Average Number Of Secondary Cases Research Articles

Immunoprophylaxis in Biosecurity Programs

In aquaculture, vaccination is a part of biosecurity measures. Vaccination reduces the risk of disease outbreak in the farm and spread of disease within the farm, to other farms, and to wild fish. Even nonvaccinated fish and poor responders may be protected as a consequence of herd immunity in the vaccinated farms. Prevention is achieved when the density of susceptible hosts is reduced sufficiently so that the basic reproduction number, the average number of secondary cases arising from a single primary infected individual, falls below 1. Based on the information about the basic reproduction number and the vaccine efficacy, the outcome of immunoprophylaxis can be predicted using modeling.

Using modelling to disentangle the relative contributions of zoonotic and anthroponotic transmission: the case of lassa fever.

BackgroundZoonotic infections, which transmit from animals to humans, form the majority of new human pathogens. Following zoonotic transmission, the pathogen may already have, or may acquire, the ability to transmit from human to human. With infections such as Lassa fever (LF), an often fatal, rodent-borne, hemorrhagic fever common in areas of West Africa, rodent-to-rodent, rodent-to-human, human-to-human and even human-to-rodent transmission patterns are possible. Indeed, large hospital-related outbreaks have been reported. Estimating the proportion of transmission due to human-to-human routes and related patterns (e.g. existence of super-spreaders), in these scenarios is challenging, but essential for planned interventions.Methodology/Principal FindingsHere, we make use of an innovative modeling approach to analyze data from published outbreaks and the number of LF hospitalized patients to Kenema Government Hospital in Sierra Leone to estimate the likely contribution of human-to-human transmission. The analyses show that almost of the cases at KGH are secondary cases arising from human-to-human transmission. However, we found much of this transmission is associated with a disproportionally large impact of a few individuals (‘super-spreaders’), as we found only of human cases result in an effective reproduction number (i.e. the average number of secondary cases per infectious case) , with a maximum value up to .Conclusions/SignificanceThis work explains the discrepancy between the sizes of reported LF outbreaks and a clinical perception that human-to-human transmission is low. Future assessment of risks of LF and infection control guidelines should take into account the potentially large impact of super-spreaders in human-to-human transmission. Our work highlights several neglected topics in LF research, the occurrence and nature of super-spreading events and aspects of social behavior in transmission and detection.

Temporal Changes in Ebola Transmission in Sierra Leone and Implications for Control Requirements: a Real-time Modelling Study.

Background: Between August and November 2014, the incidence of Ebola virus disease (EVD) rose dramatically in several districts of Sierra Leone. As a result, the number of cases exceeded the capacity of Ebola holding and treatment centres. During December, additional beds were introduced, and incidence declined in many areas. We aimed to measure patterns of transmission in different regions, and evaluate whether bed capacity is now sufficient to meet future demand. Methods: We used a mathematical model of EVD infection to estimate how the extent of transmission in the nine worst affected districts of Sierra Leone changed between 10th August 2014 and 18th January 2015. Using the model, we forecast the number of cases that could occur until the end of March 2015, and compared bed requirements with expected future capacity. Results: We found that the reproduction number, R, defined as the average number of secondary cases generated by a typical infectious individual, declined between August and December in all districts. We estimated that R was near the crucial control threshold value of 1 in December. We further estimated that bed capacity has lagged behind demand between August and December for most districts, but as a consequence of the decline in transmission, control measures caught up with the epidemic in early 2015. Conclusions: EVD incidence has exhibited substantial temporal and geographical variation in Sierra Leone, but our results suggest that the epidemic may have now peaked in Sierra Leone, and that current bed capacity appears to be sufficient to keep the epidemic under-control in most districts.

Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature.

