of the bacterial infection, as well as the corresponding inflammatory response and anatomical changes in the bone.
02:20
Monitoring Bacterial Infection and Effector Protein Delivery in Plant Cells
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2025
Source: Lee, H. et. al., Split Green Fluorescent Protein System to Visualize Effectors Delivered from Bacteria During Infection. J. Vis. Exp. (2018)This video demonstrates a method for monitoring bacterial infection and effector protein delivery in plant cells using split superfolder green fluorescent protein (sfGFP) complementation combined with confocal microscopy. The approach enables precise visualization of effector translocation into the host cell cytosol.
09:22
Monitoring Immune Cells Trafficking Fluorescent Prion Rods Hours after Intraperitoneal Infection
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Cited by 6 •
2010
Here we describe a novel assay for monitoring prion uptake and trafficking by immune cells immediately following intraperitoneal inoculation by purifying and fluorescently labeling aggregated prion rods from infected brain material then monitoring their uptake and movement from the injection site and characterizing the cells mediating these events.
05:53
Use of In vivo Imaging to Monitor the Progression of Experimental Mouse Cytomegalovirus Infection in Neonates
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Cited by 6 •
2013
Human Cytomegalovirus (HCMV) infection of neonates represents an important cause of mental retardation, yet the molecular events leading to virus-induced pathogenesis are still poorly understood. To investigate the dynamics of brain infection, we adapted whole-animal in vivo imaging to perform time-course analysis of neonates infected with a luciferase-recombinant virus.
FAQs
Incidence and prevalence answer different monitoring questions. Incidence focuses on newly occurring infections during a period, whereas prevalence summarizes infections present in a population at a specified time or period. Selecting between them affects interpretation: incidence is suited to assessing occurrence over time, while prevalence describes the population burden being observed.
Cases per person-time can improve comparability when people are observed for different lengths of time. Instead of treating every individual as having the same observation period, the calculation relates documented cases to the accumulated time at risk. This helps interpret infection frequency when population size or follow-up duration changes.
Confidence intervals show the uncertainty around an estimated infection measure rather than treating the observed value as exact. Trend analysis adds a time-based comparison, helping assess whether changes are meaningful or could reflect random variation. Together, these statistical tools support more cautious interpretation of apparent increases or decreases.
A raw case count can rise simply because more people are being observed, or fall because the population becomes smaller. Relating cases to the population at risk, and when appropriate to person-time, provides a more interpretable comparison across periods. This adjustment helps distinguish changing exposure from changing population scale.
A basic workflow identifies the population at risk and observation period, documents infection cases, selects an appropriate summary such as incidence, prevalence, or cases per person-time, and then examines uncertainty and trends. The resulting summaries can be compared across periods to identify changes that warrant interpretation or action.
Repeated statistical summaries can reveal an unusual increase in infections over time, while trend analysis helps distinguish a potentially meaningful change from random fluctuation. Because the measures are tied to a defined population and period, public health teams can use the signal to investigate conditions and consider infection-control responses.
Researchers can compare infection measures across monitoring periods to examine whether prevention or treatment programs coincide with changes in occurrence. Confidence intervals and trend analyses add context to those comparisons, reducing the risk of interpreting random variation as program impact. The findings can inform refinement of interventions and infection-control policies.