title: Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010.
abstract: BACKGROUND
  Dengue fever, a mosquito-borne viral disease, is a rapidly emerging public health problem in Ecuador and throughout the tropics. However, we have a limited understanding of the disease transmission dynamics in these regions. Previous studies in southern coastal Ecuador have demonstrated the potential to develop a dengue early warning system (EWS) that incorporates climate and non-climate information. The objective of this study was to characterize the spatiotemporal dynamics and climatic and social-ecological risk factors associated with the largest dengue epidemic to date in Machala, Ecuador, to inform the development of a dengue EWS.
  
  
  METHODS
  The following data from Machala were included in analyses: neighborhood-level georeferenced dengue cases, national census data, and entomological surveillance data from 2010; and time series of weekly dengue cases (aggregated to the city-level) and meteorological data from 2003 to 2012. We applied LISA and Moran's I to analyze the spatial distribution of the 2010 dengue cases, and developed multivariate logistic regression models through a multi-model selection process to identify census variables and entomological covariates associated with the presence of dengue at the neighborhood level. Using data aggregated at the city-level, we conducted a time-series (wavelet) analysis of weekly climate and dengue incidence (2003-2012) to identify significant time periods (e.g., annual, biannual) when climate co-varied with dengue, and to describe the climate conditions associated with the 2010 outbreak.
  
  
  RESULTS
  We found significant hotspots of dengue transmission near the center of Machala. The best-fit model to predict the presence of dengue included older age and female gender of the head of the household, greater access to piped water in the home, poor housing condition, and less distance to the central hospital. Wavelet analyses revealed that dengue transmission co-varied with rainfall and minimum temperature at annual and biannual cycles, and we found that anomalously high rainfall and temperatures were associated with the 2010 outbreak.
  
  
  CONCLUSIONS
  Our findings highlight the importance of geospatial information in dengue surveillance and the potential to develop a climate-driven spatiotemporal prediction model to inform disease prevention and control interventions. This study provides an operational methodological framework that can be applied to understand the drivers of local dengue risk.
authors.affiliation: Department of Microbiology and Immunology, Center for Global Health and Translational Science, State University of New York Upstate Medical University, 750 East Adams St, Syracuse, NY, 13210, USA. stewarta@upstate.edu.
authors.forename: Anna M
authors.initials: AM
authors.lastname: Stewart-Ibarra
authors.affiliation: International Research Institute for Climate and Society (IRI), Earth Institute, Columbia University, New York, NY, USA. agmunoz@iri.columbia.edu.|Centro de Modelado Científico (CMC), Universidad del Zulia, Maracaibo, Venezuela. agmunoz@iri.columbia.edu.
authors.forename: Ángel G
authors.initials: ÁG
authors.lastname: Muñoz
authors.affiliation: Department of Microbiology and Immunology, Center for Global Health and Translational Science, State University of New York Upstate Medical University, 750 East Adams St, Syracuse, NY, 13210, USA. sjryan@ufl.edu.|Department of Geography, Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. sjryan@ufl.edu.|School of Life Sciences, College of Agriculture, Engineering, and Science, University of KwaZulu-Natal, Durban, South Africa. sjryan@ufl.edu.
authors.forename: Sadie J
authors.initials: SJ
authors.lastname: Ryan
authors.affiliation: The National Service for the Control of Vector-Borne Diseases, Ministry of Health, Machala, El Oro Province, Ecuador. felixbeltran57@hotmail.com.|Facultad de Medicina, Universidad Técnica de Machala, Machala, El Oro Province, Ecuador. felixbeltran57@hotmail.com.
authors.forename: Efraín Beltrán
authors.initials: EB
authors.lastname: Ayala
authors.affiliation: Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador. meborbor@espol.edu.ec.
authors.forename: Mercy J
authors.initials: MJ
authors.lastname: Borbor-Cordova
authors.affiliation: Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA. jfinkelstein@cornell.edu.|Center for Geographic Analysis, Harvard University, Cambridge, MA, USA. jfinkelstein@cornell.edu.
authors.forename: Julia L
authors.initials: JL
authors.lastname: Finkelstein
authors.affiliation: National Institute of Meteorology and Hydrology, Guayaquil, Ecuador. rmejia@inamhi.gob.ec.
authors.forename: Raúl
authors.initials: R
authors.lastname: Mejía
authors.affiliation: The National Service for the Control of Vector-Borne Diseases, Ministry of Health, Machala, El Oro Province, Ecuador. taniaordonez63@hotmail.com.
authors.forename: Tania
authors.initials: T
authors.lastname: Ordoñez
authors.affiliation: Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador. gcrecalde@gmail.com.|National Institute of Meteorology and Hydrology, Guayaquil, Ecuador. gcrecalde@gmail.com.
authors.forename: G Cristina
authors.initials: GC
authors.lastname: Recalde-Coronel
authors.affiliation: National Institute of Meteorology and Hydrology, Guayaquil, Ecuador. keytiar@gmail.com.
authors.forename: Keytia
authors.initials: K
authors.lastname: Rivero
mesh_terms: D000293:Adolescent; D000328:Adult; D000330:Aedes; D000368:Aged; D000369:Aged, 80 and over; D000818:Animals; D002648:Child; D002675:Child, Preschool; D002980:Climate; D003715:Dengue / Q000453:epidemiology* / Q000517:prevention & control / Q000635:transmission; D004196:Disease Outbreaks; D004484:Ecuador / Q000453:epidemiology; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007303:Insect Vectors; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012307:Risk Factors; D012959:Socioeconomic Factors; D062211:Spatio-Temporal Analysis; D013997:Time Factors