In Region 1, the early warning model had a sensitivity of 62% (16 of 26 epidemics). Prediction improved dramatically for the late warning model, which had a sensitivity of 96% (25 of 26 epidemics). In Region 2, the early warning model had a sensitivity of 73% (32 of 44 epidemics). The late warning model, however, had a lower sensitivity of 66%.
These findings indicate that weather conditions at relatively coarse temporal and spatial resolutions can be used to predict RRV disease epidemics with sufficient accuracy and lag time for public health planning. Figure 7 summarizes the main results. Both regions had models with more than 70% sensitivity, and in Region 2 epidemics were predicted with 73% sensitivity as early as November (when cases typically commence).
The Influence of Rainfall Pattern
The timing, duration, and extent of rainfall events in epizootic regions are critical indicators of outbreaks. 29 Two different rainfall patterns were evident in this study: excess “winter” rainfall and excess “summer” rainfall. Breeding of the main transmission vector, C annulirostris, occurs from mid-spring to late autumn, 10 and populations typically peak in January or February. 30 Reports from previous epidemics in these regions have linked heavy rainfall or extensive flooding in the December to February period with intense breeding of Culex mosquitoes, and hence to a spillover of infection to human populations. Large epidemics in the years 1928, 1956, 1984, and 1971 demonstrate this pattern. 31,27,32,33 In the first epidemic year of the study period, 1992–1993, heavy rainfall (nearly twice the long-term mean) commenced in August, and above average rainfall was sustained throughout the spring and up until late January (see Figure 5).
Nicholls 34 has noted that severe outbreaks of RRV disease in temperate Australia appear to follow heavy summer rainfall, suggesting an association with La Niña, the cool phase of the ENSO cycle. Harley and Weinstein 35 found no association between the southern oscillation index (SOI) and RRV disease outbreak years for Australia as a whole, although some RRV disease outbreaks in southeastern Australia have been positively correlated with September values of the SOI. 36 We found August SST to be a better predictor of rainfall excess, and hence epidemics, than the SOI. This was not surprising, as calculation of the SOI incorporates air pressure values from both sides of the Pacific basin (Darwin and Tahiti), whereas SST records the rise and fall of temperatures directly in the El Niño region of the eastern Pacific (Table 2, note ∥).
Not all outbreaks in the Murray area have followed heavy summer rainfall. 4 In 1980–1981, abnormally high spring rainfalls were recorded before outbreaks. In the 1996–1997 epidemic year, total rainfall over summer was below the period average in both regions. Instead, higher than average rainfall commenced as early as June, and continued until late August in Region 1 and September in Region 2 (Figure 6). Although high rainfalls were recorded in some of these months in other years (1991–1992 and 1995–1996), they were interspersed with lower than average values in either July or August. These lower values may have been a factor in aborting a buildup in these years.
We speculate that sustained winter and spring rainfall, even in the absence of excess summer rainfall, could enhance transmission by two mechanisms. First, it would allow a longer time period for amplification of virus levels between the initiating vector (Aedes species) and host populations. Floodwater Aedes maintain the virus by transovarial transmission of infection through embryonated eggs. 37 These mosquitoes overwinter as drought-resistant eggs in mud flats and creek beds. 38 After heavy winter rainfall, the females emerge and infect the vertebrate hosts. 37 In 1996–1997, minimum temperatures during July and August were higher than average in both regions. The combination of these two factors is likely to have provided for early and prolific breeding of Aedes populations and an extended period of virus buildup, thus increasing transmission potential. Second, the prolonged heavy rainfall (which resulted in widespread flooding into October) acted to raise the water table in the regions, reducing absorption and runoff. As a consequence, ground pools were able to remain into summer, despite low summer rainfalls, providing sites for Culex and Aedes breeding. These species extended the infection to humans, and epidemics ensued.
Irrigation practices, with a special release from the Hume Reservoir, appear likely to have contributed to the maintenance of wet conditions throughout the 1996–1997 summer. The Hume modulates water levels for the Murray River, influencing flood patterns downstream. A “special release” of an excessive volume of water from the Hume was authorized in October 1996 (owing to fears of a crack in the reservoir wall), which artificially maintained the Murray River flow at flood levels for more than 30 days. Under normal circumstances, a maximum irrigation release of 25,000 ml per day is permitted, and negligible flooding occurs. Flow levels in October of 1996 were persistently above 25,000 ml per day (for 2 weeks they were more than 90,000 ml per day), and did not return to normal for 6 weeks. Under this volume of water the area inundated increased rapidly, 39 and billabongs and low depressions around the river filled up.
