VECTOR-BORNE AND ZOONOTIC DISEASES Volume 14, Number 2, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/vbz.2012.1215
Exploring the Potential for Ross River Virus Emergence in New Zealand Daniel M. Tompkins1 and David Slaney 2,3
Abstract
Ross River virus (RRV) is an exotic vector-borne disease considered highly likely to emerge as a future human health issue in New Zealand, with its range expansion from Australia being driven by exotic mosquito introduction and improving conditions for mosquito breeding. We investigated our ability to assess the potential for such emergence using deterministic modeling and making preliminary predictions based on currently available evidence. Although data on actual mosquito densities (as opposed to indices) were identified as a need for predictions to be made with greater confidence, this approach generated a contrasting prediction to current opinion. Only limited potential for RRV emergence in New Zealand was predicted, with outbreaks in the human population more likely of concern in urban areas (mainly should major exotic vectors of the virus establish). The mechanistic nature of the model also allowed the understanding that if such outbreaks do occur, they will most likely be driven by virus amplification in dense human populations (as opposed to the spillover infection from wildlife common in Australia). With implications for biosecurity and health care resource allocation, modeling approaches such as that employed here have much to offer both for disease emergence prediction and surveillance strategy design. Key Words:
Disease emergence—Deterministic modeling—Exotic incursion—Mosquito—Polyarthritis.
Introduction
N
ew Zealand lacks many of the vector-borne diseases in Australia and the Pacific Nations, and only a single mosquito-borne virus circulating in the country has been isolated to date (Tompkins et al. 2010). However, there is the threat of future expansions of such disease-causing agents and their vectors into the country. Ross River virus (RRV) has been identified by multiple authors as one of the most threatening such agents (Maguire 1994, Weinstein et al. 1995, Crump et al. 2001, Kelly-Hope et al. 2002, Derraik and Calisher 2004). RRV is widely endemic in Australia where it is the most common mosquito-borne pathogen causing human disease (Bi et al. 2009, Russell 2009). RRV can be transmitted without substantial involvement of nonhuman vertebrates in virus amplification; however, it generally persists in wildlife reservoir hosts (principally macropods; Harley et al. 2001, Russell 2002). In tropical regions of Australia, spillover of infection to humans occurs annually during the wet season. In
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the more temperate southern regions, human infection occurs relatively infrequently outside of epidemics. With variable environmental conditions, and vector and wildlife reservoir communities throughout the country, disease epidemiology varies regionally and even locally (Kelly-Hope et al. 2004, Jacups et al. 2008, Tong et al. 2008). Although RRV outbreaks are yet to occur in New Zealand, five factors are believed to make them likely in the future. First, RRV persists in Tasmania (Robertson et al. 2004, Werner et al. 2012), which has a similar climate envelope to parts of New Zealand (Maguire 1994, Weinstein et al. 1995). Second, the threat of RRV as an emerging disease has been demonstrated elsewhere in recent decades. In 1979, an infected Australian tourist introduced RRV to Fiji, leading to a major epidemic of polyarthritis in the Pacific area where as many as half a million people were infected (Marshall and Miles 1979, Rosen et al. 1981). Third, multiple human viremic cases of RRV are imported into New Zealand every year (Maguire 1994, Kelly-Hope et al. 2002). Fourth, the introduced brushtail possum Trichosurus vulpecula, of which
Landcare Research, Dunedin, New Zealand. Institute of Environmental Science & Research, Porirua, New Zealand. Barbara Hardy Institute, University of South Australia, Adelaide, South Australia, Australia.
