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Using climate to predict disease outbreaks: a review: Previous page: Bibliography | 1,2,3,4,5,6,7,8,9,10,11,12

Appendix 1. Selection of infectious diseases for inclusion in the report, starting with the diseases with the strongest evidence for interannual variability and link between climatic factors and disease outbreaks



Cholera is a bacterial infection causing both local outbreaks and worldwide pandemics of which the current, and longest running, began in 1961 (Colwell and Patz 1998). Regional epidemics occur seasonally and are associated with periods of excessive rainfall, warm temperatures and increases in plankton populations (Colwell and Patz 1998, Shope 1991, Lipp et al. 2002). The plankton link is due to cholera-carrying algae which are eaten by shellfish and other crustacea and thereby incorporated into the human food chain (Colwell 1996). To date it has been suggested that monthly and annual cholera deaths are positively correlated with SST (an ENSO correlate) in Bangladesh (Pascual et al. 2000, Rodo et al. 2002) and Peru (Epstein 1993, Colwell 1996, Speelmon et al. 2000). Air temperatures also have been positively associated with outbreaks in Peru (Speelmon et al. 2000).


Malaria is the most important vector-borne disease in the world today, causing an estimated annual one million deaths worldwide, 90% of which occur in sub-Saharan Africa (Greenwood and Mutabingwa 2002). It is a disease of tropical and temperate countries between the latitudinal limits of northern Korea and southern Africa with prevalence increasing generally towards the equator. Often outbreaks occur following periods of increased rain and temperatures. It is thought that primarily this is due to positive effects on vector breeding (e.g Kilian et al. 1999), development rates (e.g Jetten and Takken 1994), parasite sporogony (MacDonald 1957) and ultimately entomological inoculation rates.

Several studies have demonstrated a positive association between the abundance of Anopheles gambiae s.l and rainfall (e.g. Molineaux and Gramiccia 1980, Smith et al. 1995). Gill (1921) found that increased malaria mortality in the Punjab correlated with high rainfall the previous month. More recent studies have shown a significant relationship between fluctuations of SST (associated with ENSO) and malaria cases or mortality rates in south America (Barrera et al. 1999, Bouma et al. 1997, Bouma and Dye 1997, Poveda et al. 2001) and Asia (Bouma et al. 1996, Bouma and van der Kaay 1996). Hay et al. (2002a) concluded that there is no similar connection between malaria in Kenya and ENSO.

The role of climate as a driving force for malaria epidemics still is fiercely debated, mainly because in some areas the link is clear (e.g. fringe areas) while in others it is more complicated or absent. In this respect, the importance of intrinsic population and extrinsic non-climatic factors also should be assessed when deciding whether to proceed with the construction of operational climate-based EWS.

Meningococcal meningitis

Meningococcal meningitis is an air-borne bacterial disease which shows a highly seasonal and epidemic pattern in sub-Saharan Africa where outbreaks occur during the hot, dry season and decline when the rainy season begins (Colwell and Patz 1998, Molesworth et al. 2002). In northern Benin, Besancenot et al. (1997) suggested a positive relationship between low absolute humidity and interannual variability in meningitis. Cheesbrough et al. (1995) reviewed geographical locations of epidemics and found that outbreaks were concentrated within a zone where absolute humidity remained below 10 g/m3 throughout the year.

Dengue / dengue haemorrhagic fever

Dengue epidemics in urban areas are due to transmission by Aedes aegypti and can involve up to 70-80% of the population (Gubler and Trent 1993). Historical outbreaks of dengue and DHF characteristically have been associated with high rainfall as well as elevated temperatures and humidity (Gubler et al. 2001) due to direct and indirect effects on pathogen and vector biology. Hales et al. (1999) suggested a positive relationship between monthly dengue incidence and temperature and rainfall in the south Pacific. More recently, Hay et al. (2000a) analysed monthly time series of DHF in Bangkok in correlation with mean monthly temperature and precipitation and found that the interannual periodicity of dengue was not matched by similar periodic cycles in temperature and rainfall. The authors concluded that intrinsic factors such as population immunity were more likely than climate to be the driving factors behind the epidemics.

