Climate drivers of vector-borne diseases in Africa and their relevance to control programmes

Abstract

Background: Climate-based disease forecasting has been proposed as a potential tool in climate change adaptation for the health sector. Here we explore the relevance of climate data, drivers and predictions for vector-borne disease control efforts in Africa.
Methods: Using data from a number of sources we explore rainfall and temperature across the African continent, from seasonality to variability at annual, multi-decadal and timescales consistent with climate change. We give particular attention to three regions defined as WHO-TDR study zones in Western, Eastern and Southern Africa. Our analyses include 1) time scale decomposition to establish the relative importance of year-to-year, decadal and long term trends in rainfall and temperature; 2) the impact of the El Niño Southern Oscillation (ENSO) on rainfall and temperature at the Pan African scale; 3) the impact of ENSO on the climate of Tanzania using high resolution climate products and 4) the potential predictability of the climate in different regions and seasons using Generalized Relative Operating Characteristics. We use these analyses to review the relevance of climate forecasts for applications in vector borne disease control across the continent.
Results: Timescale decomposition revealed long term warming in all three regions of Africa – at the level of 0.1–0.3 °C per decade. Decadal variations in rainfall were apparent in all regions and particularly pronounced in the Sahel and during the East African long rains (March–May). Year-to-year variability in both rainfall and temperature, in part associated with ENSO, were the dominant signal for climate variations on any timescale. Observed climate data and seasonal climate forecasts were identified as the most relevant sources of climate information for use in early warning systems for vector-borne diseases but the latter varied in skill by region and season.
Conclusions: Adaptation to the vector-borne disease risks of climate variability and change is a priority for government and civil society in African countries. Understanding rainfall and temperature variations and trends at multiple timescales and their potential predictability is a necessary first step in the incorporation of relevant climate information into vector-borne disease control decision-making.
Keywords: Vector-borne diseases, Climate variability, Climate change, El Niño southern oscillation, Climate services, Adaptation, Africa

Full text

R E S E A R C H A R T I C L E Open Access
Climate drivers of vector-borne diseases in
Africa and their relevance to control
programmes
Madeleine C. Thomson
1,2,3,6*
, Ángel G. Muñoz
4,1
, Remi Cousin
1
and Joy Shumake-Guillemot
5
Abstract
Background: Climate-based disease forecasting has been proposed as a potential tool in climate change adaptation
for the health sector. Here we explore the relevance of climate data, drivers and predictions for vector-borne disease
control efforts in Africa.
Methods: Using data from a number of sources we explore rainfall and temperature across the African continent, from
seasonality to variability at annual, multi-decadal and timescales consistent with climate change. We give particular
attention to three regions defined as WHO-TDR study zones in Western, Eastern and Southern Africa. Our analyses
include 1) time scale decomposition to establish the relative importance of year-to-year, decadal and long term trends
in rainfall and temperature; 2) the impact of the El Niño Southern Oscillation (ENSO) on rainfall and temperature at the
Pan African scale; 3) the impact of ENSO on the climate of Tanzania using high resolution climate products and 4) the
potential predictability of the climate in different regions and seasons using Generalized Relative Operating
Characteristics. We use these analyses to review the relevance of climate forecasts for applications in vector
borne disease control across the continent.
Results: Timescale decomposition revealed long term warming in all three regions of Africa –at the level of
0.1–0.3 °C per decade. Decadal variations in rainfall were apparent in all regions and particularly pronounced
in the Sahel and during the East African long rains (March–May). Year-to-year variability in both rainfall and
temperature, in part associated with ENSO, were the dominant signal for climate variations on any timescale.
Observed climate data and seasonal climate forecasts were identified as the most relevant sources of climate
information for use in early warning systems for vector-borne diseases but the latter varied in skill by region
and season.
Conclusions: Adaptation to the vector-borne disease risks of climate variability and change is a priority for
government and civil society in African countries. Understanding rainfall and temperature variations and trends at
multiple timescales and their potential predictability is a necessary first step in the incorporation of relevant climate
information into vector-borne disease control decision-making.
Keywords: Vector-borne diseases, Climate variability, Climate change, El Niño southern oscillation, Climate services,
Adaptation, Africa
* Correspondence: mthomson@iri.columbia.edu
1
International Research Institute for Climate and Society (IRI), Earth Institute,
Columbia University, New York, USA
2
Mailman School of Public Health Department of Environmental Health
Sciences, Columbia University, New York, USA
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Thomson et al. Infectious Diseases of Poverty (2018) 7:81
https://doi.org/10.1186/s40249-018-0460-1

Multilingual abstracts
Please see Additional file 1for translations of the
abstract into the six official working languages of the
United Nations.
Background
Climate and vector borne disease
Many parasitic, viral, and bacterial diseases respond to
variations in the climate whether through their
geographic distribution, seasonality, inter-annual vari-
ability, or temporal and spatial trends. Detailed re-
views of climate variables and the impact on pathogen
and vector dynamics are available for a wide range of
diseases [1,2].
Known relationships of climate variability and change
and the climate-sensitivity of most important infectious
diseases causing considerable morbidity and mortality
worldwide suggests the potential role of climate infor-
mation in improving climate sensitive health outcomes
[3]. Although many infectious diseases of humans are
climate sensitive –those that are transmitted by arthro-
pod (insect and tick) and snail vectors are particular im-
portant in lower and middle income countries [4]. They
are therefore prioritized by the Tropical Disease Re-
search [5] initiative of the World Health Organization
and partners [5,6].
Impact of climate on vector-borne disease transmission
dynamics
Weather and climate conditions, as well as surface water
availability, that can influence positively or negatively the
transmission of arthropod-borne diseases include air and
water temperature, rainfall, humidity, surface water and
wind [7]. These conditions, may also manifest as ex-
treme events causing flooding, drought, storms and
heat/cold waves –impacting directly and indirectly on
vector transmission dynamics. The direct impacts of cli-
mate on disease vectors are via adult survival and
reproduction rates, the creation of breeding sites, and
the development rates of the juvenile stage of the vector
[8]. Pathogens transmitted to humans by insects and
ticks spend part of their life cycle in their cold-blooded
secondary (non-human) host where they are effectively
at the temperature of the local micro-climate. Here the
development rate of the pathogen (called the extrinsic
incubation period) will slow down at lower temperatures
increasing the probability that the insect/tick will not
survive long enough for disease transmission to occur.
Some interactions between vector/parasite and climate
are relatively simple to model (e.g. the relationship be-
tween rainfall and breeding sites) but others are com-
plex. For example, temperature interacts in multiple,
sometimes opposing ways with different aspects of insect
or pathogen biology. Despite this complexity, it is clear
that, to varying degrees, climatic factors determine the
geographic limitations of vector-borne diseases, their
seasonal occurrence, year to year variability as well as
medium and long term shifts in both geographic distri-
bution and intensity of transmission.
In Africa, rainfall, humidity and temperature are major
constraint to the development of vegetation, soils, water
sources, agriculture and therefore the livelihoods of the
continents diverse populations [9]. Understanding the
spatial and temporal relationships of climate and envir-
onmental direct and indirect drivers of vector-borne dis-
ease transmission is important in order to benefit from
climate information to better target current control ac-
tivities or predict future challenges.
Temporal lags in observed climate and vector-borne
diseases
The temporal dynamics of diseases transmitted by in-
sects and ticks will lag factors such as rainfall,
temperature and humidity by a number of months be-
cause of the many inbuilt delays to the transmission
process [10]. For example, rainfall creates potential
breeding sites for juvenile mosquito vectors, but newly
laid eggs need time to mature as larvae and pupae before
they emerge as adult mosquitoes capable of transmitting
disease [11]. After emergence, the adult female mosquito
needs to imbibe the pathogen (e.g. malaria parasite or
dengue virus) from an infectious human host before
transmitting it, after it completes its extrinsic incubation
period, to another person [11]. In epidemic prone re-
gions (such as semi-arid areas or highland areas border-
ing endemic zones), infection and immunity in the
human host population are low at the beginning of the
epidemic wave and therefore a number of blood meals,
each separated by the days needed to complete the
gonotrophic cycle, may be needed before a female mos-
quito encounters and infectious human host [11]. Fur-
ther delays in the development of an epidemic result
from the time taken between the human host being in-
fected and being infectious –a process that takes place
at the more or less consistent temperature of the human
host. The result of these lags is that cumulative observed
weather events and/or conditions may provide approxi-
mately 2–4 months warning of vector-borne disease out-
breaks depending on local circumstances. Shorter lags
usually occur in warmer environments where develop-
ment rates of vector and parasite are faster. However
warmer environments may be associated with drought
which will likely (but not always) reduce vector breeding
sites and adult mosquito survivorship. Understanding
how climate drives disease transmission in a particular
locale is a step towards using climate information to
control disease [4].
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 2 of 22