BackgroundThe potential impact of an influenza pandemic can be assessed by calculating a set of transmissibility parameters, the most important being the reproduction number (R), which is defined as the average number of secondary cases generated per typical infectious case.MethodsWe conducted a systematic review to summarize published estimates of R for pandemic or seasonal influenza and for novel influenza viruses (e.g. H5N1). We retained and summarized papers that estimated R for pandemic or seasonal influenza or for human infections with novel influenza viruses.ResultsThe search yielded 567 papers. Ninety-one papers were retained, and an additional twenty papers were identified from the references of the retained papers. Twenty-four studies reported 51 R values for the 1918 pandemic. The median R value for 1918 was 1.80 (interquartile range [IQR]: 1.47–2.27). Six studies reported seven 1957 pandemic R values. The median R value for 1957 was 1.65 (IQR: 1.53–1.70). Four studies reported seven 1968 pandemic R values. The median R value for 1968 was 1.80 (IQR: 1.56–1.85). Fifty-seven studies reported 78 2009 pandemic R values. The median R value for 2009 was 1.46 (IQR: 1.30–1.70) and was similar across the two waves of illness: 1.46 for the first wave and 1.48 for the second wave. Twenty-four studies reported 47 seasonal epidemic R values. The median R value for seasonal influenza was 1.28 (IQR: 1.19–1.37). Four studies reported six novel influenza R values. Four out of six R values were <1.ConclusionsThese R values represent the difference between epidemics that are controllable and cause moderate illness and those causing a significant number of illnesses and requiring intensive mitigation strategies to control. Continued monitoring of R during seasonal and novel influenza outbreaks is needed to document its variation before the next pandemic.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2334-14-480) contains supplementary material, which is available to authorized users.

A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.

Case and partnership reproduction numbers for a curable sexually transmitted infection

Sexually transmitted infections (STIs) are, by definition, transmitted between sexual partners. For curable STIs an infected index case can potentially re-infect the same partner multiple times. Thus, R0, the average number of secondary infections one typical infected individual will produce during his or her infectious period is not necessarily the same as the average number of secondary cases (infected persons). Here we introduce the new concept of the case reproduction number (Rc). In addition, we define the partnership reproduction number (Rp) as the average number of secondary partnerships consisting of two infected individuals one typical infected individual will produce over his or her infectious lifetime. Rp takes into account clearance and re-infection within partnerships, which results in a prolongation of the duration of the infectious period. The two new reproduction numbers were derived for a deterministic pair model with serial monogamous partnerships using infection parameters for Chlamydia trachomatis, an example of a curable STI. We showed that re-infection within partnerships means that curable STIs can be sustained endemically even when the average number of secondary cases a person produces during his or her infectious period is below one.

Global Analysis for a Delay-Distributed SIR Epidemic Model

One of most tools for describing incubation period in epidemiological dynamic models is distributed delays. In this paper, we propose a delay-distributed SIR epidemic model with a nonlinear incidence rate. Using Lyapunov theory and LaSalle's principle, we show global asymptotic stability of disease-free and endemic equilibria. This analysis extends and performs existing results. Setting problem The analysis of mathematical models describing spread and control of infectious disease is one of major area biology. Transmission of a disease is a dynamical process driven by interaction between susceptible and infective. The SIR infections disease models have been studied by many authors. The behavior of SIR models are greatly aected by way in which transmission between infected and susceptible individuals. It divided population being studied into three class labeled S, I and R, where S denotes number of individuals who are susceptible to disease, I denotes number of infectious individuals and R denotes number of individuals who had been infected and were removed from possibility of being infected again or spreading infection. One fundamental parameter governs spread of diseases, and is also related to long term behaviors and level of vaccination necessary for eradication. This parameter is called reproduction number, R0: R0 is dened by epidemiologists as the average number of secondary cases caused by an infectious individual in a totally susceptible population. When R0 > 1; disease can enter a totally susceptible population and number of cases will increase, whereas when R0 < 1; disease will always fail to spread. Therefore, in its simplest form R0 tells us whether a population is at risk from a given disease. Nowadays, results of many epidemiological research are presented in terms of basic reproduction number. The epidemicity of disease is closely related to stability of solutions of mathematical models. Given global asymptotic stability conditions of those equilibria is of utmost biological relevance. Much attention has been paid to analysis of stability of disease free equilibrium and endemic equilibrium of epidemic models. The fraction of papers that obtain global stability of these models is relatively few, especially models with time delays (2, 12, 15, 19).

Transmission Dynamics of Carbapenemase-Producing Klebsiella Pneumoniae and Anticipated Impact of Infection Control Strategies in a Surgical Unit

