Outbreak data and agent ID lab
Safety gate · before any work
- If using simulated biological samples, wear gloves and avoid touching face.
- Dispose of all simulated sample materials in the designated waste container, not the regular trash.
- Wash hands thoroughly with soap and water after handling any lab materials.
Do now
Students build outbreak visualizations and run identification tests to characterize the infectious agent.
- Hand in
- Completed data table with line-list summary, plotted epidemic curve (labeled with transmission type), spot-map cluster description, agent-ID results, and one stated measurement error.
- Where
- Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
You get two school days for every day you were absent, so this deadline moves with you.
Given this reunion and a set of identification tests, can you plot the outbreak, run the tests, and narrow six possible bacteria down to the one that fits both the data and the test results?
Students build outbreak visualizations and run identification tests to characterize the infectious agent.
- • Produce an accurate and map from the .
- • Narrow the infectious agent and state one procedural limitation.
- In the agent-ID procedure, what is the and what is the ?
- Name one property an identification test can detect (for example shape, a biochemical reaction, or an ).
- 1Record the SOP for completing the and recording test results.
- 2Identify the independent and dependent variables in the agent-ID procedure.
- 3Plot an and a spot map from the provided case data.
- 4Run or interpret the identification test results to narrow the candidate agent.
- 5Note sources of measurement error and one limitation of the data set.
What did this day actually feel like?
Outbreak data and agent ID lab
LAB The best lab of the semester. We took raw case data and built the line list, plotted an epidemic curve, made a spot map, and ran identification tests to narrow the agent.
Watching a pile of case records become a curve with a shape, and then a map with a visible cluster, and having those two independently point at the same thing, was the closest this class has come to feeling like actual work rather than school. Our cluster was tight around one location and our curve had a single sharp peak, and those two facts together said common source before any test came back.
Turned in: data table, epidemic curve, spot map → Data Tables folder
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
The same day, drawn.
Best lab of the semester. A pile of case records became a curve with a shape, then a map with a cluster, and the two pointed at the same thing.
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
🛠 Get unstuck · pick your level
Lab day: Tier 1 is the whole class at the bench. No extension today.
🔑 Today's words · 5
Tap a word in the lesson for a plain meaning and one example. Recycled into next week's Do-Now.
Do the work · 80-minute blockfirst 5 min = hook▸
💡 Big idea: Each identification test reads only one property of a , so narrowing to the true agent works by stacking several test results together to eliminate suspects, not by finding one test that names it.
- 0-8 minReview SOP for line-list completion and test-result recording; write it in your notebook.
- 8-15 minIdentify and record , , and controlled variables.
- 15-38 minComplete from case data; plot on graph paper or provided template.
- 38-52 minPlot spot map; identify geographic cluster.
- 52-68 minRun or interpret agent-identification test results; record candidate pathogens in .
- 68-80 minRecord one measurement error source and one data-set limitation; begin organizing for Thursday CER.
- • Today you work as an outbreak investigation team: the is your raw data and the lab is your analysis pipeline.
- • Every result you record must follow the SOP exactly, because a misrecorded test result can redirect an entire investigation.
- • This is a WebXam 072110 strand 5 day: Handling/Preparation/Storage/Disposal procedures are being assessed.
- • Document your measurement error source: it is part of the lab report, not an optional add-on.
- • : the case data provided; : the shape and agent-ID result.
- • Plotting a spot map requires converting addresses or zones into a visual cluster to reveal a common source.
- • Agent-identification tests each detect a specific property: morphology, biochemical profile, or .
Unit 3.1 Outbreak Evidence: Line lists, maps, epidemic curves, infectious-agent identification lab or simulation. · Outbreak data and agent ID lab
Day 3 of this lesson. Open this exact section in myPLTW (find it in Clever, Microsoft sign-in), then do the work below.