Development of early warning systems (EWS)
If significant temporal relationships between the occur-
rence of specific climatic/environmental variables and hu-
man cases of vector-borne diseases are demonstrated, and
an underlying mechanism is understood, then it is pos-
sible to consider the development of a climate-informed
early warning systems [12]. EWS may help disease control
services anticipate where and when outbreaks or increased
transmission are likely to occur and react proactively to
emerging changes in disease risk.
Disease early warning systems may be established
based on epidemiological data alone. For instance, an
unusual early seasonal rise in case numbers may trigger
an epidemic alert for some diseases. These are often
called “early detection systems”but in reality they are
still providing early warning of likely increase in future
cases [13]. Early warning can be extended using ob-
served environmental or climatic data which may offer
2–3 months prior notice of likely changes in transmis-
sion risk. Early warning for climate sensitive diseases can
be further extended by 3–6 months using seasonal
climate forecasts [14].
Weather forecasts (< 2 weeks), on the other hand add
little value to the prediction of a vector-borne disease
epidemics. This is because they provide only a few add-
itional days to early warning system that already have
the potential for several months lead time just using
observed climate or environmental data alone.
Sub-seasonal to seasonal (termed S2S) forecasts are cur-
rently an intense area of climate and weather research and
may, in the future, provide additional predictability at the
two week to two month time frame. Because of the short
prediction time frame in Africa of weather forecasts (1–
5 days) and the experimental nature of S2S forecasts nei-
ther are considered further here. However, as the science
advances, opportunities for using S2S forecasts in vector
disease control programmes may emerge.
Decadal (10–30 year) and long-term shifts in the
climate may also impact on vector-borne diseases by
changing their geographic range. In a recent study of
warming in the East African highlands the authors
calculated that an additional 6 million individuals
now live in regions of Ethiopia that are above the
temperature threshold for malaria transmission com-
pared with 30 years ago; this change resulting from a
slow upward shift in minimum temperature [15].
However, while decadal variations in the climate are
increasingly understood to exist, our ability to predict
such changes in an operational context is not cur-
rently developed and may yet prove impossible be-
cause of the strong stochastic character of the climate
[16]. Trends in temperature, where decadal variations
are weak, provide an indication of longer term cli-
mate changes.
The climate information regarding climate change
timescale (> 50 years) are highly uncertain and beyond
the normal decision timeframe of Ministries of health;
they are considered here in the context of historical
trends.
The African climate system and its drivers at multiple
time-scales
The health and wellbeing of African populations is
closely tied to their environment which is itself closely
linked to the regional and local climate. An extreme
range of climates span the continent, according to the
Köppen-Geiger classification system (Fig. 1)[17]. Across
the continent the climate varies from arid zones (includ-
ing the Sahara, Somali-Chalbi and Kalahari deserts),
steppe or semi-arid regions (e.g. Sahelian savannah) to
humid tropical environments (Congo river basin).
Humid subtropical climates are features found predom-
inantly in southern Africa but also include areas in the
Ethiopian highlands. In some regions these widely di-
verse climates co-exist within relatively small areas and
rainfall amount and seasonality (for example) may
change significantly over tens of kilometers [18]. The
changes in seasons (particularly the rainy and dry sea-
sons) is the dominant characteristic of regional climate
and it consequently drives the seasonal pattern of hu-
man activities as well as vector-borne diseases across the
continent. The large seasonal variations in rainfall that
distinguish different climate zones is seen clearly in
Fig. 2a–d–which indicates the fraction of mean annual
rainfall that falls within 3 month seasons (December–
February: DJF; March–May: MAM; June–August: JJA;
September–November: SON). The Fig. 2b and d indicate
that East Africa has a bimodal season while others, such
as the Sahel (see Fig. 2c) have a single rainy season,
more typical of monsoon behavior.
The most significant driver of seasonal temperature
change across Africa (where proximity to the equator
might suggest nearly constant year-round temperatures) is
the monsoonal rains, in part related to the inter-tropical
convergence zone defined previously. For instance, cloud
cover at night will tend to increase minimum tempera-
tures whereas cloud cover in the day time will tend to
reduce maximum temperatures [19]. These different re-
sponses indicate that minimum and maximum tempera-
tures are better treated as separate variables rather than
combined as mean temperature.
Whereas weather is almost entirely governed by condi-
tions in the atmosphere, the climate is substantially
driven by slower processes, particularly in the major
oceans. The climate at any location varies from its mean
historical climate state on multiple time-scales, from an-
nual to multi-decadal (10–30 years) to long-term climate
change; the latter compatible with anthropogenic climate
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 3 of 22

change signals. The magnitude of these variations and
trends may enhance or decrease the climate suitability
for different disease vectors and their pathogens.
Sea surface temperature variations in the Atlantic [20],
Indian [21] and Pacific [22] oceans influence the African
climate on different time scales. We consider three time-
scales of variability in the African climate that describe
the past and provide some indication of the future. El
Niño-Southern Oscillation (ENSO) is the most important
driver of climate variability at seasonal-to-interannual
timescales [23], a key source of climate predictability in
Africa [24](seeFig.3) and relevant to the development of
climate information services targeting health decision-
makers [3]. It is important to recognize that ENSO (El
Niño and La Niña) impact the climate (and thereby
climate-sensitive health outcomes): (a) differently accord-
ing to the variable of interest (e.g. rainfall, and minimum
and maximum temperature), (b) at different spatial scales,
(c) in some regions and not others, (d) in some seasons
and not others, (e) often according to its strength, and
sometimes in a non-linear fashion, (f) at varying periods
(from 5 months to ~ two years), with both El Niño and La
Niña events on occasions occurring in the same calendar
year (e.g., 2010), (g) often substantially conditioned on the
action of other climate drivers, such as the Indian Ocean
Dipole [25].
Natural variations in the climate at 10–30 year time
frames (decadal) have also been observed in Western,
Eastern and Southern Africa and again may be specific
to region and season. In Eastern Africa decadal rainfall
variations are largely confined to the long rains which
occur between March and May [26]. Where historical
data is sufficient, long term trends in temperature and
rainfall, consistent with climate change, may be
established once the noise from shorter term natural
variations in the climate have been removed. Unless the
impact of the different timescales can be disentangled,
there is considerable opportunity for confusion, with
important implications for decision-making and poten-
tial maladaptation. For instance, climate change models
have indicated that Eastern Africa will become wetter to-
wards the end of the twenty-first century while the re-
gion has, since 1999, experienced an increased frequency
of drought [27].
Here we aim to characterize the African climate –its
variability, trends and potential predictability –and es-
tablish the relevance of this knowledge and current tools
to operational vector-borne disease control efforts.
Fig. 1 Koppen-Geiger climate classification scheme for Africa [12]
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 4 of 22

Methods
We use a range of data sources and analytical methods
to undertake four analysis which we use to characterize
the African climate and its potential predictability.
First we use global climate products to explore the na-
ture of rainfall and temperature at multiple timescales
(seasonal, decadal and long term change) in three
regions in Africa. The regions chosen correspond to those
used by the World Health Organization (WHO)-Special
Programme for Research and Training in Tropical
Diseases (TDR) “Population Health Vulnerabilities to
Vector-Borne Diseases: Assessing and Developing Strategies
for Reducing the Impact of Social, Environmental and
Climate Change in Africa”research consortium partners
Fig. 2 Percentage of mean seasonal rainfall for Dec–Feb, Mar–May, Jun–Aug, and Sep–Nov. Data from the Global Precipitation Climatology
Centre, 1971–2000
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 5 of 22