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there are an estimated 30 million in the wild in New Zealand (Shepherd et al. 2011), has been implicated as a reservoir host of the virus in Australia (Boyd et al. 2001, Kay et al. 2007). Finally, six mosquito species that have shown at least some degree of RRV vector competence in laboratory trials currently occur in New Zealand (Holder et al. 1999): The introduced Aedes notoscriptus, Ae. australis, and Culex quinquefasciatus, and the native Ae. antipodeus, Cx. pervigilans, and Opifex fuscus (Maguire 1994, Doggett and Russell 1997, Watson and Kay 1998, Kramer et al. 2011). Ae. notoscriptus has been identified as a likely vector of the virus in urban Australia (Russell 1995, 1998, 2002, Watson and Kay 1997, Lindsay et al. 1998), whereas Ae. australis has been implicated in transmission in Tasmania (Hearnden et al. 1999). Additionally the exotic Ae. camptorhynchus, recently declared eradicated in New Zealand following its first detection in 1998 (Bader and Williams 2011), is the main vector of RRV in southern Australia (Harley et al. 2001, Russell 2002). With a likely reservoir species widespread and abundant (as well as isolated populations of captive and wild wallabies), and viremic human cases of RRV imported to New Zealand annually, the most likely bottleneck for current RRV transmission within the country is believed to be of an insufficient vectorial capacity (Kelly-Hope et al. 2002). Hence, two nonexclusive mechanisms considered likely to cause RRV emergence in New Zealand are improving conditions for mosquito breeding (caused by factors such as land use and climate change; Derraik and Slaney 2007) and further incursions and spread of major vectors (Derraik and Calisher 2004). Given the need to allocate constrained biosecurity and health care resources to where they provide the greatest benefit in any country, formal predictive exercises need to be carried out for any potential future threat, such as RRV to New Zealand, to guide decisions regarding preventive management and incursion responses. For RRV in Australia, and indeed for vector-borne diseases in general (e.g., Hales et al. 2002), such exercises generally involve correlational modeling of outbreak data against environmental variables (e.g., Werner et al. 2012). However such an approach, at least for vector-borne agents such as RRV where vector and wildlife reservoir communities are highly variable, has proven nontransferable between regions (Kelly-Hope et al. 2004, Jacups et al. 2008, Tong et al. 2008). As an alternative to correlational modeling, here we explore the use of deterministic models to investigate the potential for RRV emergence in New Zealand. Such models have been constructed previously for RRV in Australia to explore ecological questions (Glass 2005, Carver et al. 2009). However, although the understanding of the mechanisms that are implicit in deterministic modeling makes it a potentially more valid approach than correlational work for investigating disease emergence, such a benefit comes at the cost of increased information requirements. For example, while correlational models of infectious diseases can be based on general environmental data (e.g., Hales et al. 2002, Werner et al. 2012), deterministic models require rate information for specific mechanisms (Tompkins et al. 2002). Hence, our first aim was to use model construction and parameterization as an objective process for highlighting knowledge gaps regarding the assessment of RRV emergence potential in New Zealand. We then make preliminary predictions based on currently
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available data to explore the worth of filling those gaps, and inform on the utility of this approach for exploring disease emergence in general. Materials and Methods Model structure
Our model is based on that of Carver et al. (2009), formulated for investigating ecological influences on RRV dynamics in southwest Western Australia (see reference for base model details). The original model took the form of a deterministic ‘‘Susceptible, Exposed, Infected, and Resistant’’ model, constructed to simulate RRV epidemics where local transmission briefly occurs (triggered by a pulse of mosquito abundance following favorable environmental conditions) followed by local extinction of the virus. Such an approach is also relevant to New Zealand, where RRV occurrence in the foreseeable future will most likely be as similar highly seasonal epidemics rather than as endemic (Kelly-Hope et al. 2004). However, our model differs in two key respects: Humans are specifically included as hosts, and the model is extended to allow multiple vectors. Bites from each vector type were apportioned among the different host types according to the relative abundance of each host type weighted by a ‘‘relative bite factor.’’ Acknowledging that mosquitoes may prefer certain hosts over others ( Johansen et al. 2009), and that certain hosts are more accessible to mosquitoes than others (Kay et al. 2007), these factors were calculated from published blood meal identification and host abundance data and scored relative to humans. Where data were available on the same host type from different mosquitoes, no significant difference in ‘‘relative bite factor’’ was observed (data not presented for brevity). These factors were thus assumed to be invariable host traits. Vector and host categorization
