Application of the ARIMA model on the COVID-2019 epidemic dataset

MBLSTM is a contextual interaction refined method for time series prediction

Time series prediction has been widely used in the medical field to predict patient recurrence or physiological fluctuations. However, the adequacy of the existing methods for contextual information interaction is still insufficient when dealing with a longer memory need in clinical data modelling. In order to enhance the utilization of memory interaction, this paper introduces a new contextual interaction refinement method MB-LSTM by incorporating a Hidden Layer Information Interaction Intensifier. The MB-LSTM method allows for simultaneous interaction of input and hidden layer states at each time step to enhance capability of capturing complex temporal relationships. Besides, more features of time series data are learned utilizing contrastive learning and a data augmentation scheme based on Kernel Density Estimation is designed to identify more accurate features from time series data. The method is evaluated on a real clinical dataset including 1053 records of patient with Gouty arthritis from the Guangdong Provincial Traditional Chinese Medicine Hospital by predicting the subsequent status of patients. The results show the proposed method achieves state-of-the-art performance by 0.5-7.2% using four different evaluation metrics compared with baseline methods.

Distinguishing critical microbial community shifts from normal temporal variability in human and environmental ecosystems

Differentiating significant microbial community changes from normal fluctuations is vital for understanding microbial dynamics in human and environmental ecosystems. This knowledge could enable early warning systems to monitor critical changes affecting human or environmental health. We applied 16S rRNA gene sequencing and time-series analysis to model bacterial abundance trajectories in human gut and wastewater microbiomes. We evaluated various model architectures using datasets from two human studies and five wastewater settings. Long short-term memory (LSTM) models consistently outperformed other models in predicting bacterial abundances and detecting outliers, as measured by multiple metrics. Prediction intervals for each genus allowed us to identify significant changes and signaling shifts in community states. This study proposes a machine learning model capable of monitoring microbial communities and providing insights into their responses to internal and external factors in medical and environmental settings.

Global Burden of Disease Due to High Body Mass Index and Projections to 2040: A Study Based on the Global Burden of Disease Study 2019

BACKGROUND

The prevalence of high body mass index (BMI) contributes to an increased risk of various diseases. This study aimed to identify global disease burden trends associated with high BMI from 1990 to 2019 and forecasts up to 2040.

METHODS

Using data from the global burden of disease (GBD) 2019 study, we analysed the number and ratio of disability-adjusted life years (DALYs) related to high BMI. The data were analysed by sex, ages, socio-demographic index (SDI), world health organization (WHO) region, and disease level. The autoregressive integrated moving average (ARIMA) model was employed to predict high BMI-related disease burden up to 2040.

RESULTS

In 2019, the global burden of disease due to high BMI was 1932.54 (95% uncertainty interval [UI]: 1276.61, 2639.74), representing an increase of 0.18 (95% UI: 0.02, 0.42). Disease burden was consistently higher in males, middle-aged and older populations, particularly noting a narrowing gap between those aged 50-69 years and≥ 70 years in the forecast results until 2040. Additionally, regions with a middle SDI and the North Africa and Middle East WHO super-regions exhibited the highest disease burdens. Also, Cardiovascular disease ranked highest among diseases.

CONCLUSION

The rising disease burden associated with high BMI highlights the need for targeted health policies focussing on older populations, low and middle-income countries, and major conditions like cardiovascular disease and diabetes. Addressing these trends requires an integrated, equity-focused approach to health planning and management to mitigate global impacts.

Spatio-temporal epidemic forecasting using mobility data with LSTM networks and attention mechanism

The outbreak of infectious diseases can have profound impacts on socio-economic balances globally. Accurate short-term forecasting of infectious diseases is crucial for policymakers and healthcare systems. This study proposes a novel deep learning approach for short-term forecasting of infectious disease trends, using COVID-19 confirmed cases and hospitalizations in Japan as a case study. This method provides weekly updates and forecasts outcomes over 1-4 weeks. The proposed model combines long short-term memory (LSTM) networks and multi-head attention mechanism strengths and is trained on public data sourced from open-access platforms. We conduct a comprehensive and rigorous evaluation of the performance of our model. We assess its weekly predictive capabilities over a long period of time by employing multiple error metrics. Furthermore, we carefully explore how the performance of the model varies over time and across geographical locations. The results demonstrate that the proposed model outperforms baseline approaches, particularly in short-term forecasts, achieving lower error rates across multiple metrics. Additionally, the inclusion of mobility data improves the predictive accuracy of the model, especially for longer-term forecasts, by capturing spatio-temporal dynamics more effectively. The proposed model has the potential to assist in decision-making processes, help develop strategies for controlling the spread of infectious diseases, and mitigate the pandemic's impact.

Forecasting of hospitalizations for COVID-19: A hybrid intelligence approach for Disease X research

BackgroundThe COVID-19 pandemic underscores the necessity for proactive measures against emerging diseases, epitomized by WHO's "Disease X." Among the myriad of indicators tracking COVID-19 progression, the count of hospitalized patients assumes a pivotal role. This metric facilitates timely responses from government agencies, enabling proactive allocation and management of medical resources.ObjectiveIn this study, we introduce a novel hybrid intelligent approach, the EMD&LSTM-ARIMA model.Method: This model integrates three techniques: Empirical Mode Decomposition (EMD) to decompose the data into intrinsic mode functions, Long Short-Term Memory (LSTM) neural network for capturing long-term dependencies and nonlinear relationships, and the Auto-Regressive Integrated Moving Average (ARIMA) model for handling linear trends and time series forecasting. We verify its high predictive power and utility through training and forecasting COVID-19 hospitalizations in the UK, Canada, Italy, and Japan.ResultsOur analysis reveals that all forecasted error rates remain below 10%, with Mean Absolute Percentage Error (MAPE) values obtained for these four countries as 2.30%, 3.33%, 1.63%, and 2.89%, respectively.ConclusionOur proposed EMD&LSTM-ARIMA model demonstrates robust forecasting performance, particularly for COVID-19 hospitalization data.

