Hospital capacities and response to COVID-19 pandemic surges in Iran: A quantitative model-based study.

The coronavirus 2019 (COVID-19) pandemic resulted in serious limitations for healthcare systems, and this study aimed to investigate the impact of COVID-19 surges on in-patient care capacities in Iran employing the Adaptt tool. Using a cross-sectional study design, our study was carried out in the year 2022 using 1-year epidemiologic (polymerase chain reaction-positive COVID-19 cases) and hospital capacity (beds and human resource) data from the official declaration of the pandemic in Iran in February 2020. We populated several scenarios, and in each scenario, a proportion of hospital capacity is assumed to be allocated to the COVID-19 patients. In most of the scenarios, no significant shortage was found in terms of bed and human resources. However, considering the need for treatment of non- COVID-19 cases, in one of the scenarios, it can be observed that during the peak period, the number of required and available specialists is exactly equal, which was a challenge during surge periods and resulted in extra hours of working and workforce burnout in hospitals. The shortage of intensive care unit beds and doctors specializing in internal medicine, infectious diseases, and anesthesiology also requires more attention for planning during the peak days of COVID-19.

Data pipeline for real-time energy consumption data management and prediction.

With the increasing utilization of data in various industries and applications, constructing an efficient data pipeline has become crucial. In this study, we propose a machine learning operations-centric data pipeline specifically designed for an energy consumption management system. This pipeline seamlessly integrates the machine learning model with real-time data management and prediction capabilities. The overall architecture of our proposed pipeline comprises several key components, including Kafka, InfluxDB, Telegraf, Zookeeper, and Grafana. To enable accurate energy consumption predictions, we adopt two time-series prediction models, long short-term memory (LSTM), and seasonal autoregressive integrated moving average (SARIMA). Our analysis reveals a clear trade-off between speed and accuracy, where SARIMA exhibits faster model learning time while LSTM outperforms SARIMA in prediction accuracy. To validate the effectiveness of our pipeline, we measure the overall processing time by optimizing the configuration of Telegraf, which directly impacts the load in the pipeline. The results are promising, as our pipeline achieves an average end-to-end processing time of only 0.39 s for handling 10,000 data records and an impressive 1.26 s when scaling up to 100,000 records. This indicates 30.69-90.88 times faster processing compared to the existing Python-based approach. Additionally, when the number of records increases by ten times, the increased overhead is reduced by 3.07 times. This verifies that the proposed pipeline exhibits an efficient and scalable structure suitable for real-time environments.

Blood donation projections using hierarchical time series forecasting: the case of Zimbabwe's national blood bank.

BACKGROUND: The discrepancy between blood supply and demand requires accurate forecasts of the blood supply at any blood bank. Accurate blood donation forecasting gives blood managers empirical evidence in blood inventory management. The study aims to model and predict blood donations in Zimbabwe using hierarchical time series. The modelling technique allows one to identify, say, a declining donor category, and in that way, the method offers feasible and targeted solutions for blood managers to work on. METHODS: The monthly blood donation data covering the period 2007 to 2018, collected from the National Blood Service Zimbabwe (NBSZ) was used. The data was disaggregated by gender and blood groups types within each gender category. The model validation involved utilising actual blood donation data from 2019 and 2020. The model's performance was evaluated through the Mean Absolute Percentage Error (MAPE), uncovering expected and notable discrepancies during the Covid-19 pandemic period only. RESULTS: Blood group O had the highest monthly yield mean of 1507.85 and 1230.03 blood units for male and female donors, respectively. The top-down forecasting proportions (TDFP) under ARIMA, with a MAPE value of 11.30, was selected as the best approach and the model was then used to forecast future blood donations. The blood donation predictions for 2019 had a MAPE value of 14.80, suggesting alignment with previous years' donations. However, starting in April 2020, the Covid-19 pandemic disrupted blood collection, leading to a significant decrease in blood donation and hence a decrease in model accuracy. CONCLUSIONS: The gradual decrease in future blood donations exhibited by the predictions calls for blood authorities in Zimbabwe to develop interventions that encourage blood donor retention and regular donations. The impact of the Covid-19 pandemic distorted the blood donation patterns such that the developed model did not capture the significant drop in blood donations during the pandemic period. Other shocks such as, a surge in global pandemics and other disasters, will inevitably affect the blood donation system. Thus, forecasting future blood collections with a high degree of accuracy requires robust mathematical models which factor in, the impact of various shocks to the system, on short notice.

Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness.

BACKGROUND: Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness. METHODS: The study was based on routine clinical data from electronic health records from the psychiatric services of the Central Denmark Region. A total of 74,880 patients with 1.59 million psychiatric service contacts were included in the analyses. We created 1343 potential predictors from 51 source variables, covering patient-level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalised as HbA1c ≥48 mmol/mol, fasting plasma glucose ≥7.0 mmol/mol, oral glucose tolerance test ≥11.1 mmol/mol or random plasma glucose ≥11.1 mmol/mol. Two machine learning models (XGBoost and regularised logistic regression) were trained to predict T2D based on 85% of the included contacts. The predictive performance of the best performing model was tested on the remaining 15% of the contacts. RESULTS: The XGBoost model detected patients at high risk 2.7 years before T2D, achieving an area under the receiver operating characteristic curve of 0.84. Of the 996 patients developing T2D in the test set, the model issued at least one positive prediction for 305 (31%). CONCLUSION: A machine learning model can accurately predict development of T2D among patients with mental illness based on routine clinical data from electronic health records. A decision support system based on such a model may inform measures to prevent development of T2D in this high-risk population.

Prediction model for lower limb amputation in hospitalized diabetic foot patients using classification and regression trees.

BACKGROUND: The decision to perform amputation of a limb in a patient with diabetic foot ulcer (DFU) is not an easy task. Prediction models aim to help the surgeon in decision making scenarios. Currently there are no prediction model to determine lower limb amputation during the first 30 days of hospitalization for patients with DFU. METHODS: Classification And Regression Tree analysis was applied on data from a retrospective cohort of patients hospitalized for the management of diabetic foot ulcer, using an existing database from two Orthopaedics and Traumatology departments. The secondary analysis identified independent variables that can predict lower limb amputation (mayor or minor) during the first 30 days of hospitalization. RESULTS: Of the 573 patients in the database, 290 feet underwent a lower limb amputation during the first 30 days of hospitalization. Six different models were developed using a loss matrix to evaluate the error of not detecting false negatives. The selected tree produced 13 terminal nodes and after the pruning process, only one division remained in the optimal tree (Sensitivity: 69%, Specificity: 75%, Area Under the Curve: 0.76, Complexity Parameter: 0.01, Error: 0.85). Among the studied variables, the Wagner classification with a cut-off grade of 3 exceeded others in its predicting capacity. CONCLUSIONS: Wagner classification was the variable with the best capacity for predicting amputation within 30 days. Infectious state and vascular occlusion described indirectly by this classification reflects the importance of taking quick decisions in those patients with a higher compromise of these two conditions. Finally, an external validation of the model is still required. LEVEL OF EVIDENCE: III.

A deep learning-based approach for predicting COVID-19 diagnosis.

This paper focuses on forecasting the total count of confirmed COVID-19 cases in Saudi Arabia through a range of methodologies, including ARIMA, mathematical modeling, and deep learning network (DQN) techniques. Its primary aim is to anticipate the verified COVID-19 cases in Saudi Arabia, aiding in decision-making for life-saving interventions by enhancing awareness of COVID-19 infection. Mathematical modeling and ARIMA are employed for their efficacy in forecasting, while DQN approaches, particularly through comparative analysis, are utilized for prediction. This comparative analysis evaluates the predictive capacities of ARIMA, mathematical modeling, and DQN techniques, aiming to pinpoint the most reliable method for forecasting positive COVID-19 cases. The modeling encompasses COVID-19 cases in Saudi Arabia, the United Kingdom (UK), and Tunisia (TU) spanning from 2020 to 2021. Predicting the number of individuals likely to test positive for COVID-19 poses a challenge, requiring adherence to fundamental assumptions in mathematical and ARIMA projections. The proposed methodology was implemented on a local server. The DQN algorithm formulates a reward function to uphold target functional performance while balancing training and testing periods. The findings indicate that DQN technology surpasses conventional approaches in efficiency and accuracy for predictions.

