University of Cambridge > Talks.cam > Plant Sciences Research Seminars > Detecting asymptomatic infection is necessary for real-time forecasting of major outbreaks of disease

Detecting asymptomatic infection is necessary for real-time forecasting of major outbreaks of disease

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When an infectious disease is introduced into a host population of plants, the pathogen typically either dies out without causing a large epidemic or goes on to become widespread in the population. Mathematical models can be used to predict whether the pathogen will die out or whether a major outbreak will occur. For all common infectious diseases, individuals are asymptomatic immediately after acquiring the pathogen. Determining the number of individuals that are infected but not yet symptomatic is vital for accurately calculating the probability of a major outbreak. We show that estimating the quantity of asymptomatic infecteds is as important as traditional parameter estimation (focussed on estimating traits of the disease, such as the infection rate, incubation and infectious periods). The main implication of our findings is that developing affordable diagnostic tests to detect infection in asymptomatic infected individuals is of critical importance in plant epidemiology. Our results are also applicable to human and animal diseases.

This talk is part of the Plant Sciences Research Seminars series.

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