Some developments on seasonal INAR processes with application to influenza data.

Fatimah E Almuhayfith, Emmanuel W Okereke, Manik Awale, Hassan S Bakouch, Hana N Alqifari

Abstract read

In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Fatimah E AlmuhayfithDepartment of Mathematics and Statistics, College of Science, King Faisal University, Alahsa, 31982, Saudi Arabia. falmuhaifeez@kfu.edu.sa.

Emmanuel W OkerekeDepartment of Statistics, Michael Okpara University of Agriculture, Umudike, Nigeria.

Manik AwaleDepartment of Statistics, Savitribai Phule Pune University, Pune, 411007, India.

Hassan S BakouchDepartment of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia.

Hana N AlqifariDepartment of Statistics and Operation Research, College of Science, Qassim University, Buraydah, 51482, Saudi Arabia.

Funding

Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia 3290

6 · The paper itself

Abstract

Influenza epidemic data are seasonal in nature. Zero-inflation, zero-deflation, overdispersion, and underdispersion are frequently seen in such number of cases of disease (count) data. To explain these counts' features, this paper introduces a flexible model for nonnegative integer-valued time series with a seasonal autoregressive structure. Some probabilistic properties of the model are discussed for general seasonal INAR(p) model and three estimation methods are used to estimate the model parameters for its special case seasonal INAR(1) model. The performance of the estimation procedures has been studied using simulation. The proposed model is applied to analyze weekly influenza data from the Breisgau- Hochschwarzwald county of Baden-Württemberg state, Germany. The empirical findings show that the suggested model performs better than existing models.

Indexed as

Influenza, HumanModels, StatisticalComputer SimulationHumansSeasonsTime Factors

Identifiers

PMID38086947

DOI10.1038/s41598-023-48805-y

PMCPMC10716150

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.