Given the range of potential RRV vectors in New Zealand, either already present or with a possibility of incursion, mosquitoes were modeled as belonging to either one of two categories: (1) minor vectors (combining Ae. notoscriptus, Ae. antipodeus, Ae. australis, Cx. pervigilans, Cx. quinquefasciatus, and O. fuscus) or (2) major vectors (combining Ae. camptorhynchus, Ae. vigilax, and Cx. annulirostris; the latter two are the other main vectors of RRV in Australia; Harley et al. 2001, Russell 2002). Major and minor vector parameters were set to those for Ae. camptorhynchus and Ae. notoscriptus, respectively, being the species in the two categories for which most information regarding their role in RRV transmission is available. All vectors categorized as major perform at similarly high levels in laboratory competence trials (Harley et al. 2001), whereas those categorized as minor all perform significantly worse (Maguire 1994, Doggett and Russell 1997, Watson and Kay 1998, Kramer et al. 2011). Given the range of potential RRV hosts in New Zealand, relevant ecological, clinical, and immunological characteristics were used to combine hosts into meaningful functional groups with respect to RRV dynamics (Table 1). The ‘‘deadend hosts’’ category, comprising cats and birds, contains hosts in which viremia in the wild (if any) is considered too low to lead to the subsequent transmission of infection to
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Table 1. Parameter Estimates for Ross River Virus Infection Dynamics in the Six Host Categories Employed in the New Zealand Model Major vector transmission probabilities Parameter Symbol Humans Possums Macropods Minor hosts Dogs Other dead-end hosts
Minor vector transmission probabilities
Relative bite factor
Mosquito to host
Host to mosquito
Mosquito to host
Host to mosquito
Rate at which exposed hosts become infectious
a 1 2 4 6 6 1
TMH 0.5 0.3 1 0.75 0 0
THM 0.5 0.55 0.5 0.55 0 0
TMH 0.07 0.04 0.13 0.1 0 0
THM 0.5 0.55 0.5 0.55 0 0
r 0.13 1 1 1 0 0
Host recovery rate c 0.25 1 0.17 0.4 0 0
See text for the source of the minor vector transmission probability estimates. All rates are per day. Relative bite factors are calculated from blood meal identification and host abundance data and scored relative to humans. Information was obtained from Harley et al. (2001), Choi et al. (2002), Carver et al. (2009), and Johansen et al. (2009). Other model parameters are as Carver et al. (2009).
feeding mosquitoes, and ‘‘relative bite factors’’ are similarly low. Although a host species in which viremia is considered similarly low, dogs were modeled separately because their relative bite factor is much higher and they thus may act as an important sink of infection (Kay et al. 2007). The minor hosts category, comprising sheep, horses, cattle, deer, rabbits, and pigs, contains hosts in which viremia occurs, but the levels and duration are similar and consistently lower than those in the recognized primary hosts of the virus in Australia. Interestingly, hosts in this category have relative bite factors much higher than humans. This is well recognized and linked to them (and also dogs) being relatively sedentary and exposed outdoors (Kay et al. 2007). The host categories employed here align with the generally accepted conclusion of Kay and Aaskov (1989) that ‘‘marsupials are more competent hosts than placental mammals which, in turn, are more effective amplifiers than birds.’’ Estimates of RRV transmission probabilities (i.e., the probability that a bite from an ‘‘Infected’’ class mosquito will infect a ‘‘Susceptible’’ class host, or the probability that biting an Infected class host would infect a Susceptible class mosquito) in the wild are only available for Ae. camptorhynchus (Carver et al. 2009). However, for mosquito-to hosttransmission, laboratory trials have been carried out for Ae. notoscriptus. Hence, we use a comparison of these trials with similar trials for Ae. camptorhynchus to obtain a factor by which to adjust the wild estimates for Ae. camptorhynchus to give meaningful estimates of the same for Ae. notoscriptus. Laboratory trials with Ae. camptorhynchus can achieve 100% RRV transmission to infant mice (Ballard and Marshall 1986); however, similar trials with Ae. notoscriptus can only achieve 13% (Watson and Kay 1998). Hence, transmission probabilities for RRV to hosts in the wild by the minor vector Ae. notoscriptus were estimated as being only 13% of those for the major vector Ae. camptorhynchus (Table 1). In contrast, because laboratory RRV infectious doses for Ae. notoscriptus are similar to those for Ae. camptorynchus (Harley et al. 2001), host-to-mosquito transmission probabilities for Ae. notoscriptus were assumed to be the same as those estimated for Ae. camptorhynchus. No data are available on RRV transmission probabilities to and from humans. These values were thus initially assumed to equal 0.5 with respect to major
