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 (_{0}

The risk of importing

Plague is also recognized as a potential weapon for bioterrorists (

Given that primary pneumonic plague is transmissible person-to-person and outbreaks could occur as a consequence of importation or bioterrorism, it is essential to develop quantitative assessments of the transmissibility and kinetics of the disease that are as robust as possible to aid public health planning, including training exercises such as those referred to above. Without preparation, inappropriate responses such as those seen during the suspected outbreak of plague in Surat, India (1994), are inevitable; the tourist industry suffered, exports were affected, and excessive demands were placed upon healthcare systems. The losses in this case have been estimated to run into billions of U.S. dollars (

While there has been much discussion concerning the transmissibility of primary pneumonic plague, no quantitative estimates could be found in published literature. The qualitative assessments that were found varied considerably: some reports suggest that primary pneumonic plague is highly transmissible and infectious (

Using mathematical models based on historic data, we quantitatively assess the transmissibility and potential health effects of primary pneumonic plague outbreaks under a range of assumptions. In this initial analysis, we consider only the immediate health effects due to primary pneumonic plague and not the possible long-term effects due to potentially establishing the pathogen in rodent reservoirs and subsequent risks for bubonic plague. Based on available epidemiologic evidence, the modeling assumes that persons, once infected, experience a nonsymptomatic latent period followed by a symptomatic infectious period during which they can transmit primary pneumonic plague to other persons. Thereafter, if infected persons are untreated they will die. The reported case-fatality rate is close to 100% (

To estimate the duration of the latent period and the infectious period, and the probability of transmission of primary pneumonic plague, data describing cases and transmission events were sought from well-documented outbreaks. Reports of sufficiently well-documented outbreaks were rare, and each of the outbreaks resulted in relatively small numbers of new cases of primary pneumonic plague. Since therapy may affect the duration of individual latent periods and infectious periods, only the data in reports from person who had not received therapy was used in this analysis for latent periods (

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).

To estimate the transmission rate of primary pneumonic plague, only those transmission events from reports where the infecting persons could be unambiguously identified and where the infections had occurred before public health intervention were included in the analysis. The average number of infections generated by each infected person was then determined for each of the outbreaks documented in the ^{x}_{0}) with variance of 3.1. This provides a probability density function (

Y and location | Total of PP cases in outbreak^{a} | No. of PP cases before intervention^{b} | Transmission events | Average of secondary transmissions per primary transmission |
---|---|---|---|---|

Seattle USA, 1907 (30) | 5 | 5 | 4 | 0.8 |

Oakland USA, 1919 (24) | 13 | 6 | 12 | 2.0 |

Ecuador, 1939 (23) | 18 | 4 | 6 | 1.5 |

Mukden, China, 1946 (25) | 39 | 9 | 8 | 0.9 |

Rangoon, 1946 (31) | 16 | 11 | 22 | 2.0 |

NW Madagascar, 1957 (32) | 42 | 35 | 39 | 1.1 |

Zambia, 1993 (33) | 3 | 3 | 2 | 0.7 |

Madagascar, 1997 (26) | 18 | 1 | 3 | 3.0 |

^{a}Includes index case.
^{b}Only includes cases where the infecting person could be identified.

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.

Documented 20th century outbreaks of primary pneumonic plague were often rapidly contained once they came to the attention of public health authorities (_{0}

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.

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).

^{A simple} Markov-chain model was used to model disease outbreaks such that an individual _{i}_{i}_{i}_{i}_{i}_{i}^{with the exception of Mukden, 1946 (}^{25}^{), that} the ^{control measures were very effective in controlling all outbreaks; any subsequent cases occurred only as a result of infections incurred before the initiation of the control measures.}

After the introduction of latent infections into a community, infectious symptomatic cases will begin to appear over time. By the time an outbreak has been detected, there will potentially be a number of infectious persons in the community who can be estimated by using the modeling procedure described above. This number is critical in estimating the likely scale of response that might be required by public health authorities, giving a guide not only to the number of infectious people in the community at that point, but also an index for further onward transmission should responses be delayed. The model was thus used to numerically estimate a function, given by equation 1, that estimates the average number of infectious persons in the community with the potential to infect others, _{0}_{0}

_{0}e^{β}^{t}

where α = 0.3841 (SE = 0.00078) and β = 0.0734 (SE = 0.00005) for _{0}

The transmission rate derived here for primary pneumonic plague is relatively low compared to many other communicable diseases (_{0}

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.

Where _{0}_{0}_{0}_{0}_{0}_{0}_{0}_{0}_{0}_{0}_{0}_{0}

Estimates for (A) the cumulative number of people infected from the time of the first infection, and (B) daily number of infected people, where D_{0} = 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.

Variation in the expected number of cases at the end of an outbreak when N_{0}, D_{0,} and R_{0} are varied across multiple iterations (n = 27,000) of the model (red denotes R_{0} = 0.96, green denotes R_{0} = 1.3, and black denotes R_{0} = 2.3). (N.B. Note scale changes).

Reducing the average number of secondary cases per primary cases (_{0}_{0}^{2}-values derived from minimizing the log-likelihood function) are 2.3 (variance = 7.8) and 0.96 (variance = 1.9), outbreaks with higher values of _{0}

The fact that the estimated _{0}_{0} values (_{0}_{0,}_{0}_{0}_{0}

Suggested citation for this article: Gani R and Leach S. Epidemiologic Determinants for Modeling Pneumonic Plague Outbreaks. Emerg Infect Dis [serial online] 2004 Apr [

We thank C. Penn, G. Lloydd, C. Clegg, and V. Mioulet for their help with this work and the preparation of this manuscript, and S. Eley and members of the DH Steering Group for their comments and help with model parameterization.

This work was funded by the U.K. Department of Health. The views expressed in the publication are those of the authors and not necessarily those of the Department of Health.