scholarly journals Forecast number of new cases of Corona Virus Disease (COVID-19) in Ethiopia, using the case-based autoregressive integrated moving average model

2020 ◽  
pp. 017-022
Author(s):  
Eticha Alemu Bekele
Keyword(s):  
Virus Disease ◽  
Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Corona Virus ◽  
Moving Average Model ◽  
Case Based

scholarly journals Predicting the detection of leprosy in a hyperendemic area of Brazil: Using time series analysis

2021 ◽  
Vol 0 ◽  
pp. 1-9
Author(s):  
Vera Gregório ◽  
Dinilson Pedroza ◽  
Celivane Barbosa ◽  
Gilberto Bezerra ◽  
Ulisses Montarroyos ◽  
...  
Keyword(s):  
Time Series ◽  
Information System ◽  
Moving Average ◽  
Northeastern Brazil ◽  
Average Model ◽  
Detection Rates ◽  
Autoregressive Integrated Moving Average ◽  
Notifiable Diseases ◽  
Hyperendemic Area ◽  
Moving Average Model

Background: Brazil has the second highest prevalence of leprosy worldwide. Autoregressive integrated moving average models are useful tools in surveillance systems because they provide reliable forecasts from epidemiological time series. Aim: To evaluate the temporal patterns of leprosy detection from 2001 to 2015 and forecast for 2020 in a hyperendemic area in northeastern Brazil. Methods: A cross-sectional study was conducted using monthly leprosy detection from the Brazil information system for notifiable diseases. The Box–Jenkins method was applied to fit a seasonal autoregressive integrated moving average model. Forecasting models (95% prediction interval) were developed to predict leprosy detection for 2020. Results: A total of 44,578 cases were registered with a mean of 247.7 cases per month. The best-fitted model to make forecasts was the seasonal autoregressive integrated moving average ((1,1,1); (1,1,1)). It was predicted 0.32 cases/100,000 inhabitants to January of 2016 and 0.38 cases/100,000 inhabitants to December of 2020. Limitations: This study used secondary data from Brazil information system for notifiable diseases; hence, leprosy data may be underreported. Conclusion: The forecast for leprosy detection rate for December 2020 was < 1 case/100,000 inhabitants. Seasonal autoregressive integrated moving average model has been shown to be appropriate and could be used to forecast leprosy detection rates. Thus, this strategy can be used to facilitate prevention and elimination programmes.

scholarly journals Forecasting of salmonellosis epidemic proces in Ukraine using autoregressive integrated moving average model

2020 ◽  
pp. 346-354
Author(s):  
Serhii Bohdanov ◽  
Yulia Polyvianna ◽  
Tetyana Chumachenko ◽  
Dmytro Chumachenko
Keyword(s):  
Moving Average ◽  
Incidence Rates ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
Complex Models ◽  
Optimal Values ◽  
Predicted Values

The article highlights the problem of salmonellosis among the population of the Kharkov region, Ukraine. Three time series were used for calculations: a series of incidence rates for men, a series of incidence rates for women and a series of incidence rates for the general population, each of the series was an ordered set of monthly values from December 2015 to December 2018. It was revealed that the most effective tool for analyzing these statistical data is the use of the autoregressive moving average model (ARIMA). The following steps were used: identification and replacement of outliers, the use of smoothing and decomposition of the series. The developed model allows you to explicitly indicate the order of the model using the arima () function or automatically generate a set of optimal values (p, d, q) using the auto.arima () function. The validated model allows to calculate the predicted values of the incidence of salmonellosis for 50 days. In certain cases, models of exponential smoothing are able to give forecasts that are not inferior in accuracy to forecasts obtained using more complex models.

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