Burden and regional disparities of chronic obstructive pulmonary disease in India: Insights from the global burden of disease data and projections for future incidence

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality globally, particularly in low- and middle-income countries like India. This study aims to analyze regional trends and project future burden of COPD in India using data from the Global Burden of Disease (GBD) 1990-2021.

METHODS

This analysis utilized data from the GBD study to assess age-standardized prevalence (ASPR), incidence (ASIR), disability-adjusted life years (DALYs) (ASDR), and mortality rates (ASMR) for COPD across Indian states. Joinpoint regression was used to analyze temporal trends, while ARIMA models predicted future incidence rates.

RESULTS

In 2021, the highest ASIR was observed in Rajasthan at 306.28, and the highest ASMR was observed in Uttarakhand at 227.19. Projections suggest that the ASIR for COPD in India will decrease from 265.16 in 2022 to 258.19 by 2031. The heatmap analysis identified states like Uttarakhand and Rajasthan as having the highest DALYs attributable to COPD risk factors, including air pollution and tobacco use.

CONCLUSION

COPD remains a public health challenge in India, with regional variability. Targeted interventions addressing air pollution, smoking cessation, and improved healthcare access are essential to mitigate the disease's future burden, particularly in high-risk regions.

Cayley-Purser secured communication and jackknife correlative classification for COVID patient data analysis

Internet of Medical Things (IoMT) is a group of medical devices that connect the healthcare information technology to minimize the redundant hospital visit and healthcare system troubles. IoMT connect the patients to the doctor and transmit the medical data over the network. The spread of corona virus has put the people at high risk. Due to increasing number of cases and its stress on health professionals, IoMT technology is used in many healthcare centers. But, the security level and data classification accuracy was not improved by existing methods during the data communication. In order to solve these issues, Cayley-Purser Cryptographic Secured Communication based Jackknife Correlative Data Classification (CPCSC-JCDC) method is designed. The key objective of CPCSC-JCDC method is to collect the patient information through IoMT devices and send to the doctor in more secured manner. Initially in CPCSC-JCDC method, the patient data is collected. After the data collection process, the data gets encrypted with help of public key of the patient by using cayley-purser cryptosystem. After the encryption process, the data is sent to the doctor. The doctor receives and decrypts the patient data by using their private key. After decryption process, the doctor analyses the patient data and classifies the data as emergency case or normal case by using jackknife correlation function. This helps to minimize the patient readmission rate and increase the patient satisfaction level. Experimental evaluation is carried out by Novel Corona Virus 2019 dataset using different metrics like data classification accuracy, data classification time and security level. The evaluation result shows that CPCSC-JCDC method improves the security level as well as accuracy and minimizes the time consumption during data communication than existing works.

Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics-A Simulation Study

Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends on the quality of the forecasts. We compare different state-of-the-art forecasting methods in terms of forecasting performance. Differently from most existing research in logistics, we do not perform this in a case-dependent way but consider a broad set of simulated time series to give more general recommendations. We therefore simulate various linear and nonlinear time series that reflect different situations. Our simulation results showed that the machine learning methods, especially Random Forests, performed particularly well in complex scenarios, with the differentiated time series training significantly improving the robustness of the model. In addition, the time series approaches proved to be competitive in low noise scenarios.

Spatial impact on inflation of Java Island prediction using Autoregressive Integrated Moving Average (ARIMA) and Generalized Space-Time ARIMA (GSTARIMA)

Inflation is one of macroeconomic issues in Indonesia that needs to be controlled. Inflation could happen because of widespread increases in the cost of goods and services. Annual inflation rate in Indonesia on 2008 to 2023 are quite fluctuating and several periods are not achieved inflation target yet. One of the ways to control inflation is by making predictions for the upcoming period. Java Island is the biggest contributor on economy and Gross Domestic Product (GDP) in Indonesia so it can be considered as general indicator to measure overall inflation rate of Indonesia. Thus, data used in this study is monthly inflation at each province in Java Island from January 2008 to December 2023. This study using two methods, Autoregressive Integrated Moving Average (ARIMA) for univariate time series prediction and Generalized Space-Time ARIMA (GSTARIMA) for multivariate time series prediction with a spatial factor. The results of both models will be compared to determine which model has better accuracy. Based on RMSE value, GSTARIMA model has least average RMSE value, which is 0.113 compared with ARIMA model which has average RMSE value 0.319 thus it can conclude that spatial factors addition could increase accuracy on inflation prediction in Java Island.•This paper purposes to get Java Island's inflation rate prediction to determine better policy on controlling cost of goods and services.•Best model using GSTARIMA methods is GSTARMA(1,1) with distance invese matrix that indicate that coordinate point of each location increase performance of inflation rate prediction.•The result indicate GSTARIMA has better accuracy than ARIMA for inflation prediction in Java Island based on RMSE value.

The performance of interrupted time series designs with a limited number of time points: Learning losses due to school closures during the COVID-19 pandemic

Interrupted time series (ITS) designs are increasingly used for estimating the effect of shocks in natural experiments. Currently, ITS designs are often used in scenarios with many time points and simple data structures. This research investigates the performance of ITS designs when the number of time points is limited and with complex data structures. Using a Monte Carlo simulation study, we empirically derive the performance-in terms of power, bias and precision- of the ITS design. Scenarios are considered with multiple interventions, a low number of time points and different effect sizes based on a motivating example of the learning loss due to COVID school closures. The results of the simulation study show the power of the step change depends mostly on the sample size, while the power of the slope change depends on the number of time points. In the basic scenario, with both a step and a slope change and an effect size of 30% of the pre-intervention slope, the required sample size for detecting a step change is 1,100 with a minimum of twelve time points. For detecting a slope change the required sample size decreases to 500 with eight time points. To decide if there is enough power researchers should inspect their data, hypothesize about effect sizes and consider an appropriate model before applying an ITS design to their research. This paper contributes to the field of methodology in two ways. Firstly, the motivation example showcases the difficulty of employing ITS designs in cases which do not adhere to a single intervention. Secondly, models are proposed for more difficult ITS designs and their performance is tested.

Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network

Enhancing crop water productivity is crucial for regional water resource management and agricultural sustainability, particularly in arid regions. However, evaluating the spatial heterogeneity and temporal dynamics of crop water productivity in face of data limitations poses a challenge. In this study, we propose a framework that integrates remote sensing data, time series generative adversarial network (TimeGAN), dynamic Bayesian network (DBN), and optimization model to assess crop water productivity and optimize crop planting structure under limited water resources allocation in the Qira oasis. The results demonstrate that the combination of TimeGAN and DBN better improves the accuracy of the model for the dynamic prediction, particularly for short-term predictions with 4 years as the optimal timescale (R2 > 0.8). Based on the spatial distribution of crop suitability analysis, wheat and corn are most suitable for cultivation in the central and eastern parts of Qira oasis while cotton is unsuitable for planting in the western region. The walnuts and Chinese dates are mainly unsuitable in the southeastern part of the oasis. Maximizing crop water productivity while ensuring food security has led to increased acreage for cotton, Chinese dates and walnuts. Under the combined action of the five optimization objectives, the average increase of crop water productivity is 14.97%, and the average increase of ecological benefit is 3.61%, which is much higher than the growth rate of irrigation water consumption of cultivated land. It will produce a planting structure that relatively reduced irrigation water requirement of cultivated land and improved crop water productivity. This proposed framework can serve as an effective reference tool for decision-makers when determining future cropping plans.

Trends in Nationally Notifiable Infectious Diseases in Humans and Animals during COVID-19 Pandemic, South Korea

We investigated trends in notifiable infectious diseases in both humans and animals during the COVID-19 pandemic in South Korea and compared those data against expected trends had nonpharmaceutical interventions (NPIs) not been implemented. We found that human respiratory infectious diseases other than COVID-19 decreased by an average of 54.7% after NPIs were introduced. On the basis of that trend, we estimated that annual medical expenses associated with respiratory infections other than COVID-19 also decreased by 3.8% in 2020 and 18.9% in 2021. However, human gastrointestinal infectious diseases and livestock diseases exhibited similar or even higher incidence rates after NPIs were instituted. Our investigation revealed that the preventive effect of NPIs varied among diseases and that NPIs might have had limited effectiveness in reducing the spread of certain types of infectious diseases. These findings suggest the need for future, novel public health interventions to compensate for such limitations.

An Artificial Neural Network Predicts Gender Differences of Motor and Non-Motor Symptoms of Patients with Advanced Parkinson's Disease under Levodopa-Carbidopa Intestinal Gel

Background and Objectives: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson's disease (PD) under levodopa-carbidopa intestinal gel (LCIG) treatment. The aim of this study was to develop a novel deep neural network model to predict the clinical outcomes of patients with advanced PD after two years of LCIG therapy. Materials and Methods: This was a longitudinal, 24-month observational study of 59 patients with advanced PD in a multicenter registry under LCIG treatment from September 2019 to September 2021, including 43 movement disorder centers. The data set includes 649 measurements of patients, which make an irregular time series, and they are turned into regular time series during the preprocessing phase. Motor status was assessed with the Unified Parkinson's Disease Rating Scale (UPDRS) Parts III (off) and IV. The NMS was assessed by the NMS Questionnaire (NMSQ) and the Geriatric Depression Scale (GDS), the quality of life by PDQ-39, and severity by Hoehn and Yahr (HY). Multivariate linear regression, ARIMA, SARIMA, and Long Short-Term Memory-Recurrent NeuralNetwork (LSTM-RNN) models were used. Results: LCIG significantly improved dyskinesia duration and quality of life, with men experiencing a 19% and women a 10% greater improvement, respectively. Multivariate linear regression models showed that UPDRS-III decreased by 1.5 and 4.39 units per one-unit increase in the PDQ-39 and UPDRS-IV indexes, respectively. Although the ARIMA-(2,0,2) model is the best one with AIC criterion 101.8 and validation criteria MAE = 0.25, RMSE = 0.59, and RS = 0.49, it failed to predict PD patients' features over a long period of time. Among all the time series models, the LSTM-RNN model predicts these clinical characteristics with the highest accuracy (MAE = 0.057, RMSE = 0.079, RS = 0.0053, mean square error = 0.0069). Conclusions: The LSTM-RNN model predicts, with the highest accuracy, gender-dependent clinical outcomes in patients with advanced PD after two years of LCIG therapy.

An autoregressive integrated moving average and long short-term memory (ARIM-LSTM) hybrid model for multi-source epidemic data prediction

The COVID-19 pandemic has far-reaching impacts on the global economy and public health. To prevent the recurrence of pandemic outbreaks, the development of short-term prediction models is of paramount importance. We propose an ARIMA-LSTM (autoregressive integrated moving average and long short-term memory) model for predicting future cases and utilize multi-source data to enhance prediction performance. Firstly, we employ the ARIMA-LSTM model to forecast the developmental trends of multi-source data separately. Subsequently, we introduce a Bayes-Attention mechanism to integrate the prediction outcomes from auxiliary data sources into the case data. Finally, experiments are conducted based on real datasets. The results demonstrate a close correlation between predicted and actual case numbers, with superior prediction performance of this model compared to baseline and other state-of-the-art methods.

Impact of the COVID-19 outbreak and interventions on hand, foot and mouth disease in Zhengzhou, China, 2014-2022: a retrospective study

BACKGROUND

Since December 2019, COVID-19 has spread rapidly around the world, and studies have shown that measures to prevent COVID-19 can largely reduce the spread of other infectious diseases. This study explored the impact of the COVID-19 outbreak and interventions on the incidence of HFMD.

METHODS

We gathered data on the prevalence of HFMD from the Children's Hospital Affiliated to Zhengzhou University. An autoregressive integrated moving average model was constructed using HFMD incidence data from 2014 to 2019, the number of cases predicted from 2020 to 2022 was predicted, and the predicted values were compared with the actual measurements.

RESULTS

From January 2014 to October 2022, the Children's Hospital of Zhengzhou University admitted 103,995 children with HFMD. The average number of cases of HFMD from 2020 to 2022 was 4,946, a significant decrease from 14,859 cases from 2014 to 2019. We confirmed the best ARIMA (2,0,0) (1,1,0)12 model. From 2020 to 2022, the yearly number of cases decreased by 46.58%, 75.54%, and 66.16%, respectively, compared with the forecasted incidence. Trends in incidence across sexes and ages displayed patterns similar to those overall.

