Zika Virus Speed and Direction: Reconstructing Zika Introduction in Brazil
Notice bibliographique
Résumé
ObjectiveTo estimate the velocity of Zika virus disease spread in Brazil usingdata on confirmed Zika virus disease cases at the municipal-level.IntroductionLocal transmission of Zika virus has been confirmed in67 countries worldwide and in 46 countries or territories in theAmericas (1,2). On February 1, 2016 the World Health Organizationdeclared a Public Health Emergency of International Concern due tothe increase in microcephaly cases and other neurological disordersreported in Brazil (2). Several countries issued travel warnings forpregnant women travelling to Zika-affected countries with Brazil,Colombia, Ecuador, and El Salvador advising against pregnancy(3-7). The risk of local transmission in unaffected regions is unknownbut potentially significant where competent Zika vectors are present(8) and also given the additional complexities of sexual transmissionand population mobility (9,10). Despite the rapid spread of Zikavirus across the Americas and global concerns regarding its effectson fetuses, little is known about the pattern of spread. Knowledge ofthe direction and the speed of movement of disease is invaluable forpublic health response planning, including the timing and placementof interventions.MethodsData for this analysis were obtained from the Brazil Ministryof Health and consisted of confirmed cases of Zika virus disease.The centroids of the municipalities were taken in meters from theshapefiles and used to perform a surface trend analysis. Surfacetrend is a spatial interpolation method used to estimate continuoussurfaces from point data. The continuous surface of time to infectionwas estimated by regressing it against the X and Y coordinates. Timewas in days and X and Y coordinates were meters. Parameters wereestimated using least squares regression and velocity (in km per day)was obtained by inverting the final magnitude of the slope.ResultsData provided from the Brazil Ministry of Health on May 31,2016, indicated that Zika had been confirmed in 316 of the 5,564municipalities in Brazil representing 26 states, with six additionalmunicipalities identified from other reporting sources. Our modelsindicated a southward pattern of introduction of Zika starting fromthe northeast coast towards the southeastern coastal states of Rio deJanerio, Espírito Santo, and São Paulo. There was also a pattern ofwestern movement towards Bolivia. Overall, the average speed ofdiffusion was 42.1 km/day across all models was 6.9 km/day to amaximum of 634.1 km/day. The municipalities in the Northeast andNorth regions had the slowest speeds whereas the municipalities inthe Central-West and Southeast regions had the highest speeds. Thisis due to proximity of cases in time and space, with more cases havingoccurred closer in time and over larger areas in South, Southeast, andCentral-West regions resulting in faster rates of introduction.ConclusionsThe average speed of spread was 42 km per day and it tookapproximately five to six months for Zika to spread from thenortheastern coast to the southeastern coast and western border ofBrazil. The rapid spread of Zika can help us understand its possiblefuture directions and the pace at which it travels, which are key fortargeted mosquito control interventions, public health messaging, andtravel advisories. A multi-country analysis is needed to understand thecontinental spatial and temporal patterns of dispersion of Zika virus.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».