• ARIMA
• Ambulatory blood pressure
• Forecasting
• LSTM
• Missing data imputation
• Univariate time series

• Humans
• Forecasting/methods
• Blood Pressure/physiology
• Algorithms
• Neural Networks, Computer
• Models, Statistical

• DP1 AG069870 NIA NIH HHS
• P30 AG066546 NIA NIH HHS

• Data science
• Intensive care unit
• Machine learning
• Salzburg Intensive Care database

• Humans
• Databases, Factual
• Critical Care
• Intensive Care Units
• Artificial Intelligence
• Male
• Female
• Aged
• Middle Aged
• Adult
• Aged, 80 and over
• Benchmarking
• Adolescent

• Universität Leipzig (1039)

• ANN
• SARIMA
• disease surveillance
• seasonal influenza
• time series

• Animals
• Humans
• Models, Statistical
• Seasons
• Public Health
• Influenza, Human/epidemiology
• Influenza, Human/prevention & control
• Canada/epidemiology
• Incidence
• Neural Networks, Computer
• Forecasting
• China/epidemiology

• Canadian Safety and Security Program (CSSP)
• Defence Research and Development Canada (DRDC)
• CSSP-2018-CP-2334 Public Health Agency of Canada

• Amazonia
• Brazil
• COVID-19
• epidemiology
• incidence
• lethality
• mortality
• pandemic

• nonlinear phenomena
• computational science
• complex networks
• information theory and computation

• 11925103 National Natural Science Foundation of China (National Science Foundation of China)
• 22JC1401402 Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission)
• 2023ZKZD04 Shanghai Municipal Education Commission
• 12171350 National Natural Science Foundation of China (National Science Foundation of China)

• Auto-regressive integrated moving average
• COVID-19
• Epidemiology
• Excess mortality
• Pandemic
• Seroprevalence
• Time series analysis

• Acute kidney injury
• XGBoost
• aortic dissection
• cardiac surgery
• intraoperative hypotension
• machine learning
• predictive model

• Humans
• Blood Pressure
• Retrospective Studies
• Hypotension/diagnosis
• Hypotension/etiology
• Acute Kidney Injury/diagnosis
• Acute Kidney Injury/etiology
• Machine Learning

• Air pollutants
• Children respiratory system diseases
• Cost of disease method
• Disease burden
• Hospitalization risk
• Time-series study analysis

• Child
• Humans
• Air Pollutants/adverse effects
• Air Pollutants/analysis
• Environmental Pollutants
• Air Pollution/adverse effects
• Air Pollution/analysis
• Respiratory Tract Diseases/epidemiology
• China
• Particulate Matter/adverse effects
• Particulate Matter/analysis

• gxbjZD09 The Project for Top Disciplinary Talents of Majors in Universities of Anhui Province

• Bayesian filtering
• count time series
• iterated extended Kalman filter
• n onlinear state space model
• singular value decomposition

• Not Applicable Hasso Plattner Institute, Research School on Data Science and Engineering

• Artificial intelligence
• Big data
• Data analytics
• Data-driven interventions
• Digital cancer care
• Digital transformation

• Humans
• Artificial Intelligence
• Big Data
• Medical Oncology
• Digital Technology
• Neoplasms/therapy

• image and signal processing
• medical applications
• nonadditive entropies
• nonextensive statistical mechanics

• statistics
• statistical methods

• Algorithms

• R01 AG048209 NIA NIH HHS
• R01 GM129512 NIGMS NIH HHS
• R01 HL155417 NHLBI NIH HHS
• National Institutes of Health

Tuberculosis (TB) is one of the world's deadliest infectious disease killers today, and despite China's increasing efforts to prevent and control TB, the TB epidemic is still very serious. In the context of the COVID-19 pandemic, if reliable forecasts of TB epidemic trends can be made, they can help policymakers with early warning and contribute to the prevention and control of TB. In this study, we collected monthly reports of pulmonary tuberculosis (PTB) in Guiyang, China, from January 1, 2010 to December 31, 2020, and monthly meteorological data for the same period, and used LASSO regression to screen four meteorological factors that had an influence on the monthly reports of PTB in Guiyang, including sunshine hours, relative humidity, average atmospheric pressure, and annual highest temperature, of which relative humidity (6-month lag) and average atmospheric pressure (7-month lag) have a lagging effect with the number of TB reports in Guiyang. Based on these data, we constructed ARIMA, Holt-Winters (additive and multiplicative), ARIMAX (with meteorological factors), LSTM, and multivariable LSTM (with meteorological factors). We found that the addition of meteorological factors significantly improved the performance of the time series prediction model, which, after comprehensive consideration, included the ARIMAX (1,1,1) (0,1,2)₁₂ model with a lag of 7 months at the average atmospheric pressure, outperforms the other models in terms of both fit (RMSE = 37.570, MAPE = 10.164%, MAE = 28.511) and forecast sensitivity (RMSE = 20.724, MAPE = 6.901%, MAE = 17.306), so the ARIMAX (1,1,1) (0,1,2)₁₂ model with a lag of 7 months can be used as a predictor tool for predicting the number of monthly reports of PTB in Guiyang, China.

