Using climate to predict disease outbreaks: a review:
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Climate-based early warning systems for infectious diseases
This section presents an overview of the diseases highlighted in section 4 with respect to their climate sensitivity and the existence or potential development of EWS following the framework previously presented. On the basis of a literature review, each disease is assessed according to the progress made – i.e. which steps of the proposed framework have been completed successfully.
The strong, well-studied link between cholera epidemics and fluctuations in climate, suggests potential for constructing climate-based EWS for this disease. Cholera was the first disease for which surveillance and reporting was initiated on a large scale (WHO 2000). Due to its high impact (Table 2) it is one of three diseases currently reportable under the International Health Regulations (IHR) of 1969, which state that the first cases of cholera (both indigenous and imported) should be reported to WHO within 24 hours. Weekly notifications of these reports are published in WHO’s Weekly Epidemiological Records which are freely available. Annual cases and the number of deaths reported to WHO (with substantial gaps) are available for Africa, the Americas and Europe from 1970 onwards and for Asia from 1949. In 1998, 74 countries reported annual cholera cases and deaths.
It has been suggested that epidemics of cholera may be predicted by monitoring or forecasting the seasonal abundance of zooplankton in aquatic environments using remotely sensed vegetation images (Colwell 1996; Lobitz et al. 2000). Colwell (1996) suggested a positive relationship between the monthly abundance of Vibrio cholerae and the abundance of copepods in ponds in Bangladesh and presented graphical evidence that cholera cases occurred following rises in sea surface temperature (SST). Lobitz et al. (2000) used weekly 1 km resolution NOAA AVHRR data for SST and sea surface height (SSH) in combination with weekly cholera cases in Bangladesh and found a significant correlation between cycles of cholera cases and
SST during 1992, 1994 and 1995, but did not attempt to construct a predictive model. The authors state that a predictive model for cholera in the Bay of Bengal is currently under development, but to date this model has not been peer reviewed.
Despite the immense public health impact of cholera and the large amounts of data available, attempts to develop climate-based cholera predictions remain at an early research stage of development. Possible next steps include evaluating the ability of existing quantitative models for Bangladesh and Peru (based on SST anomalies - Colwell 1996, Lobitz et al. 2000, Pascual et al. 2000, 2002) to predict historical epidemics, and extending similar approaches to test and quantify climate-epidemic links in Africa. Formal tests of predictive accuracy would indicate whether there should be further efforts to incorporate climate-based predictions into operational surveillance systems. In either case, clearly it is important for national health services and their partners (e.g. NGOs and international donors) to ensure that existing disease monitoring and surveillance is improved, particularly in Africa.
The early detection, containment and prevention of malaria epidemics constitute one of the four main elements of WHO’s global malaria control strategy . Within the past 20 years, a few countries have begun to develop EWS which use climatic transmission risk indicators. Progress towards operational systems has been limited, however, because of poor inter-sectoral collaborations and lack of evidence of the cost-effectiveness of malaria EWS. WHO has supported the development of malaria EWS by establishing a technical support network together with a framework that not only defines generic concepts but also identifies early warning and detection indicators which potentially could predict the timing and severity of malaria epidemics (WHO 2001, 2002b). Several field projects have been initiated (e.g. in Ethiopia, Kenya and Sudan) but it is not possible to draw definite conclusions from these studies, as the results have yet to be analysed carefully.
Quantitative spatial models of the relationship between malaria and climatic factors have been used numerous times for geographical mapping of disease risk, with an overwhelming focus on Africa (e.g. Craig et al. 1999, Snow et al. 1999, Kleinschmidt et al. 2000, 2001). Such risk mapping is a useful preliminary stage, as it can be used to differentiate areas that experience epidemic or highly seasonal transmission, from those with more stable transmission patterns where EWS are likely to be less useful.
Monitoring of malaria cases can be used for early detection of an epidemic if collection and notification are timely (i.e. weekly). There are functioning weekly notification systems from sentinel sites in Zimbabwe, Uganda, Kenya, and Madagascar (Cox et al. 1999, WHO 2001). Computerized collection and organization of surveillance of data have begun in Niger and is proposed elsewhere (WHO 2001). However, in most epidemic regions there remains a lack of regular surveillance.