BackgroundCarbapenemase-producing Klebsiella pneumoniae (CPKP) has been established as important nosocomial pathogen in many geographic regions. Transmission from patient to patient via the hands of healthcare workers is the main route of spread in the acute-care setting.Methodology/Principal FindingsEpidemiological and infection control data were recorded during a prospective observational study conducted in a surgical unit of a tertiary-care hospital in Greece. Surveillance culture for CPKP were obtained from all patients upon admission and weekly thereafter. The Ross-Macdonald model for vector-borne diseases was applied to obtain estimates for the basic reproduction number R0 (average number of secondary cases per primary case in the absence of infection control) and assess the impact of infection control measures on CPKP containment in endemic and hyperendemic settings. Eighteen of 850 patients were colonized with CPKP on admission and 51 acquired CPKP during hospilazation. R0 reached 2 and exceeded unity for long periods of time under the observed hand hygiene compliance (21%). The minimum hand hygiene compliance level necessary to control transmission was 50%. Reduction of 60% to 90% in colonized patients on admission, through active surveillance culture, contact precautions and isolation/cohorting, in combination with 60% compliance in hand hygiene would result in rapid decline in CPKP prevalence within 8–12 weeks. Antibiotics restrictions did not have a substantial benefit when an aggressive control strategy was implemented.Conclusions/SignificanceSurveillance culture on admission and isolation/cohorting of colonized patients coupled with moderate hand hygiene compliance and contact precautions may lead to rapid control of CPKP in endemic and hyperendemic healthcare settings.

On the definition and the computation of the type-reproduction number T for structured populations in heterogeneous environments

In the context of mathematical epidemiology, the type-reproduction number (TRN) for a specific host type is interpreted as the average number of secondary cases of that type produced by the primary cases of the same host type during the entire course of infection. Here, it must be noted that T takes into account not only the secondary cases directly transmitted from the specific host but also the cases indirectly transmitted by way of other types, who were infected from the primary cases of the specific host with no intermediate cases of the target host. Roberts and Heesterbeek (Proc R Soc Lond B 270:1359-1364, 2003) have shown that T is a useful measure when a particular single host type is targeted in the disease control effort in a community with various types of host, based on the fact that the sign relation sign(R₀-1) = sign(T-1) holds between the basic reproduction number R₀ and T. In fact, T can be seen as an extension of R₀ in a sense that the threshold condition of the total population growth can be formulated by the reproduction process of the target type only. However, the original formulation is limited to populations with discrete state space in constant environments. In this paper, based on a new perspective of R₀ in heterogeneous environments (Inaba in J Math Biol 2011), we give a general definition of the TRN for continuously structured populations in heterogeneous environments and show some examples of its computation and applications.

A plague on five of your houses--statistical re-assessment of three pneumonic plague outbreaks that occurred in Suffolk, England, between 1906 and 1918.

BackgroundPlague is a re-emerging disease and its pneumonic form is a high priority bio-terrorist threat. Epidemiologists have previously analysed historical outbreaks of pneumonic plague to better understand the dynamics of infection, transmission and control. This study examines 3 relatively unknown outbreaks of pneumonic plague that occurred in Suffolk, England, during the first 2 decades of the twentieth century.MethodsThe Kolmogorov-Smirnov statistical test is used to compare the symptomatic period and the length of time between successive cases (i.e. the serial interval) with previously reported values. Consideration is also given to the case fatality ratio, the average number of secondary cases resulting from each primary case in the observed minor outbreaks (termed Rminor), and the proportion of individuals living within an affected household that succumb to pneumonic plague via the index case (i.e. the household secondary attack rate (SAR)).Results2 of the 14 cases survived giving a case fatality ratio of 86% (95% confidence interval (CI) = {57%, 98%}). For the 12 fatal cases, the average symptomatic period was 3.3 days (standard deviation (SD) = 1.2 days) and, for the 11 non index cases, the average serial interval was 5.8 days (SD = 2.0 days). Rminor was calculated to be 0.9 (SD = 1.0) and, in 2 households, the SAR was approximately 14% (95% CI = {0%, 58%}) and 20% (95% CI = {1%, 72%}), respectively.ConclusionsThe symptomatic period was approximately 1 day longer on average than in an earlier study but the serial interval was in close agreement with 2 previously reported values. 2 of the 3 outbreaks ended without explicit public health interventions; however, non-professional caregivers were particularly vulnerable - an important public health consideration for any future outbreak of pneumonic plague.

Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries

BackgroundMathematical modelling of infectious disease is increasingly used to help guide public health policy. As directly transmitted infections, such as influenza and tuberculosis, require contact between individuals, knowledge about contact patterns is a necessary pre-requisite of accurate model predictions. Of particular interest is the potential impact of school closure as a means of controlling pandemic influenza (and potentially other pathogens).MethodsThis paper uses a population-based prospective survey of mixing patterns in eight European countries to study the relative change in the basic reproduction number (R0 - the average number of secondary cases from a typical primary case in a fully susceptible population) on weekdays versus weekends and during regular versus holiday periods. The relative change in R0 during holiday periods and weekends gives an indication of the impact collective school closures (and prophylactic absenteeism) may have during a pandemic.ResultsSocial contact patterns differ substantially when comparing weekdays to the weekend and regular to holiday periods mainly due to the reduction in work and/or school contacts. For most countries the basic reproduction number decreases from the week to weekends and regular to holiday periods by about 21% and 17%, respectively. However for other countries no significant decrease was observed.ConclusionWe use a large-scale social contact survey in eight different European countries to gain insights in the relative change in the basic reproduction number on weekdays versus weekends and during regular versus holiday periods. The resulting estimates indicate that school closure can have a substantial impact on the spread of a newly emerging infectious disease that is transmitted via close (non sexual) contacts.