• Nanjing Medical Science and Technique Development Foundation

• Air Pollution
• Climate Change
• Environmental Exposure/adverse effects
• Forecasting
• Humans
• Temperature

• Climate Change
• Environmental Health
• Forecasting
• Mental Health
• Public Health

Red blood cells (RBCs) are an essential factor to consider for modern medicine, but planning the future collection of RBCs and supply efforts for coping with fluctuating demands is still a major challenge.

This study aimed to explore the feasibility of the time-series model in predicting the clinical demand of RBCs for pediatric patients each month.

Our study collected clinical RBC transfusion data from years 2014 to 2019 in the National Center for Children's Health (Beijing) in China, with the goal of constructing a time-series, autoregressive integrated moving average (ARIMA) model by fitting the monthly usage of RBCs from 2014 to 2018. Furthermore, the optimal model was used to forecast the monthly usage of RBCs in 2019, and we subsequently compared the data with actual values to verify the validity of the model.

The seasonal multiplicative model SARIMA (0, 1, 1) (1, 1, 0)₁₂ (normalized BIC = 8.740, R² = 0.730) was the best prediction model and could better fit and predict the monthly usage of RBCs for pediatric patients in this medical center in 2019. The model residual sequence was white noise (Ljung-Box Q₍₁₈₎ = 15.127, P > 0.05), and its autocorrelation function (ACF) and partial autocorrelation function (PACF) coefficients also fell within the 95% confidence intervals (CIs). The parameter test results were statistically significant (P < 0.05). 91.67% of the actual values were within the 95% CIs of the forecasted values of the model, and the average relative error of the forecasted and actual values was 6.44%, within 10%.

The SARIMA model can simulate the changing trend in monthly usage of RBCs of pediatric patients in a time-series aspect, which represents a short-term prediction model with high accuracy. The continuously revised SARIMA model may better serve the clinical environments and aid with planning for RBC demand. A clinical study including more data on blood use should be conducted in the future to confirm these results.

• Classification
• Evaluation models
• Prediction
• Time series analysis

• Daily admission
• Diabetes mellitus
• Generalized additive model
• Long short-term memory recurrent neural network
• Prediction
• Respiratory disease

• Beijing/epidemiology
• Diabetes Mellitus/epidemiology
• Female
• Hospitalization
• Humans
• Male
• Neural Networks, Computer

• Epidemic calendar
• Mutation
• N-terminal domain
• Proteome
• SARS-CoV-2
• Seasonality
• Variants

In the United States, cocaine use and mortality have surged in the past 5 years. Considering cocaine’s reputation as a fashionable social drug, the rise of cocaine mentions in popular music may provide a signal of epidemiological trends of cocaine use. We characterized the relationship between mentions of cocaine in song lyrics and incidence of cocaine use and mortality in the US. Incidence of cocaine use from 2002 to 2017 was obtained from the National Survey on Drug Use and Health and cocaine overdose mortality rate from 2000 to 2017 was obtained from the Centers for Disease Control. Distributed lag models were fit using ordinary least squares on the first difference to identify associations between changes in cocaine lyric mentions and changes in incidence of cocaine use and mortality. A total of 5955 song lyrics with cocaine mentions were obtained from Lyrics.com. Cocaine mentions in song lyrics were stable from 2000 to 2010 then increased by 190% from 2010 to 2017. The first-order distributed lag model estimated that a 0.01 increase in mentions of cocaine in song lyrics is associated with an 11% increase in incidence of cocaine use within the same year and a 14% increase in cocaine mortality with a 2-year lag. Lag-times were confirmed with cross-correlation analyses and the association remained after accounting for street pricing of cocaine. Mentions of cocaine in song lyrics are associated with the rise of incidence of cocaine use and cocaine overdose mortality. Popular music trends are a potentially valuable tool for understanding cocaine epidemiology trends.

