Anderson-May Host-Parasite Model: SIR Framework with Parasite Load

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R0 ≈ 4.2, endemic prevalence 76% with overdispersed parasite distribution (k=0.5)

With transmission rate 0.5, recovery rate 0.1, parasite mortality 0.02, and aggregation k=0.5, the basic reproduction number is 4.17. At endemic equilibrium, approximately 76% of the population is infected, with parasites following a negative binomial distribution — most individuals have low burdens, but a minority carry disproportionately heavy loads.

Formula

R0 = beta / (gamma + alpha)
Endemic prevalence = 1 - 1/R0
P(burden = j) = NegBin(mean_burden, k) = C(j+k-1,j) * (mu/(mu+k))^j * (k/(mu+k))^k

The Anderson-May Framework

Roy Anderson and Robert May revolutionized parasitology in the late 1970s by applying mathematical ecology to host-parasite systems. Their framework distinguishes macroparasites (helminths, ectoparasites) from microparasites (viruses, bacteria) based on a crucial difference: macroparasite pathology depends on burden — the number of parasites per host — not merely on infection status. This requires tracking the full distribution of parasites across the host population.

The Negative Binomial Distribution

A universal finding in parasitology is that macroparasites are overdispersed among hosts. The negative binomial distribution, characterized by mean burden and aggregation parameter k, describes this pattern. When k is small (k < 1), most hosts have few parasites while a minority carry extremely heavy burdens. This 20/80 pattern (20% of hosts harbor 80% of parasites) has profound implications for control — treating the most heavily infected individuals removes a disproportionate share of the parasite population.

R0 and Transmission Dynamics

The basic reproduction number R0 represents the average number of successful offspring per adult parasite in a fully susceptible host population. When R0 > 1, the parasite can establish and persist; below 1, it goes extinct. The model reveals how R0 depends on transmission rate, host recovery, parasite-induced mortality, and the aggregation pattern — each offering a potential point of intervention.

Control Strategy Implications

Adjust the parameters to explore different control strategies. Reducing beta (transmission) through vector control or sanitation lowers R0. Increasing gamma (recovery) through mass drug administration accelerates parasite clearance. The aggregation parameter k determines whether uniform mass treatment or targeted treatment of high-burden individuals is more efficient — a question central to current WHO helminth control policy.

FAQ

What is the Anderson-May model?

The Anderson-May model extends classical SIR epidemiology to macroparasites (helminths, arthropods) by tracking parasite burden distribution within hosts rather than just infection status. It incorporates parasite-induced mortality, aggregation (negative binomial distribution), and density-dependent reproduction.

What is parasite aggregation?

Macroparasites are characteristically overdispersed among hosts — most individuals harbor few or no parasites while a small fraction carry very heavy burdens. This is described by the negative binomial distribution with aggregation parameter k. Lower k means more aggregation.

How does R0 relate to control strategies?

The basic reproduction number R0 determines the critical vaccination or treatment coverage needed for elimination: at least 1 - 1/R0 of the population must be protected. For a parasite with R0 = 4, at least 75% coverage is required to break transmission.

Why does parasite-induced mortality matter for transmission?

Parasite-induced host mortality (alpha) reduces the infectious period, lowering R0. This creates an evolutionary trade-off: more virulent parasites kill hosts faster, limiting their own transmission. This is the basis of virulence evolution theory.

Sources

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