scholarly journals Excess cases of influenza and the coronavirus epidemic in Catalonia: a time-series analysis of primary-care electronic medical records covering over 6 million people

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e039369 ◽  
Author(s):  
Ermengol Coma Redon ◽  
Nuria Mora ◽  
Albert Prats-Uribe ◽  
Francesc Fina Avilés ◽  
Daniel Prieto-Alhambra ◽  
...  
Keyword(s):  
Public Health ◽  
Primary Care ◽  
Time Series ◽  
Moving Average ◽  
Epidemic Curve ◽  
Imported Case ◽  
Moving Average Models ◽  
Community Transmission ◽  
Auto Regressive ◽  
Primary Care Practices

ObjectivesThere is uncertainty about when the first cases of COVID-19 appeared in Spain. We aimed to determine whether influenza diagnoses masked early COVID-19 cases and estimate numbers of undetected COVID-19 cases.DesignTime-series study of influenza and COVID-19 cases, 2010–2020.SettingPrimary care, Catalonia, Spain.ParticipantsPeople registered in primary-care practices, covering >6 million people and >85% of the population.Main outcome measuresWeekly new cases of influenza and COVID-19 clinically diagnosed in primary care.AnalysesDaily counts of both cases were computed using the total cases recorded over the previous 7 days to avoid weekly effects. Epidemic curves were characterised for the 2010–2011 to 2019–2020 influenza seasons. Influenza seasons with a similar epidemic curve and peak case number as the 2019–2020 season were used to model expected case numbers with Auto Regressive Integrated Moving Average models, overall and stratified by age. Daily excess influenza cases were defined as the number of observed minus expected cases.ResultsFour influenza season curves (2011–2012, 2012–2013, 2013–2014 and 2016–2017) were used to estimate the number of expected cases of influenza in 2019–2020. Between 4 February 2020 and 20 March 2020, 8017 (95% CI: 1841 to 14 718) excess influenza cases were identified. This excess was highest in the 15–64 age group.ConclusionsCOVID-19 cases may have been present in the Catalan population when the first imported case was reported on 25 February 2020. COVID-19 carriers may have been misclassified as influenza diagnoses in primary care, boosting community transmission before public health measures were taken. The use of clinical codes could misrepresent the true occurrence of the disease. Serological or PCR testing should be used to confirm these findings. In future, this surveillance of excess influenza could help detect new outbreaks of COVID-19 or other influenza-like pathogens, to initiate early public health responses.

scholarly journals Excess cases of influenza suggest an earlier start to the coronavirus epidemic in Spain than official figures tell us: an analysis of primary care electronic medical records from over 6 million people from Catalonia

2020 ◽  
Author(s):  
Ermengol Coma ◽  
Nuria Mora ◽  
Albert Prats-Uribe ◽  
Francesc Fina ◽  
Daniel Prieto-Alhambra ◽  
...  
Keyword(s):  
Public Health ◽  
Primary Care ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Current Crisis ◽  
Epidemic Curve ◽  
Imported Case ◽  
Asymptomatic Patients ◽  
Community Transmission ◽  
Primary Care Practices

AbstractObjectivesThere is uncertainty about when the first cases of COVID-19 appeared in Spain, as asymptomatic patients can transmit the virus. We aimed to determine whether influenza diagnoses masked early COVID-19 cases and, if so, estimate numbers of undetected COVID-19 cases in a large database of primary-care records covering >6 million people in Catalonia.DesignTime-series study of influenza and COVID-19 cases, using all influenza seasons from autumn-winter 2010-2011 to autumn-winter 2019-2020.SettingPrimary care, Catalonia, Spain.ParticipantsPeople registered in one of the contributing primary-care practices, covering >6 million people and >85% of the population.Main outcome measuresWeekly new cases of influenza and COVID-19 diagnosed in primary care.AnalysesDaily counts of both cases were computed using the total cases recorded over the previous 7 days to avoid weekly effects on recording practice. Epidemic curves were characterised for the 2010-2011 to 2019-2020 influenza seasons. Influenza seasons with a similar epidemic curve and peak case number as the 2019-2020 season were used to model predictions for 2019-2020. ARIMA models were fitted to the included influenza seasons, overall and stratified by age, to estimate expected case numbers. Daily excess influenza cases were defined as the number of observed minus expected cases.ResultsFour influenza season curves (2011-2012, 2012-2013, 2013-2014, and 2016-2017) were used to estimate the number of expected cases of influenza in 2019-2020. Between 4 February 2020 and 20 March 20202, 8,017 (95% CI: 1,841 to 14,718) excess influenza cases were identified. This excess was highest in the 15-64 age group.ConclusionsCOVID-19 cases may have been present in the Catalan population when the first imported case was reported on 25 February 2020. COVID-19 carriers may have been misclassified as influenza diagnoses in primary care, boosting community transmission before public health measures were taken. In future, the surveillance of excess influenza cases using widely available primary-care electronic medical records could help detect new outbreaks of COVID-19 or other influenza-like illness-causing pathogens. Earlier detection would allow public health responses to be initiated earlier than during the current crisis.