[6]. These are: West Africa (Ivory Coast and Mauritania),
East Africa (Kenya and Tanzania) and Southern Africa
(Botswana, Zimbabwe). We then use global climate prod-
ucts to Identify regions and seasons across Africa where
the ENSO has greatest impact on local temperature and
rainfall. We then repeat the same analysis using climate
products created through the “Enhancing National Cli-
mate Services (ENACTS)”initiative [28] for Tanzania and
identify where ENSO has the greatest likely impact. Global
climate products provide an assessment of where and
when seasonal climate forecasts may be relevant to vector
control efforts across the African continent.
International research Institute for Climate and Society
(IRI) data library
The IRI Data Library [29] was used throughout this study
to access, manage, and analyse climate data as well as to
display the results via Maprooms all of which are available
to the reader (Table 1). The Data Library is an open and
free earth science data service, providing common,
high-quality, objective observations and analysis of the en-
vironment that promotes transparency in data source and
manipulation. The platform makes climate and other data
products more widely accessible through tool devel-
opment, data organization and transformation, as
well as data/technology transfer [30]. Tools devel-
oped include Maprooms which are designed for
rapid access to needed information for particular
user groups. Data Library technology has been shared
with partners around the world and underpins key climate
Fig. 3 Likely impact of El Niño rainfall in Africa. In addition, general warming of the atmosphere occurs across the tropics during an El Niño event. Local
temperature will be influenced by rainfall
Table 1 IRI Data Library Maprooms used in the analysis
Maproom
Timescale
decomposition
https://iridl.ldeo.columbia.edu/maproom/Global/
Time_Scales/
ENSO rainfall https://iridl.ldeo.columbia.edu/maproom/Health/
Regional/Africa/Malaria/ENSO_Prob/
ENSO_Prob_Precip.html
ENSO
temperature
https://iridl.ldeo.columbia.edu/maproom/Health/
Regional/Africa/Malaria/ENSO_Prob/
ENSO_Prob_Temp.html
Predictability of
climate
http://iri.columbia.edu/our-expertise/climate/forecasts/
seasonal-climate-forecasts/
ENACTS all
countries
Iri.columbia.edu/ENACTS
ENACTS
Tanzania
http://maproom.meteo.go.tz/maproom/
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 6 of 22

services in many countries including those implementing
ENACTS initiative in Africa [28].
Analysis 1. Multi-timescale climate decomposition
To better understand how much of the total variance in
rainfall and temperature anomalies across the African
continent is explained by different time-scales, a ‘timescale
decomposition’methodology [31] was used. The temporal
analysis was focused on the WHO-TDR study sites. This
approach has been used elsewhere to explore the contri-
bution of climate variations and trends at multiple time-
scales to the observed seasonal climate of Latin America
associated with the 2015 Zika virus epidemic [32].
Data
Timescale decomposition analysis was undertaken using
the most up-to-date long-term rainfall and average
temperature data available from the University of East
Anglia’s Climate Research Unit, gridded station product
version 3.4 (CRUv3.4, 0.5° resolution) [33], considering
the period 1901–2000. It is widely recognized that
changes in the number of observing station data incor-
porated into the monthly gridded data sets may signifi-
cantly affect the results of any analysis. There has been a
notable decline in stations available for incorporation
into global products post 2000, so the analysis is limited
to the twentieth century data only.
Methodology
The timescale decomposition methodology filters the as-
sociated anomalies of a climate time-series into three
components: the inter-annual (year to year), decadal (10–
30 year), and long-term trend signals. Time series, maps
and data are freely available in the IRI’s Timescale Decom-
position Maproom (https://iridl.ldeo.columbia.edu/map
room/Global/Time_Scales/) for any region in the world
with long enough quality-controlled records. Data pro-
cessing consists of three steps: (1) Screening the indi-
vidual gridboxes for filled rainfall or temperature
values, and for very dry regions and seasons; (2)
detrending in order to extract slow, trend-like changes;
and (3) filtering, to separate high and low frequency
components in the detrended data.
Analysis 2: Assessing the impact of the ENSO on rainfall
and temperature across Africa
In Africa ENSO impacts on African rainfall are well
known and vary according to region and season [24].
While the impact of ENSO on global tropical tempera-
tures is also widely appreciated [34], local effects are
amplified or muted by ENSO impacts on rainfall [19].
The rainfall response to ENSO is nearly contemporan-
eous however, this may not be true for temperature.
Once El Niño has begun, there is a ramp up of global
temperatures which are then slow to dissipate after the
return to a neutral phase although they may cool down
rapidly if La Niña conditions emerge.
Data
For sea-surface temperature (SST) data, the extended
reconstructed SST (ERSST) dataset (http://iridl.ldeo.
columbia.edu/SOURCES/.NOAA/.NCDC/.ERSST/.versi
on4/.sst/) was used. The ENSO state for each season
was defined according to the Oceanic Niño Index
(ONI) [35]. This is calculated using SST anomalies
based on the 1981–2010 normal, in the geographical
box defined by 170°W, 5°S, 120°W, 5°N. A season is
considered El Niño (La Niña) if it is part of at least 5
consecutive overlapping 3-month long seasons where
the ONI is above 0.45 °C (below–0.45 °C).
Rainfall and temperature data correspond to the University
of California Santa Barbara CHIRPS v2p0 monthly global
precipitation, and the East Anglia University Climate
Research Unit (http://iridl.ldeo.columbia.edu/SOURCES/.
UCSB/.CHIRPS/.v2p0/.monthly/.global/.precipitation/).
TS3.23 near-surface temperature on a 0.5° × 0.5° lat/long
grid (about 50 km of resolution) (http://iridl.ldeo.columbia.
edu/SOURCES/.UEA/.CRU/.TS3p23/.monthly/.tmp/).
Methodology
The historical probability of seasonal average rainfall
falling within the upper (wet/hot), middle (normal), or
bottom (dry/cool) one-third (“tercile”) of the 1981–
current historical distribution in Africa given the state of
ENSO (El Niño, Neutral, La Niña) during that same sea-
son was calculated and the results presented in an IRI
Maproom. The seasonal skill was assessed using the
Generalized Relative Operating Characteristics (GROC),
a metric similar to Kendall’s t rank correlation coeffi-
cient [36] measuring the “proportion of all available
pairs of observation of differing category whose
probability forecasts are discriminated in the correct
direction”[37]. Being a discrimination metric, GROC
provides information about how well the forecast system
can distinguish between the different categories, e.g.,
above-normal from normal rainfall. It also provides an
indication of how often the forecasts are correct, with a
value of 50% (or 0.5) being the expected score of an un-
skilled set of forecasts [36].
Analysis 3: Assessing the local impact of ENSO on rainfall
and temperature in Tanzania
The analysis for one of the WHO-TDR study sites
Monduli, Arusha, Tanzania –was further investigated
using products and services from the ENACTS initia-
tive [28]. ENACTS national climate products (rainfall
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 7 of 22

