Infectious Diseases of Humans¶
Anderson, R. M., & May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. Oxford University Press.
Cited by¶
14 citations across 14 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Associative Property Transfer
- In epidemiology it is literal disease transmission through contact networks — the causal version of the same structure that, in informational settings, becomes a fallacy.
This sourceStandard reference on disease transmission through contact networks — the literal causal version of the property-on-links structure that, in informational settings, becomes a fallacy.
Supported in partVerified against the publisher's abstract
The book's own abstract establishes it as a modelling treatment of infectious-disease dynamics in human hosts, backing the 'literal transmission' half of the claim but not the contact-network framing or the analogy to informational fallacy.
“This book combines mathematical models with extensive use of epidemiological and other data, to achieve a better understanding of the overall dynamics of populations of pathogens or parasites and their human hosts.”
- In epidemiology it is literal disease transmission through contact networks — the causal version of the same structure that, in informational settings, becomes a fallacy.
- Birthday Problem
- Astronomy — orbital-collision risk among N objects in a finite shell scales quadratically with occupancy. Epidemiology — pairwise contacts in a mixing population scale as N², the origin of density-dependent transmission.
This sourceEstablishes that pairwise contacts in a freely mixing population scale with the product of densities (mass-action transmission), the combinatorial origin of density-dependent transmission.
- Astronomy — orbital-collision risk among N objects in a finite shell scales quadratically with occupancy. Epidemiology — pairwise contacts in a mixing population scale as N², the origin of density-dependent transmission.
- Contact-Response Decomposition
- Epidemiology: force of infection = contact rate × per-contact transmission probability; the SIR β parameter is exactly this product, with distancing acting on contact and vaccination on response.
This sourceFoundational text deriving the force of infection and the transmission rate β as the product of contact rate and per-contact transmission probability.
- Epidemiology: force of infection = contact rate × per-contact transmission probability; the SIR β parameter is exactly this product, with distancing acting on contact and vaccination on response.
- Contagion
- It separates a population into states (susceptible, infected, removed) and names the rate-and-topology conditions under which the infected state propagates across the contact graph rather than fizzling at its source.
This sourceCanonical text establishing the basic reproduction number R₀ as the outbreak-versus-extinction switch, the contact-to-transmission-to-onward-transmission structure, the herd-immunity threshold (susceptible fraction below 1/R₀), and the corresponding intervention classes (reduce transmission, remove susceptibles, sever contacts).
Supported in partVerified against the publisher's abstract
The book's own abstract promises an analytical framework for evaluating control strategies and mathematics as a tool for thinking, which supports the 'reasoned about before acting' half but not the specific trichotomy.
“The authors thus provide an analytical framework for evaluating public health strategies aimed at controlling or eradicating particular infections.”
- It separates a population into states (susceptible, infected, removed) and names the rate-and-topology conditions under which the infected state propagates across the contact graph rather than fizzling at its source.
- Containment
- It appears wherever an agent, energy, infection, or consequence must be held in place: reactor vessels holding radioactive material, quarantine zones holding disease vectors, sandboxed software holding untrusted code, detention facilities holding individuals deemed dangerous, and therapeutic frames holding traumatic affect.
This sourceCanonical text establishing the basic reproduction number R₀ as the outbreak-versus-extinction switch and making explicit the presupposition that an uncontrolled infectious agent propagates if left unchecked, the condition containment is designed to interrupt.
Supported in partVerified against the publisher's abstract
Backs only the epidemiological limb of the claim: a framework for controlling or eradicating infections, not the five-domain list of reactor vessels, quarantine zones, sandboxes, detention facilities and therapeutic frames.
“The authors thus provide an analytical framework for evaluating public health strategies aimed at controlling or eradicating particular infections.”
- It appears wherever an agent, energy, infection, or consequence must be held in place: reactor vessels holding radioactive material, quarantine zones holding disease vectors, sandboxed software holding untrusted code, detention facilities holding individuals deemed dangerous, and therapeutic frames holding traumatic affect.
- Critical Mass
- Geometry, density, purity, and the presence of reflectors or moderators all shift the threshold, which is why subcritical pieces are safe and assembling them is the whole danger. Epidemiology: An outbreak sustains itself only while the effective reproduction number Rₑ stays above one; herd-immunity thresholds are exactly the immunized fraction needed to push Rₑ below one.
