IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group II: Impacts, Adaptation and Vulnerability Malaria, dengue and other infectious diseases

Studies published since the TAR support previous projections that climate change could alter the incidence and geographical range of malaria. The magnitude of the projected effect may be smaller than that reported in the TAR, partly because of advances in categorising risk. There is greater confidence in projected changes in the geographical range of vectors than in changes in disease incidence because of uncertainties about trends in factors other than climate that influence human cases and deaths, including the status of the public-health infrastructure.

Table 8.2 summarises studies that project the impact of climate change on the incidence and geographical range of malaria, dengue fever and other infectious diseases. Models with incomplete parameterisation of biological relationships between temperature, vector and parasite often over-emphasise relative changes in risk, even when the absolute risk is small. Several modelling studies used the SRES climate scenarios, a few applied population scenarios, and none incorporated economic scenarios. Few studies incorporate adequate assumptions about adaptive capacity. The main approaches used are inclusion of current ‘control capacity‘ in the observed climate–health function (Rogers and Randolph, 2000; Hales et al., 2002) and categorisation of the model output by adaptive capacity, thereby separating the effects of climate change from the effects of improvements in public health (van Lieshout et al., 2004).

Malaria is a complex disease to model and all published models have limited parameterisation of some of the key factors that influence the geographical range and intensity of malaria transmission. Given this limitation, models project that, particularly in Africa, climate change will be associated with geographical expansions of the areas suitable for stable Plasmodium falciparum malaria in some regions and with contractions in other regions (Tanser et al., 2003; Thomas et al., 2004; van Lieshout et al., 2004; Ebi et al., 2005). Projections also suggest that some regions will experience a longer season of transmission. This may be as important as geographical expansion for the attributable disease burden. Although an increase in months per year of transmission does not directly translate into an increase in malaria burden (Reiter et al., 2004), it would have important implications for vector control.

Few models project the impact of climate change on malaria outside Africa. An assessment in Portugal projected an increase in the number of days per year suitable for malaria transmission; however, the risk of actual transmission would be low or negligible if infected vectors are not present (Casimiro et al., 2006). Some central Asian areas are projected to be at increased risk of malaria, and areas in Central America and around the Amazon are projected to experience reductions in transmission due to decreases in rainfall (van Lieshout et al., 2004). An assessment in India projected shifts in the geographical range and duration of the transmission window for Plasmodium falciparum and P. vivax malaria (Bhattacharya et al., 2006). An assessment in Australia based on climatic suitability for the main anopheline vectors projected a likely southward expansion of habitat, although the future risk of endemicity would remain low due to the capacity to respond (McMichael et al., 2003a).

Dengue is an important climate-sensitive disease that is largely confined to urban areas. Expansions of vector species that can carry dengue are projected for parts of Australia and New Zealand (Hales et al., 2002; Woodruff, 2005). An empirical model based on vapour pressure projected increases in latitudinal distribution. It was estimated that, in the 2080s, 5-6 billion people would be at risk of dengue as a result of climate change and population increase, compared with 3.5 billion people if the climate remained unchanged (Hales et al., 2002).

The projected impacts of climate change on other vector-borne diseases, including tick-borne encephalitis and Lyme disease, are discussed in the chapters dealing with Europe (Chapter 12) and North America (Chapter 14).

Table 8.2. Projected impacts of climate change on malaria, dengue fever and other infectious diseases.