CONCLUSIONS

The COVID-19 outbreak and interventions reduced the incidence of HFMD compared to that before the outbreak. Strengthening public health interventions remains a priority in the prevention of HFMD.

MPSTAN: Metapopulation-Based Spatio-Temporal Attention Network for Epidemic Forecasting

Accurate epidemic forecasting plays a vital role for governments to develop effective prevention measures for suppressing epidemics. Most of the present spatio-temporal models cannot provide a general framework for stable and accurate forecasting of epidemics with diverse evolutionary trends. Incorporating epidemiological domain knowledge ranging from single-patch to multi-patch into neural networks is expected to improve forecasting accuracy. However, relying solely on single-patch knowledge neglects inter-patch interactions, while constructing multi-patch knowledge is challenging without population mobility data. To address the aforementioned problems, we propose a novel hybrid model called metapopulation-based spatio-temporal attention network (MPSTAN). This model aims to improve the accuracy of epidemic forecasting by incorporating multi-patch epidemiological knowledge into a spatio-temporal model and adaptively defining inter-patch interactions. Moreover, we incorporate inter-patch epidemiological knowledge into both model construction and the loss function to help the model learn epidemic transmission dynamics. Extensive experiments conducted on two representative datasets with different epidemiological evolution trends demonstrate that our proposed model outperforms the baselines and provides more accurate and stable short- and long-term forecasting. We confirm the effectiveness of domain knowledge in the learning model and investigate the impact of different ways of integrating domain knowledge on forecasting. We observe that using domain knowledge in both model construction and the loss function leads to more efficient forecasting, and selecting appropriate domain knowledge can improve accuracy further.

Predicting COVID-19 cases in Belo Horizonte-Brazil taking into account mobility and vaccination issues

The pandemic caused millions of deaths around the world and forced governments to take drastic measures to reduce the spread of Coronavirus. Understanding the impact of social distancing measures on urban mobility and the number of COVID-19 cases allows governments to change public policies according to the evolution of the pandemic and plan ahead. Given the increasing rates of vaccination worldwide, immunization data may also represent an important predictor of COVID-19 cases. This study investigates the impact of urban mobility and vaccination upon COVID-19 cases in Belo Horizonte, Brazil using Prophet and ARIMA models to predict future outcomes. The developed models generated projections fairly close to real numbers, and some inferences were drawn through experimentation. Brazil became the epicenter of the COVID-19 epidemic shortly after the first case was officially registered on February 25th, 2020. In response, several municipalities adopted lockdown (total or partial) measures to minimize the risk of new infections. Here, we propose prediction models which take into account mobility and vaccination data to predict new COVID-19 cases.

Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings

BACKGROUND

Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection.

METHODS

Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results.

RESULTS

The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results.

CONCLUSION

These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.

COVID-19 and Adolescent Outpatient Mental Health Service Utilization

OBJECTIVE

The COVID-19 pandemic created challenges in accessing mental health (MH) services when adolescent well-being declined. Still, little is known about how the COVID-19 pandemic affected outpatient MH service utilization for adolescents.

METHODS

Retrospective data were collected from electronic medical records of adolescents aged 12-17 years at Kaiser Permanente Mid-Atlantic States, an integrated health care system from January 2019 to December 2021. MH diagnoses included anxiety, mood disorder/depression, anxiety and mood disorder/depression, attention-deficit/hyperactivity disorder, or psychosis. We used interrupted time series analysis to compare MH visits and psychopharmaceutical prescribing before and after the COVID-19 onset. Analyses were stratified by demographics and visit modality.

RESULTS

The study population of 8121 adolescents with MH visits resulted in a total of 61,971 (28.1%) of the 220,271 outpatient visits associated with an MH diagnosis. During 15,771 (7.2%) adolescent outpatient visits psychotropic medications were prescribed. The increasing rate of MH visits prior to COVID-19 was unaffected by COVID-19 onset; however, in-person visits declined by 230.5 visits per week (P < .001) from 274.5 visits per week coupled with a rise in virtual modalities. Rates of MH visits during the COVID-19 pandemic differed by sex, mental health diagnosis, and racial and ethnic identity. Psychopharmaceutical prescribing during MH visits declined beyond expected values by a mean of 32.8 visits per week (P < .001) at the start of the COVID-19 pandemic.

CONCLUSIONS

A sustained switch to virtual visits highlights a new paradigm in care modalities for adolescents. Psychopharmaceutical prescribing declined requiring further qualitative assessments to improve the quality of access for adolescent MH.

Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models

BACKGROUND

Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast.

METHODS

We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022-2023 seasonal wave. Performance is measured against autoregressive integrated moving average (ARIMA) and Prophet time series models.

RESULTS

Across the epidemic phases the hierarchical GAM shows improved performance, at all geographic scales relative to the ARIMA and Prophet models. Temporally, the hierarchical GAM has overall an improved performance at 7 and 14 day time horizons. The performance of the GAM is most sensitive to the flexibility of the smoothing function that measures the national epidemic trend.

CONCLUSIONS

This study introduces an approach to short-term forecasting of hospital admissions for the influenza virus using hierarchical, spatial, and temporal components. The methodology was designed for the real time forecasting of epidemics. This modelling framework was used across the 2022-2023 winter for healthcare operational planning by the UK Health Security Agency and the National Health Service in England.

Trends and prediction in the incidence rate of hepatitis C in Shandong Province in China from 2004 to 2030

BACKGROUND

Hepatitis C threatens human health and brings a heavy economic burden. Shandong Province is the second most populous province in China and has uneven regional economic development. Therefore, we analyzed the incidence rate trend and regional differences of hepatitis C in Shandong Province from 2004 to 2021.

METHODS

The monthly and annual incidence rates of hepatitis C in Shandong Province from 2022 to 2030 were predicted by fitting Autoregressive Integrated Moving Average model (ARIMA), Long Short-Term Memory (LSTM) and ARIMA-LSTM combined model.

