Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data

Shoko, Claris; Sigauke, Caston

doi:10.1016/j.ajic.2023.03.010

Cited by 6 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By introducing the -insensitive loss function, SVM can be converted to a support vector regression machine, where the role of the OSH is to minimize the error of all samples from this plane. SVR has a theoretical basis in statistical learning and relatively high learning performance, making it suitable for performing predictions in small-sample, non-linear, and multi-dimensional fields [ 30 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

Zhang,

2024

BMC Health Serv Res

View full text Add to dashboard Cite

Background Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the “last line of defense” for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies. Objective This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves. Methods A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients. Results Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic. Conclusion The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

show abstract

Section: Methodsmentioning

confidence: 99%

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

Zhang,

2024

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

“…Similar to Section 4, we calculate MAPE, RMSE, MAE and R 2 to evaluate the predictive performance. Since the time series data are positively skewed, MAE and MASE are the best evaluation metrics for evaluating the model performance [35]. The results are shown in Table 5.…”

Section: Application To Real Datasetsmentioning

confidence: 99%

Variational Bayesian Variable Selection for High-Dimensional Hidden Markov Models

Zhai,

Liu,

Jin

et al. 2024

Mathematics

View full text Add to dashboard Cite

The Hidden Markov Model (HMM) is a crucial probabilistic modeling technique for sequence data processing and statistical learning that has been extensively utilized in various engineering applications. Traditionally, the EM algorithm is employed to fit HMMs, but currently, academics and professionals exhibit augmenting enthusiasm in Bayesian inference. In the Bayesian context, Markov Chain Monte Carlo (MCMC) methods are commonly used for inferring HMMs, but they can be computationally demanding for high-dimensional covariate data. As a rapid substitute, variational approximation has become a noteworthy and effective approximate inference approach, particularly in recent years, for representation learning in deep generative models. However, there has been limited exploration of variational inference for HMMs with high-dimensional covariates. In this article, we develop a mean-field Variational Bayesian method with the double-exponential shrinkage prior to fit high-dimensional HMMs whose hidden states are of discrete types. The proposed method offers the advantage of fitting the model and investigating specific factors that impact the response variable changes simultaneously. In addition, since the proposed method is based on the Variational Bayesian framework, the proposed method can avoid huge memory and intensive computational cost typical of traditional Bayesian methods. In the simulation studies, we demonstrate that the proposed method can quickly and accurately estimate the posterior distributions of the parameters with good performance. We analyzed the Beijing Multi-Site Air-Quality data and predicted the PM2.5 values via the fitted HMMs.

show abstract

“…An autoregressive integrated moving average (ARIMA) model was utilized to forecast the trend of the disease in four African countries which reported the most cases: South-Africa, Egypt, Nigeria and Ghana [23] and in Southern Africa [38]. Shoko et al [39] used a support vector regression for short-term forecasting of the disease in Zimbabwe. However, the reported COVID-19 impact (cases, hospitalizations, and deaths) in Africa has likely underestimated the actual extent of infection and thus the transmission dynamics.…”

Section: Introductionmentioning

confidence: 99%

SARS-CoV-2 transmission dynamics in Mozambique and Zimbabwe during the First Three Years of the Pandemic

Kaondera-Shava,

Galanti,

Perini

et al. 2024

Preprint

View full text Add to dashboard Cite

The 2019 emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid spread created a public health emergency of international concern. However, the impact of the pandemic in Sub-Saharan Africa, as documented in cases, hospitalizations and deaths, appears far lower than in the Americas, Europe, and Asia. Characterization of the transmission dynamics is critical for understanding how SARS-CoV-2 spread and the true scale of the pandemic. Here, to better understand SARS-CoV-2 transmission dynamics in two southern African countries, Mozambique and Zimbabwe, we developed a dynamic model-Bayesian inference system to estimate key epidemiologic parameters, namely the transmission and ascertainment rates. Total infection burdens (reported and unreported) during the first three years of the pandemic were reconstructed using a model-inference approach. Transmission rates rose with each successive wave, which aligns with observations in other continents. Ascertainment rates were found to be low matching prior assumption and consistent with other African countries. Overall, the estimated disease burden was higher than the documented cases, indicating need for improved reporting and surveillance. These findings aid understanding of COVID-19 disease and respiratory virus transmission dynamics in two African countries little investigated to date, and can help guide future public health planning and control strategies.

show abstract

Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data

Cited by 6 publications

References 23 publications

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

Variational Bayesian Variable Selection for High-Dimensional Hidden Markov Models

SARS-CoV-2 transmission dynamics in Mozambique and Zimbabwe during the First Three Years of the Pandemic

Contact Info

Product

Resources

About