Influenza: H1N1 Goes to School

A key determinant of the success of influenza containment is the transmission rate of the novel strain. C. Fraser et al. [“Pandemic potential of a strain of influenza A (H1N1): Early findings,” Reports, 19 June, p. [1557][1]] estimated the basic reproduction number ( R ) of the Mexican outbreak of influenza A (H1N1) to be in the range of 1.2 to 1.6. The value of R is a key measure of transmissibility and estimates the number of secondary cases in a completely susceptible population. Their findings were comparable to lower estimates for the 1918 pandemic, where R ranged from 2 to 3 ([ 1 ][2]). To further investigate the transmissibility of this novel virus, we conducted a secondary analysis of the largest reported cluster of influenza A (H1N1) ([ 2 ][3]). We used survey data from students of the St. Francis Preparatory School outbreak in the United States to calculate the effective reproduction number ( R ) in a school-based setting. R is the average number of secondary cases generated by an infectious case during an epidemic and is usually comparable to R . This survey collected data on self-reported fever and either cough or sore throat between 8 and 28 April 2009. We used the method proposed by Vynncky et al. ([ 3 ][4]) to calculate R from the growth rate of the epidemic. We based our parameter assumptions on estimates for seasonal influenza commonly reported in the literature, because such values are not yet available for H1N1. The parameters were as follows: incubation period of 2 days; infectious period of 3 days; and a calculated serial interval of 5 days. The serial interval is the time between the onset of symptoms for first and second generation cases. Using daily data from the outbreak growth phase, we calculated R to be 2.69 [95% confidence interval: 2.20 to 3.22; degrees of freedom (df) = 13]. Increasing the estimated infectious period to 5 days results in an R of 3.45 (95% confidence interval: 2.74 to 4.28; df = 13). The confidence interval for R was derived from a Monte Carlo simulation based on the uncertainty of the slope estimate. Estimates of R were relatively insensitive to the use of data from the growth phase or entire outbreak. Our calculated R is specific to this school setting and, with the increased transmission potential of a close-contact setting such as a school, is likely to be higher than transmission rates in the community-wide Mexican outbreak. The use of parameters estimated from seasonal influenza will need confirmation for the 2009 influenza A H1N1 virus. Our analysis supports the findings from Fraser et al. that this H1N1 virus has a transmission rate comparable to the lower R estimates of 2 for the 1918 pandemic ([ 1 ][2]). 1. [↵][5] 1. G. Chowell, 2. H. Nishiura, 3. L. Bettencourt , J. R. Soc. Interface 4, 155 (2007). [OpenUrl][6][Abstract/FREE Full Text][7] 2. [↵][8] 1. T. R. Frieden , Commissioner, “St. Francis Prep Update: Swine Flu Outbreak” (New York City Department of Health and Mental Hygiene, New York, 2009); ([www.nyc.gov/html/doh/downloads/pdf/cd/h1n1\_stfrancis\_survey.pdf][9]). 3. [↵][10] 1. E. Vynncky, 2. A. Trindall, 3. P. Mangtani , Int. J. Epidemiol. 36, 881 (2007). 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Strategies for Pandemic and Seasonal Influenza Vaccination of Schoolchildren in the United States

Vaccinating school-aged children against influenza can reduce age-specific and population-level illness attack rates. Using a stochastic simulation model of influenza transmission, the authors assessed strategies for vaccinating children in the United States, varying the vaccine type, coverage level, and reproductive number R (average number of secondary cases produced by a typical primary case). Results indicated that vaccinating children can substantially reduce population-level illness attack rates over a wide range of scenarios. The greatest absolute reduction in influenza illness cases per season occurred at R values ranging from 1.2 to 1.6 for a given vaccine coverage level. The indirect, total, and overall effects of vaccinating children were strong when transmission intensity was low to intermediate. The indirect effects declined rapidly as transmission intensity increased. In a mild influenza season (R = 1.1), approximately 19 million influenza cases could be prevented by vaccinating 70% of children. At most, nearly 100 million cases of influenza illness could be prevented, depending on the proportion of children vaccinated and the transmission intensity. Given the current worldwide threat of novel influenza A (H1N1), with an estimated R of 1.4–1.6, health officials should consider strategies for vaccinating children against novel influenza A (H1N1) as well as seasonal influenza.

