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

Introduction

  Contents

Early identification of an infectious disease outbreak is an important first step towards implementing effective disease interventions and reducing resulting mortality and morbidity in human populations. In the majority of cases, however, epidemics are generally well under way before authorities are notified and able to control the epidemic or mitigate its effects.

Both geographical and seasonal distributions of many infectious diseases are linked to climate, therefore the possibility of using seasonal climate forecasts as predictive indicators in disease early warning systems (EWS) has long been a focus of interest. During the 1990s, however, a number of factors led to increased activity in this field: significant advances in data availability, epide-miological modelling and information technology, and the implementation of successful EWS outside the health sector. In addition, convincing evidence that anthropogenic influences are causing the world’s climate to change has provided an added incentive to improve understanding of climate-disease interactions. Projections indicate an approximate average global warming of 2-5 ºC within the twenty-first century (IPCC 2001), accompanied by an increase in the frequency of extreme and anomalous weather events such as heat-waves, floods and droughts (McMichael 2001). It has been widely speculated that these projected changes may have significant impacts on the timing and severity of infectious disease outbreaks.

A range of infectious (particularly vector-borne) diseases are geographically and temporally limited by environmental variables such as climate and vegetation patterns. Climatic factors’ impact on infectious diseases can be divided into three main effects: on human behaviour; on the disease pathogen; on the disease vector, where relevant:

Human behaviour

Climate variability directly influences human behaviour, which in turn can determine disease transmission patterns. The strong seasonal pattern of influenza infections in Europe, for example, is thought to reflect humans’ increased tendency to spend more time indoors during winter months (Halstead 1996). Also, the peak of gastro-enteritis in temperate developed countries during summer months can be related to changes in human behaviour (e.g. more picnics and barbecues) associated with warmer temperatures (Altekruse et al. 1998).

Disease pathogens

For infectious diseases where the pathogen replicates outside the final host (i.e. in the environment or an intermediate host or vector), climate factors can have a direct impact on the development of the pathogen. Most viruses, bacteria and parasites do not replicate below a certain temperature threshold (e.g. 18 ºC for the malaria parasite Plasmodium falciparum and 20 ºC for the Japanese encephalitis virus; Macdonald 1957, Mellor and Leake 2000). Ambient temperature increases above this threshold will shorten the development time of the pathogen.

Disease vectors

The geographical distribution and development rate of insect vectors is strongly related to temperature, rainfall and humidity. A rise in temperature accelerates the insect metabolic rate, increases egg production and makes blood feeding more frequent (e.g. Mellor and Leake 2000). The influence of rainfall also is significant, although less easy to predict. Rainfall has an indirect effect on vector longevity through its effect on humidity; relatively wet conditions may create favourable insect habitats, thereby increasing the geographical distribution and seasonal abundance of disease vectors. In other cases excess rainfall may have catastrophic effects on local vector populations if flooding washes away breeding sites.

Even where linkages between disease and climate are relatively strong, other non-climatic factors also may have a significant impact on the timing and severity of disease outbreaks. One such factor is population vulnerability (e.g. influenced by herd immunity and malnutrition). In Kenya, for example, Shanks et al. (2000) have argued that malaria epidemics in the western highlands may occur only when the non-immune proportion of the population has grown by recovery, births and immigration because local children surviving to adulthood develop immunity. When developing an EWS, factors influencing the population dynamics of the pathogen (e.g. drug resistance) also may have to be considered. Human-related factors such as population movements and agricultural practices also can have considerable impact on disease patterns at various spatial scales. For example, the prevalence of malaria and leishmaniasis sometimes is strongly related to irrigation schemes and deforestation (e.g. Campbell-Lendrum et al. 2001, Guthmann et al. 2002). Arguably, the importance of non-climatic factors should be assessed and compared to that of climate variability in order to justify the development of climate-based EWS for infectious diseases. The relative contributions of climatic and non-climatic risk factors in explaining temporal variability in disease incidence will, to a large degree, determine the practical utility of a climate-based EWS.

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

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