This sourceCanonical text establishing the basic reproduction number R₀ as the outbreak-versus-extinction switch and the herd-immunity threshold (immunize a fraction 1 − 1/R₀ to push Rₑ below one), the exact intervention logic the prime's epidemiology use-case asserts.
- Geometry, density, purity, and the presence of reflectors or moderators all shift the threshold, which is why subcritical pieces are safe and assembling them is the whole danger. Epidemiology: An outbreak sustains itself only while the effective reproduction number Rₑ stays above one; herd-immunity thresholds are exactly the immunized fraction needed to push Rₑ below one.
- Escape and Leakage
- A disease outbreak in a quarantine zone is escape-and-leakage; the subsequent spreading of that disease beyond the zone is propagation, a distinction Anderson and May (1991) maintain throughout their canonical treatment of infectious-disease dynamics.
This sourceCanonical text on infectious-disease dynamics establishing the basic reproduction number and the distinction between an outbreak within a population and the onward propagation/spread of disease; supports separating bounded escape from subsequent propagation.
- A disease outbreak in a quarantine zone is escape-and-leakage; the subsequent spreading of that disease beyond the zone is propagation, a distinction Anderson and May (1991) maintain throughout their canonical treatment of infectious-disease dynamics.
- Evolutionarily Stable Strategy
- Pathogen virulence is the biological mirror: an intermediate virulence level is often an ESS because too-virulent mutants kill hosts before transmitting (lower fitness) while too-mild mutants transmit less, so the resident virulence re-extinguishes both deviations — and the prime's payoff-alteration intervention predicts that changing the transmission structure (e.g., reducing host mobility) shifts the ESS virulence, a lever used in epidemiological control.
This sourceDevelops the transmission-mortality trade-off underlying intermediate optimal virulence (an ESS), and how transmission structure shifts the evolutionarily stable virulence level.
Supported in partVerified against the publisher's abstract
The book's abstract supports a mathematical, control-oriented framework for infectious disease dynamics, but not the specific ESS virulence trade-off claim.
“This book combines mathematical models with extensive use of epidemiological and other data, to achieve a better understanding of the overall dynamics of populations of pathogens or parasites and their human hosts.”
- Pathogen virulence is the biological mirror: an intermediate virulence level is often an ESS because too-virulent mutants kill hosts before transmitting (lower fitness) while too-mild mutants transmit less, so the resident virulence re-extinguishes both deviations — and the prime's payoff-alteration intervention predicts that changing the transmission structure (e.g., reducing host mobility) shifts the ESS virulence, a lever used in epidemiological control.
- Fracture Toughness
- In epidemiology the reproduction number just below or above one is the propagation threshold, and ring vaccination, contact tracing, and firebreaks are toughness interventions operating after initiation.
This sourceEstablishes the basic reproduction number R0 as the propagation threshold for epidemics and the basis of containment interventions such as ring vaccination.
Supported in partVerified against the publisher's abstract
The abstract confirms an epidemiological modelling framework for infection control, but states neither the R0 threshold nor ring vaccination, contact tracing or firebreaks.
“The authors thus provide an analytical framework for evaluating public health strategies aimed at controlling or eradicating particular infections.”
- In epidemiology the reproduction number just below or above one is the propagation threshold, and ring vaccination, contact tracing, and firebreaks are toughness interventions operating after initiation.
- Inoculation Theory
- The six-primitive model supports several inferences. Dose-response: too weak a stimulus fails to activate the defense, too strong overwhelms it, so every inoculation has an effective range. Boomerang risk: exposure without successful refutation can increase susceptibility — the vaccine that gives the disease, the simulation with no debrief that normalizes the unsafe behavior. Generalization gap: immunity holds when test-time threats fall within the training distribution and fails outside it, the structural analogue of antigenic drift and of novel attack vectors. Decay and boosters: adaptive memory fades, so periodic re-exposure maintains resistance. Population versus individual immunity: inoculating enough of a population produces herd effects where low-level threats cannot propagate, with an organizational analogue in security.
This sourceEstablishes herd-immunity thresholds: inoculating a sufficient fraction of a population prevents low-level propagation.
Supported in partVerified against the publisher's abstract
The book's abstract establishes an epidemiological framework for controlling infections in populations, but states no herd-immunity threshold or the six-primitive model.
“The authors thus provide an analytical framework for evaluating public health strategies aimed at controlling or eradicating particular infections.”
