The global distribution of yellow fever and dengue

Abstract

Yellow fever has been subjected to partial control for decades, but there are signs that case numbers are now increasing globally, with the risk of local epidemic outbreaks. Dengue case numbers have also increased dramatically during the past 40 years and different serotypes have invaded new geographical areas. Despite the temporal changes in these closely related diseases, and their enormous public health impact, few attempts have been made to collect a comprehensive dataset of their spatial and temporal distributions. For this review, records of the occurrence of both diseases during the 20th century have been collected together and are used to define their climatic limits using remotely sensed satellite data within a discriminant analytical model framework. The resulting risk maps for these two diseases identify their different environmental requirements, and throw some light on their potential for co-occurrence in Africa and South East Asia.

Figure 1

Yellow fever transmission cycles for (a) South America and (b) Africa. Simian host species in South America include Alouatta sp., Ateles sp., Callithrix sp., Cebus sp. and Saimiri sp. while those in Africa include Colobus abyssinicus, C. polycomos, C. badius, Cercopithicus sp., Cercocebus sp., Erythrocebus sp., Papio papio, P. anubis and Pan troglodytes.

Figure 2

Global distribution of Aedes aegypti (top) and Aedes albopictus (bottom), two important vector species of yellow fever and dengue. Aedes albopictus distributions are provided at national scale. Australia, New Zealand and South Africa have all reported mosquito interception at ports (see Tatem et al., this volume, pp. 293–343). Source: Center for International Earth Science Information Network (CIESIN) [http://www.ciesin.org/docs/001-613/map15.gif], supplemented with information from Gratz (2004); Gubler (2003); Lounibos (2002); Medlock et al. (2005); Moore (1999); and Moore and Mitchell (1997).

Figure 3

Plot of the number of yellow fever cases and the number of vaccinations in French West Africa for the period of 1934–1953, demonstrating the effect of the French vaccine on case numbers. Source: Vainio and Cutts (1998).

Figure 4

Yellow fever vaccination certificate requirements by country. □E1 requirements, E2 requirements E3 requirements E4 requirements ■E5 requirements. There are five levels of certification: E1—immunisation is an essential requirement for entry to the country concerned and a certificate is required, except for infants under one year, E2—immunisation is an essential requirement for entry to the country concerned and a certificate is required (except for infants under one year) unless arriving from non-infected areas and staying for less than two weeks, E3—immunisation is an essential requirement for entry to the country concerned and a certificate is required if the traveller arrives from an infected country or area, E4—immunisation is an essential requirement for entry to the country concerned and a certificate is required if arriving within six days of having visited an infected country, E5—immunisation is an essential requirement for entry to the country concerned and a certificate is required for entry to the country from endemic areas, travelling to Easter island. Source: From data obtained at [http://www.dh.gov.uk/PolicyAndGuidance/HealthAdviceForTravellers/GeneralHealthAdvice/Diseases/DiseasesArticle/fs/en?].

Figure 5

Yellow fever outbreak distribution for 1900–1959 (top) and 1960–2005 (bottom). The maps are displayed between 40°N and 40°S as these latitudes encompass all known areas of the disease.

Figure 6

All-sera dengue outbreak distribution for 1960–2005. The map is displayed between 40°N and 40°S as these latitudes encompass all known areas of the disease.

Plate 6.7

Risk map for yellow fever. This risk map is the average of 100 bootstrap models each based on a sample of 300 presence and 300 absence pixels selected at random with replacement from the training set for this disease. Risk is on a probability scale from zero to 1.0. Probabilities from 0.0 to 0.49 are coloured green (darker to lighter) and indicate conditions not suitable for the disease (i.e. predicted absence of disease). Probabilities from 0.50 to 1.0 are coloured yellow through to dark red, indicating conditions increasingly suitable for the disease. The database observations of presence are indicated by the blue dots and the WHO 2003 map for yellow fever by the thick black outline.

Plate 6.7

Risk map for yellow fever. This risk map is the average of 100 bootstrap models each based on a sample of 300 presence and 300 absence pixels selected at random with replacement from the training set for this disease. Risk is on a probability scale from zero to 1.0. Probabilities from 0.0 to 0.49 are coloured green (darker to lighter) and indicate conditions not suitable for the disease (i.e. predicted absence of disease). Probabilities from 0.50 to 1.0 are coloured yellow through to dark red, indicating conditions increasingly suitable for the disease. The database observations of presence are indicated by the blue dots and the WHO 2003 map for yellow fever by the thick black outline.

Plate 6.8

Risk map for dengue. This risk map is the average of 100 bootstrap models each based on a sample of 900 presence and 900 absence pixels selected at random with replacement from the training set for this disease. Risk is on a probability scale from zero to 1.0. Probabilities from 0.0 to 0.49 are coloured green (darker to lighter) and indicate conditions not suitable for the disease (i.e. predicted absence of disease). Probabilities from 0.50 to 1.0 are coloured yellow through to dark red, indicating conditions increasingly suitable for the disease. The database observations of presence are indicated by the blue dots and the WHO 2003 map for dengue by the thick black outline.

Plate 6.8

Risk map for dengue. This risk map is the average of 100 bootstrap models each based on a sample of 900 presence and 900 absence pixels selected at random with replacement from the training set for this disease. Risk is on a probability scale from zero to 1.0. Probabilities from 0.0 to 0.49 are coloured green (darker to lighter) and indicate conditions not suitable for the disease (i.e. predicted absence of disease). Probabilities from 0.50 to 1.0 are coloured yellow through to dark red, indicating conditions increasingly suitable for the disease. The database observations of presence are indicated by the blue dots and the WHO 2003 map for dengue by the thick black outline.

Plate 1.3

Results from the 100 bootstrap models for (left) Rift Valley Fever, (middle) Yellow Fever and (right) Dengue. Each row in the image refers to one of the models, which are arranged in rank order, with 1 (lowest AIC_c value) at the top and 100 (highest AIC_c value) at the bottom. Each of the 31 columns on the right of the image indicates one of the satellite predictor variables available to describe the disease. The first column of these 31 columns is for the digital elevation layer or DEM. There then follow three sets of 10 columns referring to the Fourier-processed AVHRR MIR, LST and NDVI imagery. These layers are in the following order: mean, phase of annual cycle, amplitude of annual cycle; phase of bi-annual cycle, amplitude of bi-annual cycle; phase of tri-annual cycle, amplitude of tri-annual cycle; maximum of fitted Fourier cycles (summed annual to tri-annual), minimum of fitted Fourier cycles and variance of the original signal. In any single model (row) the top (i.e. first selected) predictor variable is coloured red, the second most important variable is coloured orange and so on according to the rainbow colour scale to the right of the image. Variables not chosen in any model are not coloured at all in that row. The red line down the first image indicates variable 14 in the variable list, which is the annual amplitude of LST. This variable is consistently chosen in all RVF models, and is usually the most important variable, but there is no other single variable which is consistently chosen second. (The left-most column refers to the model number in the sequence; this, and the grey area to the left of the variable columns should be ignored.) The other two images may be similarly interpreted (see Rogers et al., this volume, pp. 181–220, for more details).

Figure 10

(a) Total numbers of humans within each category of yellow fever or dengue risk as shown in Figures 8 and 9 (thick lines, millions scale) and numbers of 0.10° grid squares within each risk category (dashed lines, thousands scale). (b) Mean human population density per 0.10° grid square for each category of yellow fever and dengue risk.

Figure 11

Reported number of yellow fever outbreaks per year per continent. Source: Weekly Epidemiological Record archives.