A preliminary estimation of the reproduction ratio for new influenza A(H1N1) from the outbreak in Mexico, March-April 2009

As of 12 May 2009, 5,251 cases of the new influenza A(H1N1) have been officially reported to the World Health Organization (WHO) from 30 countries, with most of the identified cases exported from Mexico where a local epidemic has been going on for the last two months. Sustained human-to-human transmission is necessary to trigger influenza pandemic and estimating the reproduction ratio (average number of secondary cases per primary case) is necessary for forecasting the spread of infection. We use two methods to estimate the reproduction ratio from the epidemic curve in Mexico using three plausible generation intervals (the time between primary and secondary case infection). As expected, the reproduction ratio estimates were highly sensitive to assumptions regarding the generation interval, which remains to be estimated for the current epidemic. Here, we suggest that the reproduction ratio was less than 2.2 - 3.1 in Mexico, depending on the generation interval. Monitoring and updating the reproduction ratio estimate as the epidemic spreads outside Mexico into different settings should remain a priority for assessing the situation and helping to plan public health interventions.

Effectiveness of control measures during the SARS epidemic in Beijing: a comparison of theRtcurve and the epidemic curve

One of the areas most affected by SARS was Beijing with 2521 reported cases. We estimate the effective reproductive number Rt for the Beijing SARS epidemic, which represents the average number of secondary cases per primary case on each day of the epidemic and is therefore a measure of the underlying transmission dynamics. Our results provide a quantitative assessment of the effectiveness of public health control measures. More generally, our results illustrate how changes in Rt will reflect changes in the epidemic curve.

Time variations in the transmissibility of pandemic influenza in Prussia, Germany, from 1918–19

BackgroundTime variations in transmission potential have rarely been examined with regard to pandemic influenza. This paper reanalyzes the temporal distribution of pandemic influenza in Prussia, Germany, from 1918–19 using the daily numbers of deaths, which totaled 8911 from 29 September 1918 to 1 February 1919, and the distribution of the time delay from onset to death in order to estimate the effective reproduction number, Rt, defined as the actual average number of secondary cases per primary case at a given time.ResultsA discrete-time branching process was applied to back-calculated incidence data, assuming three different serial intervals (i.e. 1, 3 and 5 days). The estimated reproduction numbers exhibited a clear association between the estimates and choice of serial interval; i.e. the longer the assumed serial interval, the higher the reproduction number. Moreover, the estimated reproduction numbers did not decline monotonically with time, indicating that the patterns of secondary transmission varied with time. These tendencies are consistent with the differences in estimates of the reproduction number of pandemic influenza in recent studies; high estimates probably originate from a long serial interval and a model assumption about transmission rate that takes no account of time variation and is applied to the entire epidemic curve.ConclusionThe present findings suggest that in order to offer robust assessments it is critically important to clarify in detail the natural history of a disease (e.g. including the serial interval) as well as heterogeneous patterns of transmission. In addition, given that human contact behavior probably influences transmissibility, individual countermeasures (e.g. household quarantine and mask-wearing) need to be explored to construct effective non-pharmaceutical interventions.

Pneumonic Plague, Northern India, 2002

Pneumonic Plague, Northern India, 2002

Comparative estimation of the reproduction number for pandemic influenza from daily case notification data