Yellow fever

Yellow fever is a zoonotic viral disease which causes severe epidemics among humans in urban settings where Ae. aegypti is the only vector. The recent expansion of the geographical distribution of Ae. aegypti in many parts of the world (e.g. Asia and Europe) has caused concern about the emergence of yellow fever transmission, particularly in relation to climate changes (IPCC 2001). The development of Ae. aegypti as well as the extrinsic incubation period of the yellow fever virus are highly dependent on temperature (Shope 1991) but the importance of temperature fluctuations in the inter-annual variation of disease is unclear (Reiter 2001). Vasconcelos et al. (2001) recently suggested that an increase in temperatures and rainfall in Bahia State, Brazil, may have contributed significantly to the epidemic in 2000. The role of rainfall in yellow fever epidemics also remains unquantified, but increases in precipitation are thought to be the principal driving factor behind epidemics by increasing the number of mosquito breeding sites (Reiter 2001).

African trypanosomiasis

Large scale epidemics of Rhodesiense African trypanosomiasis (sleeping sick-ness) are currently spreading across central and eastern Africa (Rickman 2002). There has been much recent interest in analysing the relationship between spatial patterns of African trypanosomiasis and climatic factors (Rogers 2000), with particular focus on constructing predictive maps of tsetse distributions and abundance for future control purposes (e.g. Rogers et al. 1996, Robinson 1998, Hendrickx et al. 1999). Past studies have suggested a link between temperature and vegetation and the distribution of tsetse in Africa (e.g. Robinson et al. 1997, Rogers and Williams 1994, Brightwell et al. 1992). At present there is no clear link between climate and interannual variability of sleeping sickness. Rogers (2000) reported a significant correlation between monthly cases of sleeping sickness and LST in Uganda. Also it is likely that rainfall patterns may be related to temporal distribution of disease. However, because of the strong association between cattle and human infections (e.g. Fevre et al. 2001, Rogers 2000) and other non-climatic factors such as population movements, deforestation and drug resistance, the climate’s exact role in sleeping sickness epidemics remains unclear.

Japanese encephalitis and St. Louis encephalitis

Japanese encephalitis (JE) is the leading cause of viral encephalitis in Asia with 30 000 – 50 000 cases reported annually . The disease is transmitted by Culex mosquitoes and maintained in a zoonotic system of pigs, water birds and humans. JE causes severe epidemics which are highly seasonal, occurring during the monsoon season when temperatures reach 30 °C or above (Mellor and Leake 2000). Rao et al. (2000) observed that JE cases peaked with an increase in temperature and rainfall in India while epidemics in China have been shown to be associated with rice cultivation (Okuno et al. 1975).

St. Louis encephalitis (SLE) is transmitted by Culex mosquitoes and normally circulates in wild birds with occasional outbreaks in humans. Before an SLE epidemic, the number of virus-infected mosquitoes increases through amplifi-cation, resulting in a rapidly increasing number of infected birds and humans (Day and Stark 2000). It has been proposed that certain biotic and abiotic conditions favour early season virus amplification and transmission. For instance, an increase in temperature favours the development of mosquitoes and virus incubation (Hurlbut 1973). The 1999 outbreak in New York City occurred during the hottest and driest summer on record (Day 2001). Recently, Shaman et al. (2002) suggested a positive relationship between droughts and SLE in chickens in Florida, indicating that periods of drought followed by heavy rain may be a driving factor behind SLE epidemics (Day 2001).

Rift Valley fever

Rift Valley fever (RVF) is a zoonotic disease which is transmitted by female culicine mosquitoes and causes occasional serious outbreaks in humans. Throughout history, these epidemics have been associated with above average rainfall and temperatures but until the late 1980s this link was based mainly on observations and anecdotal evidence (Davies et al. 1985). Linthicum et al. (1990) were among the first to provide evidence that significant increases in rainfall results and associated flooding were linked to RVF outbreaks. Recently, it has been shown (Anyamba et al. 2002) that RVF outbreaks are positively associated with warm ENSO events and above-normal precipitation (indicated by remotely sensed vegetation patterns). A quantitative analysis of Kenya data from 1950 to 1998 suggested that RVF activity was significantly correlated with SST and NDVI obtained from satellite images (Linthicum et al. 1999).