and temperature) are created by quality –controlling
all national station observations and combining this
data with data from proxies –satellite estimates for
rainfall, digital elevation models, and reanalysis prod-
ucts for temperature. The approach thus combines
the spatial information from the proxies with the ac-
curacy from point station measurements. The final
products are datasets with 30 or more years of rain-
fall and temperature time-series data at a ten-daily
(dekadal) time scale for a 4-km grid across the coun-
try. ENACTS products and services are disseminated
online via Maprooms that are developed using the lRI
Data Library which is installed at the Tanzanian Me-
teorological Agency [30]aswellasinanumberof
other African countries (iri.columbia.edu/resources/
ENACTS). This online mapping service provides
user-friendly tools for the analysis, visualization, and
download of climate information products via the
NMHS websites.
Data
For ENSO the NOAA NCDC ERSST (version 4) was used
when analyzing SSTs was used. For climate the ENACTS
historical rainfall and temperature (minimum) databases
(1983–2014) generated from combining quality-controlled
station observations with satellite data and downscaled re-
analysis data respectively were used.
Methodology
The approach used was the same as that undertaken for
assessing the impact of the ENSO on rainfall and
temperature across Africa (Analysis 2).
Analysis 4: Assessment of seasonal rainfall and
temperature predictability across Africa
Having identified the dominant signals of rainfall and
temperature variability and trends in the different regions
of the African continent, we explore their predictability
using a two-tiered atmospheric global circulation model
forecast system based on sea surface temperatures.
Data
The gridded global Climate Anomaly Monitoring System
dataset from National Oceanic and Atmospheric Admin-
istration (NOAA) [12] is used for temperature. For pre-
cipitation, two datasets are used, depending on the
period of interest: from 1979 onward the dataset is the
Climate Prediction Center [38] Merged Analysis of Pre-
cipitation [39], while for 1961–1978 data from the Cli-
mate Research Unit of the University of East Anglia [40]
is used.
Output from a total of nine atmospheric circulation
models were used in this study: the National Aeronaut-
ics and Space Administration, Center for Ocean-Land-
Atmosphere Studies, Geophysical Fluid Dynamics La-
boratory and Scripps models have an horizontal reso-
lution of ~ 2.0°, while the European Center for Medium
Range Weather Forecasts model and National Center
for Atmospheric Research Community Climate Model
have an horizontal resolution of ~ 2.8°. With this set of
models, retrospective probabilistic forecasts were pro-
duced using total of 144 members forced by evolving
sea-surface temperatures, and 68 members forced by
persisted sea-surface temperatures. For additional details
see Table 2in Barnston et al. [37].
Results
The results from the analyses described above are all
presented using the IRI Data Library Maproom capabil-
ity and can therefore be explored directly by any inter-
ested reader (Table 1for links).
Analysis 1. Multi-timescale climate decomposition
The results of the timescale decomposition analysis for
rainfall and temperature are presented in Figs. 4and 5.
Note that while the decomposition of a signal into trend,
low- and high-frequency components may seem straight-
forward, the analysis presented involves a number of
subtleties that are described in detail in documentation
that can be found on the timescale decomposition Map-
room site (see Table 1). The documentation also offers a
number of caveats regarding the interpretation of Map-
room displays.
Rainfall
The dominant source of variability in rainfall across the
continent comes from the inter-annual timescale. Sig-
nificant decadal variability also exists –especially across
the Sahel region including Mauritania. There is minimal
evidence of long term trends in rainfall across the con-
tinent using the University of East Anglia gridded rain-
fall data set.
Temperature
The UEA temperature data set has far fewer observa-
tions than for rainfall and consequently the poor quality
of the century long, continent wide, data set limits the
areas where robust analysis can be undertaken. However,
despite these limitations it can clearly be seen that long
term trends, decadal shifts and short-term variability in
temperature all contribute to the observed variations in
temperature across the three regions where the
WHO-TDR consortium projects study sites are based.
Analysis 2. Assessing the impact of the ENSO on rainfall
and temperature across Africa
The positive and negative impact of the El Niño on rain-
fall in the October to December for Eastern Africa and
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 8 of 22

July to September seasons (for the Sahel) respectively are
presented in Fig. 6a&b, while Fig. 6c indicates the positive
impact of La Niña conditions on the rainfall of Southern
Africa during the main season (December to February).
On the other hand, Fig. 6d shows no impact of El Niño on
the main rainy season (March to May) in Eastern Africa.
Additional analyses for other seasons and for temperature
can be obtained directly from the Maproom (Table 1).
The relationship of ENSO states to seasonal rainfall
totals and average annual temperature time series are
presented for Botswana in Fig. 7. The colour bars indi-
cate the ENSO phase for an individual year, and the
horizontal lines show the historical tercile limits. The
image allows a quick assessment of the historical impact
of ENSO by region and season and gives a visual indica-
tor of the spread of results.
Note that the ENSO Maproom does not provide a fore-
cast, but is a good tool for exploring the effect of different
ENSO phases on seasonal rainfall and temperature. It is
based on historical observations of rainfall and SST alone.
Where a strong signal is found, it suggests that there is an
opportunity for skillful seasonal forecasts since such fore-
casts substantially rely on a strong ENSO signal.
Analysis 3. Assessing the impact of the ENSO on rainfall
and temperature in Tanzania
The results of the Pan-African ENSO analysis above
were repeated in a national scale analysis using ENACTS
Table 2 Potential utility of weather and climate predictions for vector borne disease control
Time frame Climate driver Availability for operational use How forecast may be used in vector
control
Weather forecasts Numerical weather predictions
provide the most robust short
term weather forecasts.
In Africa few countries have
capacities to skillfully predict
the weather beyond 2 days.
Extending such forecasts to 5
or even 10 days may be possible
in some areas. Global weather
forecasts are often poorly calibrated
for local use.
Short term weather forecasts
give little additional time for a
vector-borne disease early
warning system although they
might provide valuable information
on extreme events that may
impact the health system more
broadly.
Sub- Seasonal weather forecast
(S2S)
The Madden-Julian Oscillation
(MJO) is the dominant mode of
sub-seasonal climate variability in
the global tropics and a driver of
predictability in S2S forecasts.
S2S experimental forecasts are
becoming available from global
producers.
S2S forecasts have yet to be
shown as operationally useful for
vector-borne disease control.
Seasonal climate forecasts Slow changes in sea surface
temperature (eg. equatorial
Pacific ENSO events).
Operational seasonal forecasts
are available from national,
regional and global producrers.
They are useful for predictable
regions (Sahel, JAS), Eastern Africa,
OND) and Southern Africa, DJF) -
highest skill during ENSO periods.
When predictability is high seasonal
climate forecasts can add months
to early warning system that are
already developed based on
monitored rainfall and temperature.
Transition time scale forecasts
(1–9 years)
Transition time scale between
seasonal prediction and decadal
variability.
Forecasts that are longer than
seasonal climate forecast are highly
desirable for planning purposes.
However they are not operationally
available for Africa.
Decadal forecasts (10–30 years) Decadal SSTs for example SST
variations over the Pacific Ocean
are highly correlated with decadal
rainfall variations in Eastern Africa
March–May season.
Experimental forecasts only. Decadal predictions are at the
forefront of climate research but
operational forecasts may not be
realistic any time soon. However,
where decadal variations are
limited temperature may follow
long term climate change trend.
Climate change scenarios Long term changes in
athropogenic gas emissions.
IPCC scenarios for global and
regional scale. - regions where
models agree. Downscaling of
climate change scenarios is
essential to relate this information
to national and subnational
decision-making.
Climate change scenarios provides
some strong indication of long term
warming trend but largely outside
of operational vector-borne disease
decision-time frames. Where the time
line is relevant, e.g. in assessing climate
risks to malaria eradication, rainfall
scenarios are highly uncertain.
Temperature trends, especially in the
absence of strong decadal variability,
may provide valuable information.
S2S Sub-seasonal to seasonal, ENSO El Niño Southern Oscillation, JAS July–August-September, OND October–November-December, DJF December–January-
February, SST sea surface temperatures
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 9 of 22

products and services made available by the Tanza-
nian Meteorological Agency on their website
(Table 1). The analysis indicates a moderate to
strong impact of El Niño across the country associ-
ated with the Oct–Dec short rains (Fig. 8). A de-
tailed analysis of the ENSO rainfall and temperature
interaction for Monduli District, Tanzania (Fig. 9)for
October–November-December (OND) is presented in
Fig. 10a&b. Figure 10a indicates that El Niño years
[41] have rainfall amounts predominantly within the
normal to above normal range whereas La Niña
years (blue) have rainfall amounts predominantly
within the normal to below normal range. Figure 10b
indicates that El Niño years [41]haveminimum
temperatures that are predominantly within the nor-
mal to above normal range whereas La Niña years
(blue) have minimum temperatures predominantly
within the normal to below normal range. Similar
analysis indicting the correlation of the positive and
negative phases of the Indian Ocean Dipole where
completed using the Tanzanian Meteorological
Agency (TMA) Maproom (not shown here). The
same analysis can be done for Kenya and other EN-
ACTS countries.
Analysis 4. Assessing the predictability of seasonal rainfall
and temperature across Africa
The skill of seasonal climate forecast across Africa, as
measure by the Generalized Relative Operating Charac-
teristics (GROC) metric, for temperature and rainfall
forecasts averaged over the entire year is poor (see
Fig. 11a&b). However, both temperature and rainfall sea-
sonal forecasts demonstrate skill in certain regions when
particular seasons are considered. For example, during
DJF, temperature forecasts tend to be good in southern
Africa where they coincide with the main rainy season
and also in parts of western Africa. They are also skillful
in eastern Africa for both rainfall and temperature des-
pite the short rainy season being largely confined to
OND (see Fig. 11c). Rainfall in the Sahel exhibits some
predictability during the main July–August-September
(JAS) season. Although it is not very high, the skill of
forecasts for rainfall for this season is on average higher
than surface temperature skill (see Fig. 11e&f ). Note that
the crude nature of the climate data used in the analysis
will limit the evidence of predictability.
A summary of the predictability of the climate drivers
(ENSO, Decadal, Long Term Change) over the climate
of the WHO TDR study regions is provided in Table 3.
ab c
de f
Fig. 4 a–fClimate timescale decomposition for rainfall a,b&c and temperature d,e&f across Africa. Boxes indicate source of time series analysis for
Western, Eastern and Southern Africa for Fig. 5a–f
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 10 of 22