Do this: Open myPLTW and locate the Lesson 3.1 Nosocomial Nightmare outbreak lab activity. Use the platform data or reference materials to guide your construction and agent-ID interpretation.
Submit test-result responses in myPLTW alongside your handwritten .
Platform responses for this Lesson 3.1 lab should be submitted before you leave today.
Handwritten with and map plus platform submission.
The official PLTW activity stays inside myPLTW. If myPLTW will not open, use F1 and E1-E3 on this page to complete today's local evidence decision, then make up the official activity when access returns. Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
Check things off as you work, then submit. This tells Mr. Mendoza how you're doing so he can help the class. It does not replace turning in your producible through the submission route shown below.
Use the code Mr. Mendoza gave you, not your name. Saved on this device.
Unit 3.1 Outbreak Evidence: Line lists, maps, epidemic curves, infectious-agent identification lab or simulation. · Outbreak data and agent ID lab
Open myPLTW and locate the Lesson 3.1 Nosocomial Nightmare outbreak lab activity. Use the platform data or reference materials to guide your construction and agent-ID interpretation.
Platform responses for this Lesson 3.1 lab should be submitted before you leave today.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Students build outbreak visualizations and run identification tests to characterize the infectious agent.
- Record the SOP for completing the and recording test results.
- Identify the independent and dependent variables in the agent-ID procedure.
- Plot an and a spot map from the provided case data.
- Run or interpret the identification test results to narrow the candidate agent.
- Note sources of measurement error and one limitation of the data set.
Data table: Completed with line-list summary, plotted (labeled with type), spot-map cluster description, agent-ID results, and one stated measurement error.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Use the checklist just below and upload by 11:29 PM for full credit. Absent with an excused absence? You get two school days for every day you were absent, so this deadline moves with you.
| Task | Who |
|---|---|
| Record the SOP for completing the and recording test results. | _______ |
| Identify the independent and dependent variables in the agent-ID procedure. | _______ |
| Plot an and a spot map from the provided case data. | _______ |
| Run or interpret the identification test results to narrow the candidate agent. | _______ |
| Note sources of measurement error and one limitation of the data set. | _______ |
Working solo? Put your own name in "Who" for every row.
- Produce an accurate and map from the .
- Narrow the infectious agent and state one procedural limitation.
- 1Do thisStudents build outbreak visualizations and run identification tests to characterize the infectious agent.
- 2Use this resource
- 3Submit thisData table: Completed data table with line-list summary, plotted epidemic curve (labeled with transmission type), spot-map cluster description, agent-ID results, and one stated measurement error.
- 4Submit it here
- 1Open the drop folder.
- 2Sign in with your district Microsoft account, not a personal one.
- 3Upload the file, named Lastname_Firstname__Assignment Title.
- 4Your own upload panel says Uploaded with a green check: that is your receipt.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Principles of Biomedical Technology (Principles of Biomedical Science) › Unit 3.1 Outbreak Evidence: Line lists, maps, epidemic curves, infectious-agent identification lab or simulation. › Data tableOpen the drop folder
Learn it · deck, reading, and vocabulary▸
The deck carries the prior idea forward, lets you inspect an analogy, maps the rule to biology, and ends with the same evidence decision and exit ticket used on this page.
Generated from this lesson's canonical data with a red-team citation check.
saves lives because it shares case data, so every name on a is both a public-health tool and a patient's private record that must be protected.
Each identification test reads only one property of a , so narrowing to the true agent works by stacking several test results together to eliminate suspects, not by finding one test that names it.
Wet footprints appear across connected rooms after one person enters from the rain.
- Which footprint came first?
- Which rooms connect?
- What pattern would support more than one entry point?
Patterns across time and connection can narrow a explanation without proving it by themselves.
People change behavior, infections have periods, and surveillance data can be incomplete.
- • Footprints map to recorded cases.
- • Room connections map to exposures.
- • The route hypothesis maps to a limited claim.