RESULTS

From 2004 to 2021, annual new cases of hepatitis C in Shandong Province increased from 635 to 5834, with a total of 61,707 cases. The incidence rate increased from 0.69/100 thousand in 2004 to 6.40/100 thousand in 2019, with a slight decrease in 2020 and 2021. The average annual incidence rate was 3.47/100 thousand. In terms of regional distribution, the hepatitis C incidence rate in Shandong Province was generally high in the west and low in the east. It is estimated that the hepatitis C incidence rate in Shandong Province will be 9.21 per 100 thousand in 2030.

CONCLUSION

The hepatitis C incidence rate in Shandong Province showed an increasing trend from 2004 to 2019 and a decreasing trend in 2020 and 2021. Significant regional variations in incidence rate existed. An upward trend in incidence rate is predicted from 2022 to 2030. It is necessary to strengthen the prevention and control of hepatitis C to achieve the goal of eliminating viral hepatitis by 2030.

Reproduction number projection for the COVID-19 pandemic

The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-STR) prototype is a deterministic compartment model for epidemics and an alternative to the Susceptible-Infected-Removed (SIR) model. The HI-STR predicts that pathogen transmission depends on host population characteristics including population size, population density and social behaviour common within that population.

The HI-STR prototype is applied to the ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) to show that the original estimates of the Coronavirus Disease 2019 (COVID-19) basic reproduction number $\mathcal{R}_{0}$ R 0 for the United Kingdom (UK) could have been projected onto the individual states of the United States of America (USA) prior to being detected in the USA.

The Imperial College London (ICL) group’s estimate of $\mathcal{R}_{0}$ R 0 for the UK is projected onto each USA state. The difference between these projections and the ICL’s estimates for USA states is either not statistically significant on the paired Student t-test or not epidemiologically significant.

The SARS-CoV2 Delta variant’s $\mathcal{R}_{0}$ R 0 is also projected from the UK to the USA to prove that projection can be applied to a Variant of Concern (VOC). Projection provides both a localised baseline for evaluating the implementation of an intervention policy and a mechanism for anticipating the impact of a VOC before local manifestation.

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting

COVID-19 has resulted in significant morbidity and mortality globally. We develop a model that uses data from thirty days before a fixed time point to forecast the daily number of new COVID-19 cases fourteen days later in the early stages of the pandemic. Various time-dependent factors including the number of daily confirmed cases, reproduction number, policy measures, mobility and flight numbers were collected. A deep-learning model using Bidirectional Long-Short Term Memory (Bi-LSTM) architecture was trained on data from 22nd Jan 2020 to 8 Jan 2021 to forecast the new daily number of COVID-19 cases 14 days in advance across 190 countries, from 9 to 31 Jan 2021. A second model with fewer variables but similar architecture was developed. Results were summarised by mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and total absolute percentage error and compared against results from a classical ARIMA model. Median MAE was 157 daily cases (IQR: 26-666) under the first model, and 150 (IQR: 26-716) under the second. Countries with more accurate forecasts had more daily cases and experienced more waves of COVID-19 infections. Among countries with over 10,000 cases over the prediction period, median total absolute percentage error was 33% (IQR: 18-59%) and 34% (IQR: 16-66%) for the first and second models respectively. Both models had comparable median total absolute percentage errors but lower maximum total absolute percentage errors as compared to the classical ARIMA model. A deep-learning approach using Bi-LSTM architecture and open-source data was validated on 190 countries to forecast the daily number of cases in the early stages of the COVID-19 outbreak. Fewer variables could potentially be used without impacting prediction accuracy.

Burden of ischemic stroke in mainland China and Taiwan province from 1990 to 2019: with forecast for the next 11 years

Ischemic stroke is featured with high incidence, mortality, and disability. The aim of this study is to use Global Burden of Disease database to describe and compare the burden of ischemic stroke in mainland China and Taiwan province and to further predict the expected changes in the next 11 years using statistical modeling methods. Information on ischemic stroke incidence and mortality in China (mainland and Taiwan province) during 1990-2019 was obtained from the Global Burden of Disease database to analyze the effects of region, gender, and age on the incidence and mortality of ischemic stroke in China. The autoregressive integrated moving average model was used to predict the age-standardized incidence rate and age-standardized mortality rate of ischemic stroke in males and females in mainland China and Taiwan province in the next 11 years. The period from 1990 to 2019 witnessed an overall upward trend in the number of incidence and deaths in mainland China and Taiwan province. In 2019, there were nearly 2.87 million ischemic incidence cases with stroke in mainland China, with more female patients than male in the age group of over 60 years. Among the nearly 1.03 million deaths, the death toll of men under the age of 85 years was higher than that of women, while in Taiwan province, the number of incidence was 28 771, with more female patients of all ages than male. Among the 6788 deaths, the death toll of men under the age of 80 years was higher than that of women. In 2019, the age group with the highest number of patients in the two regions was 65-69 years, while the highest number of deaths was found in people aged 85 years and above. As our autoregressive integrated moving average model predicted, the age-standardized incidence rate value of ischemic stroke is expected to be 163.23/100 000 persons in mainland China by 2030, which would continue to increase, while the age-standardized mortality rate value of ischemic stroke is expected to be 16.41/100 000 persons in Taiwan province by 2030, which showed a decreasing trend. Disease burden of ischemic stroke is still increasing in mainland China and Taiwan province, and health resources should be deployed to implement effective prevention and control strategies, taking into account region, gender, and age.

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.

Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models

The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.

Automatic Forecast of Intensive Care Unit Admissions: The Experience During the COVID-19 Pandemic in Italy

The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system.Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background.This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont.The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas.In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions.

Epilepsy-related mortality during the COVID-19 pandemic: A nationwide study of routine Scottish data

OBJECTIVE

To examine whether epilepsy-related deaths increased during the COVID-19 pandemic and if the proportion with COVID-19 listed as the underlying cause is different between people experiencing epilepsy-related deaths and those experiencing deaths unrelated to epilepsy.