Discussion
Climate information into national decision-making for
vector control purposes
Forecasting vector-borne diseases, such as malaria,
using climate information is not new. Over a century
ago records of unusual rainfall along with impover-
ished foods stocks were used as indicators of forth-
coming malaria epidemics in the Punjab region of
India [42]. In recent years an extensive research lit-
erature has emerged on the predictive relationship of
observed and forecasted climate events in Africa and
the spatial, seasonal, year to year and longer term
shifts in vector-borne diseases [1]. Furthermore there
has been an increase in studies providing evidence of
the skillfulness of vector-borne disease forecasts based
on climate monitoring products and seasonal climate
predictions [14,43,44] and a greater interest in such
analysis by policy-makers [4].
However, the promise of skillful and useful climate
based early warning systems in Africa has been slow to
materialize in practice. This is in part because:
1) Climate and disease mechanisms and relationships
are often poorly understood and may not be
consistent across space or time;
2) Seasonal climate forecasts are not universally
applicable and should only be used when and
where they are shown to be skillful. Because
ENSO is a major source of predictability of the
African climate forecasts have the greatest
Fig. 5 a–fClimate timescale decomposition for rainfall and temperature in Western (a&b) Eastern (c&d) and Southern Africa (e&f) with analysis
averaged over boxed areas identified in Fig. 4a–f
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 11 of 22

Fig. 6 a–dThese maps show the historical probability (given in percentile) of seasonal average of CHIRPS monthly rainfall falling within the upper
(wet), one-third (“tercile”) of the 1983–2015 distribution in the country given the occurrence of El Niño/La Niña during that same season. A dry mask is
used whenever the sum total of rainfall is ≤10 mm for the three month period. a) the probability of El Niño associated above normal rainfall for Oct–
Dec (note the severe impact in Eastern Equatorial Africa); and b) El Niño associated below normal rainfall impact for Jul–Sep (note the severe impact
in Ethiopia); c) La Niña associated above normal rainfall for Dec–Feb (note the severe impact in Southern Africa; d) El Niño associated above normal
rainfall for Mar–May (note the absence of impact for this main rainy season in Eastern Africa
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 12 of 22

predictability during ENSO years, and in certain
regions and seasons;
3) Concomitant disease and climate data of sufficient
quality, historic length and appropriate spatial scale
and coverage for development of evidence are
needed to develop robust analysis but are not
readily available;
4) Where data is available research may not be
translatable to local operational systems; for
example, if a forecast system is developed using
historical data, such as reanalysis, which is not
updated in real time, the research results will not
translate into an operational system where near
real time data is needed.
5) Where research results could technically translate
to operational systems, institutional relationships,
data policy issues, resources and capacity gaps may
limit the development operationalization, and
sustainability of Early Warning Systems.
A key challenge to accurately using climate infor-
mation for vector-borne disease prediction is the
spatial and temporal variability in climate variables of
interest. While a range of variables may be relevant
to transmission they may not be available for use in
operational systems which require national coverage,
relevance at the local scale and near real time up-
dates. Temperature and precipitation conditions may
be predictable in one region or season but this does
not necessarily mean that it can be extended to an-
other. The series of analysis presented here are de-
signed to establish which timeframes of variability are
most important and reliable for disease prediction in
thedifferentstudyregions.
Analysis 1. Multi-timescale climate decomposition
The timescale decomposition analysis revealed that
while century long term changes in rainfall were not a
major historical concern across Sub-Saharan Africa dur-
ing the twentieth century, decadal-scale variability has
significant impacts on the climate, and hence popula-
tions and economies, in strongly affected areas such as
the Sahel. This region shows the most extreme varia-
tions of seasonal climate anywhere in the world. Dra-
matic year to year variability in rainfall (in part related
to ENSO events) is super-imposed upon decadal shifts
in the climate as well as a long term drying trend. How-
ever, climate change models are uncertain as to the sign
(wetter or drier), let alone the magnitude of potential
changes in rainfall in this region. The decadal fluctuation
in West African rainfall observed in Fig. 5b has been
linked, in other studies, to SST variations in the Atlantic
Ocean although the Indian Ocean may also be playing a
role [20]. The long decline in rainfall during the 1970s
and 1980s in the Sahel contributed to the retreat of mal-
aria in this region [45]. The return to a higher rainfall re-
gime in the last two decades (also likely a decadal
variation) may have contributed to the re-emergence of
Anopheles funestus to some areas, including Niger, after
an absence of many years [46].
In East Africa, there has been a significant drying in
the climate over the last two decades (Fig. 5c). This has
occurred at a time when climate change models project
that East Africa is getting wetter in the future –a
Fig. 7 Spatially averaged yearly seasonal rainfall (Dec–Feb) time series for Botswana using CHIRPS (1982–2017). The color of the bars depicts the
El Niño Southern Oscillation phase of the year, and the horizontal lines show the historical terciles limits. Note that 11/13 El Niño years (red) [41]
have rainfall amounts within the normal to below normal range whereas 7/9 La Niña years (blue) have rainfall amounts predominantly within the
normal to above normal range. Grey bars are for neutral years
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 13 of 22

phenomena called the “East African Climate Paradox”
[22]. According to Lyon, the observed drying started
abruptly in 1998 with a steep decline in the long
rains (MAM) and is found to be driven strongly (al-
though not necessarily exclusively) by natural decadal
variability in the tropical Pacific rather than anthropo-
genic climate change [47]. The East African short
rains (OND) are not affected by this decadal process
further indicating distinct nature of these two seasons.
As March–May is the main rainy season throughout
much of Eastern Africa a dramatic decline in rainfall
amounts in this season is likely to have a profound
effect on vector-borne diseases such as malaria in af-
fected areas [48].
There is also evidence of decadal variability in rainfall
in Southern Africa (Fig. 5e) which has a tendency to be-
come wetter during decadal periods when the eastern
Pacific Ocean is cooler than average [47]. Mason and
Jury [49] suggest there may be some periodicity of dec-
adal variations in the climate of South Africa having a
dominant period of about 18 years.
Continued warming of the planet is the most certain
feature of climate change models [50]. Warming trends
over the last century (and in particular from the 1970s,
Fig. 8 This map of Tanzania shows the historical probability of seasonal average monthly rainfall falling within the upper (wet) one-third (“tercile”)
of the 1983–2010 historical distribution in the country given the occurrence of El Niño during that same season. The image depicts the probability of
rainfall being above normal for the October–December season
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 14 of 22

is evident in all regions of Africa where data is suffi-
cient for analysis (see Figs. 4d and 5bd,f). For in-
stance there is now substantive evidence that the East
African highland region has been warming over the
last 30 years [19,51] with potential impacts on mal-
aria and other vector-borne disease transmission in
areas where transmission has hitherto been con-
strained by low temperatures.
Analysis 2: Assessing the impact of the ENSO on rainfall
and temperature across Africa
Our results are consistent with what is known about
ENSO and the climate of Eastern Africa. Here the an-
nual cycle of rainfall tends to be bi-modal, with two
physically and statistically uncorrelated rainy seasons
[26] occurring in October–December (short rains) and
March–May (long rains). Year-to-year variability of the
short rains is frequently associated with ENSO [24]; but
this connection depends critically on sea surface tem-
peratures in the Indian Ocean, not just the Pacific. El
Niño is typically associated with wetter than average
conditions, while La Niña is frequently associated
with drought in the short (OND) rainy season. A
positive Indian Ocean Dipole (IOD) [52]isalsoasso-
ciated with enhanced short rains; its opposite phase
with drier than average conditions. While we have
not undertaken an IOD analysis the relationship and
can be explored in local East African ENACTS Map-
rooms (iri.columbia.edu/ENACTS).
Rainfall in many parts of the northwestern region of
Eastern Africa (western Ethiopia and parts of western
Kenya) have a boreal summer rainy season from June–
September which is more in common with the timing of
the Sahelian rainy season. The climate of the Sahel ex-
hibits typical monsoon behavior, with a single peak in
the rainy season between June–September. Our results
support other studies which find a modest connection
between ENSO and seasonal rainfall variability in the
Sahel [53] with El Niño events associated with drier than
average conditions and La Niña with wetter than average
conditions.
Our results are also consistent with what we now
about the climate of Southern Africa which is influenced
by atmospheric circulations in both the tropics and the
mid-latitudes. The main rainy season typically extends
from October–April across much of the region, peaking
during the southern-most extension of the inter-tropical
convergence zone. By contrast, the southern tip of South
Africa has a maximum in rainfall during the southern
hemisphere winter season (May–September), associated
primarily with the passage of mid-latitude storm systems
[49]. A relationship between seasonal rainfall variability
and ENSO has been observed in the region [54]. El Niño
events are typically associated with drought in Southern
Africa with La Niña linked to wetter than average
Fig. 9 The geographic location of Monduli district, Arusha, Tanzania
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 15 of 22