vectors (and adjusted as above for minor vectors), but later varied across the range of values observed for non–dead-end hosts to assess the robustness of model predictions to such variation (data not shown; impact on predictions is reported in the Discussion section). Model simulation
There is a global lack of absolute count data for adult mosquito populations; the general practice is to use numbers caught per trap-night as an index. These data are easily incorporated into correlational models, but they are insufficient for mechanistic approaches like that adopted here. Thus, we treat mosquito density as a variable, assessing the potential for RRV outbreaks across mosquito pulses of different densities with either only the minor vector category included (i.e., the current situation) or both the minor and major vector categories included (i.e., the situation if further incursions and spread of known major vectors occurs). Mosquito densities from 0 to 1 m - 2 were explored in 0.01-m - 2 steps, with the upper limit being the same order of magnitude as the maximum local density of 2.6 m - 2 estimated from a 1-ha area in Western Australia by Carver et al. (2009). The model was simulated on a daily basis at a spatial scale of 1 km2. This is similar to the scale employed by Carver et al. (2009) and is representative of the average dispersal range of the mosquito species involved (Watson et al. 2000, Robertson 2006, Jardine 2007). Simulations were initiated with a single Exposed class human being present in an otherwise Susceptible host community, and run for 100 days; RRV emergence in New Zealand being seeded by an infected traveler is considered the most likely route of future incursion. Because the potential combinations of different RRV hosts present across the country are countless, simulations were conducted for six common scenarios chosen to span the potential range (Table 2), two of which were simulated in the presence and absence of a low-density macropod population to investigate the effect of wallaby population presence on predicted dynamics. The proportion of the human population infected during the simulated outbreak was the default predicted variable (equivalent to the proportion in the Recovered class
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Table 2. Representative Host Densities (km - 2) for the Six Host Community Scenarios Simulated Using the New Zealand Ross River Virus Model
Scenario Humans Possums Macropods Minor hosts Dogs Other dead-end hosts
Highdensity urban
Lowdensity urban
Highdensity pastoral
Lowdensity pastoral
Broadleaf/ podocarp forest
Beech forest/ plantation
500 0 0 0 0 200
250 10 0 0 50 400
10 100 0 (100) 1000 5 600
1 200 0 (100) 1000 1 200
1 1000 0 10 0 200
1 200 0 10 0 200
Note that high- versus low-density refers to the human population as per table values. Numbers in brackets denote densities used only in a few specific simulations (see main text for details). Densities based on information in King (2006), MacLeod et al. (2006, 2009), Ministry for Primary Industries (2012), New Zealand Government (2012), and Statistics New Zealand (2012).
at the end of each simulation). For those scenarios in which humans are not present or are at very low density, the proportion of the possum population infected during the simulated outbreak became the predicted variable, with the probability that a human visitor to the area would be infected also evaluated. Results
The model simulated RRV outbreaks with infectious humans (or possums) present for approximately 3 months, a timescale similar to that observed in outbreaks in Southern Australia (Fig. 1; Jardine et al. 2008). The general characteristics of its predictions were as expected. First, major vectors were more efficient than minor vectors, causing outbreaks (and resulting in higher proportions of hosts being infected) at lower vector densities. Second, the presence of dead-end hosts inhibited outbreaks and reduced proportions
FIG. 1. Outbreak of Ross River virus (RRV) infection in the human population, generated by the model for the highdensity urban scenario, at minor and major vector densities of 0.5 m - 2 (i.e., one minor and one major vector every 2 m2). The y axis shows number of people in each of the four model disease classes; the x axis shows days since the introduction of a single Exposed class individual into the population.