METHODS

This was a Scotland-wide, population-based, cross-sectional study of routinely-collected mortality data pertaining to March-August of 2020 (COVID-19 pandemic peak) compared to the corresponding periods in 2015-2019. ICD-10-coded causes of death of deceased people of any age were obtained from a national mortality registry of death certificates in order to identify those experiencing epilepsy-related deaths (coded G40-41), deaths with COVID-19 listed as a cause (coded U07.1-07.2), and deaths unrelated to epilepsy (death without G40-41 coded). The number of epilepsy-related deaths in 2020 were compared to the mean observed through 2015-2019 on an autoregressive integrated moving average (ARIMA) model (overall, men, women). Proportionate mortality and odds ratios (OR) for deaths with COVID-19 listed as the underlying cause were determined for the epilepsy-related deaths compared to deaths unrelated to epilepsy, reporting 95% confidence intervals (CIs).

RESULTS

A mean number of 164 epilepsy-related deaths occurred through March-August of 2015-2019 (of which a mean of 71 were in women and 93 in men). There were subsequently 189 epilepsy-related deaths during the pandemic March-August 2020 (89 women, 100 men). This was 25 more epilepsy-related deaths (18 women, 7 men) compared to the mean through 2015-2019. The increase in women was beyond the mean year-to-year variation seen in 2015-2019. Proportionate mortality with COVID-19 listed as the underlying cause was similar between people experiencing epilepsy-related deaths (21/189, 11.1%, CI 7.0-16.5%) and deaths unrelated to epilepsy (3,879/27,428, 14.1%, CI 13.7-14.6%), OR 0.76 (CI 0.48-1.20). Ten of 18 excess epilepsy-related deaths in women had COVID-19 listed as an additional cause.

CONCLUSIONS

There is little evidence to suggest there have been any major increases in epilepsy-related deaths in Scotland during the COVID-19 pandemic. COVID-19 is a common underlying cause of both epilepsy-related and unrelated deaths.

Analysis and prediction of the incidence and prevalence trends of gonorrhea in China

To understand the epidemiological trend of gonorrhea in China from 2004 to 2021, predict the prevalence of the disease, and provide basic theory and data support for monitoring and managing gonorrhea. Gonorrhea incidence data in China from 2004 to 2021 were collected through the China Public Health Science Data Center and National Administration of Disease Prevention and Control, and the incidence and epidemiological characteristics were analyzed. Statistical analysis was performed using Joinpoint and autoregressive integrated moving average (ARIMA) models. A linear correlation model was used to analyze the correlation between gross domestic product (GDP) and the incidence rate. From 2004 to 2021, a total of 2,289,435 cases of gonorrhea were reported in China, with an average reported incidence rate of 9.46/100,000 people and a downward followed by an upward trend. Individuals with gonorrhea were primarily 20-30 y of age, with 1,034,847 cases (53.38%) from 2004 to 2018. The trend of increasing incidence was most obvious in the 10-20 age group (5,811 cases in 2004 to 12,752 cases in 2018, AAPC = 6.1, P < .001). The incidence of gonorrhea in China was negatively correlated with GDP from 2004 to 2021 (r = -0.547, P = .019). The correlation coefficient between the average incidence growth rate of each region from 2012 to 2018 and the average growth rate of regional GDP was 0.673 (P < .01). The root mean square error (RMSE) of the ARIMA model was 4.89%, showing powerful performance. There would be 97,910 gonorrhea cases in 2023 as predicted by the model.

Variability in the psychological impact of four waves of COVID-19: a time-series study of 60 000 text-based counseling sessions

BACKGROUND

Continuous exposure to stressors can lead to vulnerability and, in some cases, resilience. This study examined the variation in its psychological impact across the first four waves of COVID-19 in Hong Kong.

METHODS

Transcripts from Open Up, an online text-based counseling service, between January 2019 and January 2021 were analyzed (N = 60 775). We identified COVID-19 mentioned sessions using keywords and further categorized them into those that also mentioned symptoms of common mental disorders (CMDs) and those that did not. Autoregressive integrated moving average models were used to analyze the associations between the severity of the outbreak and the mention of COVID-19 and CMDs.

RESULTS

Results revealed that the pandemic led to increased psychological distress. Compared to prior to its advent, more people sought help in the initial months of the outbreak. Furthermore, associations were found between the severity of the outbreak and the number of help-seeker mentioning the pandemic, as well as between the outbreak severity and the number of help-seekers disclosing psychological distress. However, these relationships were not uniform across the four waves of outbreaks; a dissociation between outbreak severity and help-seekers' concern was found in the fourth wave.

CONCLUSION

As the pandemic waxes and wanes, people may become habituated to its psychological toll. This may be interpreted as a form of resilience. Instead of worsening with time, the psychological impact of COVID-19 may reduce with repeated exposure.

The convergence epidemic volatility index (cEVI) as an alternative early warning tool for identifying waves in an epidemic

This manuscript introduces the convergence Epidemic Volatility Index (cEVI), a modification of the recently introduced Epidemic Volatility Index (EVI), as an early warning tool for emerging epidemic waves. cEVI has a similar architectural structure as EVI, but with an optimization process inspired by a Geweke diagnostic-type test. Our approach triggers an early warning based on a comparison of the most recently available window of data samples and a window based on the previous time frame. Application of cEVI to data from the COVID-19 pandemic data revealed steady performance in predicting early, intermediate epidemic waves and retaining a warning during an epidemic wave. Furthermore, we present two basic combinations of EVI and cEVI: (1) their disjunction cEVI + that respectively identifies waves earlier than the original index, (2) their conjunction cEVI- that results in higher accuracy. Combination of multiple warning systems could potentially create a surveillance umbrella that would result in early implementation of optimal outbreak interventions.

Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach

Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data.

Implications of war on the food, beverage, and tobacco industry in South Korea

The Food, Beverage & Tobacco (F&B) industry is an essential sector in the competitive economy. Procurement of production factors mainly depends on sales forecasting and the supply chain of raw materials. However, the conflict between Russia and Ukraine has jeopardized the global supply chain. As the conflict worsened, the world faced a food crisis, which was already a significant challenge due to the Covid-19 pandemic. Understanding how conflict-related disruptions in global food markets might affect the stock return of the F&B industry of South Korea, this study forecasts the stock returns on the KOSDAQ F&B sector. This paper highlights that the conflict resulted in immediate and far-reaching consequences on the global food supply chain and future crop harvesting in South Korea. As numerous algorithms have been widely used in predicting stock market returns, we use Autoregressive Integrated Moving Average (ARIMA) model for the prediction. Using daily returns from the KOSDAQ F&B industry from January 1999 to October 2022, the study proposes an ARIMA (2,2,3) model to forecast future movements of the stock returns. With an RMSE of 0.012, the prediction performance holds good using the ARIMA model. The results show a negative trend observed in the F&B sector returns for a few months, implying that sector stock returns decline as the conflict between Russia and Ukraine becomes more pronounced. This study also suggests that South Korea has massive scope to stabilize the demand for healthy, safe food, give more attention to domestic agribusiness, and make itself a self-sufficient agri-economy.

