Your browser doesn't support javascript.
loading
A review of models applied to the geographic spread of Zika virus.
Li, Sabrina L; Messina, Jane P; Pybus, Oliver G; Kraemer, Moritz U G; Gardner, Lauren.
  • Li SL; School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.
  • Messina JP; School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.
  • Pybus OG; School of Global and Area Studies, University of Oxford, 12 Bevington Road, Oxford, OX2 6LH, UK.
  • Kraemer MUG; Department of Zoology, University of Oxford, 11a Mansfield Rd, Oxford, OX1 3SZ, UK.
  • Gardner L; Department of Zoology, University of Oxford, 11a Mansfield Rd, Oxford, OX1 3SZ, UK.
Trans R Soc Trop Med Hyg ; 115(9): 956-964, 2021 09 03.
Article en En | MEDLINE | ID: mdl-33570155
In recent years, Zika virus (ZIKV) has expanded its geographic range and in 2015-2016 caused a substantial epidemic linked to a surge in developmental and neurological complications in newborns. Mathematical models are powerful tools for assessing ZIKV spread and can reveal important information for preventing future outbreaks. We reviewed the literature and retrieved modelling studies that were developed to understand the spatial epidemiology of ZIKV spread and risk. We classified studies by type, scale, aim and applications and discussed their characteristics, strengths and limitations. We examined the main objectives of these models and evaluated the effectiveness of integrating epidemiological and phylogeographic data, along with socioenvironmental risk factors that are known to contribute to vector-human transmission. We also assessed the promising application of human mobility data as a real-time indicator of ZIKV spread. Lastly, we summarised model validation methods used in studies to ensure accuracy in models and modelled outcomes. Models are helpful for understanding ZIKV spread and their characteristics should be carefully considered when developing future modelling studies to improve arbovirus surveillance.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Epidemias / Virus Zika / Infección por el Virus Zika Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans / Newborn Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Epidemias / Virus Zika / Infección por el Virus Zika Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans / Newborn Idioma: En Año: 2021 Tipo del documento: Article