Disease surveillance for early detection of malaria epidemics has been used in Thailand where deviations from seasonal averages were used to detect outbreaks (i.e. where monthly case numbers exceed the long-term mean plus two standard deviations). This approach detected 228 out of 237 epidemics in 114 districts from 1973-1981 (Cullen et al. 1984). Using data for Ethiopia, Abeku et al. (2002) have since demonstrated that this simple approach outperforms more advanced methods – although the authors concluded that epidemic early warnings could be improved further by including meteorological factors.
As outlined in section 3, early detection of malaria epidemics potentially can be supplemented by prediction. Monitoring data on the various risk factors (e.g. temperature and precipitation measurements from remotely sensed images and ground-based meteorological measurements) can be used as an input to mathematical models, based on correlations between risk factors and disease rates in the past. Currently there are several constraints on this approach for malaria. The first is the relative paucity of long-term disease datasets for model construction. The most extensive collection of data has been undertaken by the Malaria Risk in Africa (MARA) project, which has established a database on all available malaria data in Africa . Extensive historical datasets (with gaps) also are available for Europe (Kuhn 2002), India (D. Bradley personal communication) and north America (A. Ter Veen personal communication). However, these data sets lack continuous long time-series at high temporal resolution and therefore have been used principally for mapping geo-graphical variation in risk (e.g. Craig et al. 1999) or investigating relatively long-term trends (Kuhn et al. 2003), rather than epidemic prediction.
In addition, non-climatic risk factors such as vector abundance, population immunity and control activities are known to have a strong influence on the potential occurrence of an epidemic (e.g. Thomson and Connor 2001, Lindblade et al. 2000). At present, however, these relationships are not sufficiently well quantified to incorporate into mathematical models that can be widely applied. In addition, it may be impractical or too expensive continually to monitor these risk factors in many endemic regions.
Perhaps because of these constraints, relatively few studies have attempted to predict malaria epidemics by either monitoring or advance forecasting of the risk factors (i.e. seasonal climate forecasts) . Within Africa, Hay et al. (1998) used a model containing NDVI to predict malaria seasons in Kenya, but there was no formal assessment of the accuracy of predictions (apart from a visual comparison to historical maps). More recently, Hay et al. (2002b, 2003) concluded that a malaria emergency in four districts in western Kenya could have been predicted on the basis of rainfall data available in the previous month. In contrast, they suggest that early epidemic detection through case monitoring would not have been possible, due to the weakness of the surveillance system, and that seasonal rainfall forecasts were too unreliable to predict the epidemic with a longer lead-time (although Thomson et al. (2003) suggest that seasonal forecasting remains a promising tool).
Outside Africa, the only quantitative models which could be used for predicting malaria seasons based on climatic variables are those developed for the Punjab and Sri Lanka (Bouma et al. 1996, Bouma and Van der Kaay 1996), Venezuela (Bouma and Dye 1997) and Colombia (Bouma et al. 1997). These models are not very robust, however, mainly because they operate at a very low resolution for both climate and disease data.
As concluded by previous authors, the recent advances in satellite imagery and GIS should provide sufficient environmental data to build satisfactory models of malaria transmission (Thomson and Connor 2001, Rogers et al. 2002). However, there are no existing climate-based EWS in use for malaria. In research terms, the main limitations have been a lack of high-resolution long-time series of malaria cases, insufficient explanatory (climate) data at an appropriate resolution and lack of funds for in-depth studies. Further progress towards accurate predictive models is likely to come through using a wider range of long-term datasets to quantify the links between climatic factors and interannual variability in malaria cases and/or deaths. Although most easily accessible datasets already have been investigated, there are non-computerized surveillance records in Africa, Asia and potentially elsewhere, that could add to the evidence base relating variations in climate to malaria incidence.