Excess mortality in Ukraine during the course of COVID-19 pandemic in 2020-2021

In this work, the COVID-19 pandemic burden in Ukraine is investigated retrospectively using the excess mortality measures during 2020-2021. In particular, the epidemic impact on the Ukrainian population is studied via the standardized both all-cause and cause-specific mortality scores before and during the epidemic. The excess mortality counts during the pandemic were predicted based on historic data using parametric and nonparametric modeling and then compared with the actual reported counts to quantify the excess. The corresponding standardized mortality P-score metrics were also compared with the neighboring countries. In summary, there were three "waves" of excess all-cause mortality in Ukraine in December 2020, April 2021 and November 2021 with excess of 32%, 43% and 83% above the expected mortality. Each new "wave" of the all-cause mortality was higher than the previous one and the mortality "peaks" corresponded in time to three "waves" of lab-confirmed COVID-19 mortality. The lab-confirmed COVID-19 mortality constituted 9% to 24% of the all-cause mortality during those three peak months. Overall, the mortality trends in Ukraine over time were similar to neighboring countries where vaccination coverage was similar to that in Ukraine. For cause-specific mortality, the excess observed was due to pneumonia as well as circulatory system disease categories that peaked at the same times as the all-cause and lab-confirmed COVID-19 mortality, which was expected. The pneumonias as well as circulatory system disease categories constituted the majority of all cases during those peak times. The seasonality in mortality due to the infectious and parasitic disease category became less pronounced during the pandemic. While the reported numbers were always relatively low, alcohol-related mortality also declined during the pandemic.

Interrelationships between urban travel demand and electricity consumption: a deep learning approach

The analysis of infrastructure use data in relation to other components of the infrastructure can help better understand the interrelationships between infrastructures to eventually enhance their sustainability and resilience. In this study, we focus on electricity consumption and travel demand. In short, the premise is that when people are in buildings consuming electricity, they are not generating traffic on roads, and vice versa, hence the presence of interrelationships. We use Long Short Term Memory (LSTM) networks to model electricity consumption patterns of zip codes based on the traffic volume of the same zip code and nearby zip codes. For this, we merge two datasets for November 2017 in Chicago: (1) aggregated electricity use data in 30-min intervals within the city of Chicago and (2) traffic volume data captured on the Chicago expressway network. Four analyses are conducted to identify interrelationships: (a) correlation between two time series, (b) temporal relationships, (c) spatial relationships, and (d) prediction of electricity consumption based on the total traffic volume. Overall, from over 250 models, we identify and discuss complex interrelationships between travel demand and electricity consumption. We also analyze and discuss how and why model performance varies across Chicago.

Machine learning-based analytics of the impact of the Covid-19 pandemic on alcohol consumption habit changes among United States healthcare workers

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States.

A Review of the Mental Health Sequelae of the SARS-CoV-2 (COVID-19): Preparedness Perspective

Despite the three significant epidemics that have rattled the world in the last two decades, many questions remain unanswered! The concept of unwanted psychological distress remains looming after any epidemic or pandemic. The public health burden of the COVID-19 pandemic still resonates with different aspects of life with predicted mental health sequelae. This review will focus on the role of natural disasters and past infectious epidemic-related mental health complications. Additionally, the study provides recommendations and policy suggestions for mitigating COVID-19-related mental health prevalence.

COVID-19 forecasting using shifted Gaussian Mixture Model with similarity-based estimation

The COVID-19 pandemic has caused a pronounced disturbance in the social environments and economies of many countries worldwide. Credible forecasting methods to predict the pandemic's progress can allow countries to control the disease's spread and decrease the number of severe cases. This study presents a novel approach, called the Shifted Gaussian Mixture Model with Similarity-based Estimation (SGSE), that forecasts the future of a specific country's daily new case values by examining similar behavior in other countries. The model uses daily new case values collected since the pandemic began and finds countries with similar trends using a specific time offset. The daily new case values data between the first day and ( t o d a y - N ) th day are transformed by employing the Gaussian Mixture Model (GMM) and, subsequently, a new vector of features is obtained for each country. Using these feature vectors, countries that show similar statistics in the past are found for any forecasted country. The future of the corresponding country is forecasted by taking the mean of the time-series plots after the offset points of similar countries are calculated. A brand new metric called a trend similarity score, which calculates the similarity between forecasted and actual values is also presented in this study. While the SGSE trend similarity score median varies between 0.903-0.947, based on the selection of the distance metric, the ARIMA model yields only 0.642. The performance of the SGSE was compared in seven European countries using four different public projects submitted to The European COVID-19 Forecast Hub. The SGSE gives the most accurate forecasts compared to all other models. The test sets' results show that trends and plateaus are predicted accurately for many countries.

Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020–2022

Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average–least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

A proposed novel adaptive DC technique for non-stationary data removal

The stationarity of a time series is an important assumption in the Box-Jenkins methodology. Removing the non-stationary feature from the time series can be done using a differencing technique or a logarithmic transformation approach, but it is not guaranteed from the first step. This paper proposes a new adaptive DC technique, a novel technique for removing a non-stationary time series from the first step. The technique involves transferring non-stationary data into another domain that deals with it as a stationary time series, as it is much easier to be forecasted in that domain. The adaptive DC technique has been applied to different time series, including gasoline and diesel fuel prices, temperature, demand side, inflation rate and number of internet users time series. The performance of the proposed technique is evaluated using different statistical tests, including Augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Phillips Perron (PP). Additionally, the technique is validated by comparing it with a differencing technique, and the results show that the proposed technique slightly outperforms the differencing method. The importance of the proposed technique is its capability to get the stationarity data from the first step, whereas the differencing technique sometimes needs more than one step.

