Chapter summary
Infection, vaccination and disease spread, at a glance
Scan the key ideas, or hide the answers and try to recall them.
01
How viruses disrupt host function
Why do influenza and HIV damage different systems?
Key idea and reminders
Viral tissue tropism links receptor recognition and replication to the host functions that are disrupted.
- Influenza targets respiratory epithelium.
- HIV impairs helper T-cell coordination.
- Tissue specificity shapes physiological consequences.
Keep in mind: HIV is the virus; AIDS is a syndrome of advanced immune deficiency.
02
Tuberculosis: transmission and infection
Why does inhalation matter more than simply touching an infected person?
Key idea and reminders
Airborne particles can deliver M. tuberculosis to the lungs; infection and active, transmissible disease are distinct states.
- M. tuberculosis is a bacterium.
- Airborne inhalation can seed the lungs.
- Latent infection differs from active disease.
Keep in mind: Latent infection is distinct from infectious active pulmonary/laryngeal disease.
03
Antibiotics target bacterial processes
Why can penicillin affect growing bacteria without treating a virus?
Key idea and reminders
Selective toxicity exploits bacterial structures or processes; viruses lack the bacterial targets of antibiotics.
- Antibiotics have specific bacterial targets.
- Penicillin interferes with peptidoglycan cross-linking.
- Viruses lack those bacterial structures.
Keep in mind: Bacteria, viruses and other pathogens have different targets; the agent and mechanism must match.
04
Vaccination and interrupted transmission
How can one person's immunity reduce another person's exposure?
Key idea and reminders
Vaccination primes adaptive protection; sufficient effective population immunity can interrupt chains of transmission.
- Vaccination primes adaptive responses.
- Effective immunity can break transmission chains.
- Benefits and risks require frequency and severity, not anecdotes alone.
Keep in mind: Effectiveness is assessed by comparative risk and outcomes, not a requirement that every case be prevented.
05
R0, outbreaks, epidemics and pandemics
What does R0 predict, and what does it leave out?
Key idea and reminders
R0 is an expected number of secondary infections in a fully susceptible population under specified conditions, not a fixed property of the virus alone.
- R0 assumes full susceptibility and stated conditions.
- R above/below 1 suggests growth/decline in a simple model.
- Pandemic concerns geographical spread, not a fixed severity score.
Keep in mind: It is an average total secondary-infection count under stated conditions, not a daily fixed count for every person.