Abrupt events and population synchrony in the dynamics of Bovine Tuberculosis (original) (raw)
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A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance
PLoS ONE, 2010
Background: Detailed analysis of the dynamic interactions among biological, environmental, social, and economic factors that favour the spread of certain diseases is extremely useful for designing effective control strategies. Diseases like tuberculosis that kills somebody every 15 seconds in the world, require methods that take into account the disease dynamics to design truly efficient control and surveillance strategies. The usual and well established statistical approaches provide insights into the cause-effect relationships that favour disease transmission but they only estimate risk areas, spatial or temporal trends. Here we introduce a novel approach that allows figuring out the dynamical behaviour of the disease spreading. This information can subsequently be used to validate mathematical models of the dissemination process from which the underlying mechanisms that are responsible for this spreading could be inferred.
Tuberculosis, a highly infectious disease which is transmitted within and between communities when infected and susceptible individuals interact. Tuberculosis at present is a major public health problem and continues to take toll on the most productive members of the community. An understanding of disease spread dynamics of infectious diseases continues to play a critical role in design of disease control strategies. Modeling of Tuberculosis is useful in understanding disease dynamics as it will guide the importance of basic science as well as public policy, prevention and control of the emerging infectious disease and modeling the spread of the disease. This study sought to establish how long under different frameworks will TB disease recede to extinction. In this study,deterministic and stochastic models for the trends of tuberculosis cases over time in Kenya were developed. Susceptible Infective (SI), Susceptible Infective and Recovered (SIR) and Susceptible Exposed Infective and Recovered (SEIR) models were considered. These models were modified in order to fit the data more precisely (age structure and predisposing factors of the incident cases).The SIR and SEIR model with non-linear incidence rates were further looked at and the stability of their solutions were evaluated. The results indicate that both deterministic and stochastic models can give not only an insight but also an integral description of TB transmission dynamics. Both deterministic and stochastic models fit well to the Kenyan TB epidemic model however with varying time periods. The models show that for deterministic model the number of infected individuals increases dramatically within three years and begins to fall quickly when the transmissible acts are 10 and 15 and falls to close to zero by 15 years but when the transmissible act is 5 the number infected peaks by the 11 th year and declines to zero by year 31, while for stochastic models the number infected falls exponentially but when the transmissible acts is 15 the decline is slow and will get to zero by the 53 rd year while for 10 transmissible acts to declines to zero by the 18 th year. The other transmissible acts (1,3,5) decline to zero by the 9 th year.From this study we conclude that if the national control program continues with the current interventions it could take them upto the next 31 years to bring the infection numbers to zero if the deterministic model is considered, while in the stochastic model with accelerated interventions and high recovery rate and assuming that there is no change in the risk factors it could take them upto 11 years to bring the infections to zero.
1. Increasing sophistication of population viability analysis has broadened our capacity to model population change while accounting for system complexity and uncertainty. However, many emergent properties of population dynamics, such as the coupling of demographic processes with transmission and spread of disease, are still poorly understood. 2. We combined an individual-based demographic (Vortex) and epidemiological (Outbreak) model using a novel command-centre module (MetaModel Manager) to predict the progression of bovine tuberculosis in introduced swamp buffalo Bubalus bubalis in northern Australia and validated the model with data from a large-scale disease-monitoring and culling programme. We also assessed the capacity to detect disease based on incrementing sentinel (randomly sampled individuals) culling rates. 3. We showed that even high monitoring effort (1000 culled sentinels) has a low (<10%) probability of detecting the disease, and current sampling is inadequate. 4. Testing proportional and stepped culling rates revealed that up to 50% of animals must be killed each year to reduce disease prevalence to near-eradication levels. 5. Sensitivity analysis indicated that prevalence depended mainly on population demography (e.g. female age at primiparity) and the additional mortality induced by disease, with only minor contributions from epidemiological characteristics such as probability of transmission and encounter rate. This is a useful finding because the disease parameters are the least well known. 6. Synthesis and applications. Our models suggest that details of population demography should be incorporated into epidemiological models to avoid extensive bias in predictions of disease spread and effectiveness of control. Importantly, we demonstrate that low detection probabilities challenge the effectiveness of existing disease-monitoring protocols in northern Australia. The command-centre module we describe linking demographic and epidemiological models provides managers with the tools necessary to make informed decisions regarding disease management.
