Journal article

A nonparametric estimation of the infection curve

HuaZhen Lin, Paul SF Yip, Richard M Huggins



Predicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals. However, while back-projection techniques allow reliable estimation of the numbers of infected individuals in the more distant past, they are less reliable in the recent past. We propose two new nonparametric methods to estimate the unobserved numbers of infected individuals in the recent past in an epidemic. The proposed methods are noniterative, easily computed and asymptotically normal with simple variance formulas. Simulations show that the proposed methods are much more robust and accurate than the existing back projection method, especially for the recent past, whi..

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University of Melbourne Researchers


Awarded by National Natural Science Foundation of China

Funding Acknowledgements

Lin's research was supported in part by National Natural Science Foundation of China (Grant Nos. 10771148, 11071197). Yip's research was supported by an RGC grant, the Chief Executive Community Project and Hong Kong Jockey Club Charities Trust.