ArticleStatistics in medicine2017
Infectious disease prediction with kernel conditional density estimation.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 64 citations in OpenAlex.
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- A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data.Epidemics · 2025Article
- Integrating information from historical data into mechanistic models for influenza forecasting.PLoS computational biology · 2024Article
- Towards Understanding the Microepidemiology of Lymphatic Filariasis at the Community Level in Ghana.Tropical medicine and infectious disease · 2024Article
- Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.PLoS computational biology · 2023Article
- Learning from the past: A short term forecast method for the COVID-19 incidence curve.PLoS computational biology · 2023Article
- Session 3 of the RSS Special Topic Meeting on Covid-19 Transmission: Replies to the discussion.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Dynamic downscaling and daily nowcasting from influenza surveillance data.Statistics in medicine · 2022Article
- Estimating and forecasting the burden and spread of Colombia's SARS-CoV2 first wave.Scientific reports · 2022Article
- Spatial and Temporal Analysis of COVID-19 in the Elderly Living in Residential Care Homes in Portugal.International journal of environmental research and public health · 2022Article
- An endemic-epidemic beta model for time series of infectious disease proportions.Journal of applied statistics · 2022Article
- Adaptively stacking ensembles for influenza forecasting.Statistics in medicine · 2021Article
- Multiscale influenza forecasting.Nature communications · 2021Article
- Evaluating epidemic forecasts in an interval format.PLoS computational biology · 2021Article
- Incorporating human mobility data improves forecasts of Dengue fever in Thailand.Scientific reports · 2021Article
- Evaluating the ALERT algorithm for local outbreak onset detection in seasonal infectious disease surveillance data.Statistics in medicine · 2020Article
- Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Creating statistical models that generate accurate predictions of infectious disease incidence is a challenging problem whose solution could benefit public health decision makers. We develop a new approach to this problem using kernel conditional density estimation (KCDE) and copulas. We obtain predictive distributions for incidence in individual weeks using KCDE and tie those distributions together into joint distributions using copulas. This strategy enables us to create predictions for the timing of and incidence in the peak week of the season. Our implementation of KCDE incorporates 2 novel kernel components: a periodic component that captures seasonality in disease incidence and a component that allows for a full parameterization of the bandwidth matrix with discrete variables. We demonstrate via simulation that a fully parameterized bandwidth matrix can be beneficial for estimating conditional densities. We apply the method to predicting dengue fever and influenza and compare to a seasonal autoregressive integrated moving average model and HHH4, a previously published extension to the generalized linear model framework developed for infectious disease incidence. The KCDE outperforms the baseline methods for predictions of dengue incidence in individual weeks. The KCDE also offers more consistent performance than the baseline models for predictions of incidence in the peak week and is comparable to the baseline models on the other prediction targets. Using the periodic kernel function led to better predictions of incidence. Our approach and extensions of it could yield improved predictions for public health decision makers, particularly in diseases with heterogeneous seasonal dynamics such as dengue fever.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.