Outbreak Investigation: Key Concepts and Definitions

Linkage and Association in Epidemiology

• Opening idea: Asking whether a particular risk factor is linked with a disease outcome. Examples include whether eating a specific food causes food poisoning, exposure in a barn causes respiratory disease, or whether a treatment is linked to a complication or more effective than another. These questions of linkage and association are central to epidemiology.
• The universal tool: A cell phone can be used for any epidemiologic investigation.

Three Learning Outcomes of the Session

• By the end of this activity, you should be able to describe and explain the steps in an outbreak investigation.
• Define what an outbreak looks like by collecting data and plotting graphs that show the number of cases over time; this helps assess if the outbreak is spreading and suggests exposure patterns (single exposure vs multiple exposures, short-term vs long-term exposure).
• Develop a hypothesis about what might be happening and why (the classic scientific approach). Once suspicion arises, it’s sensible to call the outbreak even if not totally sure, to avoid delays and harm.

Step-by-step in Outbreak Investigation

• Start with the definition of the outbreak: observe data, plot time-series, and assess whether the outbreak is beginning, middle, or ending.
• Collect data and plot graphs showing cases over time to infer spread and exposure patterns.
• Use the data to hypothesize exposure(s) and possible causes.
• Develop a hypothesis: e.g., exposure to a particular source or a particular event as the driver of cases.
• Decide when to declare an outbreak; in practice, it is prudent to call it an outbreak when there is suspicion and data support.
• Note a humorous aside from the session: a line suggesting that the lesson is to be cautious with jokes like “never let old people near town”—a reminder of the importance of data over anecdotes; the speaker clarifies that the point is not to rely on gut feelings.
• Emphasize the caution: you can’t rely on gut feeling alone in science, especially when there’s potential for widespread events; data and systematic analysis are required.
• Acknowledge that outbreaks can be difficult to identify: diarrheal syndromes may have many potential causes, and the exact pathogen or toxin may not be known initially.
• The director’s perspective: a decision (e.g., lockdown) should not be based on feeling alone; data and evidence guide action.
• Reference to real-world remark: an outbreak in Chile was described as being about more than just the numbers; context matters.

What is an Outbreak? Distinguishing Feelings from Data

• An outbreak is not simply a gut feeling; it requires data and systematic investigation.
• A syndrome (e.g., vomiting and diarrhea) can have many causes, and initial information may be incomplete; follow outbreak investigation steps to identify the cause.
• When you lack a clear diagnosis, you still begin with the outbreak investigation steps to gather evidence and refine hypotheses.
• The session notes a common phenomenon: although a sudden cluster may feel like an outbreak, you must verify with data before declaring a formal outbreak.
• Example discussion: someone might note that an outbreak appears in one country and then consider whether there’s a broader pattern; the emphasis is on data-driven decisions.

Baseline Concepts: Incidence, Prevalence, and Background Incidence

• Important definitions (as to be memorized for analyses):
• Incidents (Incidence): the number of new cases over a period of time.
• Prevalence: a snapshot of the number of current cases at a specific moment in time.
• Both are often reported over a defined time frame (e.g., daily, monthly, yearly).
• Background incidence is the expected number of cases in a population over a given period in the absence of an outbreak; knowing this is essential to determine whether observed cases indicate an outbreak.
• In short: an outbreak is more likely when observed cases exceed the background incidence or expected baseline.

Exotic Disease Example: African Horse Sickness

• An example discussed: African horse sickness exists only in a region of South Africa and does not occur across other countries in that region.
• The key point: the expected incidence in the UAE is zero for this disease.
• Therefore, observing even a single case in the UAE would constitute an outbreak under the logic of “more than the expected incidence.”
• This illustrates how background incidence informs outbreak decisions.