conditions, although even strong El Niño events are not
necessarily accompanied by drought [55]. There is sub-
stantive evidence that malaria in southern Africa is af-
fected by SSTs in the Eastern Pacific (the Niño 3.4
region) with La Niña events frequently associated with
an increased occurrence of cases [56,57].
While we have not considered in detail the climate of
Central Africa, we note that it contains the second lar-
gest area of tropical rainforest on earth and is therefore
an important, but poorly studied, part of the global cli-
mate system [41]. It also has a high burden of malaria.
The annual cycle of rainfall shows a bimodal behavior,
with relative rainy seasons peaking in March–May and
October–December, although there is substantial rainfall
outside these seasons. The variability of the climate of
Central Africa has received comparatively little attention
compared to other parts of the continent [58]. On
seasonal to inter-annual timescales, some studies have
suggested a relationship between rainfall variability in
Central Africa and SSTs in the tropical southern Atlantic
Ocean [59]. For example, warmer than average SSTs off
the Angolan coast are associated with increased rainfall,
particularly in the March–May season and in the west-
ern part of the region. It should be noted that the quality
a
b
Fig. 10 a&bSpatially averaged yearly seasonal rainfall time series for, Monduli, Tanzania using ENACTS climate products (1983–2014) for the
October–December Season. The color of the bars depicts the ENSO phase of the year (El Niño red; La Niña blue bar; neutral grey) and the
horizontal lines show the historical terciles limits; a) rainfall and b) minimum temperature. Note that El Niño years tend to be wet and warm
relative to La Niña years
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 16 of 22

ab
cd
ef
Fig. 11 Forecast skill as measured by the Generalized Relative Operating Characteristics (GROC) metric, for the African continent. Surface temperature
is shown on the left column, and rainfall is on the right. (a&b) All seasons, (c&d)Dec–Jan–Feb, (e&f)Jul–Aug–Sep. Lead time is 0.5 months
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 17 of 22

Table 3 Climate drivers and levels of predictability for WHO-TDR study regions + provides an indication of the strength of the relationship
Region ENSO impact ENSO predictability Decadal Impact Decadal predictability Long term change Climate Change
predictions
Eastern Africa +++ for rainfall for OND.
+++ for temperature in tropics
for extended period following
ENSO onset.
Tanzania - rainfall impact
focused on northern and
western regions
Kenya - with temperature
signal particular important in
extensive highland areas.
+++ for rainfall OND in
conjunction with Indian
Ocean Dipole.
+++ for temperature in
tropics for extended
period following ENSO
onset.
Tanzania - with rainfall
forecast skill focused on
regions where OND
rainfall occurs.
Kenya - with temperature
prediction particular
important in extensive
highland areas.
+++ for rainfall
for MAM.
+++ for
temperature.
Rainfall not predictable in operational context.
+ Temperature predictable from long term
change if decadal signal is weak.
+++ for temperature
warming.
+ Rainfall scenarios indicate
wet.
+++ for temperatures
warming.
+ for rainfall getting
wetter.
Western Africa
(including Sahel)
(Valid for
Mauretania and
Ivory Coast)
++ for rainfall for JAS in Sahel.
+++ for temperature in tropics
for extended period following
ENSO onset.
++ for rainfall for JAS in
Sahel.
+++ for temperature
following ENSO onset.
+++ rainfall JAS.
+++
temperature.
Rainfall not predictable in operational context.
+ Temperature not predictable in operational
but long term change signal may be visible
at shorter timescales if not masked by
decadal signal.
+++ for temperature
warming. Rainfall scenarios
indicate both wet and dry.
+++ for temperatures
warming.
Rainfall highly
uncertainty.
Southern Africa
(Valid for
Botswana and
Zimbabwe)
+++ for rainfall in DJF.
+++ for temperature in tropics
for extended period following
ENSO onset.
+++ for rainfall in NDJ.
+++ for temperature
in tropics for extended
period following ENSO
onset.
++ for rainfall
season.
+++ for temperature
in tropics.
Rainfall not predictable in operational context.
+ Temperature long term change signal may
be visible at shorter timescales if not masked
by decadal signal.
+++ for temperature
warming.
++ Rainfall observations
indicate dry.
+++ for temperatures
getting warming.
++ for rainfall drying.
+ = weak; ++ moderate; +++ strong; ENSO El Niño Southern Oscillation, MAM March–April-May, JAS July–August-September, OND October–November-December, NDJ November–December-January, DJF December–January-February
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 18 of 22

of climate data for this region is extremely poor with
few operational meteorological stations available. Con-
sequently, global products for this region are likely
also poor.
Our results (Fig. 5a, b) are consistent with other stud-
ies that show only a weak link between seasonal rainfall
variability and ENSO in Central Africa with the largest
connection found during the boreal fall season where El
Niño (La Niña) events are associated with drier (wetter)
than average conditions [60].
It is to be expected that the signal of the inter-annual re-
lationship between climate and vector-borne diseases in
Central Africa will also be weak as the environment is con-
sistently warm and humid with high levels of rainfall
throughout much of the year. Variations are likely insuffi-
cient to impact on transmission although there is scant vec-
tor or case data to establish whether or not this is the case.
The value of high resolution climate data in assessing the
impact of ENSO on rainfall and temperature at the
subnational level
National climate datasets made available through the
Enhancing National Climate Services (ENACTS) initia-
tive, provide additional insights into the relationship of
ENSO (and the Indian Ocean Dipole) to rainfall and
temperature variations at spatial scales which are rele-
vant for vector-borne disease monitoring and prediction.
The higher quality data sets are created from a blend of
all the relevant observations made available by the Na-
tional Meteorological and Hydrological Services, with
the best global products. The improved quality of the
data sets over global products make it easier to reveal
the predictability that exists. Similar analysis are now
possible in all countries where ENACTS is being imple-
mented (see Table 1).
The relative importance of climate drivers and their
potential predictability
The relative importance of the three categories of cli-
mate drivers and their predictability are region and vari-
able specific. For year-to-year-variations, ENSO is the
predominant driver of variability in rainfall and
temperature and ENSO impacts on the climate can be
observed most strongly during the single rainy seasons
of Southern Africa and the Sahel and the short rains of
Eastern Africa. Decadal variations in rainfall are also sig-
nificant in the Sahel and have been observed for the
March–April–May rainy season in Eastern Africa (not
shown). Long term trends are observed the temperature
data for southern and western Africa but the analysis for
eastern Africa is constrained by data quality. Challenges
encountered when seeking predictions at climate time-
scales are outlined in Table 2. In particular, our ability to
assess forecast/prediction/scenario skill at different time
scales is constrained by the lack of sufficiently long his-
torical climate data. To observe the accuracy of a wea-
ther forecast one needs to wait a day or two and then
the expired forecasts can be assessed against what is ob-
served. Within a season there is plenty of data which
can be used to assess forecast skill. For seasonal predic-
tion, many regions only have one or at most two rainy
seasons. Since seasons may act independently they each
need to be treated in separate analysis. Thus assessing
the skill of a probabilistic seasonal climate forecasts re-
quires a minimum of 30 or more years of climate data
against which the forecast models can be run in “hind-
cast mode”. Seasonal climate forecasts (both rainfall and
temperature) are predicted shifts in the probability dens-
ity function of seasonal rainfall totals or temperature
means relative to a climatological baseline. The forecasts
are commonly expressed in tercile probability format
(i.e., probabilities of below-normal [BN], near-normal
[NN] and above-normal [AN] rainfall or temperature
categories). Thus, within a pdf of 30 years of climate
data we have 10 years BN, 10 years NN and 10 years
AN. With this short time series signals have to be very
strong to be statistically significant. Describing a year as
above-normal, provides little indication of the likely out-
come in terms of disease. Is the season likely to be ex-
tremely wet? above a certain rainfall threshold? with
rainfall events well distributed over time?. These types of
questions are increasingly being addressed by climate
scientists and we may expect much more nuanced sea-
sonal forecasts to be available in the near future.
The quality of the data used to assess forecast skill also
matters. If the data set gives a poor indication of actual
climate conditions, then the skill test results are likely to
be poor. Where available the ENACTS historical climate
data (30+ years) provides a high quality climate data set
for use as the forecast predictand.
The challenge of verifying forecasts that will happen
decades into the future become even more onerous. As
there are few places in the world where historical cli-
mate datasets go back sufficiently long in the past to as-
sess variability over 10–30 year time frames, a more
general validation of the model is needed. This is based
on an understanding of its underlying mechanisms and
the relationship of model outputs to historical climate
characteristics of the region of interest. This is also true
for the assessment of climate change model outputs.
Conclusions
Climate varies across the African continent. These varia-
tions have the potential to significantly impact vector-borne
disease dynamics at multiple space and time scales. Wea-
ther and climate information (past, present and future) may
be used for operational vector programmes; their advan-
tages and limitations are summarized below:
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 19 of 22