Health effect Metric Model Climate scenario, with time slices Temperature increase and baseline Population projections and other assumptions Main results Reference 
Malaria, global and regional Population at risk in areas where climate conditions are suitable for malaria transmission Biological model, calibrated from laboratory and field data, for falciparum malaria HadCM3, driven by SRES A1FI, A2, B1, and B2 scenarios. 2020s, 2050s, 2080s   SRES population scenarios; current malaria control status used as an indicator of adaptive capacity Estimates of the additional population at risk for >1 month transmission range from >220 million (A1FI) to >400 million (A2) when climate and population growth are included. The global estimates are severely reduced if transmission risk for more than 3 consecutive months per year is considered, with a net reduction in the global population at risk under the A2 and B1 scenarios. van Lieshout et al., 2004 
Malaria, Africa Person-months at risk for stable falciparum transmission MARA/ARMAa model of climate suitability for stable falciparum transmission HadCM3, driven by SRES A1FI, A2a, and B1 scenarios. 2020s, 2050s, 2080s 1.1 to 1.3°C in 2020s; 1.9 to 3.0°C in 2050s; 2.6 to 5.3°C in 2080s Estimates based on 1995 population By 2100, 16 to 28% increase in person-months of exposure across all scenarios, including a 5 to 7% increase in (mainly altitudinal) distribution, with limited latitudinal expansion. Countries with large areas that are close to the climatic thresholds for transmission show large potential increases across all scenarios. Tanser et al., 2003 
Malaria, Africa  Map of climate suitability for stable falciparum transmission [minimum 4 months suitable per year] MARA/ARMAa model of climate suitability for stable falciparum transmission  HadCM2 ensemble mean with medium-high emissions. 2020s, 2050s, 2080s   Climate factors only (monthly mean and minimum temperature, and monthly precipitation) Decreased transmission in 2020s in south-east Africa. By 2050s and 2080s, localised increases in highland and upland areas, and decreases around Sahel and south central Africa. Thomas et al., 2004 
Malaria, Zimbabwe, Africa Climate suitability for transmission MARA/ARMAa model of climate suitability for stable falciparum transmission 16 climate projections from COSMIC. Climate sensitivities of 1.4 and 4.5°C; equivalent CO2 of 350 and 750 ppm 2100   None Highlands become more suitable for transmission. The lowlands and regions with low precipitation show varying degrees of change, depending on climate sensitivity, emissions scenario and GCM. Ebi et al., 2005 
Malaria, Britain  Probability of malaria transmission Statistical multivariate regression, based on historic distributions, land cover, agricultural factors and climate determinants 1 to 2.5°C average temperature increase 2050s 1 to 2.5°C average temperature increase None. No changes in land cover or agricultural factors. Increase in risk of local malaria transmission of 8 to 15%; highly unlikely that indigenous malaria will be re-established. Kuhn et al., 2002 
Malaria, Portugal Percentage days per year with favourable temperature for disease transmission Transmission risk based on published thresholds  PROMES for 2040s and HadRM2 for 2090s Average annual temperature increase of 3.3°C in 2040s and 5.8°C in 2090s, compared with 1981-1990 and 2006-2036, respectively Some assumptions about vector distribution and/or introduction Significant increase in the number of days suitable for survival of malaria vectors; however, if no infected vectors are present, then the risk is very low for vivax and negligible for falciparum malaria. Casimiro and Calheiros, 2002 
Malaria, Australia Geographical area suitable/unsuitable for maintenance of vector Empirical-statistical model (CLIMEX) based on current distribution, relative abundance, and seasonal phenology of main malaria vector  CSIROMk2 and ECHAM4 driven by SRES B1, A1B, and A1FI emissions scenarios 2020, 2050 0.4 to 2.0°C annual average temperature increase in the 2030s, and 1.0 to 6.0°C in the 2070s, relative to 1990 (CSIRO) Assumes adaptive capacity; used Australian population projections ‘Malaria receptive zone‘ expands southward to include some regional towns by 2050s. Absolute risk of reintroduction very low.  McMichael et al., 2003b 

a The Mapping Malaria Risk in Africa/Atlas du Risque de la Malaria en Afrique Project

Table 8.2. Continued.