Driving question: Given this reunion and a set of identification tests, can you plot the outbreak, run the tests, and narrow six possible bacteria down to the one that fits both the data and the test results?
What you already know: saves lives because it shares case data, so every name on a is both a public-health tool and a patient's private record that must be protected.
New idea: Each identification test reads only one property of a , so narrowing to the true agent works by stacking several test results together to eliminate suspects, not by finding one test that names it.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Outbreak data and agent ID lab. Use it with E1-E3; it is a model or context image, not experimental or patient data. What to notice: Trace the labeled observation or evidence sequence before choosing an explanation.
- Observe or measure the relevant feature in Outbreak data and agent ID lab.
- Organize the observation with a stable evidence ID.
- Apply this rule: Patterns across time and connection can narrow a explanation without proving it by themselves.
- Choose the option the evidence supports and state the limit of the conclusion.
Real biomedical example: Given this reunion and a set of identification tests, can you plot the outbreak, run the tests, and narrow six possible bacteria down to the one that fits both the data and the test results?
What the evidence supports: E1-E3 and F1 support the daily take-home when the response meets the stated success criteria.
What it cannot prove: The package does not support claims beyond this lesson's or any real patient diagnosis.
- • epidemiology: The study of how diseases spread, who they affect, and what causes them across populations, used to find patterns and guide prevention.
- • : A table used in outbreak investigations where each row records one patient's key details, such as symptoms, dates, and exposures.
- • : A bar graph showing the number of new disease cases over time, used to track how fast an outbreak is growing or shrinking.
- • : Keeping samples or cultures at a controlled warm temperature for a set time so cells, microbes, or reactions can grow or proceed.
- • prevalence: The share of a population that has a particular disease or condition at a given time, often shown as a percentage.
- • incidence: The number of new cases of a disease that appear in a population during a set period of time.
- • : The specific organism or factor, such as a bacterium, virus, or , that directly produces a particular disease.
Use it now: Choose one decision option. Cite E1 and E3, then explain how the rule connects the evidence to your choice.
Go further, optional: The source links below are optional enrichment. Every fact required for today's local evidence decision appears in this lesson package.
: the case data provided; : the shape and agent-ID result.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
Each identification test reads only one property of a , so narrowing to the true agent works by stacking several test results together to eliminate suspects, not by finding one test that names it.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
Produce an accurate and map from the .
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-PBT-2026-11-18 · Simulated classroom evidence scenario
Your role: biomedical investigator
Decision: Your team must decide what the evidence from Outbreak data and agent ID lab supports before submitting the labeled and result claim named on the lesson page.
- • Choose the strongest supported explanation.
- • Choose the next evidence to collect.
- • Hold the decision because the evidence is insufficient.
Response: State one choice, cite at least two evidence IDs, explain the rule that connects them, and add one limitation. Submit it as the labeled and result claim.
Claim ceiling: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Outbreak data and agent ID lab. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Reason for review: Your team must decide what the evidence from Outbreak data and agent ID lab supports before submitting the labeled and result claim named on the lesson page.
Context: Identifying an outbreak agent is applied science under uncertainty: you convert case data into a curve and a map, then use tests that each read one property of the to rule suspects out until a short list remains.
- • T1: Record the SOP for completing the and recording test results.
- • T2: Identify the independent and dependent variables in the agent-ID procedure.
- • T3: Plot an and a spot map from the provided case data.
- • T4: Run or interpret the identification test results to narrow the candidate agent.
- • T5: Note sources of measurement error and one limitation of the data set.
- • E1: : the case data provided; : the shape and agent-ID result.
- • E2: Each identification test reads only one property of a , so narrowing to the true agent works by stacking several test results together to eliminate suspects, not by finding one test that names it.
- • E3: Produce an accurate and map from the .
Measurements: Use only the measurements, units, graph, or counts supplied in today's task. No additional patient measurement is implied.