1) Historical observations of rainfall, temperature
and humidity provide valuable information for
understanding past variations in vector-borne
disease if quality information is available at the
space and time scales of the vector/health data
(for example, ENACTS-implementing countries).
2) Recent and current observations of rainfall and
temperature (and humidity when available) provide
a significant resource for predicting changes in
vector-borne diseases months ahead of time if
quality information is available at relevant space
time scales and in near-real time.
3) Weather forecasts provide limited advanced notice
(only a few days at best) of epidemics above what is
available from rainfall and temperature monitoring
information.
4) Sub-seasonal climate forecasts are an area of
significant research and, while not very skillful,
may help bridge the gap between weather and
seasonal forecasts in some areas.
5) ENSO impacts on rainfall on the African continent
are observed predominantly in Eastern and
Southern Africa with a more moderate impact in
the Sahel. Predictions of ENSO state (El Niño,
Neutral and La Niña) can provide some limited
early warning of drought or wetter conditions in
some regions and seasons.
6) Seasonal climate forecasts, available from Regional
Climate Centers or National Meteorological
Agencies, which integrate ENSO state and other
predictors, are likely to be most useful as a
component of early-warning systems for vector-
borne diseases. This assessment is expected to be
especially true for the single rainy season in
Southern Africa (December–February), and for
the short rains (October–December) in Eastern
Africa, where they are most skillful.
7) Decadal variations in climate are significant in some
regions (e.g. the Sahel) and seasons (e.g. March–
May in Eastern Africa). Decadal variations can
impact the perception or expectations of
anthropogenic climate change, as short-term
shifts in the climate (10–30 year) are easily
confused with longer-term trends. Decadal
climate prediction is in its infancy and it is
not certain that skillful forecasts will emerge
that can be used operationally.
8) Long-term trends in warming are most likely to
have the greatest impact in the highland areas of
Eastern and Southern Africa where current
temperatures restrict the development rates of
vectors and pathogens. Climate change projections
may provide relevant information on long term
trends (e.g. for 2080 and beyond), but these are
commonly too far into the future to be use of use
to policy makers concerned with considerations of
disease control. In the absence of significant decadal
variations long-term trends can be used to provide
a strong indication of likely trends at shorter time
scales, e.g., the next few decades.
Given the above, EWS for vector-borne diseases
should be developed using an integration of historical
knowledge, current climate context as well as skillful op-
erational seasonal climate forecasts.
Additional file
Additional file 1: Multilingual abstracts in the six official working
languages of the United Nations. (PDF 577 kb)
Abbreviations
CRU: Climate Research Unit of the University of East Anglia; DJF: December–
January-February; ENACTS: Enhancing National Climate Services; ENSO: El
Niño Southern Oscillation; ERSST: Extended reconstructed sea surface
temperature; EWS: Early warning systems; GPCC: Global Precipitation
Climatology Center; GROC: Generalized Relative Operating Characteristics;
IOD: Indian Ocean Dipole; IRI: International Research Institute for Climate and
Society; JAS: July–August-September; JJA: June–July-August; MAM: March–
April-May; NCDC: National Climate Data Center; NOAA: National Oceanic
and Atmospheric Administration; OND: October–November-December;
ONI: Oceanic Niño Index; S2S: sub-seasonal to seasonal; SST: sea surface
temperature; TDR: Tropical Disease Research; WHO: World Health
Organization; WMO: World Meteorological Organization
Acknowledgements
This paper was written following valuable discussions with the WHO-TDR
research teams in relation to the IDRC-funded project: “Population health
vulnerabilities to vector-borne diseases: increasing resilience under climate
change conditions in Africa”. Pietro Ceccato and Bernadette Ramirez are
thanked for their support throughout the project. Lisa Van Aardenne is
thanked for helpful review of an early draft of the manuscript. John del
Corral and other members of the IRI Data Library team are acknowledged
for their work on Maproom development. Francesco Fiondella is thanked
for support with the figures. The Tanzanian Meteorological Agency (TMA)
are thanked for their implementation of the TMA ENACTS Maproom.
Funding
Funding for the work came from WHO PO 21353027 (PI MCT) in support of
WHO-TDR IDRC-funded project: “Population health vulnerabilities to vector-
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 20 of 22

