Epidemiologic determinants for modeling pneumonic plague outbreaks

Abstract

Pneumonic plague poses a potentially increasing risk to humans in plague nonendemic regions either as a consequence of an aerosolized release or through importation of the disease. Pneumonic plague is person-to-person transmissible. We provide a quantitative assessment of transmissibility based on past outbreaks that shows that the average number of secondary cases per primary case (R0) was 1.3 (variance = 3.1), assuming a geometric probability distribution, prior to outbreak control measures. We also show that the latent and infectious periods can be approximated by using lognormal distributions with means (SD) of 4.3 (1.8) and 2.5 (1.2) days. Based on this parameter estimation, we construct a Markov-chain epidemic model to demonstrate the potential impact of delays in implementing outbreak control measures and increasing numbers of index cases on the incidence of cases in simulated outbreaks.

Figure 1

A. Frequency distribution for the latent period with a fitted lognormal distribution (n=224); B. frequency distribution of the length of the infectious period with a fitted lognormal distribution (n=225).

Figure 2

Frequency distributions for the number of secondary cases per primary case of primary pneumonic plague. Observations from outbreaks in Table are in black and the fitted geometric distribution in gray.

Figure 3

Epidemic curves for outbreaks in the Table and from the model. The curves plot cumulative cases at time of onset. Day 0 is the time of onset of index case, the circles represent the times at which disease control measures begin, those without circles ended without public health interventions. Dotted lines indicate missing data. The thicker yellow line represents the upper 95 percentile from the epidemic model, which rises roughly exponentially to a value of 256 by day 35.

Figure 4

Distributions for the contexts of the transmission events for PPP by (A) type of contact with infectious individual (n=91), and (B) location of infectious contact when infected (n=86). Data aggregated from multiple sources (23–26,30–33), where these data were specified).

Figure 5

Frequency distributions for (A) the expected number of cases at the end of outbreaks, and (B) the expected lengths of outbreaks when different numbers of deaths are required to trigger public health interventions.

Figure 6

Estimates for (A) the cumulative number of people infected from the time of the first infection, and (B) daily number of infected people, where D₀ = 1 (black), 5 (red) and 10 (blue). Solid lines represent the median number of cases from multiple iterations (n = 1000) of the model and the dotted lines give the upper and lower 95 percentiles.

Figure 7

Variation in the expected number of cases at the end of an outbreak when N₀, D_0, and R₀ are varied across multiple iterations (n = 27,000) of the model (red denotes R₀ = 0.96, green denotes R₀ = 1.3, and black denotes R₀ = 2.3). (N.B. Note scale changes).