Additional steps are necessary if research on EWS is to be implemented in control activities in the field. As for other diseases, these include strengthening of reporting systems to promote early detection of epidemics, and better definition of the control responses that should follow an epidemic warning. For example, it may be important to differentiate between maintenance or intensification of regular control activities (Hay et al. 2003), as compared to a qualitatively different response. In east Africa, a major project to develop and test operational EWS within national malaria control programmes was initiated in 2001 and is exploring these and other operational issues . Preliminary results from this project are expected in 2004.
3 Meningococcal meningitis
Climate’s role in meningitis outbreaks is poorly understood; as yet there have been no attempts to initiate the development of climate-based EWS for this disease. Although the transmission of meningococci has been linked to areas with low absolute humidity, this relationship has not been quantified. However, it is well-known that more important risk factors for meningitis outbreaks are human-related, including vaccination programmes and socioeconomic determinants.
In 1998, a total of 98 countries regularly reported meningitis cases to WHO (WHO 2000). Since 1997, countries in the African meningitis belt have undertaken weekly surveillance of disease activity during the meningitis season and provided total annual case numbers to WHO as input for the International Coordinating Group on Vaccine Provision for Epidemic Meningitis Control (ICG). In addition, various NGOs in vulnerable areas regularly supply information on meningitis outbreaks.
For modelling purposes, WHO holds non-continuous annual data on meningitis cases from 1966 onwards from reporting countries as well as the (more or less continuous) weekly reports from countries in the African meningitis belt. Although reporting differences mean that the data are not always completely reliable, they should still allow testing of potential correlations with climate variables at low resolution.
Currently there is little basis for the development of climate-based EWS for meningococcal meningitis, as a link between epidemics and climate variability has not been established. However, the existence of the long-term datasets would allow such associations to be tested. Progress could be made by testing and quantifying the link between historical outbreaks of meningitis and climate variables using (1) annual data collected worldwide from 1966 onwards and (2) weekly data from the meningitis belt from 1997 onwards. Depending on the results of these analyses, prediction models could be constructed and tested. As for all other diseases, strengthening of surveillance is essential to further develop and test predictive models and, more importantly, support control responses.
The MALSAT group, at the Liverpool School of Tropical Medicine in the United Kingdom of Great Britain and Northern Ireland, is currently developing a climate-based system for predicting meningococcal meningitis in Africa. This involves the collection and quantitative analysis of epidemiological data as the baseline for developing a predictive model. The results of this study are not yet available.
4 Dengue/dengue haemorrhagic fever (DHF)
There has been considerable discussion of the development of dengue early warning systems because of its comparatively high impact in epidemic and endemic areas. Significant progress was made towards the construction of EWS for this disease during the 1990s .
Today, passive surveillance of dengue and DHF cases is undertaken in most endemic countries (Gubler 1989). In the United States, local health departments monitor cases which are reported to the Centers for Disease Control and Prevention (CDC) and distributed by the VECTOR list server (Gubler et al. 2001). There has been particular interest in surveillance in Florida and Texas due to recent introductions of cases from nearby Mexico (Gill et al. 2000). In Puerto Rico, an active, laboratory-based surveillance programme receives serum specimens from ambulatory and hospitalized patients throughout the island, clinical reports on hospitalized cases, and copies of death certificates that list dengue as a cause of death. The WHO-managed DengueNet is a global surveillance of dengue and DHF which collects and analyses case data reported from participating partners. Data can be entered directly and accessed via the Internet.
Using the extensive dengue database from Puerto Rico, Schreiber (2001) developed a model to predict dengue cases with two week intervals and a three week lead-time. The model uses a quantified relationship between dengue cases and daily temperatures, precipitation and water budget to make predictions. Although this approach is promising, the predictive power is very low for epidemic years (definition of which also is unclear). Additionally, the authors do not indicate whether the assessment was made on independent data (i.e. data not included in the model).