Health effect Metric Model Climate scenario, with time slices Temperature increase and baseline Population projections and other assumptions Main results Reference 
Malaria, India, all states Climate suitability for falciparum and vivax malaria transmission Temperature transmission windows based on observed associations between temperature and malaria cases HadRM2 driven by IS92a emissions scenario 2 to 4°C increase compared with current climate None By 2050s, geographical range projected to shift away from central regions towards south-western and northern states. The duration of the transmis-sion window is likely to widen in northern and western states and shorten in southern states. Bhattacharya et al., 2006 
Dengue, global  Population at risk  Statistical model based on vapour pressure. Baseline number of people at risk is 1.5 billion. ECHAM4, HadCM2, CCSR/NIES, CGCMA2, and CGCMA1 driven by IS92a emissions scenarios   Population growth based on region-specific projections By 2085, with both population growth and climate change, global population at risk 5 to 6 billion; with climate change only, global population at risk 3.5 billion.  Hales et al., 2002 
Dengue, New Zealand Map of vector ‘hotspots‘; dengue currently not present in New Zealand Threshold model based on rainfall and temperature DARLAM GCM driven by A2 and B2 emissions scenarios 2050, 2100   None Potential risk of dengue outbreaks in some regions under the current climate. Climate change projected to increase risk of dengue in more regions. de Wet et al., 2001 
Dengue, Australia Map of regions climatically suitable for dengue transmission  Empirical model (Hales et al., 2002) CSIROMk2, ECHAM4, and GFDL driven by high (A2) and low (B2) emissions scenarios and a stabilisation scenario at 450 ppm 2100 1.8 to 2.8°C global average temperature increase compared with 1961-1990 None Regions climatically suitable increase southwards; size of suitable area varies by scenario. Under the high-emissions scenario, regions as far south as Sydney could become climatically suitable. Woodruff et al., 2005 
Lyme disease, Canada Geographical range and abundance of Lyme disease vector Ixodes scapularis Statistical model based on observed relationships; tick-abundance model CGCM2 and HADCM2 driven by SRES A2 and B2 emissions scenarios 2020s, 2050s, 2080s   None Northward expansion of approximately 200 km by 2020s under both scenarios, and approximately 1000 km by 2080s under A2. Under the A2 scenario, tick abundance increases 30 to 100% by 2020s and 2- to 4-fold by 2080s. Seasonality shifts. Ogden et al., 2006 
Tick-borne encephalitis, Europe Geographical range Statistical model based on present-day distribution HadCM2 driven by low, medium-low, medium-high, and high degrees of change (not further defined) 2020s, 2050s, 2080s 3.45°C increase in mean temperature in 2050s under high scenario, baseline not defined None From low to high degrees of climate change, tick-borne encephalitis is pushed further northeast of its present range, only moving westward into southern Scandinavia. Only under the low and medium-low scenarios does tick-borne encephalitis remain in central and eastern Europe by the 2050s. Randolph and Rogers, 2000 
Diarrhoeal disease, global, 14 world regions Diarrhoea incidence (mortality)  Statistical model, derived from cross-sectional study, including annual average temperature, water supply and sanitation coverage, and GDP per capita SRES A1B, A2, B1 and B2 emissions scenarios 2025, 2055   SRES population growth Results vary by region and scenario. Generally, diarrhoeal disease increases with temperature increase.  Hijioka et al., 2002 
Diarrhoeal disease, Aboriginal community, central Australia (Alice Springs) Hospital admissions in children aged under 10 Exposure–response relationship based on published studies CSIROMk2 and ECHAM4 driven by SRES B1, A1B and A1FI emissions scenarios 2020, 2050 0.4 to 2.0°C annual average temperat-ure increase in the 2030s, and 1.0 to 6.0°C in the 2070s, relative to 1990 (CSIRO) None Compared with baseline, no significant increase by 2020 and an annual increase of 5 to 18% by 2050.  McMichael et al., 2003b 
Food poisoning, England and Wales Notified cases of food poisoning (non-specific) Statistical model, based on observed relationship with temperature UKCIP scenarios 2020s, 2050s, 2080s 0.57 to 1.38°C in 2020s; 0.89 to 2.44°C in 2050s; 1.13 to 3.47°C in 2080s compared with 1961-1990 baseline None For +1, +2 and +3°C temperature increases, absolute increases of approximately 4,000, 9,000, and 14,000 notified cases of food poisoning Department of Health and Expert Group on Climate Change and Health in the UK, 2001