![]() ![]() This allows a user to understand not only the relationship between the current state as a function of the past states, commonly referred to as endogenous variables, but also the influence of inputs outside the state of the series, also called exogenous variables. Like ordinary least-square regressions the relationship between the independent variables and the dependent variables are easily understood based on the assumptions of the model. First, they can be easily interpreted in retrospective studies. These models have two distinct advantages. Traditionally, time series predictions are performed using the autoregressive integrated moving average (ARIMA) models, which attempt to filter out high-frequency noise in the data to detect local trends based on linear dependence in observations in the series. Therefore, development of better prediction models for avian influenza outbreaks in Egypt could aid efforts to control and prevent disease in both birds and humans.įor diseases that occur in cyclic or repeating patterns, time series models have been used to predict future outbreaks. As a result, by tracking and predicting the number of bird outbreaks we hope to predict increases in risk for the zoonotic (animal to human) transmission of the virus. It has also been reported that daily temperature could affect the risk of disease transmission. In Egypt, it has been reported that H5N1 outbreaks in bird populations occur in close temporal proximity to human H5N1 cases, with human cases generally occurring within 30 days of an initial bird outbreak. These events may increase the likelihood global pandemic among humans. Furthermore, continued transmission of the pathogen between birds (and from birds) to humans has the potential to drive virus evolution with the possibility of increased virulence, and human-to-human transmission. Along with being a menace to human health, the continued presence of the pathogen in Egypt impacts the country’s protein sources, farmer’s livelihoods, the economy as a whole. In addition to bird cases, 168 humans in Egypt have been confirmed as infected with H5N1 as of August, 2012, and 60 of those cases resulted in death due to the disease. These outbreaks have caused the death of millions of birds due to illness or due to culling to prevent further spread of the disease. Outbreaks of H5N1 among small farming operations have remained prevalent over time, despite control measures. ![]() H5N1 was first detected in poultry populations in Egypt in February of 2006 and was shortly afterward found in 15 governorates around the country. ![]() Therefore, an important application of new, improved predictive models should be related to the emergence of zoonotic disease outbreaks in animals that could constitute health risk to nearby human populations. An example is the recent emergence of highly pathogenic avian influenza. More than two thirds of emerging infectious diseases in recent decades are zoonotic in origin (crossing from animals to humans). For such prevention to take place, timely and accurate prediction of outbreaks is critical. Examples of such preventive steps include vector control, public health messaging to avoid high-risk behaviors or areas, and raising clinician awareness for early diagnosis and treatment. By understanding when a future outbreak may occur, preventative steps can be taken to minimize its impact. Certain infectious diseases and disease events occur in a cyclic or rhythmic pattern related to climate or other factors that allows for modeling and prediction of future outbreaks. ![]()
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