Leishmaniasis is caused by a protozoan parasite which is transmitted by the bite of phlebotomine sandflies. Visceral leishmaniasis (VL) is highly epidemic in certain areas such as Afghanistan, where a serious epidemic was reported recently , and in large areas of north Africa, south-west Asia and south America (e.g. Seaman et al. 1996, Sundar et al. 2000, Werneck et al. 2002). Outbreaks of VL have been associated with population movements (Mansour et al. 1989), environmental modifications such as dam constructions and deforestation (Molyneux 1997) and changes in the availability of zoonotic reservoirs. Climatic factors are thought to have been responsible for outbreaks in Sudan in 1985 and 1986 where heavy rains favoured sandfly breeding (Elsafi et al. 1991). Franke et al. (2002) demonstrated a positive relationship between the incidence of VL and ENSO in Brazil. In Turkmenistan, Neronov and Malkhazova (1999) suggested a significant positive relationship between the incidence of zoonotic cutaneous leishmaniasis, soil moisture and temperatures. Broutet et al. (1994) concluded that epidemics of CL in Brazil between 1986 and 1990 may have been attributable to climatic factors. In addition, the seasonal abundance of sand-flies in south-west Asia also has been shown to be dependent on temperature and humidity (Cross and Hyams 1996, Cross et al. 1996).

West Nile virus

West Nile virus (WNV) is a zoonotic arbovirus which is transmitted by urban culex mosquitoes and occasionally produces illness in humans. Despite the relatively low importance to human health, WNV has received intense attention during the past two to three years because of much publicised outbreaks in United States’ cities (e.g. Crook et al. 2002, Epstein 2001). All recent WNV epidemics have occurred during unusually hot and dry periods (Epstein 2001), fuelling speculation about a strong climate-disease link. It has been suggested that the emergence of virus transmission in north America and parts of Europe (Hubálek and Halouzka 1999) may be due to climate change (Epstein 2001). Although there have been no attempts to identify and quantify statistically the link between climate and outbreaks of WNV, the disease is considered relevant to this report.

Ross River virus and Murray Valley encephalitis

Ross River virus (RRV) is an enzootic arthritic infection transmitted by Aedes and Culex mosquitoes with occasional spill-over to humans following virus amplification (see above). Epidemics of RRV occur mainly in southern Australia (Hales and Hearnden 1999) and typically are initiated in early summer. Previous studies have suggested a positive association between interannual RRV fluctuations and rainfall and temperatures (Tong and Hu 2001, Woodruff et al. 2002) as well as ENSO patterns (Maelzer et al. 1999). Woodruff et al. (2002) undertook the first quantitative analysis of monthly RRV cases and climatic factors which suggested that excess winter and summer rainfall were significantly associated with RRV cases. Murray Valley encephalitis (MVE) is caused by a JE-related flavivirus, transmitted by culex mosquitoes in Australia. Epidemics have been associated with above average rainfall (e.g. Kay 1980). Nicholls (1986) suggested that below average atmospheric pressure (an index of the Southern Oscillation and a precursor of heavy rainfall) was positively related to the occurrence of MVE the following year.

Epidemic diseases with a weak or non-existing climate link

Influenza is highly epidemic and local outbreaks or pandemics occur due to changes in the viral antigenic proteins. Although seasonal flu-ctuations are associated with decreases in temperatures (Lina et al. 1996), non-climatic factors such as virus type, vaccination pro-grammes, human behaviour etc. are more strongly related to epidemics.

Non-cholera diarrhoeal diseases present a very strong seasonal pattern with an increase in cases during hotter seasons in developing countries with poor sanitation. Studies have indicated that hospital admissions for diarrhoea can increase with increasing temperatures in Asia and south America (e.g. Pinfold et al. 1991, Callejas et al. 1999). Checkley et al. (2000) demonstrated that temperature increases related to an ENSO event in Peru coincided with a 200 % increase in diarrhoea-related admissions. Singh et al. (2001) suggested a negative association between water availability and diarrhoea rates indicating that non-climatic factors such as sewage system quality and general sanitation also are strongly related to outbreaks, both independently, and through interaction with climate effects.

