scholarly journals Time series analysis of foodborne diseases during 2012–2018 in Shenzhen, China

2021 ◽  
Author(s):  
Siguo Li ◽  
Zhao Peng ◽  
Yan Zhou ◽  
Jinzhou Zhang
Keyword(s):  
Disease Surveillance ◽  
Moving Average ◽  
Disease Incidence ◽  
Arima Model ◽  
Percentage Error ◽  
Foodborne Diseases ◽  
Foodborne Disease ◽  
Surveillance Network ◽  
Adequate Model ◽  
Incidence Data

AbstractThe present study aimed to use the autoregressive integrated moving average (ARIMA) model to forecast foodborne disease incidence in Shenzhen city and help guide efforts to prevent foodborne disease. The data of foodborne diseases in Shenzhen comes from the infectious diarrhea surveillance network, community foodborne disease surveillance network, and student foodborne disease surveillance network. The incidence data from January 2012 to December 2017 was used for the model-constructing, while the data from January 2018 to December 2018 was used for the model-validating. The mean absolute percentage error (MAPE) was used to assess the performance of the model. The monthly foodborne disease incidence from January 2012 to December 2017 in Shenzhen was between 954 and 32,863 with an incidence rate between 4.77 and 164.32/100,000 inhabitants. The ARIMA (1,1,0) was an adequate model for the change in monthly foodborne disease incidence series, yielding a MAPE of 5.34%. The mathematical formula of the ARIMA (1,1,0) model was (1 − B) × log(incidencet) = 0.04338 + εt/(1 + 0.51106B). The predicted foodborne disease incidences in the next three years were 635,751, 1,069,993, 1,800,838, respectively. Monthly foodborne disease incidence in Shenzhen were shown to follow the ARIMA (1,1,0) model. This model can be considered adequate for predicting future foodborne disease incidence in Shenzhen and can aid in the decision-making processes.

scholarly journals Comparing Statistical Models to Predict Dengue Fever Notifications

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Arul Earnest ◽  
Say Beng Tan ◽  
Annelies Wilder-Smith ◽  
David Machin
Keyword(s):  
Dengue Fever ◽  
Statistical Models ◽  
Disease Surveillance ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Problem ◽  
Percentage Error ◽  
Mosquito Vector ◽  
Autoregressive Integrated Moving Average ◽  
The Mean

Dengue fever (DF) is a serious public health problem in many parts of the world, and, in the absence of a vaccine, disease surveillance and mosquito vector eradication are important in controlling the spread of the disease. DF is primarily transmitted by the femaleAedes aegyptimosquito. We compared two statistical models that can be used in the surveillance and forecast of notifiable infectious diseases, namely, the Autoregressive Integrated Moving Average (ARIMA) model and the Knorr-Held two-component (K-H) model. The Mean Absolute Percentage Error (MAPE) was used to compare models. We developed the models using used data on DF notifications in Singapore from January 2001 till December 2006 and then validated the models with data from January 2007 till June 2008. The K-H model resulted in a slightly lower MAPE value of 17.21 as compared to the ARIMA model. We conclude that the models' performances are similar, but we found that the K-H model was relatively more difficult to fit in terms of the specification of the prior parameters and the relatively longer time taken to run the models.

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.

scholarly journals Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo State, Brazil, using a SARIMA model

2011 ◽  
Vol 27 (9) ◽  
pp. 1809-1818 ◽  
Author(s):  
Edson Zangiacomi Martinez ◽  
Elisângela Aparecida Soares da Silva
Keyword(s):  
Moving Average ◽  
Dengue Infection ◽  
Arima Model ◽  
Sao Paulo ◽  
São Paulo ◽  
Incidence Data ◽  
Paulo State ◽  
Control And Prevention ◽  
São Paulo State ◽  
Predicted Values

This study aimed to develop a forecasting model for the incidence of dengue in Ribeirão Preto, São Paulo State, Brazil, using time series analysis. The model was performed using the Seasonal Autoregressive Integrated Moving Average (SARIMA). Firstly, we fitted a model considering monthly notifications of cases of dengue recorded from 2000 to 2008 in Ribeirão Preto. We then extracted predicted values for 2009 from the adjusted model and compared them with the number of cases observed for that year. The SARIMA (2,1,3)(1,1,1)12 model offered best fit for the dengue incidence data. The results showed that the seasonal ARIMA model predicts the number of dengue cases very effectively and reliably, and is a useful tool for disease control and prevention.