Spatio-temporal models of bovine tuberculosis in the Irish cattle population, 2012-2019
Spatial and Spatio-temporal Epidemiology, 2021
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Journal of The Royal Society Interface, 2006
Infection with Mycobacterium tuberculosis leads to tuberculosis (TB) disease by one of the three possible routes: primary progression after a recent infection; re-activation of a latent infection; or exogenous re-infection of a previously infected individual. Recent studies show that optimal TB control strategies may vary depending on the predominant route to disease in a specific population. It is therefore important for public health policy makers to understand the relative frequency of each type of TB within specific epidemiological scenarios. Although molecular epidemiologic tools have been used to estimate the relative contribution of recent transmission and re-activation to the burden of TB disease, it is not possible to use these techniques to distinguish between primary disease and re-infection on a population level. Current estimates of the contribution of re-infection therefore rely on mathematical models which identify the parameters most consistent with epidemiological d...
Preventive Veterinary Medicine, 2012
(C.M. O'Connor). complexity of infectious disease dynamics. Few diseases exclusively affect only one species or a group of hosts (Daszak et al., 2000; Cleaveland et al., 2001), and these host populations are themselves embedded within dynamic ecological systems and subject to complex behavioural interactions. The often sensitive interactions that govern the regulation of disease mean that any change to the disease ecosystem can perturb the balance of host, agent and environment. Disturbances to these systems can result in outbreaks of disease (e.g. zoonotic cases of hantavirus pulmonary syndrome and nephropathia epidemica,
Epidemics, 2012
For infections that are typically asymptomatic, targeted surveillance systems (whereby individuals at increased risk are tested more frequently) will detect infections earlier on average than systems with random testing or in systems where all individuals are tested at the same intervals. However, estimating temporal trends in infection risk using data from such targeted surveillance systems can be challenging. This is similarly a problem for targeted surveillance to detect faults of individual industrial components. The incidence of bovine tuberculosis (TB) in British cattle has been generally increasing in the last thirty years. Cattle herds are routinely tested for evidence of exposure to the aetiological bacteria Mycobacterium bovis, in a targeted surveillance programme in which the testing interval is determined by past local TB incidence and local veterinary discretion. The UK Department for Environment, Food and Rural Affairs (Defra) report the monthly percentage of tests on officially TB-free (OTF) herds resulting in a confirmed positive test for M. bovis (i.e. the percentage of tested herds with OTF status withdrawn), which contains substantial fluctuations (three years apart) within the increasing trend. As the number of herds tested changes over time, this cyclic trend is difficult to interpret. Here we evaluate an alternative to the Defra method in which we distribute each incident event across the period at risk to infer the underlying trends in infection incidence using a stochastic model of cattle herd incidence and testing frequencies fitted to data on the monthly number of herds tested and number of these with OTF status withdrawn in 2003-2010. We show that for an increasing underlying incidence trend, the current Defra approach can produce artefactual fluctuations whereas the alternative method described provides more accurate descriptions of the underlying risks over time.
Modeling the spread of Tuberculosis in semi-closed communities
In this paper we address the problem of long-term dynamics of tuberculosis (TB) and latent tuberculosis (LTB) in semi-closed communities. These communities are congregate settings with the potential for sustained daily contact for weeks, months and even years between its members. Basic examples of these communities are prisons, but certain urban/rural communities, some schools, among others could possibly fit well into this definition. These communities present a sort of ideal conditions for TB spread. In order to describe key relevant dynamics of the disease in these communities we consider a five compartments SEIR model with five possible routes toward TB infection: primary infection after a contact with infected and infectious individuals (fast TB), endogenous reactivation after a period of latency (slow TB), relapse by natural causes after a cure, exogenous reinfection of latently infected and exogenous reinfection of recovered individuals. We discuss the possible existence of multiple endemic equilibrium states and the role that could play the two types of exogenous reinfections in the long term dynamics of the disease.
Assessing the effects of multiple infections and long latency in the dynamics of tuberculosis
Theoretical Biology and Medical Modelling, 2010
In order to achieve a better understanding of multiple infections and long latency in the dynamics of Mycobacterium tuberculosis infection, we analyze a simple model. Since backward bifurcation is well documented in the literature with respect to the model we are considering, our aim is to illustrate this behavior in terms of the range of variations of the model's parameters. We show that backward bifurcation disappears (and forward bifurcation occurs) if: (a) the latent period is shortened below a critical value; and (b) the rates of super-infection and re-infection are decreased. This result shows that among immunosuppressed individuals, super-infection and/or changes in the latent period could act to facilitate the onset of tuberculosis. When we decrease the incubation period below the critical value, we obtain the curve of the incidence of tuberculosis following forward bifurcation; however, this curve envelops that obtained from the backward bifurcation diagram.
Stochastic Environmental research and Risk Assessment
Bovine Tuberculosis (TB) is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5 %, while the accuracy of the test employed to detect infected cattle explained less than 3 % of the variance in the number of infected cattle.