Figure finding: Teaching diagram for Outbreak data and agent ID lab. Trace the labeled observation or evidence sequence before choosing an explanation. This is a teaching model, not patient or experimental data.
Uncertainty: This is a composite classroom scenario. Missing history, measurements, or confirmation tests remain unknown and limit the conclusion.
Rate or percent = part / comparison total x 100%. Percent change = (new - comparison) / comparison x 100%.
If 18 of 60 records meet a condition, the frequency is 18 / 60 x 100% = 30%.
Name the comparison total. A percent describes the supplied group and does not automatically predict an individual's outcome.
Use today's supplied counts to calculate one rate, risk, frequency, or percent change. Show the denominator and interpretation.
- • The solution must address the stated need in Outbreak data and agent ID lab.
- • The decision must be supported by E1-E3.
- • The final product must make the success criteria visible.
- • Complete the work inside the 80-minute block.
- • Use only supplied or teacher-approved materials and evidence.
- • Do not trade , accessibility, or privacy for speed.
- • and evidence quality: must pass before scoring other criteria.
- • User need and effectiveness: highest scored criterion.
- • Time, cost, and ease of use: compare only after and effectiveness pass.
Test evidence: For each option, record the E1-E3 result that supports or fails each criterion. Do not assign a score without a named observation.
- Version or option tested
- Criterion met or missed
- Evidence ID and result
- Revision made
- Reason for the revision
- Need and user
- Criteria and constraints
- Chosen option and evidence
- Test result
- Revision and reason
Students often think Students expect one master test that reads out the 's name, like a barcode scanner that just tells you the answer.. The trap: No single test names the agent, because each test only detects one property (a shape, a sugar it can ferment, an it carries), and identification is the logic of stacking several results together to eliminate suspects until one candidate survives.
SOP I recorded: complete one line-list row per case, then plot onset dates for the curve and locations for the map, then record each identification-test result exactly as observed.
Variables:
- Independent variable: the case data provided (onset dates, locations, test results).
- Dependent variable: the epidemic-curve shape and the narrowed agent ID.
Line-list summary: 18 cases, onset clustered November 14 to 16, most near the east cafeteria.
Epidemic curve: single sharp peak on November 15, which reads as a point-source pattern.
Spot map: a tight cluster around the east cafeteria, pointing to a common exposure there.
Agent-ID results: Gram stain showed gram-negative rods; the lactose test was positive; the candidate was narrowed to a gram-negative enteric bacterium.
Measurement error: two onset dates were self-reported from memory, so the curve's exact peak day could be off by a day. That is a data-quality limit, not a plotting mistake.
| Case | Onset date | Location | Gram stain | Lactose test |
|---|---|---|---|---|
| 1 | Nov 14 | East cafeteria | Negative rod | Positive |
| 2 | Nov 15 | East cafeteria | Negative rod | Positive |
| 3 | Nov 15 | East cafeteria | Negative rod | Positive |
| 4 | Nov 16 | West hall | Negative rod | Positive |
This model shows the level of evidence and organization needed to complete: Completes the outbreak lab: a line-list summary, a plotted epidemic curve and spot map, agent-ID results, and one stated measurement error.
- Name the variables and include units.
- Enter observations without changing the raw values.
- Check labels, calculations, and patterns before interpreting the data.
Keep the structure. Replace the question, facts, measurements, and evidence. Then recheck units, vocabulary, and whether the conclusion goes beyond the evidence.
Also due today: Submit your data table and visualization on the class site, or hand it to Mr. Mendoza in class before leaving.
- CER:
- Claim, Evidence, Reasoning: make a claim, back it with evidence, explain your reasoning.
- SOP:
- Standard Operating Procedure, the exact steps to follow (especially in a lab).
- Tracker:
- Your PLTW progress log where you record completed evidence.
- myPLTW:
- The PLTW course site where you do the online activities. Find it in Clever with your Microsoft sign-in, right next to Schoology.