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons.

This study uses operational data from a 180 kWp grid-connected solar PV system to train and compare the performance of single and hybrid machine learning models in predicting solar PV production a day-ahead, a week-ahead, two weeks ahead and one month-ahead. The study also analyses the trend in solar PV production and the effect of temperature on solar PV production. The performance of the models is evaluated using R2 score, mean absolute error and root mean square error. The findings revealed the best-performing model for the day ahead forecast to be Artificial Neural Network. Random Forest gave the best performance for the two-week and a month-ahead forecast, while a hybrid model composed of XGBoost and Random Forest gave the best performance for the week-ahead prediction. The study also observed a downward trend in solar PV production, with an average monthly decline of 244.37 kWh. Further, it was observed that an increase in the module temperature and ambient temperature beyond 47 °C and 25 °C resulted in a decline in solar PV production. The study shows that machine learning models perform differently under different time horizons. Therefore, selecting suitable machine learning models for solar PV forecasts for varying time horizons is extremely necessary.

Prospective validation of a seizure diary forecasting falls short.

OBJECTIVE: Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS: We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. RESULTS: The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE: The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.

Impact of coronavirus pandemic on stock index: A polynomial regression with time delay.

Motivation: Under contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the possible nonlinear relations between the two indicators. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated. Objectives: To contribute to filling the gaps identified in existing research, this study models relationship between the stock market index and coronavirus pandemic by leveraging volatility in the stock market and covid data through time delay and best degree in a polynomial environment. The resultant optimal time delay and best degree model is used to derive a high-accuracy prediction of stock market index. Novelty: In line with the possible relations, the novelty of this study is that it proposes, validates and implements polynomial regression with time delay to model nonlinear relationship between the stock index and covid. Methods: This study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment. Findings: The empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation of the nonlinear relationship such predictors have with stock index for China. Relative to results from the polynomial regression without delay, the empirical evidence from the model with delay show that an optimal time delay of 17 weeks makes it possible to predict the stock index at high accuracy and record improvements of 16-fold or higher. The representative delay model is used to project for up to 17 weeks for future trends in the stock index. Implication: The implication of the findings herein is that the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases to achieve better results.

Projected wind and solar energy potential in the eastern Mediterranean and Middle East in 2050.

The ongoing energy transition from conventional fuels to renewable energy sources (RES) has given nations the potential to achieve levels of energy self-sufficiency previously thought unattainable. RES in the form of utility-scale solar and wind energy are currently the leading alternatives to fossil-fuel generation. Precise location siting that factors in efficiency limitations related to current and future climate variables is essential for enabling the green energy transition envisioned for 2050. In this context, understanding and mapping the intermittency of RES provides insights to energy system operators for their seamless integration into the grid. The Eastern Mediterranean and Middle East (EMME) region has the potential to harness vast amounts of RES. The scarcity of observations from weather station networks and the lack of private sector incentives for transitioning to RES mean that relevant, supporting weather and climate studies have been limited. This study employs the Weather Research and Forecasting model with Chemistry (WRF-CHEM) to estimate the RES technical potential of EMME countries and map the hourly generation profiles per source and country, simulated for the reference year 2015 and considering future conditions. The findings indicate that by 2050, seven countries within the region could transform into net energy exporters, while the remaining nine might remain reliant on energy imports or fossil fuels. Egypt emerges as a "powerhouse", potentially enjoying a potential surplus energy generation of 76 GW per hour, whereas the United Arab Emirates may face an annual deficit of 955 TWh. Further, we derived the hourly generation profiles for wind and solar during different seasons. Four dominant patterns were identified. We find a complementary relationship for six countries, and for four countries, a substitute relationship between solar and wind energy generation. Greece stands out with a near-constant wind energy source, which would facilitate its integration into the national grid.

Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes.

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction.

The usage of the autoregressive integrated moving average model for forecasting milk production in Egypt (2022-2025).

