Cargando…

Predictable Chikungunya Infection Dynamics in Brazil

Chikungunya virus (CHIKV) was first imported into the Caribbean in 2013 and subsequently spread across the Americas. It has infected millions in the region and Brazil has become the hub of ongoing transmission. Using Seasonal Autoregressive Integrated Moving Average (SARIMA) models trained and valid...

Descripción completa

Detalles Bibliográficos
Autor principal: Yakob, Laith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505030/
https://www.ncbi.nlm.nih.gov/pubmed/36146696
http://dx.doi.org/10.3390/v14091889
_version_ 1784796370738085888
author Yakob, Laith
author_facet Yakob, Laith
author_sort Yakob, Laith
collection PubMed
description Chikungunya virus (CHIKV) was first imported into the Caribbean in 2013 and subsequently spread across the Americas. It has infected millions in the region and Brazil has become the hub of ongoing transmission. Using Seasonal Autoregressive Integrated Moving Average (SARIMA) models trained and validated on Brazilian data from the Ministry of Health’s notifiable diseases information system, we tested the hypothesis that transmission in Brazil had transitioned from sporadic and explosive to become more predictable. Consistency weighted, population standardized kernel density estimates were used to identify municipalities with the most consistent inter-annual transmission rates. Spatial clustering was assessed per calendar month for 2017–2021 inclusive using Moran’s I. SARIMA models were validated on 2020–2021 data and forecasted 106,162 (95%CI 27,303–200,917) serologically confirmed cases and 339,907 (95%CI 35,780–1035,449) total notifications for 2022–2023 inclusive, with >90% of cases in the Northeast and Southeast regions. Comparing forecasts for the first five months of 2022 to the most up-to-date ECDC report (published 2 June 2022) showed remarkable accuracy: the models predicted 92,739 (95%CI 20,685–195,191) case notifications during which the ECDC reported 92,349 case notifications. Hotspots of consistent transmission were identified in the states of Para and Tocantins (North region); Rio Grande do Norte, Paraiba and Pernambuco (Northeast region); and Rio de Janeiro and eastern Minas Gerais (Southeast region). Significant spatial clustering peaked during late summer/early autumn. This analysis highlights how CHIKV transmission in Brazil has transitioned, making it more predictable and thus enabling improved control targeting and site selection for trialing interventions.
format Online
Article
Text
id pubmed-9505030
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-95050302022-09-24 Predictable Chikungunya Infection Dynamics in Brazil Yakob, Laith Viruses Article Chikungunya virus (CHIKV) was first imported into the Caribbean in 2013 and subsequently spread across the Americas. It has infected millions in the region and Brazil has become the hub of ongoing transmission. Using Seasonal Autoregressive Integrated Moving Average (SARIMA) models trained and validated on Brazilian data from the Ministry of Health’s notifiable diseases information system, we tested the hypothesis that transmission in Brazil had transitioned from sporadic and explosive to become more predictable. Consistency weighted, population standardized kernel density estimates were used to identify municipalities with the most consistent inter-annual transmission rates. Spatial clustering was assessed per calendar month for 2017–2021 inclusive using Moran’s I. SARIMA models were validated on 2020–2021 data and forecasted 106,162 (95%CI 27,303–200,917) serologically confirmed cases and 339,907 (95%CI 35,780–1035,449) total notifications for 2022–2023 inclusive, with >90% of cases in the Northeast and Southeast regions. Comparing forecasts for the first five months of 2022 to the most up-to-date ECDC report (published 2 June 2022) showed remarkable accuracy: the models predicted 92,739 (95%CI 20,685–195,191) case notifications during which the ECDC reported 92,349 case notifications. Hotspots of consistent transmission were identified in the states of Para and Tocantins (North region); Rio Grande do Norte, Paraiba and Pernambuco (Northeast region); and Rio de Janeiro and eastern Minas Gerais (Southeast region). Significant spatial clustering peaked during late summer/early autumn. This analysis highlights how CHIKV transmission in Brazil has transitioned, making it more predictable and thus enabling improved control targeting and site selection for trialing interventions. MDPI 2022-08-26 /pmc/articles/PMC9505030/ /pubmed/36146696 http://dx.doi.org/10.3390/v14091889 Text en © 2022 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yakob, Laith
Predictable Chikungunya Infection Dynamics in Brazil
title Predictable Chikungunya Infection Dynamics in Brazil
title_full Predictable Chikungunya Infection Dynamics in Brazil
title_fullStr Predictable Chikungunya Infection Dynamics in Brazil
title_full_unstemmed Predictable Chikungunya Infection Dynamics in Brazil
title_short Predictable Chikungunya Infection Dynamics in Brazil
title_sort predictable chikungunya infection dynamics in brazil
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505030/
https://www.ncbi.nlm.nih.gov/pubmed/36146696
http://dx.doi.org/10.3390/v14091889
work_keys_str_mv AT yakoblaith predictablechikungunyainfectiondynamicsinbrazil