Is the incidence of breast cancer increasing? Using the NCDB and autoregressive integrated moving average models (ARIMA) to forecast breast cancer to 2030.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13612-e13612
Author(s):  
Leah Elson ◽  
Nadeem Bilani ◽  
Elizabeth Blessing Elimimian ◽  
Rafael Arteta-Bulos ◽  
Zeina A. Nahleh
Keyword(s):  
Public Health ◽  
Breast Cancer ◽  
Time Series ◽  
Cancer Incidence ◽  
Moving Average ◽  
Breast Cancer Incidence ◽  
Multiple Models ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Models ◽  
Best Fit

e13612 Background: The observed increase in the incidence rate of BC has been described due, in part, to higher, effective public health campaigns for screening, higher sensitivity among diagnostic testing modalities as well as an aging population. While the mortality rate associated with breast cancer has reported to fall by 1.8% every year since 2007, the disease burden itself has continued to grow and increased breast cancer rates will contribute to the strain already experienced within the United States healthcare system infrastructure. Therefore, the authors sought to use a large, national registry to develop a time series model that might help forecast the approximate breast cancer incidence rate, to the year 2030, as captured by the National Cancer Database (NCDB). Methods: In this time series forecast, autoregressive integrated moving average models (ARIMA) were constructed based on 2004-2016 historic breast cancer incidence rates, as reported by the NCDB. Multiple models were generated, using differing autoregressive parameters, and the most predictive model was chosen using the lowest Bayesian Information Criteria (BIC), and mean absolute percentage error (MAPE). Similar methodology has already been published to predict prostate cancer incidence. The best fit model was applied to forecast annual incidence in the NCDB to the year 2030. Statistics were performed using modeling systems in SPSS, version 26. Results: For this model, 12 years of NCDB breast cancer diagnoses were used, which included n = 1,924,425 cases, overall. Using ARIMA modeling, a best fit, stationary average was identified with autoregressive and difference terms of 1 (ARIMA (1,1,0), coefficient = 0.598; P = 0.028). Of the multiple models tested the model with the lowest BIC was chosen, with a MAPE of 4.71%. The best fit model forecasted n = 325,048 new breast cancer diagnoses to be captured annually by the NCDB, by 2030. Conclusions: In this analysis, the annual breast cancer incidence within the NCDB is predicted to increase by 21%, by 2030. This forecast, while slightly lower than previously reported by the National Cancer Institute, utilizes more recent historical data that reflects a period of leveling-off in disease incidence, during 2014-2016, as reported to the NCDB. This innovative model can be utilized to proactively plan public health strategies and allocate appropriate resources focused on reducing the burden of cancer.

scholarly journals On Modeling Murder Crimes in Nigeria

2018 ◽  
pp. 157-162
Author(s):  
Obubu Maxwell ◽  
Ikediuwa Udoka Chinedu ◽  
Anabike Charles Ifeanyi ◽  
Nwokike Chukwudike C
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Data Set ◽  
Adequate Model ◽  
Resource Center ◽  
Moving Average Models ◽  
Augmented Dickey Fuller ◽  
Auto Regressive

This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

Modeling Time Series for Prediction of Thalassemia in Nineveh Governorate

2021 ◽  
Vol 2 (3) ◽  
pp. 120-131
Author(s):  
Shaymaa Riyadh Thanoon
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Monthly Data ◽  
Number Of Patients ◽  
Auto Regressive

The aim of this research is to analyze the time series of Thalassemia cancer cases by making assumptions on the number of cases to formulate the problem to find the best model for predicting the number of patients in Nineveh governorate using (Box and Jenkins) method of analysis based on the monthly data provided by Al Salam Hospital in Nineveh for the period (2014-2018). The results of the analysis showed that the appropriate model of analysis is the Auto-Regressive Integrated Moving Average (ARIMA) (2,1,0) and based on this model the number of people with this disease was predicted for the next two years where the results showed values ​​consistent with the original values which indicates the good quality of the model.

scholarly journals Influence of seasonality, salinity and temperature on catch trend of Penaeus indicus H. Milne-Edwards, 1837 in a coastal lagoon, India

2019 ◽  
Vol 66 (1) ◽  
Author(s):  
R.K. Raman ◽  
V.R. Suresh ◽  
S.K. Mohanty ◽  
K.S. Bhatta ◽  
S.K. Karna ◽  
...  
Keyword(s):  
Time Series ◽  
Coastal Lagoons ◽  
Moving Average ◽  
Positive Influence ◽  
Chilika Lagoon ◽  
Explanatory Variables ◽  
Structural Time Series ◽  
Water Temperature Data ◽  
Penaeus Indicus ◽  
Auto Regressive

The catch pattern of P. indicus in coastal lagoons is influenced by seasonal changes in physicochemical parameters of the lagoon ecosystem. In this study the effects of seasonality, salinity and water emperature of lagoon on P. indicus catch were analysed using Structural Time Series Model (STSM) and ARIMAX (Auto Regressive Integrated Moving Average with explanatory variables) modeling approach using monthly time series catch, salinity and water temperature data of the Chilika Lagoon (a Ramsar site) in India for the period from 2001 to 2015. Results showed a significant (p<0.05) increasing stochastic upward trend and two seasonal cycles for P. indicus catch in the lagoon. Salinity was found to have significant positive influence (p<0.05) and temperature to have insignificant positive influence on P. indicus catch in the lagoon.

scholarly journals Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems

2021 ◽  
Author(s):  
Ines Sansa ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Cloud Cover ◽  
Moving Average ◽  
Arma Model ◽  
Weather Conditions ◽  
Pv Systems ◽  
Auto Regressive ◽  
Micro Grid ◽  
One Year

Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.

Moving average models—time series in m-dimensions

1980 ◽  
Vol 9 (5) ◽  
pp. 467-489 ◽  
Author(s):  
D.A. Voss ◽  
C.A. Oprian ◽  
L.A Aroian
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Moving Average Models
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