Background: Milk is considered one of the most important capital goods and essential sources of animal protein in the diet of the Egyptian family, as well as an effective means to improve the economic condition of farmers, considering this important view, the policymakers need accurate and advance information regarding future supply for planning on the both short and long term. Aim: The study aims to forecast the production of milk in Egypt during the period from 2022 to 2025 using the Autoregressive Integrated Moving Average (ARIMA) model using time series data of milk production (MP) (1970-2021) obtained from the Central Agency for public mobilization and statistics (CAPMS). Methods: Augmented Dickey-Fullar Unit Root test, Partial autocorrelation function (PACF), and Autocorrelation function (ACF) of the time series sequence were used to judge the stationarity of the data. After confirming the stationarity of the data, the appropriate ARIMA model was selected based on certain statistical parameters like significant coefficients, values of adjusted R-squared, Akaike information criteria (AIC), Schwarz criterion (SC), and Standard Error of Regression. After the selection of the model based on the previous parameters, the verification of the model was employed by checking the residuals of the Correlogram-Q-Statistics test. Results: The most fitted model to predict the future levels of MP in Egypt was ARIMA (1, 1, and 3). Conclusion: Using the ARIMA (1, 1, 3) model, it could be forecasted that the production of milk in Egypt would show an increasing trend from 6,152.606 thousand tons in 2022 to 6,360.829 thousand tons in 2025.

ORTHOPOD: Linking ambulatory future trauma injury distribution from fragility proximal femur fracture caseload.

INTRODUCTION: The age of those experiencing traumatic injury and requiring surgery increases. The majority of this increase seen in older patients having operations after accidents is in fragility proximal femur fractures (FPFF). This study designed a model to predict the distribution of fractures suitable for ambulatory trauma list provision based on the number of FPFF patients. METHODS: The study utilized two datasets which both had data from 64 hospitals. One derived from the ORTHOPOD study dataset, and the other from National Hip Fracture Database. The model tested the predictability of 12 common fracture types based on FPFF data from the two datasets, using linear regression and K-fold cross-validation. RESULTS: The predictive model showed some promise. Evaluation of the model with mean RMSE and Std RMSE demonstrated good predictive performance for some fracture types, although the r-squared values showed that large variation in these fracture types was not always captured by the model. The study highlighted the dominance of FPFFs, and the strong correlation between these and numbers of ankle and distal radius fractures at a given unit. DISCUSSION: It is possible to model the numbers of ankle and distal radius fractures based off the number of patients admitted with hip fractures. This has great significance given the drive for increased day case utilisation and bed pressures across health services. While the model's current predictability was limited, with methodological improvements and additional data, a more robust predictive model could be developed to aid in the restructuring of trauma networks and improvement of patient care and surgical outcomes.

SmartLaundry: A Real-Time System for Public Laundry Allocation in Smart Cities.

Smart cities facilitate the comprehensive management and operation of urban data generated within a city, establishing the foundation for smart services and addressing diverse urban challenges. A smart system for public laundry management uses artificial intelligence-based solutions to solve the challenges of the inefficient utilization of public laundries, waiting times, overbooking or underutilization of machines, balancing of loads across machines, and implementation of energy-saving features. We propose SmartLaundry, a real-time system design for public laundry smart recommendations to better manage the loads across connected machines. Our system integrates the current status of the connected devices and data-driven forecasted usage to offer the end user connected via a mobile application a list of recommended machines that could be used. We forecast the daily usage of devices using traditional machine learning techniques and deep learning approaches, and we perform a comparative analysis of the results. As a proof of concept, we create a simulation of the interaction with our system.

Proposal of a Machine Learning Approach for Traffic Flow Prediction.

Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learning models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered.

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method.

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.

An explainable multiscale LSTM model with wavelet transform and layer-wise relevance propagation for daily streamflow forecasting.

Developing an accurate and reliable daily streamflow forecasting model is important for facilitating the efficient resource planning and management of hydrological systems. In this study, an explainable multiscale long short-term memory (XM-LSTM) model is proposed for effective daily streamflow by integrating the à trous wavelet transform (ATWT) for decomposing data, the Boruta algorithm for identifying model inputs, and the layer-wise relevance propagation (LRP) for explaining the prediction results. The proposed XM-LSTM is tested by performing multi-step-ahead forecasting of daily streamflow at four stations in the middle and lower reaches of the Yangtze River basin and compared with the X-LSTM. The X-LSTM is formed by coupling the long short-term memory (LSTM) with the LRP. For comparison, the inputs of these two models are identified by the Boruta selection algorithm. The results show that all models exhibit good ability to forecast daily streamflow, however, the prediction performance decreases as the lead time increases. The XM-LSTM provides a better forecasting performance than the X-LSTM, suggesting the ability of the ATWT to improve the LSTM for daily streamflow forecasting. Moreover, the correlation scores analysis by the LRP shows that the ATWT can extract useful information that influences the daily streamflow from the original predictors, and the water level has the most significant contribution to streamflow prediction. Accordingly, the XM-LSTM model can be viewed as a potentially useful approach for increasing the accuracy and explainability of streamflow forecasting.