A relatively basic system, the dengue early warning system (DEWS) is based on a malaria EWS, where simple comparisons of the monthly observed number of cases and the epidemic threshold (mean + 2SD, as above) provides information on the onset of an epidemic (Cullen et al. 1984). DEWS uses data from Bangkok and the four main regions of Thailand in combination with remotely sensed environmental data to identify vulnerable areas. Forecasts are made on the basis of time-series analysis of past case numbers, but although the model accurately describes historical epidemics, as yet it is unable to capture epidemic cycles (Myers et al. 2000).
A more complicated two-part model has been developed to predict various parameters of the dengue transmission cycle (Focks et al. 1993 a,b). The model consists of the CIMSiM (mosquito) and the DENSiM (dengue) and estimates mosquito density and survival as well as the prevalence and incidence of dengue in a human population, according to site-specific variables such as microclimate. Model simulations have been validated in Bangkok, New Orleans and Honduras during epidemics and overall predictive accuracy of the number of cases ranged from 30-85 % (Focks et al. 1993a, 1993b, Focks et al. 1995). This model represents a full biological approach to an EWS, and requires specific information on a range of parameters such as mosquito breeding, population density, virus serotypes, vertebrate hosts etc. Such monitoring may be costly and time consuming for use in developing countries. Also, there is no attempt to predict deviations from the seasonal pattern (i.e. epidemics) although the authors mention that this may be a future use of the model.
The development of EWS for dengue have reached an important stage. In the context of this report, it is necessary to stress that the likelihood and severity of dengue epidemics probably depend at least as much on socioeconomic factors, virus characteristics and human-related variables such as immunity as on climatic factors (Gubler et al. 2001). In light of this, the most important next steps for the establishment of climate-based dengue EWS are to:
- properly identify and quantify the re-lationship between climatic factors and the occurrence of dengue epidemics in vulnerable locations above that explained by other variables,
- simplify the CIMSiM and DENSiM models in order to make them more suitable for use in developing countries where funds and time resources are restricted,
- ensure that local surveillance centres are maintained and expanded to facilitate case reporting at regular intervals (weekly or monthly). If possible, active case detection should be employed.
5 African trypanosomiasis
During the twentieth century there were three severe epidemics of African trypanosomiasis; the third began in the 1970s and is continuing. In endemic countries, systematic population screening is under-taken currently for Gambiense sleeping sickness (i.e. the non-epidemic form of disease). Although there are extensive national datasets on the annual prevalence of Rhodesiense trypanosomiasis in individual African countries, they are not reported automatically to WHO. Ge-nerally, these data date back to the beginning of the twentieth century but it is expected that they contain large gaps: in Uganda, for example, no data are available for 1970 to 1975 (WHO 2000). In order to assess the quality of these data, first it is necessary to inspect national databases. The DAVID (disease and vector integrated database), which started in the 1990s, contains data on trypanosomiasis cases, tsetse distribution and abundance and cattle densities for Mozambique, Malawi, Zimbabwe, Zambia, South Africa and Ethiopia. It provides a promising means for linking long-term disease data with climate (Robinson 2002). A major limitation of this database, however, is the fact that it does not cover the areas most severely affected by human sleeping sickness (i.e. central and west Africa).
Currently there is little evidence to suggest that outbreaks of African trypanosomiasis are linked to climatic factors. However, the DAVID database and WHO data from the early twentieth century should be used to investigate rigorously any potential link between climatic variables, non-climate factors (such as cattle density and environmental modifications) and sleeping sickness epidemics. The results of such analyses would indicate whether there is any potential to develop and test climate-based warning systems.
6 Yellow fever
Yellow fever is reportable to WHO under the International Health Regulations. Annual reports of cases and deaths date back to 1948, although it is thought that only a small fraction of cases are reported (WHO 2000). In 1998, only 10 out of a total of approximately 40 epidemic countries reported yellow fever cases and deaths to WHO. .
Despite the current lack of quantitative evidence to support the role of climate in driving yellow fever epidemics, there is a biologically plausible link that could be explored using the extensive historical dataset. Statistical modelling could be used to test for and quantify climatic influences on the interannual variation of yellow fever cases and deaths. Where other potential predictor data are available (e.g. monitoring mosquito abundances in many affected urban areas of Asia, Africa and south America, and infection rates in sylvatic monkeys) they should be included in statistical models.
