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1.
Zoonoses Public Health ; 63(1): 20-33, 2016 Feb.
Article in English | MEDLINE | ID: mdl-25923926

ABSTRACT

Avian influenza virus (H5N1) is a rapidly disseminating infection that affects poultry and, potentially, humans. Because the avian virus has already adapted to several mammalian species, decreasing the rate of avian-mammalian contacts is critical to diminish the chances of a total adaptation of H5N1 to humans. To prevent the pandemic such adaptation could facilitate, a biology-specific disease surveillance model is needed, which should also consider geographical and socio-cultural factors. Here, we conceptualized a surveillance model meant to capture H5N1-related biological and cultural aspects, which included food processing, trade and cooking-related practices, as well as incentives (or disincentives) for desirable behaviours. This proof of concept was tested with data collected from 378 Egyptian and Nigerian sites (local [backyard] producers/live bird markets/village abattoirs/commercial abattoirs and veterinary agencies). Findings revealed numerous opportunities for pathogens to disseminate, as well as lack of incentives to adopt preventive measures, and factors that promoted epidemic dissemination. Supporting such observations, the estimated risk for H5N1-related human mortality was higher than previously reported. The need for multidimensional disease surveillance models, which may detect risks at higher levels than models that only measure one factor or outcome, was supported. To develop efficient surveillance systems, interactions should be captured, which include but exceed biological factors. This low-cost and easily implementable model, if conducted over time, may identify focal instances where tailored policies may diminish both endemicity and the total adaptation of H5N1 to the human species.


Subject(s)
Influenza, Human/epidemiology , Abattoirs , Adult , Africa/epidemiology , Aged , Animal Diseases/epidemiology , Animals , Birds , Disease Outbreaks/prevention & control , Egypt/epidemiology , Female , Food Microbiology , Health Surveys , Humans , Influenza A Virus, H5N1 Subtype , Influenza in Birds/epidemiology , Male , Middle Aged , Models, Biological , Nigeria/epidemiology , Poultry , Pregnancy , Risk Assessment , Risk Factors , Young Adult
2.
Prev Vet Med ; 42(3-4): 271-95, 1999 Dec 01.
Article in English | MEDLINE | ID: mdl-10619160

ABSTRACT

The simulation model InterCSF was developed to simulate the Dutch Classical Swine Fever (CSF) epidemic of 1997-98 as closely as possible. InterCSF is a spatial, temporal and stochastic simulation model. The outcomes of the various replications give an estimate of the variation in size and duration of possible CSF-epidemics. InterCSF simulates disease spread from an infected farm to other farms through three contact types (animals, vehicles, persons) and through local spread up to a specified distance. The main disease-control mechanisms that influence the disease spread in InterCSF are diagnosis of the infected farms, depopulation of infected farms, movement-control areas, tracing, and pre-emptive slaughter. InterCSF was developed using InterSpread as the basis. InterSpread was developed for foot-and-mouth disease (FMD). This paper describes the process of modifying InterSpread into InterCSF. This involved changing the assumptions and mechanisms for disease spread from FMD to CSF. In addition, CSF-specific control measures based on the standard European Union (EU) regulations were included, as well as additional control measures that were applied during the Dutch epidemic. To adapt InterCSF as closely as possible to the Dutch 1997/98 epidemic, data from the real epidemic were analysed. Both disease spread and disease-control parameters were thus specifically based on the real epidemic. In general, InterSpread turned out to be a flexible tool that could be adapted to simulate another disease with relative ease. The most difficult were the modifications necessary to mimic the real epidemic as closely as possible. The model was well able to simulate an epidemic with a similar pattern over time for number of detected farms as the real outbreak; but the absolute numbers were (despite many relevant modifications) not exactly the same--but were within an acceptable range. Furthermore, the development of InterCSF provided the researchers with a better insight into the existing knowledge gaps. In part II (see the final paper in this issue), InterCSF was used to compare various control strategies as applied to this epidemic.


Subject(s)
Classical Swine Fever/prevention & control , Disease Outbreaks/veterinary , Models, Theoretical , Animal Husbandry/methods , Animals , Classical Swine Fever/economics , Classical Swine Fever/transmission , Disease Transmission, Infectious/veterinary , Netherlands , Swine
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