borne diseases: increasing resilience under climate change conditions in
Africa”and WHO PO 201487225 (PI MCT) as a technical contribution to the
Global Framework for Climate Services. ÁM was supported via the Atmospheric
and Oceanic Sciences (AOS) Program at Princeton University.
Availability of data and materials
All data and tools used in this analysis are available free of charge via the web
as indicated in the text.
Authors’contributions
MCT, Am and JS-G conceived the paper. MCT developed the initial draft, AM
undertook the climate analyses and associated text, RC developed the initial
ENSO Maprooms and associated text, all reviewed the manuscript, revised
the text and agreed the final submission.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
International Research Institute for Climate and Society (IRI), Earth Institute,
Columbia University, New York, USA.
2
Mailman School of Public Health
Department of Environmental Health Sciences, Columbia University, New
York, USA.
3
IRI-World Health Organization (WHO) Collaborating Centre (US
430) on Early Warning Systems for Malaria and other Climate Sensitive
Diseases, New York, USA.
4
Atmospheric and Oceanic Sciences, Princeton
University, Princeton, NJ, USA.
5
World Health Organization- World
Meteorological Organization Joint Climate and Health Office, WMO, Geneva,
Switzerland.
6
International Research Institute for Climate and Society, LDEO,
Palisades, New York 10964, USA.
Received: 8 November 2017 Accepted: 11 July 2018
References
1. Kelly-Hope LA, et al. In: Thomson MC, Garcia-Herrera R, Beniston M, editors.
Climate and Infectious Disease in Seasonal Forecasts, Climatic Change, and
Human Health. Dordrecht: Springer Science+Business Media; 2008. p. 31–70.
2. Ceccato P. In: G.E.O.R.G.I.A. Committee, editor. GEO Task US-09-01a: Critical
Earth Observation Priorities, Human Health: Infectious Diseases Societal
Benefit Area. Geneva: GEO; 2010. p. 149.
3. Guillemot J. Health Exemplar: Annex to the Implementation Plan for the
Global Framework for Climate Services. Geneva: WMO; 2014.
4. Campbell-Lendrum D, et al. Climate change and vector-borne diseases:
what are the implications for public health research and policy? Philos Trans
R Soc Series B. 2015;370(1665)
5. WHO-TDR, Assessment of research needs for public health adaptation to
social, environmental and climate change impacts on vector-borne diseases
in Africa: an informal expert consultation convened by the Special
Programme for Research and Training in Tropical Diseases (TDR), Addis
Ababa, Ethiopia, February 27–29, 2012.
6. Ramirez BL. Supporting research towards understanding the population
health vulnerabilities to vector-borne diseases: increasing resilience under
climate change conditions in Africa. Infect Dis Poverty. 2017;6:E164.
7. Bai L, et al. Climate change and mosquito-borne diseases in China: a review.
Glob Health. 2013;9(10)
8. Campbell-Lendrum D. How much does the health community care about
global environmental change? Global Environ Change Human Policy
Dimens. 2005;15(4):296–8.
9. Hellmuth ME, et al. In: Climate and Society Series, H. M.E, editor. Climate Risk
Management in Africa: Learning from Practice. Palisades, New York: IRI;
2007. p. 97.
10. Stratton MD, et al. A comparative analysis of three vector-borne diseases
across Australia using seasonal and meteorological models. Sci Rep. 2017;
7(40186)
11. Beck-Johnson LM, et al. The effect of temperature on Anopheles mosquito
population dynamics and the potential for malaria transmission. PLoS One.
2013;8(11):e79276.
12. Ropelewski CF, et al. The analysis and display of real time surface climate
data. Weather Rev. 1985;113:1101–5.
13. Cox J, et al. Detecting epidemic malaria, Uganda. Emerg Inf Dis. 2007;13(5):779–80.
14. Thomson MC, et al. Malaria early warnings based on seasonal climate
forecasts from multi-model ensembles. Nature. 2006;439:576–9.
15. Lyon B, et al. Temperature suitability for malaria climbing the Ethiopian
highlands. Environ Res Lett. 2017;12(6):064015.
16. Goddard L, et al. A verification framework for interannual-to-decadal
predictions experiments. Clim Dynam. 2013;40(1–2):245–72.
17. Peel MC, et al. Updated world map of the Köppen-Geiger climate
classification. Hydrol Earth Syst Sci. 2007;11:1633–44.
18. Nicholson SE. Climate and climatic variability of rainfall over eastern Africa.
Rev Geophys. 2017;55(3):590–635.
19. Omumbo J, et al. Raised temperatures over the Kericho tea estates:
revisiting the climate in the east African highlands malaria debate. Malaria J.
2011;10:12.
20. Giannini A, et al. Oceanic Forcing of Sahel Rainfall on Interannual to
Interdecadal Time Scales. Science. 2003;302(5647):1027–30.
21. Behera SK, et al. Paramount impact of the Indian Ocean dipole on the east
African short rains: a CGCM study. J Clim. 2005;18:4514–30.
22. Lyon B. Seasonal drought in the greater horn of Africa and its recent
increase during the march-may long rains. J Clim. 2014;27:7953–75.
23. Zebiak SE, et al. Investigating El Niño-southern oscillation and society
relationships. WIRES Climate Change. 2014;6(1):17–34.
24. Mason SJ, et al. Probabilistic precipitation anomalies associated with ENSO.
Bull Am Meteol Soc. 2001;82:619–38.
25. Saji NH, et al. A dipole mode in the tropical Indian Ocean. Nature.
1999;401:360–3.
26. Lyon B, et al. A recent and abrupt decline in the east African long rains.
Geophys Res Lett. 2012;39(2):L02702.
27. Lyon B, et al. In: Wang S-YS, et al., editors. Unraveling East Africa's Climate
Paradox, in Climate Extremes: Patterns and Mechanisms. Hoboken: John
Wiley & Sons Inc; 2017. p. 265–81.
28. Dinku T, et al. THE ENACTS APPROACH: Transforming climate services in
Africa one country at a time, in World Policy Journal. New York: World
Policy Institute; 2016.
29. del Corral J, et al. Climate Information for Public Health: the role of the IRI
Climate Data Library in an Integrated Knowledge System. Geospat Health.
2012;(Suppl) 6(3):S15–24.
30. Blumenthal MB, et al. IRI Data Library: Enhancing Accessibility of Climate
Knowledge: The International Research Institute for Climate & Society:
Shaping the Landscape of Climate Services. Earth Perspect. 2014;1:19.
31. Greene AM, et al. Web tool deconstructs variability in twentieth-century
climate. Eos Trans Am Geophys Union. 2011;92(45):397–8.
32. Muñoz AG, et al. Analyzing climate variations at multiple timescales can
guide Zika virus response measures. GigaScience. 2016;20165:41.
33. Harris I, et al. Updated high-resolution grids of monthly climatic
observations - the CRU TS3.10 dataset. Int J Climatol. 2014;34(3):623–4.
34. Chiang JCH, et al. Tropical tropospheric temperature variations caused by ENSO
and their influence on the remote tropical climate. J Clim. 2002;15:2616–31.
35. Kousky VE, et al. An alert classification system for monitoring and assessing
the ENSO cycle. Weather Forecast. 2007;22:353–71.
36. Mason SJ, et al. A generic forecast verification framework for administrative
purposes. Mon Wea Rev. 2009;139:331–49.
37. Barnston AG, et al. Verification of the first 11 years of IRI's seasonal climate
forecasts. J Appl Meteorol Climatol. 2010;49:493–520.
38. ACPC. Assessment of Africa’s Climatic Records and Recording Networks
Including Strategic for Rescuing of Climatic Data. Addis Ababa: UNECA-
ACPC; 2011. p. 25.
39. Xie P, et al. Global monthly precipitation from satellite-observed outgoing
longwave radiation. J Clim. 1998;11:137–64.
40. Mitchell TD, et al. An improved method of constructing a database of
monthly climate observations and associated high-resolution grids. Int J
Climatol. 2005;25(6):693–712.
41. Malhi Y, et al. African rainforests: past, present and future. Philos Trans Royal
Soc London B Biol Sci. 2013;368:1625.
42. Gill CA. The prediction of malaria epidemics: with special reference to an
actual forecast in 1921. Indian J Med Res. 1923;10:1136–43.
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 21 of 22

43. Lowe R, et al. The development of an early warning system for climate-
sensitive disease risk with a focus on dengue epidemics in Southeast Brazil.
Stat Med. 2013;32:864–83.
44. Muñoz AG, et al. Could the recent Zika epidemic have been predicted?
Front Microbiol. 2017;8:1291.
45. Mouchet J, et al. Drought and malaria retreat in the Sahel, West Africa.
Lancet. 1996;348(9043):1735–6.
46. Labbo R, et al. Anopheles funestus in Sahel: new evidence from Niger.
Lancet. 2004;363(9409):660.
47. Lyon B, et al. Tropical pacific forcing of a 1998–1999 climate shift:
observational analysis and climate model results for the boreal spring
season. Clim Dynam. 2013;13:1891–9.
48. Thomson MC, et al. Using Rainfall and Temperature Data in the Evaluation
of National Malaria Control Programs in Africa. Am J Trop Med Hyg.
2017;97(Suppl. 3):32–45.
49. Mason SJ, et al. Climatic variability and change over southern Africa: a
reflection on underlying processes. Prog Phys Geogr. 1997;21(1):23–50.
50. IPCC, IPCC, 2012: Change Adaptation. A Special Report of Working Groups I
and II of the Intergovernmental Panel on Climate Change. IPPC reports, ed.
C.B. Field, et al. 2012, Cambridge UK and New York, USA: Cambridge
University Press, Cambridge, UK. 582.
51. Christy JR, et al. Surface temperature variations in East Africa and possible
causes. J Clim. 2009;22:3342–56.
52. Shaaban AA, Roundy PE. OLR perspective on the Indian Ocean Dipole
with application to East African precipitation. Q J Royal Meteorol Soc.
2017;143(705):1828–43.
53. Parhi P, et al. Resolving Contrasting Regional Rainfall Responses to El Niño
over Tropical Africa. J Climate. 2016;29:1461–76.
54. Landman WA, et al. Change in the association between Indian Ocean Sea-
surface temperatures and summer rainfall over South Africa andf Namibia.
Int J Climatol. 1999;19:1477–92.
55. Lyon B, et al. The 1997–98 summer rainfall season in southern Africa. Part I:
Observations. J Climate. 2007;20:5134–48.
56. Mabaso MLH, et al. El Niño southern oscillation (ENSO) and annual malaria
incidence in southern Africa. Trans Roy Soc Trop Med Hyg. 2007;101(4):326–30.
57. Thomson MC, et al. Use of rainfall and sea surface temperature monitoring for
malaria early warning in Botswana. Am J Trop Med Hyg. 2005;73(1):214–21.
58. Todd MC, et al. Climate variability in central equatorial Africa: Influence from
the Atlantic sector. Geophys Res Lett. 2004;31(23)
59. Hirst A, et al. Atmosphere-ocean mechanisms of climate anomalies in
Angola-tropical Atlantic sector. J Phys Oceanogr. 1983;13:1146–57.
60. Camberlin P, et al. Seasonality and atmospheric dynamics of the
teleconnection between African rainfall and tropical sea surface
temperature: Atlantic vs.ENSO. Int J Climatol. 2001;21:973–1005.
Thomson et al. Infectious Diseases of Poverty (2018) 7:81 Page 22 of 22