Fig 1.
Geography and arbovirus seasonality of Ecuador.
(A) Map of Ecuador, colored to indicate geographic and climate regions (coast, mountains, vs. Amazon). The base layer of the map is publicly provided by INEC [41]. (B) Temperature (grey dots and dotted lines), precipitation (dots colored by region, solid black line), and monthly distribution of arbovirus cases (black histograms). Annual province-level temperature and precipitation data from 2005–2019 were derived from the Universidad Tecnológica Equinoccial (UTE)/Centro de Investigación en Salud Pública y Epidemiología Clínica (CISPEC) database. Trend lines were fitted using local weighted regression (LOESS) models [42,43]. Monthly arbovirus case distributions for dengue, Zika, and chikungunya from 2014 to 2017 are derived from the ViEpi dataset.
Fig 2.
Age-specific incidence of dengue, normalized by year, for each province and period.
Data are fitted using LOESS regression with 95% confidence intervals. Data are from the INEC database.
Table 1.
Median and interquartile range (IQR) of population-adjusted dengue incidence by province, from 2000–2009 and 2010–2019.
Median and IQR of the population during the same period are shown for reference.
Fig 3.
DENV FoI estimates for provinces across Ecuador.
Point estimates are colored by geographic region, 95% credible intervals are shown.
Fig 4.
Age-specific incidence of laboratory-confirmed and suspected Dengue with Warning Signs/Severe Dengue, chikungunya and Zika for each province in 2015–2016.
Data are fitted using LOESS regression with 95% confidence intervals. In some provinces, unusual distributions for Zika suggest case-confirmation focused on specific age groups.
Fig 5.
Age-stratified incidence of different arboviral diseases by time since emergence.
Expected changes in age-specific incidence for a viral disease that emerged 20 years ago with an annual FoI of 50/1000, assuming the first infection causes disease (like chikungunya or Zika) vs. when there are four circulating viruses that emerged 20 years ago each with an annual FoI of 50/1000 and the second infection causes disease (like dengue).
Fig 6.
Maximum entropy geographic models using environmental and demographic variables to predict Aedes aegypti presence data.
The base layer of the map is publicly provided by INEC [41].
Table 2.
Variable importance of risk factors predicting Aedes aegypti presence data.