The reproduction number, R, defined as the average number of secondary cases generated by a primary case, is a crucial quantity for identifying the intensity of interventions required to control an epidemic. Current estimates of the reproduction number for seasonal influenza show wide variation and, in particular, uncertainty bounds for R for the pandemic strain from 1918 to 1919 have been obtained only in a few recent studies and are yet to be fully clarified. Here, we estimate R using daily case notifications during the autumn wave of the influenza pandemic (Spanish flu) in the city of San Francisco, California, from 1918 to 1919. In order to elucidate the effects from adopting different estimation approaches, four different methods are used: estimation of R using the early exponential-growth rate (Method 1), a simple susceptible-exposed-infectious-recovered (SEIR) model (Method 2), a more complex SEIR-type model that accounts for asymptomatic and hospitalized cases (Method 3), and a stochastic susceptible-infectious-removed (SIR) with Bayesian estimation (Method 4) that determines the effective reproduction number Rt at a given time t. The first three methods fit the initial exponential-growth phase of the epidemic, which was explicitly determined by the goodness-of-fit test. Moreover, Method 3 was also fitted to the whole epidemic curve. Whereas the values of R obtained using the first three methods based on the initial growth phase were estimated to be 2.98 (95% confidence interval (CI): 2.73, 3.25), 2.38 (2.16, 2.60) and 2.20 (1.55, 2.84), the third method with the entire epidemic curve yielded a value of 3.53 (3.45, 3.62). This larger value could be an overestimate since the goodness-of-fit to the initial exponential phase worsened when we fitted the model to the entire epidemic curve, and because the model is established as an autonomous system without time-varying assumptions. These estimates were shown to be robust to parameter uncertainties, but the theoretical exponential-growth approximation (Method 1) shows wide uncertainty. Method 4 provided a maximum-likelihood effective reproduction number 2.10 (1.21, 2.95) using the first 17 epidemic days, which is consistent with estimates obtained from the other methods and an estimate of 2.36 (2.07, 2.65) for the entire autumn wave. We conclude that the reproduction number for pandemic influenza (Spanish flu) at the city level can be robustly assessed to lie in the range of 2.0-3.0, in broad agreement with previous estimates using distinct data.

The effect of a live vaccine on the horizontal transmission of Mycoplasma gallisepticum

The effect of a live Mycoplasma gallisepticum vaccine on the horizontal transmission of this Mycoplasma species was quantified in an experimental animal transmission model in specific pathogen free White Layers. Two identical trials were performed, each consisting of two experimental groups and one control group. The experimental groups each consisted of 20 birds 21 weeks of age, which were housed following a pair-wise design. One group was vaccinated twice with a commercially available live attenuated M. gallisepticum vaccine, while the other group was not vaccinated. Each pair of the experimental group consisted of a challenged chicken (10(4) colony-forming units intratracheally) and a susceptible in-contact bird. The control group consisted of 10 twice-vaccinated birds housed in pairs and five individually housed non-vaccinated birds. The infection was monitored by serology, culture and quantitative polymerase chain reaction. The vaccine strain and the challenge strain were distinguished by a specific polymerase chain reaction and by random amplified polymorphic DNA analysis. In both experiments, all non-vaccinated challenged chickens and their in-contact 'partners' became infected with M. gallisepticum. In the vaccinated challenged and corresponding in-contact birds, a total of 19 and 13 chickens, respectively, became infected with M. gallisepticum. Analysis of the M. gallisepticum shedding patterns showed a significant effect of vaccination on the shedding levels of the vaccinated in-contact chickens. Moreover, the Cox Proportional Hazard analysis indicated that the rate of M. gallisepticum transmission from challenged to in-contact birds in the vaccinated group was 0.356 times that of the non-vaccinated group. In addition, the overall estimate of R (the average number of secondary cases infected by one typical infectious case) of the vaccinated group (R = 4.3, 95% confidence interval = 1.6 to 49.9) was significantly lower than that of the non-vaccinated group (R = infinity, 95% confidence interval = 9.9 to infinity). However, the overall estimate of R in the vaccinated group still exceeded 1, which indicates that the effect of the vaccination on the horizontal transmission M. gallisepticum is insufficient to stop its spread under these experimental conditions.

The effect of vaccination on foot and mouth disease virus transmission among dairy cows

The aim of this study was to quantify the effect of a single vaccination of dairy cows on foot and mouth disease virus (FMDV) transmission. To estimate if vaccination could significantly reduce virus transmission, we performed two replicates of a transmission experiment with one group of vaccinated and one group of non-vaccinated dairy cows (ten animals per group). Half of both groups were intranasally inoculated, with FMDV field isolate O/NET2001, and housed with the other half of the group (contact-exposed cows) from the next day onwards. Virus transmission was quantified by estimating the reproduction ratio R, which is the average number of secondary cases caused by one infectious animal. In the non-vaccinated groups all cows became infected and R nv was significantly above 1. In the vaccinated groups infection was demonstrated in three inoculated cows, and no transmission was observed ( R v was 0, not significantly below 1). Transmission was significantly reduced in the groups of vaccinated cows when compared to the groups of unvaccinated cows. Our findings indicate that after a single vaccination cows are protected against infection of FMD and that most likely no virus transmission will occur within a vaccinated herd.