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

Surveillance of Foodborne Disease IV. Dissemination and Uses of Surveillance Data

1997 ◽  
Vol 60 (6) ◽  
pp. 715-723 ◽  
Author(s):  
EWEN C. D. TODD ◽  
JOHN J. GUZEWICH ◽  
FRANK L. BRYAN
Keyword(s):  
Food Safety ◽  
Disease Surveillance ◽  
Information Needs ◽  
Hazard Analysis ◽  
Public Information ◽  
Surveillance Data ◽  
Foodborne Disease ◽  
Control Points ◽  
Surveillance Network ◽  
Critical Control Points

Comparisons of etiologic agents, vehicles, significant ingredients, place of mishandling, and method of food processing or preparation with specific contributory factors are particularly useful in identifying specific hazards, specifying operations that are candidates for designation as critical control points, and assessing risks. After foodborne disease surveillance data have been received, tabulated, and appropriately interpreted, summary information needs to be disseminated in a timely fashion to those who can use it for preventing foodborne diseases. This action should be taken at all levels of the surveillance network. Surveillance information is used to determine the need for food safety actions, which involves planning and implementing programs and assessing the effectiveness of the actions taken. Uses of the data include (a) developing new policies and procedures and revising priorities, (b) evaluating effectiveness of programs, (c) justifying food safety program budgets based on estimated costs offoodborne illness, (d) modifying regulations so that they relate to contemporary foodborne disease issues, (e) conducting hazard analyses and risk assessments and instituting programs oriented to hazard analysis critical control points (HACCP), (f) starting or improving a public information campaign and educating the public, (g) notifying and training food industry personnel, (h) training agency staff and public health students and professionals, and (i) identifying new problems and research needs from the data. Implementing these approaches will necessitate changes in traditional food safety activities. This four-part series of articles concludes with recommendations to be considered by local, state/provincial, national, and international agencies responsible for foodborne disease surveillance.

Surveillance of Foodborne Disease II. Summary and Presentation of Descriptive Data and Epidemiologic Patterns; Their Value and Limitations

1997 ◽  
Vol 60 (5) ◽  
pp. 567-578 ◽  
Author(s):  
FRANK L. BRYAN ◽  
JOHN J. GUZEWICH ◽  
EWEN C. D. TODD
Keyword(s):  
Disease Surveillance ◽  
Foodborne Diseases ◽  
Foodborne Disease ◽  
Foodborne Illnesses ◽  
Descriptive Data ◽  
Contributory Factors ◽  
Common Diseases ◽  
Etiologic Agents ◽  
The Common

This second part of a four-part series on foodborne disease surveillance concentrates on tabulation of data to show the common diseases and to detect those emerging in a community, region or nation. Over several years, these data give a continued description of foodborne illnesses. The presentation starts with a summarization of the incidents, outbreaks, and cases that occur over a defined interval and continues with a breakdown of specific etiologic agents or diseases that make up the summary figures. Suggested tables showing time and place of occurrences are given. These, along with data on persons who acquire these diseases, form the epidemiologic patterns of foodborne diseases. Definitions of the terms used in the table and criteria for confirming categories of etiologic agents are given. The value and limitations of each set of data in the tables are critically reviewed. The same sort of presentation is continued for data on vehicles, method of processing and preparation, and contributory factors in the next part of this series.

