Objectives:Serological surveys were used to infer the infection attack rate in different populations.The sensitivity of the testing assay,Abbott,drops fast over time since infection which makes the serological data di...Objectives:Serological surveys were used to infer the infection attack rate in different populations.The sensitivity of the testing assay,Abbott,drops fast over time since infection which makes the serological data difficult to interpret.In this work,we aim to solve this issue.Methods:We collect longitudinal serological data of Abbott to construct a sensitive decay function.We use the reported COVID-19 deaths to infer the infections,and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities.Results:Our model simulated seroprevalence matchs the reported seroprevalence in most of the 12 Indian cities.We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities.Conclusions:Using both reported COVID-19 deaths data and serological survey data,we infer the infection attack rate and infection fatality rate with increased confidence.展开更多
Plant epidemics are often associated with weather-related variables.It is difficult to identify weather-related predictors for models predicting plant epidemics.In the article by Shah et al.,to predict Fusarium head b...Plant epidemics are often associated with weather-related variables.It is difficult to identify weather-related predictors for models predicting plant epidemics.In the article by Shah et al.,to predict Fusarium head blight(FHB)epidemics of wheat,they explored a functional approach using scalar-on-function regression to model a binary outcome(FHB epidemic or non-epidemic)with respect to weather time series spanning 140 days relative to anthesis.The scalar-on-function models fit the data better than previously described logistic regression models.In this work,given the same dataset and models,we attempt to reproduce the article by Shah et al.using a different approach,boosted regression trees.After fitting,the classification accuracy and model statistics are surprisingly good.展开更多
基金supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(HKU C7123-20G).
文摘Objectives:Serological surveys were used to infer the infection attack rate in different populations.The sensitivity of the testing assay,Abbott,drops fast over time since infection which makes the serological data difficult to interpret.In this work,we aim to solve this issue.Methods:We collect longitudinal serological data of Abbott to construct a sensitive decay function.We use the reported COVID-19 deaths to infer the infections,and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities.Results:Our model simulated seroprevalence matchs the reported seroprevalence in most of the 12 Indian cities.We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities.Conclusions:Using both reported COVID-19 deaths data and serological survey data,we infer the infection attack rate and infection fatality rate with increased confidence.
基金supported by the National Natural Science Foundation of China(Grant No.12071173 and 12171192)Huaian Key Laboratory for Infectious Diseases Control and Prevention(HAP201704).
文摘Plant epidemics are often associated with weather-related variables.It is difficult to identify weather-related predictors for models predicting plant epidemics.In the article by Shah et al.,to predict Fusarium head blight(FHB)epidemics of wheat,they explored a functional approach using scalar-on-function regression to model a binary outcome(FHB epidemic or non-epidemic)with respect to weather time series spanning 140 days relative to anthesis.The scalar-on-function models fit the data better than previously described logistic regression models.In this work,given the same dataset and models,we attempt to reproduce the article by Shah et al.using a different approach,boosted regression trees.After fitting,the classification accuracy and model statistics are surprisingly good.