• Myocardial infarction
• Particulate matter
• Pollutants
• Smog

• Acute Coronary Syndrome/chemically induced
• Acute Coronary Syndrome/epidemiology
• Air Pollutants/analysis
• Air Pollutants/toxicity
• Air Pollution/adverse effects
• Air Pollution/analysis
• China
• Environmental Exposure/analysis
• Follow-Up Studies
• Humans
• Incidence
• Particulate Matter/analysis
• Particulate Matter/toxicity

• Chlamydia Infections/diagnosis
• Chlamydia Infections/epidemiology
• Chlamydia trachomatis
• Humans
• Incidence
• Sweden/epidemiology

• Atmospheric pressure
• Chronic obstructive pulmonary disease
• Distributed lag non-linear model
• Hospital admission
• Nitrogen dioxide

Theoretical frameworks have recommended organisational-level interventions to decrease employee withdrawal behaviours such as sickness absence and employee turnover. However, evaluation of such interventions has produced inconclusive results. The aim of this study was to investigate if mixed-effects models in combination with time series analysis, process evaluation, and reference group comparisons could be used for evaluating the effects of an organisational-level intervention on employee withdrawal behaviour.

Monthly data on employee withdrawal behaviours (sickness absence, employee turnover, employment rate, and unpaid leave) were collected for 58 consecutive months (before and after the intervention) for intervention and reference groups. In total, eight intervention groups with a total of 1600 employees participated in the intervention. Process evaluation data were collected by process facilitators from the intervention team. Overall intervention effects were assessed using mixed-effects models with an AR (1) covariance structure for the repeated measurements and time as fixed effect. Intervention effects for each intervention group were assessed using time series analysis. Finally, results were compared descriptively with data from process evaluation and reference groups to disentangle the organisational-level intervention effects from other simultaneous effects.

All measures of employee withdrawal behaviour indicated statistically significant time trends and seasonal variability. Applying these methods to an organisational-level intervention resulted in an overall decrease in employee withdrawal behaviour. Meanwhile, the intervention effects varied greatly between intervention groups, highlighting the need to perform analyses at multiple levels to obtain a full understanding. Results also indicated that possible delayed intervention effects must be considered and that data from process evaluation and reference group comparisons were vital for disentangling the intervention effects from other simultaneous effects.

When analysing the effects of an intervention, time trends, seasonal variability, and other changes in the work environment must be considered. The use of mixed-effects models in combination with time series analysis, process evaluation, and reference groups is a promising way to improve the evaluation of organisational-level interventions that can easily be adopted by others.

• Employee turnover
• Mixed-effects models
• Organisation
• Organisational-level intervention
• Process evaluation
• Public sector
• Sickness absence
• Time series analysis
• Work environment
• Workplace interventions

• Female
• Forecasting
• Hospital Planning/statistics & numerical data
• Humans
• Male
• Mexico/epidemiology
• Models, Statistical
• Neural Networks, Computer
• Patient Discharge/statistics & numerical data
• Retrospective Studies
• Seasons
• Tertiary Care Centers/statistics & numerical data

• autoregressive integrated moving average
• difference-in-difference
• interrupted time series
• segmented regression

• P30 AG028741 NIA NIH HHS
• P30 CA196521 NCI NIH HHS
• the National Cancer Institute Cancer Center Support Grant

Time-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology. In this analysis, adjustment for seasonality and long-term time-trend is crucial to obtain valid findings. When applying this analysis for long-term exposure (e.g., months, years) of which effects are usually studied via survival analysis with individual-level longitudinal data, unlike its application for short-term exposure (e.g., days, weeks), a standard adjustment method for seasonality and long-term time-trend can extremely inflate standard error of coefficient estimates of the effects. Given that individual-level longitudinal data are difficult to construct and often available to limited populations, if this inflation of standard error can be solved, rich case-only data over regions and countries would be very useful to test a variety of research hypotheses considering unique local contexts.

We discuss adjustment methods for seasonality and time-trend used in time-series analysis in environmental epidemiology and explain why standard errors can be inflated. We suggest alternative methods to solve this problem. We conduct simulation analyses based on real data for Seoul, South Korea, 2002–2013, and time-series analysis using real data for seven major South Korean cities, 2006–2013 to identify whether the association between long-term exposure and health outcomes can be estimated via time-series analysis with alternative adjustment methods.

Simulation analyses and real-data analysis confirmed that frequently used adjustment methods such as a spline function of a variable representing time extremely inflate standard errors of estimates for associations between long-term exposure and health outcomes. Instead, alternative methods such as a combination of functions of variables representing time can make sufficient adjustment with efficiency.

