Cargando…

Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020

Background: Brucellosis runs rampant endemically with sporadic outbreaks in Algeria. The present study aimed to provide insights into the epidemiology of brucellosis and compare the performance of some prediction models using surveillance data from Tebessa province, Algeria. Study Design: A retrospe...

Descripción completa

Detalles Bibliográficos
Autores principales: Akermi, Seif Eddine, L’Hadj, Mohamed, Selmane, Schehrazad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hamadan University of Medical Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315461/
https://www.ncbi.nlm.nih.gov/pubmed/36511254
http://dx.doi.org/10.34172/jrhs.2022.79
_version_ 1784754567600144384
author Akermi, Seif Eddine
L’Hadj, Mohamed
Selmane, Schehrazad
author_facet Akermi, Seif Eddine
L’Hadj, Mohamed
Selmane, Schehrazad
author_sort Akermi, Seif Eddine
collection PubMed
description Background: Brucellosis runs rampant endemically with sporadic outbreaks in Algeria. The present study aimed to provide insights into the epidemiology of brucellosis and compare the performance of some prediction models using surveillance data from Tebessa province, Algeria. Study Design: A retrospective study. Methods: Seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly brucellosis notifications. The prediction performance of these models was compared using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results: Overall, 13670 human brucellosis cases were notified in Tebessa province from 2000-2020 with a male-to-female ratio of 1.3. The most affected age group was 15-44 years (56.2%). The cases were reported throughout the year with manifest seasonality. The annual notification rate ranged from 30.9 (2013) to 246.7 (2005) per 100000 inhabitants. The disease was not evenly distributed, rather spatial and temporal variability was observed. The SARIMA (2,1,3) (1,1,1)(12(')), NNAR (12,1,6)(12(') ), and SARIMA (2,0,2) (1,1,0)(12)- NNAR (5,1,4)(12) were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were by far lower than those of the NNAR model. Moreover, the SARIMA-NNNAR hybrid model achieved a slightly better prediction accuracy for 2020 than the SARIMA model. Conclusion: As evidenced by the obtained results, both SARIMA and hybrid SARIMA-NNAR models are suitable to predict human brucellosis cases with high accuracy. Reasonable predictions, along with mapping brucellosis incidence, could be of great help to veterinary and health policymakers in the development of informed, effective, and targeted policies, as well as timely interventions.
format Online
Article
Text
id pubmed-9315461
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Hamadan University of Medical Sciences
record_format MEDLINE/PubMed
spelling pubmed-93154612022-08-10 Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020 Akermi, Seif Eddine L’Hadj, Mohamed Selmane, Schehrazad J Res Health Sci Original Article Background: Brucellosis runs rampant endemically with sporadic outbreaks in Algeria. The present study aimed to provide insights into the epidemiology of brucellosis and compare the performance of some prediction models using surveillance data from Tebessa province, Algeria. Study Design: A retrospective study. Methods: Seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly brucellosis notifications. The prediction performance of these models was compared using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results: Overall, 13670 human brucellosis cases were notified in Tebessa province from 2000-2020 with a male-to-female ratio of 1.3. The most affected age group was 15-44 years (56.2%). The cases were reported throughout the year with manifest seasonality. The annual notification rate ranged from 30.9 (2013) to 246.7 (2005) per 100000 inhabitants. The disease was not evenly distributed, rather spatial and temporal variability was observed. The SARIMA (2,1,3) (1,1,1)(12(')), NNAR (12,1,6)(12(') ), and SARIMA (2,0,2) (1,1,0)(12)- NNAR (5,1,4)(12) were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were by far lower than those of the NNAR model. Moreover, the SARIMA-NNNAR hybrid model achieved a slightly better prediction accuracy for 2020 than the SARIMA model. Conclusion: As evidenced by the obtained results, both SARIMA and hybrid SARIMA-NNAR models are suitable to predict human brucellosis cases with high accuracy. Reasonable predictions, along with mapping brucellosis incidence, could be of great help to veterinary and health policymakers in the development of informed, effective, and targeted policies, as well as timely interventions. Hamadan University of Medical Sciences 2021-10-31 /pmc/articles/PMC9315461/ /pubmed/36511254 http://dx.doi.org/10.34172/jrhs.2022.79 Text en © 2022 The Author(s); Published by Hamadan University of Medical Sciences. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Akermi, Seif Eddine
L’Hadj, Mohamed
Selmane, Schehrazad
Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020
title Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020
title_full Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020
title_fullStr Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020
title_full_unstemmed Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020
title_short Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020
title_sort epidemiology and time series analysis of human brucellosis in tebessa province, algeria, from 2000 to 2020
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315461/
https://www.ncbi.nlm.nih.gov/pubmed/36511254
http://dx.doi.org/10.34172/jrhs.2022.79
work_keys_str_mv AT akermiseifeddine epidemiologyandtimeseriesanalysisofhumanbrucellosisintebessaprovincealgeriafrom2000to2020
AT lhadjmohamed epidemiologyandtimeseriesanalysisofhumanbrucellosisintebessaprovincealgeriafrom2000to2020
AT selmaneschehrazad epidemiologyandtimeseriesanalysisofhumanbrucellosisintebessaprovincealgeriafrom2000to2020