- The six-primitive model supports several inferences. Dose-response: too weak a stimulus fails to activate the defense, too strong overwhelms it, so every inoculation has an effective range. Boomerang risk: exposure without successful refutation can increase susceptibility — the vaccine that gives the disease, the simulation with no debrief that normalizes the unsafe behavior. Generalization gap: immunity holds when test-time threats fall within the training distribution and fails outside it, the structural analogue of antigenic drift and of novel attack vectors. Decay and boosters: adaptive memory fades, so periodic re-exposure maintains resistance. Population versus individual immunity: inoculating enough of a population produces herd effects where low-level threats cannot propagate, with an organizational analogue in security.
- Propagation
- Where can we insert friction?"—a continuous-control reframing developed extensively by Anderson and May (1991) for infectious disease dynamics.
This sourceCanonical text establishing the basic reproduction number R₀ as the outbreak-versus-extinction switch, the contact-to-transmission-to-onward-transmission structure, the herd-immunity threshold (susceptible fraction below 1/R₀), and the corresponding intervention classes (reduce transmission, remove susceptibles, sever contacts).
Supported in partVerified against the publisher's abstract
Anderson & May's own abstract frames the book as an analytical framework for controlling infections — the control frame, but not the 'insert friction' wording.
“The authors thus provide an analytical framework for evaluating public health strategies aimed at controlling or eradicating particular infections.”
- Where can we insert friction?"—a continuous-control reframing developed extensively by Anderson and May (1991) for infectious disease dynamics.
- Threshold
- A pharmacologist's threshold analysis transfers to neuroscience (the firing threshold of a neuron), to engineering (material yield strength and fatigue limits), and to epidemiology (the R_0 threshold for outbreak potential), as Anderson and May (1991) make explicit when they generalize threshold logic from R_0 in epidemiology to dose-response thresholds and physiological tolerance limits across host-parasite systems.
This sourceCanonical text establishing the basic reproduction number R₀ as the outbreak-versus-extinction switch, the contact-to-transmission-to-onward-transmission structure, the herd-immunity threshold (susceptible fraction below 1/R₀), and the corresponding intervention classes (reduce transmission, remove susceptibles, sever contacts).
Supported in partVerified against the publisher's abstract
The abstract shows only that Anderson & May (1991) is a mathematical framework for pathogen/parasite-host dynamics; it does not state the claimed generalisation to dose-response thresholds and tolerance limits.
“This book combines mathematical models with extensive use of epidemiological and other data, to achieve a better understanding of the overall dynamics of populations of pathogens or parasites and their human hosts.”
- A pharmacologist's threshold analysis transfers to neuroscience (the firing threshold of a neuron), to engineering (material yield strength and fatigue limits), and to epidemiology (the R_0 threshold for outbreak potential), as Anderson and May (1991) make explicit when they generalize threshold logic from R_0 in epidemiology to dose-response thresholds and physiological tolerance limits across host-parasite systems.
Domain-specific¶
- Differential equation
- The SIR model underpins epidemic forecasting and vaccination policy
This sourceTextbook combining mathematical models with epidemiological data to give an analytical framework for evaluating public-health strategies aimed at controlling or eradicating particular infections. A 1991 mathematical-epidemiology framework for evaluating control programmes against infections such as measles, malaria and schistosomiasis.
Supported in partVerified against the publisher's abstract
“The authors thus provide an analytical framework for evaluating public health strategies aimed at controlling or eradicating particular infections.”
- The SIR model underpins epidemic forecasting and vaccination policy
Mechanisms¶
- Epidemic Growth Control
- Its strength is leverage: because the target is a multiplier, a modest sustained reduction below one converts exponential growth into exponential decline — the same math that makes contagion dangerous makes control decisive once the reproduction number is under one.
This sourceDocuments because the target is a multiplier; a modest sustained reduction below one converts exponential growth into exponential decline; the same math that makes contagion dangerous makes control decisive once the reproduction number is under one.
- Its strength is leverage: because the target is a multiplier, a modest sustained reduction below one converts exponential growth into exponential decline — the same math that makes contagion dangerous makes control decisive once the reproduction number is under one.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 9 of 14 citations: 9 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
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See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 3 other ways.
- https://doi.org/10.1093/oso/9780198545996.001.0001 ×3
- https://academic.oup.com/book/53038 ×1
- https://search.worldcat.org/title/22766547 ×1
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