of hosts being infected, due to wasted bites by infected mosquitoes on these hosts causing parasite dilution. Finally, lower host population sizes led to higher proportions of hosts being infected due to the frequency dependent nature of mosquito to host transmission. The relationship between the proportion of the human population infected during the simulated outbreak and mosquito density, with either only minor vectors present or both minor and major vectors present, is shown in Figure 2 for the three host community scenarios with appreciable human populations. Outbreak size is consistently low with only minor vectors present. It is not until a relatively high mosquito density of 0.35 m - 2 that the proportion infected reaches even 0.01 for any scenario (the high-density urban one). Even at the highest mosquito density simulated, only 0.06 of the human population is predicted to be infected during the outbreak. With both minor and major vectors present, outbreak size is markedly higher for all three scenarios (Fig. 2). This is particularly the case for the high-density urban scenario, where the proportion of the human population infected during the outbreak reaches 0.01 at minor and major vector densities of only 0.05 m - 2, with half infected at 0.35 m - 2. For the low-density urban and high-density pastoral (without macropods) scenarios, the minor and major vector densities required for 0.01 infected were 0.10 m - 2 and 0.24 m - 2, respectively. Including macropods in the last scenario had little influence on infection dynamics (data not shown). The relationship between the proportion of the possum population infected during the simulated outbreak and mosquito density, with either only minor vectors present or both minor and major vectors present, is shown in Figure 3 for the three scenarios without appreciable human populations. As for the previous three scenarios, outbreak size is low with only minor vectors present for the low-density pastoral (without macropods) and broadleaf/podocarp forest scenarios, with the proportion infected not reaching even 0.01. However, for the beech forest/plantation scenario, 0.01 infected is reached at a mosquito density of 0.22 m - 2 (the lowest for any scenario, with just minor vectors present), with the proportion infected reaching 0.23 at the highest mosquito density simulated. At this point, there is a similar probability of a single human visitor being infected. With both minor and major vectors present, outbreak size is again markedly higher for all three scenarios (Fig. 3). This
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FIG. 2. Proportion of the human population infected by Ross River virus (RRV) during the simulated outbreak, after the introduction of a single Exposed class individual. Model predictions for the high-density urban, low-density urban, and high-density pastoral (without macropods) scenarios are shown, across a range of mosquito densities with either only minor vectors present or both minor and major vectors present. is particularly the case for the beech forest/plantation scenario, where the proportion of the possum population infected during the outbreak reaches 0.01 at minor and major vector densities of only 0.09 m - 2, with half infected at 0.20 m - 2, and all infected at 0.71 m - 2 (at which density there is again a similar probability of a single human visitor being infected). For both the low-density pastoral (without macropods) and broadleaf/podocarp forest scenarios, the minor and major vector densities required for 0.01 infected were 0.21 m - 2. As was observed previously for the highdensity pastoral scenario, including macropods in the low-
density pastoral scenario had little influence on infection dynamics. Discussion
Although we were able to obtain data from New Zealand and Australia to parameterize our RRV model, the process of model construction highlighted our lack of absolute adult mosquito count data as a key knowledge gap. Thus, we interpret our model output in relation to mosquito density thresholds. While the magnitude of mosquito outbreak
FIG. 3. Proportion of the possum population infected by Ross River virus (RRV) during the simulated outbreak, after the introduction of a single Exposed class person. Model predictions for the low-density pastoral (without macropods), hardwood/podocarp forest, and beech forest/plantation scenarios are shown, across a range of mosquito densities with either only minor vectors present or both minor and major vectors present.