Our findings suggest that time-series analysis with case-only data can be applied for estimating long-term exposure effects. Rich case-only data such as death certificates and hospitalization records combined with repeated measurements of environmental determinants across countries would have high potentials for investigating the effects of long-term exposure on health outcomes allowing for unique contexts of local populations.

The online version contains supplementary material available at 10.1186/s12874-020-01199-1.

• Case-only data
• Environmental exposure
• Long-term exposure
• Seasonality
• Time-series analysis
• Time-trend

• Heat waves
• Mortality
• Public health
• System dynamics model

• China/epidemiology
• Cities/epidemiology
• Climate Change
• Extreme Heat/adverse effects
• Hot Temperature/adverse effects
• Humans
• Models, Theoretical
• Mortality
• Public Health
• Risk
• Systems Analysis

• 2016YFC0502702 National Key R&D Program of China
• 41671113 National Natural Science Foundation of China
• 51761135024 National Natural Science Foundation of China
• AJYG-643BJQ Met Office CSSP Academic Partnership (GB)
• R034214/1 Engineering and Physical Sciences Research Council of UK
• ALWSD.2016.007 Nederlandse Organisatie voor Wetenschappelijk Onderzoek
• National Key R&D Program of China

The interactive effects between particulate matter (PM) and heat waves on circulatory mortality are under-researched in the context of global climate change. We aimed to investigate the interaction between heat waves and PM on circulatory mortality in Fuzhou, a city characterized by a humid subtropical climate and low level of air pollution in China. We collected data on deaths, pollutants, and meteorology in Fuzhou between January 2016 and December 2019. Generalized additive models were used to examine the effect of PM on circulatory mortality during the heat waves, and to explore the interaction between different PM levels and heat waves on the circulatory mortality. During heat waves, circulatory mortality was estimated to increase by 8.21% (95% confidence intervals (CI): 0.32–16.72) and 3.84% (95% CI: 0.28–7.54) per 10 μg/m³ increase of PM_2.5 and PM₁₀, respectively, compared to non-heat waves. Compared with low-level PM_2.5 concentration on non-heat waves layer, the high level of PM_2.5 concentration on heat waves layer has a significant effect on the cardiovascular mortality, and the effect value was 48.35% (95% CI: 6.37–106.89). Overall, we found some evidence to suggest that heat waves can significantly enhance the impact of PM on circulatory mortality.

• circulatory mortality
• heat waves
• interactive effects
• particulate matter

• Air Pollutants/analysis
• Air Pollution/adverse effects
• Air Pollution/analysis
• Air Pollution/statistics & numerical data
• Cardiovascular Diseases/epidemiology
• China/epidemiology
• Cities
• Environmental Exposure/adverse effects
• Environmental Exposure/statistics & numerical data
• Hot Temperature
• Mortality
• Particulate Matter/analysis
• Particulate Matter/toxicity

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

• data aggregation
• harmonic regression
• infectious diseases
• seasonality
• time series

• cardiac arrest
• cerebral microdialysis
• hypothermic cardiac arrest
• outcome
• targeted temperature management
• tau protein

Vitamin D status is relatively poor in the general population, potentially leading to various conditions. The present study evaluates the relationship between vitamin D status and intake in the UK population and the impact of vitamin D fortified ready-to-eat cereals (RTEC) on this status via data from the National Diet and Nutrition Survey (NDNS: 2008–2012). Four cohorts were addressed: ages 4–10 (n = 803), ages 11–18 (n = 884), ages 19–64 (n = 1655) and ages 65 and higher (n = 428). The impact of fortification by 4.2 μg vitamin D per 100 g of RTEC on vitamin D intake and status was mathematically modelled. Average vitamin D daily intake was age-dependent, ranging from ~2.6 (age range 4–18 years) to ~5.0 μg (older than 64 years). Average 25(OH)D concentration ranged from 43 to 51 nmol/L, the highest in children. The relationship between vitamin D intake and status followed an asymptotic curve with a predicted plateau concentration ranging from 52 in children to 83 nmol/L in elderly. The fortification model showed that serum concentrations increased with ~1.0 in children to ~6.5 nmol/L in the elderly. This study revealed that vitamin D intake in the UK population is low with 25(OH)D concentrations being suboptimal for general health. Fortification of breakfast cereals can contribute to improve overall vitamin D status.