scholarly journals Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Debasis Mithiya ◽  
Lakshmikanta Datta ◽  
Kumarjit Mandal
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Group Method ◽  
Data Handling ◽  
Agricultural Economy ◽  
Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

scholarly journals Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

scholarly journals Using Autoregressive Integrated Moving Average (ARIMA) Technique to Forecast the Production of Kharif Cereals in Odisha (India)

2020 ◽  
pp. 104-113
Author(s):  
Abhiram Dash ◽  
A. Mangaraju ◽  
Pradeep Mishra ◽  
H. Nayak
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Percentage Error ◽  
Arima Models ◽  
Training Set ◽  
Autoregressive Integrated Moving Average ◽  
Future Production ◽  
Best Fit ◽  
Testing Set

Cereals are the most important kharif season crop in Odisha. The present study was carried out to forecast the production of kharif cereals in Odisha by using the forecast values of area and yield of kharif cereals obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1970-71 to 2010-11 are considered as training set data and used for model building and from 2011-12 to 2015-16 are considered as testing set data and used for cross-validation of the selected model on the basis of the absolute percentage error. The ARIMA models are fitted to the stationary data which may be the original data or the differenced data. The different ARIMA models are evaluated on the basis of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) at various lags. The possible ARIMA models are selected on the basis of significant coefficient of autoregressive and moving average components by using the training set data. The best fitted models are then selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under kharif cereals and yield of kharif cereals are ARIMA (1,1,0) without constant and ARIMA (0,1,2) without constant respectively which are successfully cross-validated with the testing set data. The respective best fit ARIMA model has been used to forecast the area and yield of kharif cereals for the years 2016-17, 2017-18 and 2018-19. The forecast values of area shows a decrease, whereas, the forecast values of yield shows an increase. The decrease in area might have been the result of limited availability of area for cereals due to shifting towards non-food grain crops. The forecast values of production of kharif cereals obtained from the forecast values of area and yield of kharif cereals shows an increase which is due to the increase in forecast values of yield. Since there is limited scope for area expansion, the future production of kharif cereals can only be increased by increasing the yield to achieve the goal of food security for the growing population.

scholarly journals Descriptive Study of Foodborne Disease Using Case Monitoring Data in Shandong Province, China, 2016-2017

2019 ◽  
Author(s):  
Guangjian WU ◽  
Liansen WANG ◽  
Qiang WANG ◽  
Ru HAN ◽  
Jinshan ZHAO ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Disease Surveillance ◽  
Linear Trend ◽  
Incidence Rates ◽  
Shandong Province ◽  
Monitoring Data ◽  
Foodborne Diseases ◽  
Foodborne Disease ◽  
Chi Square ◽  
Cross Sectional

Background: In order to generate data on the burden of foodborne diseases in Shandong Province, we aimed to use the case monitoring data of foodborne diseases from 2016 to 2017 to estimate. Methods: Data were obtained from the foodborne disease surveillance reporting system with dates of onset from Jan 1, 2016, to Dec 31, 2017, in Shandong, China. Results: The places of food exposure were categorized by settings as follows: private home, catering facility, collective canteens, retail markets, rural banquets and other. Exposed food is divided into 23 categories. Overall incidence rate and proportions by exposure categories, age, and sex-specific incidence rates were calculated and sex proportions compared. Approximately 75.00% of cases who had at least one exposure settings were in private homes. The most frequently reported exposed food was a variety of food (meaning more than two kinds of food). The two-year average incidence rate was 75.78/100,000, sex-specific incidence rate was much higher for females compared to males (78.23 vs. 74.69 cases per 100,000 population). An age-specific trend was observed in the cases reported (Chi-Square for linear trend, χ2=4.39, P=0.036<0.05). Conclusion: A preliminary estimate of 14 million cases of foodborne diseases in Shandong province each year. Future studies should focus on cross-sectional and cohort studies to facilitate the assessment of the distribution and burden of foodborne disease of the population in Shandong. Considering strengthening the burden of foodborne diseases in foodborne disease surveillance is also a feasible way.

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