2017
DOI: 10.1002/sim.7488
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Infectious disease prediction with kernel conditional density estimation

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 inc… Show more

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Cited by 58 publications
(82 citation statements)
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“…For the stochastic prediction of infectious disease spread between individuals, mechanistic models (such as agent‐based and compartmental susceptible‐infectious‐recovered, among others) are well developed and have been implemented as stand‐alone forecasting models . Autoregressive integrated moving average (ARIMA) or seasonal ARIMA (SARIMA) models are well‐known statistical approaches for modeling time‐series, such as infectious disease case counts, that correlate with past observations . Both statistical and mechanistic models have been used successfully in infectious disease forecasting .…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…For the stochastic prediction of infectious disease spread between individuals, mechanistic models (such as agent‐based and compartmental susceptible‐infectious‐recovered, among others) are well developed and have been implemented as stand‐alone forecasting models . Autoregressive integrated moving average (ARIMA) or seasonal ARIMA (SARIMA) models are well‐known statistical approaches for modeling time‐series, such as infectious disease case counts, that correlate with past observations . Both statistical and mechanistic models have been used successfully in infectious disease forecasting .…”
Section: Introductionmentioning
confidence: 99%
“…9,10 Autoregressive integrated moving average (ARIMA) or seasonal ARIMA (SARIMA) models are well-known statistical approaches for modeling time-series, such as infectious disease case counts, that correlate with past observations. 11,12 Both statistical and mechanistic models have been used successfully in infectious disease forecasting. 13,14 However, these methods on their own are not designed specifically to detect onset periods or guide real-world policy.…”
Section: Introductionmentioning
confidence: 99%
“…Each week, participating teams submit weekly estimates of incidence for the next four weeks, season onset, and timing and intensity of the peak. Methods used by teams include purely statistical models, [7][8][9] mechanistic models [10,11] machine learning and hybrid approaches [12][13][14]. Expertopinion surveys have also been used and performed well.…”
Section: Introductionmentioning
confidence: 99%
“…For the stochastic prediction of infectious disease spread between individuals, mechanistic models (such as agent-based [7] and compartmental susceptible-infectious-recovered [8], among others) are well developed and have been implemented as stand-alone forecasting models [9,10]. Autoregressive integrated moving average (ARIMA) or seasonal ARIMA (SARIMA) models are well-known statistical approaches for modeling time-series, such as infectious disease case counts, that correlate with past observations [11,12]. Both statistical and mechanistic models have been used successfully in infectious disease forecasting [13,14].…”
Section: Introductionmentioning
confidence: 99%