Tap the speaker to hear a term. Add two of these to your notebook glossary with a definition and an example in your own words.
Pick just 2 or 3 words from today and make them yours: write what each one means in your own words, name the context clue or evidence that helped, then give one example from what you actually did in Outbreak data and agent ID lab. Try your own words first; the glossary is there if you get stuck. This is voluntary and counts as extra credit, so keep it short.
Saved on this device. Show Mr. Mendoza or add these to your notebook glossary to claim the extra credit.
Hand-picked readings and interactives for this lesson, from authoritative open organizations and PLTW's own public course outline.
Check yourself · commit, then reveal▸
Claim ceiling for this check: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Outbreak data and agent ID lab. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Your candidate list is E. coli, Salmonella, Shigella, and Staphylococcus aureus. A gram stain of the outbreak sample shows gram-negative rods. Which suspect can you cross off right now, and why can't the gram stain alone name the agent?
Write an answer and pick a confidence to unlock the key.
Fast retrieval with instant answers, not the commit-then-reveal check above. Try each from memory first: write what you remember about the earlier units, then check yourself here.
Go further and get help▸
I can name the procedure's purpose and the evidence I will record. I can identify each named hazard and the control that reduces it: If using simulated biological samples, wear gloves and avoid touching face. My data table is ready before materials are handled.
Finish the checklist before you handle any material.
- • If using simulated biological samples, wear gloves and avoid touching face.
- • Dispose of all simulated sample materials in the designated waste container, not the regular trash.
- • Wash hands thoroughly with soap and water after handling any lab materials.
- • Keep all printed case data at your station; do not share papers between stations to prevent data contamination.
- 1Before materials are handled, identify the purpose, variables or comparison, controls, measurement units, and stop-work condition.
- 2Record the SOP for completing the line list and recording test results.
- 3Identify the independent and dependent variables in the agent-ID procedure.
- 4Plot an epidemic curve and a spot map from the provided case data.
- 5Run or interpret the identification test results to narrow the candidate agent.
- 6Note sources of measurement error and one limitation of the data set.
- 7Record each result in the prepared table before interpreting it. Mark missing, repeated, or invalid results truthfully.
- 8Complete the named cleanup and waste route, remove PPE safely, wash hands when required, and confirm the station is ready for the next group.
| Trial or sample ID | Independent condition | Measured result with units | Observation before interpretation | Quality-control note |
|---|---|---|---|---|
Before the procedure, predict the result and cite the rule behind the prediction.
After the procedure, compare the result with the prediction and name one limitation or source of uncertainty.
What today's skills lead to. These are real health-science careers this course builds toward. Tap one to see, on the US Department of Labor's O*NET site, what the job actually involves, what it pays, and how fast it is growing.
Hands-on outbreak lab: complete the , plot the and spot map, and run the agent-identification tests to record candidate pathogens.
CDC: Quick-Learn Epi CurvesThen submit your Data table. Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
Class still runs. Complete the online activity above (it's self-guided). Need the concept taught without a teacher? Use this authoritative explainer:
CDC: principles of epidemiology and outbreak investigationYou've passed Unit 2, so the optional extra-credit track is open. Complete reserved-unit work from home, including virtual labs, for extra credit. Each item shows its correct submission route.
Open the extra-credit track- CompleteEvery required part of the artifact is present, nothing left blank.
- AccurateThe science and the data are correct and match the evidence.
- Scientific reasoningYou explain your claim with evidence and reasoning (CER), not just an answer.
- Professional communicationClear, organized, labeled, and written the way a clinician or scientist would.
- SubmittedTurned in the right way, on the class site or handed to Mr. Mendoza in class, and confirmed. Not in Schoology: that is where the report-card grade appears later.
- Error analysis and method · counts doubleName a specific limit of the method and how it moved your result, and compare what you predicted to what happened. "Human error" does not count; say what about the procedure or instrument caused it.