7 Japanese encephalitis and St. Louis encephalitis
To date, the only EWS for Japanese encephalitis (JE) is based on passive surveillance of human cases which are reported to national reference laboratories in endemic countries. To our knowledge, the most extensive long-term datasets of cases exist in Japan and Thailand (IDSC 2002). A quantitative model has been developed to predict JE epidemics in Thailand using remotely sensed vegetation, rainfall and temperature (Suwannee et al. 1997). It was estimated that increases in rainfall and temperature, of 10% and 20% respectively, would increase the expected number of JE cases by 2-5% in relation to the annual mean. However, there was no attempt to predict future inter-annual variation in JE.
Surveillance of St. Louis encephalitis (SLE) in north and South America is part of the CDC arbovirus surveillance programme which consists of vector abundance monitoring, surveillance of sentinel chickens and human case monitoring. In Florida, a state-wide sentinel chicken arbovirus surveillance system has been in place since 1978 (Day 2001). Human cases are detected by active surveillance either weekly or monthly, but so far there have been no attempts to develop climate-based EWS for SLE. This is due mainly to the success of bird monitoring in providing warnings a few weeks in advance of an epidemic, sufficient to initiate control responses (Day 2001). It also reflects the fact that links between climate and SLE epidemics still have to be quantified.
The feasibility of developing EWS for both JE and SLE should be relatively easy to investigate using available datasets. Existing long-term JE datasets from Thailand and Japan and SLE datasets from north American states could be used to build statistical models to quantify the role of climate. For SLE, it would be important to compare the predictive accuracy with that obtained from current bird monitoring, and evaluate the added-value of incorporating climate inputs. In addition, efficient monitoring programmes in some areas could be expanded to include other endemic countries with less developed programmes (e.g. all affected south American countries for SLE, India and China for JE).
8 Rift Valley fever
There are no existing EWS in use for Rift Valley fever (RVF), although their development has been proposed and some important steps of the preliminary phase have been completed or are under way.
In Kenya, the RVF activity database has facilitated initial risk mapping studies. The database contains monthly information on clinical RVF cases, infected mosquitoes, and antibodies in humans and animals dating back to 1950 (Linthicum et al. 1999, Anyamba et al. 2002). A similar database exists in Zimbabwe (with gaps from the mid 1950s to early 1990s) but information about the maintenance of this database is not available. The successful prediction (but not prevention) of the 1987 epidemic with a lead-time of only a week in Senegal, using only surveillance data on virus activity, indicates that such databases potentially are useful in determining the onset of an epidemic. More work is needed to assess whether the lead-times obtained through this approach are sufficient for planning effective epidemic response.
To date the only attempt to predict RVF outbreaks using a quantitative, climate-based model, was published by Linthicum et al. (1999). Their model incorporated SST and NDVI and successfully predicted three out of three RVF outbreaks between 1982 and 1998. Although this approach is promising, the predictive power of the model should now be assessed by its ability to forecast future epidemics. The main limitation of this model is the fact that it was not validated independently (i.e. the epidemics predicted were included in the model). Additionally there is no information on how an epidemic was defined.
Recently, the CDC in the United States has established RVF International Programmes in south and east Africa with the aims of (1) assessing the relative importance of climatic and environmental factors on RVF transmission and (2) constructing an environmentally driven model to predict future RVF activity in these areas. These programmes are designed to use recent Landsat satellite images as well as historical climate and vegetation data from the FEWS database (see above). From the information provided, however, it is unclear whether active or passive disease surveillance will be included in the project. Another project, organized by FAO, is using environmental predictors to model RVF seroprevalence in domestic animal species. This project will use existing databases from Senegal and Ethiopia and begin new surveys in Ethiopia (D. Pfeiffer, Royal Veterinary College, personal communication).
The development of EWS for RVF is at an early stage. Further progress could be made by ensuring that RVF activity surveillance is maintained in Kenya and Zimbabwe, and expanded to South Africa (where RVF research has been strong for many decades). In addition, it is important to assess the value of sentinel animal (lamb) surveillance to provide epidemic warning (see St. Louis encephalitis), and test the ability of the Linthicum model, using SST and NDVI, to predict historical epidemics outside Kenya, and epidemics within Kenya not included in the model building process.