Extrinsic (ie, climatic) factors need to be combined with host-virus population dynamics in epidemic prediction. 40 Below-average rainfall in the spring of the preceding year was a necessary, but not sufficient, explanatory variable in all the models. We attribute this to the influence of vector activity on vertebrate host immunity. Higher than normal rainfalls during an October–December period would initiate and support mosquito virus activity and result in the infection of a proportion of the vertebrate population. As the breeding cycle of the primary vertebrate host (macropods) takes more than 1 year to complete, 41 the pool of susceptible vertebrates in the following year would be reduced, virus amplification would be minimal, and the probability of human cases would also be small. Conversely, in the year after an epidemic year, the high level of vertebrate population immunity would be sufficient to reduce the probability of successive epidemic years to very low levels, even if other climatic conditions were suitable (as occurred in 1993–1994 in the study regions). This finding has also been demonstrated by studies on RRV disease epidemic activity in arid northwestern Australia. 42
Limitations of the Study
The principal limitation of this study lies with the notification data, which were reported by place of residence rather than place of suspected infection. We made the assumption that in rural areas with relatively large SLAs, work and recreation (and hence transmission) generally occur within the same SLA. However, it is likely that a small portion of cases were misclassified, which would weaken the association. Furthermore, because of a lack of data, we were not able to control for the possible confounding effect of rice irrigation practices, which may vary by SLA. Although we understood mosquito control interventions to have been minimal throughout the period, estimates of the timing and effectiveness of sprays would have strengthened the findings.
The short span of data was a problem for some climate variables. Given the unusual number of El Niño events that occurred during the study period (1990–1994 and 1997–1998), we cannot be certain about the strength of the predictive power of sea surface temperature in the Region 2 early warning model. One La Niña (cool phase) occurred in 1998–1999, but was only of weak intensity during the critical late winter to spring months.
The cross-validation method assessed how well the variables in each of the models were able to predict epidemics, rather than the predictive performance of the estimated coefficients for the 8-year model. Epidemics occurred infrequently in this dataset (in general, in only 2 of the 8 years). In the rotating validation process, when 1 year of epidemic information was removed, only half of the data on epidemics remained to derive the parameter estimates of the predictive model for that year. Given that there are several variables in each model, it may be that some of these predicted the outcome by chance.
Climate, Climate Change, and Health
Beyond the seasonal effects studied here, our changing climate—including interannual cycles and longer-term natural and human-induced cycles—will continue to influence the extrinsic and intrinsic factors that drive vector-borne diseases. Climate projections for 2030 indicate that temperatures may rise by 0.4–2°C over most of Australia. By 2070 they may rise by 1–6°C. 43 Winter rainfall in the southeast of the country may decrease by up to 10% by 2030 (35% by 2070), and summer rainfall in these regions may increase by 10% to 20% by 2030 (35% to 60% by 2070) depending on whether the results of slab or coupled models are consulted. 43
The effect of these changes on the breeding and survival of arthropods and vertebrate host populations, even in a region as “localized” as southeastern Australia, is difficult to anticipate. Russell speculates that Aedes populations in dry areas (such as Region 1) may be adversely affected by decreased winter rainfall, possibly resulting in delayed or precluded virus activity. 6 We would expect this to result in an overall reduction in epidemics in years when the “winter pattern” might have occurred. Conversely, the predicted increase in summer rainfall may increase the availability of mosquito habitat that, combined with higher average temperatures, may lead to higher humidity, a lengthened season of abundance, and greater transmission levels. 6 Understanding how ENSO may change with global climate change is also essential in anticipating the impact of future climate on vector-borne diseases. Currently, climate models have mixed success in estimating interannual influence. 44
Weather forecasts can be used in conjunction with other surveillance techniques to identify conditions suitable for an epidemic of RRV disease. Seasonal arbovirus activity is already monitored in parts of southeastern Australia, with weekly trapping of mosquitoes throughout the Ross River season used to record population profiles and virus isolates. The multistaged approach (early and late warning models) developed in this study enables response plans to be adjusted as forecast certainty increases, allowing health authorities to make the most of limited resources. Although both weather and climate are pivotal in generating the conditions needed for epidemics of arboviral disease, human influence (through environmental modifications such as irrigation) can also contribute to events.
We acknowledge Keith Moodie and others at SILO, Queensland Department of Natural Resources and Mines, for their assistance with compiling the climate data. We acknowledge the Communicable Diseases Network of Australia and New Zealand for the disease data, and the Murray-Darling Basin Commission for providing data on releases from the Hume Reservoir. We thank staff at the Animal Health Science Unit, Department of Agriculture Fisheries and Forestry Australia, for the use of computing resources.
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Keywords:© 2002 Lippincott Williams & Wilkins, Inc.
arbovirus; vector-borne; climate; climate change; early warning; prediction; Ross River virus disease; Australia