Adaptive Lossy Compression of Complex Environmental Indices Using Seasonal Auto-Regressive Integrated Moving Average Models

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.

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A new hybrid for improvement of auto-regressive integrated moving average models applying particle swarm optimization

Expert Systems with Applications ◽

10.1016/j.eswa.2011.11.002 ◽

2012 ◽

Vol 39 (5) ◽

pp. 5332-5337 ◽

Cited By ~ 19

Author(s):

Shahrokh Asadi ◽

Akbar Tavakoli ◽

Seyed Reza Hejazi

Keyword(s):

Particle Swarm Optimization ◽

Moving Average ◽

Particle Swarm ◽

Swarm Optimization ◽

Moving Average Models ◽

Auto Regressive

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Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA)

10.1101/2020.05.03.20089615 ◽

2020 ◽

Author(s):

Belkacem Balah ◽

Messaoud Djeddou

Keyword(s):

Long Memory ◽

Moving Average ◽

Prediction Method ◽

Decision Support Tool ◽

Upward Trend ◽

Support Tool ◽

Moving Average Models ◽

Auto Regressive ◽

Health Database ◽

Arfima Model

AbstractIn this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks ahead. In the present study, public health database from Algeria health ministry has been used to build an ARFIMA model and used to forecast COVID-19 new cases in Algeria until May 11, 2020.BackgroundThe aim of this study is first to find the best prediction method among the two techniques used and type of memory, either short or long, of the model constructed for the daily confirmed cases in Algeria, then make forecasts of the confirmed cases in the fifteen next days.MethodsThis study was conducted based on daily new cases of COVID-19 that were collected from the official website of Algerian Ministry of Health from March 1, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARFIMA) model was used to predict the trend of confirmed cases. The evaluation of the fractional differentiation parameter (d) is carried out using OxMetrics 6 software.ResultsThe ARFIMA model (0, 0.431779, 0) build for Algeria, has a long memory and an upward trend over the next fifteen days and which coincides with the holy month of Ramadhan.ConclusionsThe forecasted results obtained by the proposed ARFIMA model can be used as a decision support tool to manage medical efforts and facilities against the COVID-19 pandemic crisis.

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