As discussed above, there is some evidence that climatic factors can influence epidemics of visceral leishmaniasis (VL) and cutaneous leishmaniasis (CL) in Asia. The existence of current surveillance systems, at least for VL, provide the possibility to develop EWS for this disease.
The worldwide increase in VL prevalence over the past 20 years has caused a renewed interest in disease surveillance that has generated considerable datasets useful for modelling purposes. In Europe, this increase has been attributed mainly to the increase in HIV. VL is notifiable in 33 out of 88 endemic countries and, since 1994, WHO has received annual data from 13 countries, most of which are in Europe (WHO 2000). Surveillance for CL, and VL in tropical countries, is patchy and the existence of full datasets is questionable. Because of the lack of long-term time series there have been no attempts to quantify the role of climate in the epidemics of leishmaniasis and no EWS have been developed.
Leishmaniasis is similar to other diseases described above, in that there is a likely link to climate, but no quantitative studies to test the relative importance of climatic and non-climatic influences have been carried out. This could be addressed by quantifying climate’s role in the interannual variation in VL using the existing datasets from southern and eastern Europe, and the output from these models to predict epidemics of both human and canine VL in selected areas of the Mediterranean. It is also important to strengthen surveillance in other areas subject to epidemics, particularly for VL in south Asia, east Africa and south America, and for CL in Asia. In these areas, it would be useful to identify possible long-term datasets which could be used to quantify climate-epidemic links.
10 West Nile virus
The well-publicised recent epidemics of West Nile virus (WNV) in the United States have all occurred during years with warm winters followed by hot, dry summers (Epstein 2001). Although there has been much debate about the role of climate changes in the emergence of WNV in north America (Epstein 2000, Reiter 2000, Epstein 2001) the relative importance of direct climate influences, as opposed to factors such as the availability of mosquito breeding sites and avian hosts, remains a matter of speculation.
Since the first outbreak in 1999, surveillance of WNV in the United States has reached a highly efficient stage. A total of 49 states, five cities (e.g. New York) and the District of Colombia have initiated special WNV surveillance programmes which include active monitoring of dead or ill birds, active surveillance of mosquitoes and passive detection of human cases (CDC 2001). Virus activity is reported regularly by state health departments of the CDC from which data are freely available via the Internet. Reports of infected birds, mosquitoes, humans and horses are accumulated at state level and used to produce retrospective maps of disease occurrence (Figure 2).
To date, no climate-based EWS have been developed for WNV mainly because the link between climate and WNV epidemics remains unquantified. Instead there has been much focus on predicting outbreaks using surveillance of animal hosts. Eidson et al. (2001) evaluated a system of dead bird surveillance as an EWS for WNV in the state of New York. They found that dead bird reports preceded confirmation of viral activity in humans by at least three months. In 2000, a system based on dead bird surveillance (both sightings and laboratory testing of birds) provided temporal and geographical early warning of virus activity before the first human cases (Eidson et al. 2001).
The emphasis on animal surveillance so far has provided encouraging results, but it is not clear whether climate-based models would improve predictive accuracy. However, the extensive data collected in North America show continuous monthly trends in virus activity and can be combined easily with low (state-level) resolution climate and vegetation data to test for possible associations. This analysis could be used to identify climatic risk factors which should be monitored in order to make predictions about coming outbreaks. As for other diseases, if climate variables are shown to be important they should be incorporated into predictive models, and their precision and economic costs compared to predictions from bird surveillance alone. Again as with many other diseases, ongoing surveillance could be expanded to other epidemic-prone areas, such as southern Europe, North Africa and Asia.