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection.

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient's characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient's ICS response in the next year based on the patient's characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

Forecasting Hepatitis C Virus Status for Children in the United States: A Modeling Study.

BACKGROUND: Virtually all cases of hepatitis C virus (HCV) infection in children in the United States occur through vertical transmission, but it is unknown how many children are infected. Cases of maternal HCV infection have increased in the United States, which may increase the number of children vertically infected with HCV. Infection has long-term consequences for a child's health, but treatment options are now available for children ≥3 years old. Reducing HCV infections in adults could decrease HCV infections in children. METHODS: Using a stochastic compartmental model, we forecasted incidence of HCV infections in children in the United States from 2022 through 2027. The model considered vertical transmission to children <13 years old and horizontal transmission among individuals 13-49 years old. We obtained model parameters and initial conditions from the literature and the Centers for Disease Control and Prevention's 2021 Viral Hepatitis Surveillance Report. RESULTS: Model simulations assuming direct-acting antiviral treatment for children forecasted that the number of acutely infected children would decrease slightly and the number of chronically infected children would decrease even more. Alone, treatment and early screening in individuals 13-49 years old reduced the number of forecasted cases in children and, together, these policy interventions were even more effective. CONCLUSIONS: Based on our simulations, acute and chronic cases of HCV infection are remaining constant or slightly decreasing in the United States. Improving early screening and increasing access to treatment in adults may be an effective strategy for reducing the number of HCV infected children in the United States.

Quantifying the impact of hospital catchment area definitions on hospital admissions forecasts: COVID-19 in England, September 2020-April 2021.

BACKGROUND: Defining healthcare facility catchment areas is a key step in predicting future healthcare demand in epidemic settings. Forecasts of hospitalisations can be informed by leading indicators measured at the community level. However, this relies on the definition of so-called catchment areas or the geographies whose populations make up the patients admitted to a given hospital, which are often not well-defined. Little work has been done to quantify the impact of hospital catchment area definitions on healthcare demand forecasting. METHODS: We made forecasts of local-level hospital admissions using a scaled convolution of local cases (as defined by the hospital catchment area) and delay distribution. Hospital catchment area definitions were derived from either simple heuristics (in which people are admitted to their nearest hospital or any nearby hospital) or historical admissions data (all emergency or elective admissions in 2019, or COVID-19 admissions), plus a marginal baseline definition based on the distribution of all hospital admissions. We evaluated predictive performance using each hospital catchment area definition using the weighted interval score and considered how this changed by the length of the predictive horizon, the date on which the forecast was made, and by location. We also considered the change, if any, in the relative performance of each definition in retrospective vs. real-time settings, or at different spatial scales. RESULTS: The choice of hospital catchment area definition affected the accuracy of hospital admission forecasts. The definition based on COVID-19 admissions data resulted in the most accurate forecasts at both a 7- and 14-day horizon and was one of the top two best-performing definitions across forecast dates and locations. The "nearby" heuristic also performed well, but less consistently than the COVID-19 data definition. The marginal distribution baseline, which did not include any spatial information, was the lowest-ranked definition. The relative performance of the definitions was larger when using case forecasts compared to future observed cases. All results were consistent across spatial scales of the catchment area definitions. CONCLUSIONS: Using catchment area definitions derived from context-specific data can improve local-level hospital admission forecasts. Where context-specific data is not available, using catchment areas defined by carefully chosen heuristics is a sufficiently good substitute. There is clear value in understanding what drives local admissions patterns, and further research is needed to understand the impact of different catchment area definitions on forecast performance where case trends are more heterogeneous.