Evolutionary Repercussions of Avian Culling on Host Resistance and Influenza Virulence

Keeping pandemic influenza at bay is a global health priority. Of particular concern is the continued spread of the influenza subtype H5N1 in avian populations and the increasing frequency of transmission to humans. To decrease this threat, mass culling is the principal strategy for eradicating influenza in avian populations. Although culling has a crucial short-term epidemiological benefit, evolutionary repercussions on reservoir hosts and on the viral population have not been considered.To explore the epidemiological and evolutionary repercussions of mass avian culling, we combine population genetics and epidemiological influenza dynamics in a mathematical model parameterized by clinical, epidemiological, and poultry data. We model the virulence level of influenza and the selection on a dominant allele that confers resistance against influenza [1, 2] in a poultry population. Our findings indicate that culling impedes the evolution of avian host resistance against influenza. On the pathogen side of the coevolutionary race between pathogen and host, culling selects for heightened virulence and transmissibility of influenza.Mass culling achieves a short-term benefit at the expense of long-term detriments: a more genetically susceptible host population, ultimately greater mortality, and elevated influenza virulence

Pause on avian flu transmission research

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Transmission studies resume for avian flu

Lette

Improving reliability of judgmental forecasts

All judgmental forecasts will be affected by the inherent unreliability, or inconsistency, of the judgment process. Psychologists have studied this problem extensively, but forecasters rarely address it. Researchers and theorists describe two types of unreliability that can reduce the accuracy of judgmental forecasts: (1) unreliability of information acquisition, and (2) unreliability of information processing. Studies indicate that judgments are less reliable when the task is more complex; when the environment is more uncertain; when the acquisition of information relies on perception, pattern recognition, or memory; and when people use intuition instead of analysis. Five principles can improve reliability in judgmental forecasting: 1. Organize and present information in a form that clearly emphasizes relevant information. 2. Limit the amount of information used in judgmental forecasting. Use a small number of really important cues. 3. Use mechanical methods to process infomation. 4. Combine several forecasts. 5. Require justification of forecasts