• National Diet and Nutrition Survey
• breakfast cereals
• vitamin D
• vitamin D fortification
• vitamin D intake and status

• Implementation science
• Mental health
• Quality improvement
• Suicide
• Suicide prevention

• Infectious disease data
• latent autoregressive model
• numerical integration
• pairwise likelihood
• time series

Application of time series modeling to predict reports related to maltreatment of vulnerable adults can be helpful for efficient early planning and resource allocation to handle a high volume of investigations. The goal of this study is to apply: (1) autoregressive integrated moving average (ARIMA) time series modeling to fit and forecast monthly maltreatment reports accepted for assessment reported to adult protective services (APS), and (2) interrupted time series analysis to test whether the implementation of intake hubs have a significant impact in the number of maltreatment reports after the implementation period.

A time series analysis on monthly APS intake reports was conducted using administrative data from SC Child and Adult Protective Services between January 2014 and June 2018. Monthly APS data were subjected to ARIMA modeling adjusting for the time period when intake hubs were implemented. The coefficient of determination, normalized SBC, AIC, MSE, and Ljung-Box Q-test were used to evaluate the goodness-of-fit of constructed models. The most parsimonious model was selected to predict the monthly APS intakes from July to December 2018. Poisson regression was fit to examine the association of the implementation of the hubs and the number of intake reports received to APS, adjusting for confounders.

Over 24,000 APS intakes accepted for investigation were identified over a period of four calendar years with an increase in the monthly average of APS intakes between 2014 and 2017. An increase in the number of monthly APS intakes was found after the intake hubs were implemented in 2015 (Phase-1) and 2017 (Phase-2). Of all the models tested, an ARIMA (12), 1, 1 model was found to work best after evaluating all fit measures for both models. For Phase-1, the optimum model predicted an average of 488 APS intake reports between July and December 2018, representing a 9% increase from January–June 2018 (median = 445). For Phase-2, the percent increase was 32%.

The implementation of the intake hubs has a significant impact in the number of reports received after the implementation period. ARIMA time series is a valuable tool to predict future reports of maltreatment of vulnerable adults, which could be used to allow appropriate planning and resource allocation to handle a high volume of monthly intake reports.

• Auto-regressive integrated moving average (ARIMA)
• Forecast
• Modeling
• Vulnerable adults

• asthma
• chronic obstructive pulmonary disease
• ozone
• particulate matter
• respiratory infection

• ARIMA
• ARIMAX
• Addiction
• SVAR
• VAR
• VECM
• time series

• Behavior, Addictive/epidemiology
• Data Interpretation, Statistical
• Interrupted Time Series Analysis/methods
• Models, Statistical
• Research Design
• Software

• 22962 Cancer Research UK

• autocorrelation
• disease counts
• prediction
• public health surveillance
• time-series analysis

• Foetal death
• influenza (seasonal)
• maternal infection
• seasonality

A large body of epidemiologic research has concentrated on the 1918 influenza pandemic, but more work is needed to understand spatial variation in pandemic mortality and its effects on natality. We collected and analyzed 35,151 death records from Arizona for 1915–1921 and 21,334 birth records from Maricopa county for 1915–1925. We estimated the number of excess deaths and births before, during, and after the pandemic period, and we found a significant decline in the number of births occurring 9–11 months after peak pandemic mortality. Moreover, excess mortality rates were highest in northern Arizona counties, where Native Americans were historically concentrated, suggesting a link between ethnic and/or sociodemographic factors and risk of pandemic-related death. The relationship between birth patterns and pandemic mortality risk should be further studied at different spatial scales and in different ethnic groups.

• Air pollution
• Climate variables
• Hip fracture
• Osteoporosis
• Seasonality
• Spain
• Weather

• Aged
• Aged, 80 and over
• Air Pollution/adverse effects
• Air Pollution/analysis
• Environmental Exposure/adverse effects
• Environmental Exposure/analysis
• Environmental Monitoring/methods
• Female
• Hip Fractures/epidemiology
• Hip Fractures/etiology
• Hospitalization/statistics & numerical data
• Humans
• Incidence
• Male
• Middle Aged
• Nitrogen Dioxide/adverse effects
• Nitrogen Dioxide/analysis
• Osteoporotic Fractures/epidemiology
• Osteoporotic Fractures/etiology
• Particulate Matter/adverse effects
• Retrospective Studies
• Spain/epidemiology
• Sulfur Dioxide/adverse effects
• Sulfur Dioxide/analysis
• Weather

• K99 ES027023 NIEHS NIH HHS
• R00 ES027023 NIEHS NIH HHS