Fig. 2 - Spread of West Nile Virus by state, 1999-2000. West Nile Virus activity in the U.S. in birds, horses, mosquitoes, animals or humans (based on information from the US Centers for Disease Control (CDC)
11 Ross River virus and Murray Valley encephalitis
Ross River virus (RRV) is the most important arbovirus in Australia so there has been significant regional interest in both surveillance and epidemic prediction of this disease. Since 1991, RRV has been a notifiable disease in all Australian states and territories, from which monthly and annual cases are reported directly to the Communicable Diseases Network Australia (CDNA). The CDNA now possesses annual and monthly data at national and state level from 1991 to the present, freely available via the Internet . These data serve partly as an EWS in their own right, but also have provided the basis for the development of early warning models based on rainfall (Woodruff et al. 2002). The models were constructed for early and late season and predicted 62-96% correct in 38 districts, with non-epidemics predicted more successfully than epidemics. This shortcoming most likely is due to the lack of data (because of passive disease surveillance) and the non-inclusion of host-related factors such as virus population dynamics (Woodruff et al. 2002). In spite of the limitations, this study shows that a relatively simple method based on easily obtained variables can be used to construct a functioning EWS.
Murray Valley encephalitis (MVE) also is a notifiable disease in Australia with monthly and annual cases reported to the CDNA at state and national level. However, this disease became separately notifiable only in January 2001 therefore the reliability of data before this date may be questionable. The potential for assessing the impact of climate on the interannual variability of MVE is therefore weaker than for RRV. Nicholls (1986) suggested that Darwin spring pressure could be used for predicting MVE epidemics (with a lead-time of weeks rather than months) but made no attempt to develop a predictive model. However, as Kay (1980) concluded, mammalian host and mosquito factors also play a crucial role in transmission; ideally these should be included in an EWS if the improvements to predictive power justify the costs of data collection.
Clear climatic influences, coupled with relatively long-term, reliable datasets, suggest that RRV and MVE are strong potential candidates for the development of climate-based EWS. Progress towards this could be made through:
- Expanding the RRV model to include non-climatic factors and assess whether this improves the predictive ability of epidemics.
- Developing a similar model, initially based on Darwin spring pressure, to predict epidemics of MVE using existing data from 1991 onwards.
- Improving surveillance of RRV particularly in the affected southern states of Australia, continuing the separate surveillance of MRV to establish a longer running dataset, and if feasible, changing passive case detection to active surveillance.
Although influenza epidemics are associated with winter and thus lower temperatures (Fleming and Cohen 1996), the existing EWS relies on the constant monitoring of virus activity in humans and animals (WHO 2000).
The international network for influenza surveillance was established with WHO in 1948. It now consists of 110 National Influenza Centres in 83 countries and 4 WHO Collaborating Centres for Virus Reference and Research (Figure 3). The network is complemented by a web-based database (FluNet) in which weekly reports of influenza activity in each location are entered . Results from the network are reviewed by WHO in February and September in order to assess the likelihood of an influenza epidemic and make recommendations to vaccination manufacturers about the antigenic strain likely to be prevalent in the following year. This system has operated for more than 50 years and generally is considered to be successful (WHO 2000), although there have been no formal assessments of the accuracy of epidemic prediction.
A collaboration of eight European networks, The European Influenza Surveillance Scheme is an integral part of WHO’s influenza surveillance system which collects information on, among other things, the number of influenza encounters per general practitioner, virus isolation and mortality (Snacken et al. 1992). These data are assessed in comparison to the epidemic threshold discussed above and previous background rates of influenza (Fleming and Cohen 1996) in order to provide early warning of an outbreak.
This system of influenza surveillance and early warning is a useful example of how similar systems can be set up for other infectious diseases. Indeed, it is feasible to envisage a scenario where these influenza centres could be equipped to monitor other infectious diseases in the region: for instance, the National Center for Infectious Diseases Surveillance Resources esta-blished by the CDC in Atlanta, Public Health Laboratory Services (PHLS) in the United Kingdom and Agence Française de Sécurité Sanitaire des Aliments in France. However, the WHO influenza network suffers from a lack of geographical coverage, and could be expanded.
Fig. 3 - WHO influenza surveillance network (WHO 2000)
Using climate to predict disease outbreaks: a review:
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