Practical Illustration: UK Gastroenteritis

• A numerical illustration discussed in the session: in the UK, about 17,000,000 people get gastroenteritis per year.
• Calculation concept described (step-by-step, with approximate numbers mentioned):
  • Daily cases if 17 million occur per year:
    ext{Daily cases} \ ext{per day} \\approx rac{17{,}000{,}000}{365} \\approx 46{,}575 ext{ cases/day}
  • UK population assumed around 66–70 million.
  • Daily incidence rate per person:
    ext{Daily incidence rate per person} \ ext{(per day)} \\approx rac{46{,}000}{66{,}000{,}000} \\approx 7.0 imes 10^{-4} \ (0.07 ext{% per person per day})
  • Annual incidence (proportion of population affected in a year):
    ext{Annual incidence} \\approx rac{17{,}000{,}000}{66{,}000{,}000} \\approx 0.258 \ (25.8 ext{% per year})
• The session notes include a rough calculation that results in a figure like “roughly 12–13%,” acknowledging the calculation was not perfectly precise in the narration.
• The point: these calculations illustrate the concepts of incidence (new cases over time) and how to compare observed events to background expectations.
• The calculations demonstrate how to translate a population-level burden into per-day and per-year metrics, and how to think about whether an observed pattern is unusual.

Case Definition: Why It Matters and How It Works

• The concept of a case definition is introduced: to narrow down who counts as a case.
• A broad case definition may include people who ate meat, or people who ate fish, or other exposure criteria; over time, definitions become more precise to reduce misclassification.
• Potential errors with a broad definition:
  • Overestimation: including individuals who are not true cases.
  • Underestimation: missing actual cases.
• The balance: starting with a broad definition and refining it helps to avoid missing cases while gradually tightening inclusion as more information becomes available.
• In real investigations, the case definition is iteratively refined to improve accuracy.

Key Takeaways and Practical Implications

• Outbreak investigation is a structured process: define, collect data, plot time trends, formulate hypotheses, and decide when to declare an outbreak.
• Data-driven decisions are essential; avoid relying solely on gut feelings or anecdotal observations.
• Incidence vs. prevalence: understand the difference and use appropriate time frames for analysis.
• Background incidence is essential for interpreting whether observed counts constitute an outbreak; even diseases with low baseline risk can appear as outbreaks if observed counts significantly exceed expectations.
• Real-world examples (African Horse Sickness, UK gastroenteritis) illustrate how to apply these concepts to different diseases and settings.
• Case definitions are critical for consistent case identification and controlling misclassification risk.

Practical Notes for Exam Preparation

• Be able to explain the steps in an outbreak investigation and why each step matters.
• Be able to interpret time-series data for an outbreak (what does an increasing trend imply? what about a plateau? what if it’s decreasing?).
• Be able to distinguish between incidence and prevalence and explain when each is used.
• Understand the role of background incidence and how to determine whether observed cases exceed expectations.
• Explain how a strict or broad case definition affects case counts and outbreak assessment.
• Recognize the importance of moving from gut feelings to data-supported conclusions in outbreak management.

Quick Formulas to Memorize (LaTeX)

• Incidents (number of new cases over a period):
  ext{Incidents} = N_{ ext{cases}} ext{ over } T
• Prevalence (snapshot at time t):
  ext{Prevalence}(t) = rac{N{ ext{cases at time } t}}{N{ ext{population}}}
• Outbreak condition (simplified):
  N{ ext{observed}} > N{ ext{expected}} \Rightarrow ext{Outbreak}
• Background incidence (illustrative): if expected cases in a region without an outbreak are zero for a disease, then any observed case signals an outbreak:
  N{ ext{expected}} = 0 \text{observed } N{ ext{observed}}
  ightarrow ext{outbreak}
• Example: UK gastroenteritis annual incidence (illustrative):
  ext{Annual incidence} \approx rac{17{,}000{,}000}{66{,}000{,}000} \approx 0.258 ext{ (25.8% per year)}
• Daily incidence rate (illustrative):
  ext{Daily incidence per person} \approx rac{17{,}000{,}000}{66{,}000{,}000 imes 365} \approx 7.0 imes 10^{-4} \text{ per day} \approx 0.07 ext{0% per day}
• Example fraction for interpretation of a proportion:
  rac{15}{120} = 0.125 = 12.5\%

End of Notes