Childhood diseases, including measles, pertussis and poliomyelitis, are highly epidemic in nature with outbreaks in the western world concentrated mainly during the school season. There is no direct link between climate factors and epidemics of these diseases which are strongly influenced by vaccination programmes, human behaviour (contact rates) and population movements.

Sexually transmitted diseases, including HIV, are listed as the most important infectious diseases worldwide. Although moderately epidemic in nature, there is no evidence that the prevalence or outbreaks of these diseases are associated in any way with climatic factors. The most important determinants of outbreaks are human-related and include sexual habits, contraception use and a range of socioeconomic variables.

Tuberculosis (TB) is a contagious air-borne disease which kills approximately two million people each year. The current worldwide epidemic is increasing and becoming more dangerous due to the emergence of multi-drug resistant TB and the spread of HIV/AIDS. Epidemics of TB are strongly linked to drug resistance, HIV prevalence and general socioeconomic conditions but there is no immediate connection to climatic factors.

Non-epidemic diseases with some climate link

Intestinal nematodes develop in soil and it is known that factors such as soil humidity and temperature have a strong influence on the developmental rates of the immature stages (Brooker and Michael 2000). As there is little or no interannual variation in helminth infection incidence, research has focused on developing geographical risk maps (Brooker et al. 2002a). Thus, remotely sensed and ground-measured climate data have been used to construct risk maps of helminth infections in west Africa (Brooker et al. 2002b) with the aim of designing mass treatment programmes.

The geographical distribution of schistosomiasis is related to environmental factors such as rainfall, temperature and water body composition (Brooker and Michael 2000). Remotely sensed surrogates of these climatic variables have been used for preliminary risk mapping of schistosomiasis in the Caribbean, Philippines and Egypt (Malone et al. 1994, Cross and Bailey 1984, Cross et al. 1984). Although infection patterns in most areas show a seasonal trend, following rainfall and temperature fluctuations (Brooker and Michael 2000), transmission is usually relatively stable from year to year.

Lymphatic filariasis is transmitted by a range of culicine and anopheline mosquito species in the tropics. Epidemics do not occur because the disease is chronic and clinical symptoms usually do not arise until years after infection. Recent studies have demonstrated that the geographical distribution of filariasis and its vectors in Africa is related to temperature and precipitation (Lindsay and Thomas 2000). This information was used to predict the distribution of lymphatic filariasis and construct disease risk maps for Africa.

Chagas disease (South American trypano-somiasis) is caused by the protozoan parasite Trypanosoma cruzi. The disease is primarily a zoonotic infection with small sylvatic mammals such as opossums and rodents acting as reservoirs. Human disease is chronic with a long latency period and infection often can be asymptomatic. Chagas disease is not epidemic in nature and therefore EWS are not considered important for this disease. As with the other examples discussed above, there has been much interest in identifying the climatic correlates of disease and vector distributions (Peterson et al. 2002). Thus, it has been demonstrated that the presence of triatomine bugs is associated with high temperatures and low humidity (Carcavallo 1999, Lorenzo and Lazzari 1999) as well as particular types of vegetation (Dumonteil et al. 2002).

Lyme disease is widely distributed in endemic foci in north America, Europe and Asia and limited only by the distribution of the ixodid tick vector. The relationship between environmental factors and the distribution of ticks and Lyme disease is well understood. A series of studies in north America have suggested that NDVI and temperatures are good predictors of both tick distributions and Lyme disease risk (Kitron et al. 1997, Estrada-Peña 2002) and these relationships have been used for predictive risk mapping at state and national level (Nicholson and Mather 1996, Dister et al. 1997, Kitron et al. 1997, Estrada-Peña 1998). Similar approaches, using remotely sensed vegetation data, have been taken for Europe (Randolph 2001, Randolph 2000).

The incidence of Lyme disease shows a peak during the summer months (Orloski et al. 2000). There is no evidence that such peaks are climate-related and it is commonly accepted that seasonal increases are due to changes in human behaviour (i.e. outdoor activities) which increase the rate of contact between humans and ticks.

Using climate to predict disease outbreaks: a review: 1,2,3,4,5,6,7,8,9,10,11,12 | Next page: Acknowledgements

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