Estimation of the Effectiveness of a Tighter, Reinforced Quarantine for the Coronavirus Disease 2019 (COVID-19) Outbreak: Analysis of the Third Wave in South Korea

It has been claimed that a tighter, reinforced quarantine strategy was advocated to reduce the transmission of coronavirus disease 2019 (COVID-19) during major outbreaks; however, there have been no prior quantitative studies examining the effectiveness and duration of such a reinforced quarantine. Consequently, the purpose of this research was to determine the impact of a “tighter, reinforced” quarantine during the third COVID-19 breakout wave in South Korea, which occurred between late 2020 and early 2021. The efficacy of the quarantine was determined by comparing the number of newly diagnosed COVID-19 patients between the “prediction model” and “actual observed data.” Two prediction models were developed using the autoregressive integrated moving average (ARIMA; 1, 0, 0) model. The effect of a “tighter, reinforced” quarantine, which would show as an immediate drop in the number of new cases, predicted its efficacy by lowering the number of new cases by 20,400. In addition, the efficacy of the quarantine lasted up to more than three months. The findings of our investigation confirmed the beneficial influence of “tighter, controlled” quarantine laws during a widespread COVID-19 epidemic. During an epidemic, when the population has not yet developed immunity to respiratory viral diseases, our study may be evidence for implementing stricter quarantine restrictions in order to reduce the number of new cases.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and clustering techniques

Purpose

The COVID-19 pandemic has affected more than 192 countries. The condition results in a respiratory illness (e.g., influenza) with signs and symptoms such as cold, cough, fever, and breathing difficulties. Predicting new instances of COVID-19 is always a challenging task.

Methods

This study improved the autoregressive integrated moving average (ARIMA)-based time series prediction model by incorporating statistical significance for feature selection and k-means clustering for outlier detection. The accuracy of the improved model (ARIMAI) was examined using World Health Organization's official data on the COVID-19 pandemic worldwide and compared with that of many modern, cutting-edge algorithms.

Results

The ARIMAI model (RSS score = 0.279, accuracy = 97.75%) outperformed the current ARIMA model (RSS score = 0.659, accuracy = 93%).

Conclusions

The ARIMAI model is not only an efficient but also a rapid and simple technique to forecast COVID-19 trends. The usage of this model enables the prediction of any disease that will affect patients in the future pandemics.

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters

The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.

Autoregressive models in environmental forecasting time series: a theoretical and application review

Though globalization, industrialization, and urbanization have escalated the economic growth of nations, these activities have played foul on the environment. Better understanding of ill effects of these activities on environment and human health and taking appropriate control measures in advance are the need of the hour. Time series analysis can be a great tool in this direction. ARIMA model is the most popular accepted time series model. It has numerous applications in various domains due its high mathematical precision, flexible nature, and greater reliable results. ARIMA and environment are highly correlated. Though there are many research papers on application of ARIMA in various fields including environment, there is no substantial work that reviews the building stages of ARIMA. In this regard, the present work attempts to present three different stages through which ARIMA was evolved. More than 100 papers are reviewed in this study to discuss the application part based on pure ARIMA and its hybrid modeling with special focus in the field of environment/health/air quality. Forecasting in this field can be a great contributor to governments and public at large in taking all the required precautionary steps in advance. After such a massive review of ARIMA and hybrid modeling involving ARIMA in the fields including or excluding environment/health/atmosphere, it can be concluded that the combined models are more robust and have higher ability to capture all the patterns of the series uniformly. Thus, combining several models or using hybrid model has emerged as a routinized custom.

Improved neural network for predicting blood donations based on two emergent factors

BACKGROUND

Blood donation forecasting is a critical part of blood supply chain management. However, few studies have focused on modeling blood donation with different emergency factors. The purpose of this study was to investigate the effects of different emergency events on blood donation and to build a suitable blood volume prediction model.

MATERIALS AND METHODS

The amount of blood donation from 2015 to December 2021 at Beijing Tongzhou District Central Blood Station was selected as the time series data. First, statistical methods were employed to analyze the effect of weather and epidemic factors on blood donation. Second, a hybrid model of SARIMAX and a neural network was built to predict the blood donation in the next two weeks with two factors.

RESULTS

We identified significant differences in blood donations under different emergency conditions and a high correlation between epidemic status and blood donations. In addition, the decision coefficient improved by 60.7%, and the Root Mean Square Error(RMSE) decreased by 1.668 when using the hybrid model of SARIMAX and the neural network, indicating that the model was effective in reducing the prediction error of blood donation.

CONCLUSION

The hybrid model approach allows long-term forecasting of blood donations under emergency conditions and provides reliable and accurate forecasting results for blood stations up to 2 weeks in advance, facilitating warnings on the blood supply to relevant hospitals and improving hospital treatment rates while reducing blood transportation costs.

Application of Piezoelectric PLLA Braided Cord as Wearable Sensor to Realize Monitoring System for Indoor Dogs with Less Physical or Mental Stress

We attempted to realize a prototype system that monitors the living condition of indoor dogs without physical or mental burden by using a piezoelectric poly-l-lactic acid (PLLA) braided cord as a wearable sensor. First, to achieve flexibility and durability of the piezoelectric PLLA braided cord used as a sensor for indoor dogs, the process of manufacturing the piezoelectric PLLA fiber for the piezoelectric braided cord was studied in detail and improved to achieve the required performance. Piezoelectric PLLA braided cords were fabricated from the developed PLLA fibers, and the finite element method was used to realize an e-textile that can effectively function as a monitoring sensor. As a result, we realized an e-textile that feels similar to a high-grade textile and senses the complex movements of indoor dogs without the use of a complex computer system. Finally, a prototype system was constructed and applied to an actual indoor dog to demonstrate the usefulness of the e-textile as a sensor for indoor dog monitoring.

A computational supervised neural network procedure for the fractional SIQ mathematical model

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjs/s11734-022-00738-9.

Machine Learning and COVID-19: Lessons from SARS-CoV-2

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.