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

Martinez, Edson Zangiacomi; Silva, Elisângela Aparecida Soares da

doi:10.1590/s0102-311x2011000900014

Cited by 32 publications

(23 citation statements)

References 28 publications

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“…SARIMA models are potentially useful when there are time dependences between each observation [36,52]. The assumption that each observation is correlated to previous ones makes it possible to model a temporal structure, with more reliable predictions, especially for climate-sensitive diseases (e.g., mosquito-borne diseases), than those obtained by other statistical methods.…”

Section: Resultsmentioning

confidence: 99%

Climate change and dengue: a critical and systematic review of quantitative modelling approaches

et al. 2014

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BackgroundMany studies have found associations between climatic conditions and dengue transmission. However, there is a debate about the future impacts of climate change on dengue transmission. This paper reviewed epidemiological evidence on the relationship between climate and dengue with a focus on quantitative methods for assessing the potential impacts of climate change on global dengue transmission.MethodsA literature search was conducted in October 2012, using the electronic databases PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search focused on peer-reviewed journal articles published in English from January 1991 through October 2012.ResultsSixteen studies met the inclusion criteria and most studies showed that the transmission of dengue is highly sensitive to climatic conditions, especially temperature, rainfall and relative humidity. Studies on the potential impacts of climate change on dengue indicate increased climatic suitability for transmission and an expansion of the geographic regions at risk during this century. A variety of quantitative modelling approaches were used in the studies. Several key methodological issues and current knowledge gaps were identified through this review.ConclusionsIt is important to assemble spatio-temporal patterns of dengue transmission compatible with long-term data on climate and other socio-ecological changes and this would advance projections of dengue risks associated with climate change.

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Section: Resultsmentioning

confidence: 99%

Climate change and dengue: a critical and systematic review of quantitative modelling approaches

et al. 2014

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“…In 1970, Box and Jenkins introduced Autoregressive Integrated Moving Average models, ARIMA. SARIMA model is useful in situations when the time series data exhibit seasonality-periodic fluctuations that recur with about the same intensity periodically, for example, quarterly (Martinez et al, 2011). This property makes the SARIMA model appropriate for studies concerning quarterly inflation rate data.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Inflation Rate in Kenya Using SARIMA Model

Gikungu¹,

Waititu²,

Kihoro³

2015

AJTAS

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It is the desire of the policy makers in a country is to have access to reliable forecast of inflation rate. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast Kenya's inflation rate using quarterly data for the period 1981 to 2013 obtained from KNBS. SARIMA (0,1,0) (0,0,1) 4 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. Diagnostic checks using Jarque-Bera Normality Test indicated that they were normally distributed. ACF and PACF plots for the residuals and squared residuals revealed that they followed a white noise process and were homoskedastic respectively. The predictive ability tests RMSE=0.2871, MAPE=3.9456 and MAE= 0.2369 showed that the model was appropriate for forecasting the inflation rate in Kenya.

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“…Where μ is a constant, φ^'=(φ_1,φ_2,… ,φ_p) is a vector of autoregressive coefficients, θ'=(θ_1,θ_2,…,θ_p) is a vector of moving average coefficients, and ε_t are error terms assumed to be independent, identically-distributed random variables sampled from a distribution with mean equal to zero and variance σ_ε^2. In time series analyses, the variables ε_t are commonly referred to white noise, and they interpreted as an exogenous effect that the model is not able to explain (Martinez & Silva, 2011).…”

Section: Introductionmentioning

confidence: 99%

An An Approach to Measure the Death Impact of Covid-19 in Jakarta using Autoregressive Integrated Moving Average (ARIMA)

Fadly

Sari

2020

UJPH

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Coronavirus disease 2019 (COVID-19) is a pandemic in more than 200 countries around the world. As the fourth most populous nation in the world, Indonesia is predicted to face a big threat to this pandemic particularly Jakarta as the epicenter of the virus in Indonesia. However, the nature of COVID-19 that can easily spread and also many undetected cases that do not present symptoms make it more difficult to determine the real mortality effects of COVID-19.The deaths in Jakarta from the new coronavirus may be higher than officially reported. To overcome this issue, this paper will provide an approach to measure the death impact of COVID-19 using the Autoregressive Integrated Moving Average model (ARIMA). The model will predict the ‘what if’ normal condition of the number of funerals in Jakarta compared to the real situation in March 2020 as an approach of the actual effect of COVID-19 in Jakarta. This research revealed a discrepancy of 450-1070 funerals in March 2020 that could not be predicted by the ARIMA model. This funeral gap, a forecast error, could be an approach to the potential number of possible death impacts of COVID-19 in Jakarta that should be significantly higher than the report. The people should be more conscious and alert of COVID-19 situation.

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

Abstract: Abstract

Cited by 32 publications

References 28 publications

Climate change and dengue: a critical and systematic review of quantitative modelling approaches

Climate change and dengue: a critical and systematic review of quantitative modelling approaches

Forecasting Inflation Rate in Kenya Using SARIMA Model

An An Approach to Measure the Death Impact of Covid-19 in Jakarta using Autoregressive Integrated Moving Average (ARIMA)

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