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densities explored (up to 1 m - 2) was based on maximum local densities estimated from Western Australia, averages there are an order of magnitude lower than maximum local densities (Carver et al. 2009). In addition, trap catch indices for Cx. pervigilans (New Zealand’s most common mosquito, occurring in the widest range of habitats including highly modified ones; Holder et al. 1999) tend to be an order of magnitude lower than what can be observed in Australia (Leisnham et al. 2007). Densities up to 0.1 m - 2 are thus reasonable estimates of what can be achieved by the mosquitoes currently established in New Zealand (i.e., the minor vectors for RRV) under contemporary conditions in their optimal habitats, with greater densities potentially achievable should conditions for mosquito breeding improve. For the major vectors of RRV (all of which are exotic species not currently established in New Zealand), densities of up to 0.1 m - 2 are the maximum considered likely should incursion occur, as current conditions in Australia are markedly more conducive to the development of such species than most parts of New Zealand in the foreseeable future (de Wet et al. 2005). According to these thresholds, the potential for RRV emergence in New Zealand is predicted to be less than current opinion suggests, being highly variable across the scenarios simulated. With only minor vectors present, appreciable outbreaks could occur in the high-density urban and beech forest/plantation host community scenarios, and then only should conditions for mosquito breeding improve. With both minor and major vectors present, appreciable outbreaks may occur under contemporary conditions in the high-density urban, low-density urban, and beech forest/plantation scenarios. For the beech forest/plantation scenario, outbreaks are enabled by virus amplification in the high-density possum population present; for the urban scenarios, outbreaks are enabled by virus amplification in the high-density human populations present. Current RRV outbreak potential in New Zealand is likely to be low. Although minor vector densities over 0.1 m - 2 were able to drive appreciable outbreaks in some scenarios, RRV is currently absent in the country. In addition, although major vector densities over 0.1 m - 2 were able to drive appreciable outbreaks in pastoral scenarios, antibodies to RRV were never detected in cattle in farmland where populations of the exotic Ae. camptorhynchus were present prior to eradication (McFadden et al. 2009). Hence, although having absolute mosquito count data would undoubtedly improve confidence in model predictions, we consider our mosquito density thresholds to be realistic. Our predictions with regard to these thresholds held with varying RRV transmission probabilities to and from humans. Higher probabilities could also lead to RRV outbreaks with only minor vectors present in the lowdensity urban scenario (and then only should conditions for mosquito breeding improve). This prediction was the only increase in disease emergence potential observed (simulations not presented for brevity). However, this was at the level of virus transmission observed in marsupials, the hosts in which RRV evolved, and is thus unlikely to occur in placental mammals such as humans (Harley et al. 2001, Russell 2002). Finally, with (1) all general mosquito parameters (such as those relating to vector competence, biting rates and transmission success) based on Australian estimates, (2) all minor vectors assumed to share the same mosquito-specific parameters as Ae. notoscriptus (implicated as playing a key
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role in RRV transmission in certain parts of Australia (Russell 1995, 1998, 2002, Watson and Kay 1997, Lindsay et al. 1998), rather than being supported only by laboratory competence trials as are others), and (3) exotic mosquitoes able to reach similar densities in New Zealand as do native mosquitoes, we have aimed to err on the side of caution to overestimate risk in our modeling approach. Thus, the prediction that actual RRV outbreak potential in New Zealand is less than current opinion suggests stands. With little in the way of human populations to be infected in scenarios with high possum densities (Table 2), this study predicts that RRV outbreaks in humans in New Zealand are likely to be of concern in urban areas, and then mainly should exotic major exotic vectors of the virus should be established. Within this scenario, environmental conditions are considered to be currently suitable for exotic major vectors of Ross River virus (C. annulirostris, Ae. vigilax, and/or Ae. camptorhynchus) in parts of Auckland, Hamilton, Gisborne, Palmerston North, and possibly also Christchurch (de Wet et al. 2005). This study thus justifies the efforts made to eradicate Ae. camptorhynchus, the main vector of RRV in Southern Australia, from New Zealand following its detected incursion in 1998. In addition, arguments based on vectorial competence have recently been put forward in support of calls for extensive surveillance for exotic vector-borne diseases and mosquito vectors to be undertaken on a regular basis across New Zealand (Kramer et al. 2011). Our approach, however, shows that variation in host competence is just as important and likely acts to limit the potential for disease emergence. As a result, should human RRV outbreaks happen in New Zealand, they will most likely be driven by virus amplification in dense human populations, as occurred in the Pacific Nations outbreak of late last century, as opposed to the spillover infection from wildlife common in Australia. However, our results for scenarios with high possum densities do indicate that model extension (through the inclusion of host and vector reproductive and seasonal parameters) should be used to explore the potential for RRV to become endemic in such host communities in the longer term. With predictions indicating that the potential for RRV emergence in New Zealand is less than current opinion suggests, and the mechanistic insight afforded, this study demonstrates how deterministic modeling can provide an additional approach for both disease emergence prediction and surveillance strategy design. The costs associated with the greater information needs of such approaches would likely be more than offset by the savings from targeted biosecurity and health care resource allocation. Thus, addressing key knowledge gaps impacting our ability to conduct these approaches in a robust manner, such as the lack of absolute count data for mosquito populations (with respect to vector borne diseases), would be highly beneficial. Two research directions to fill this specific gap can be identified. First, data on current adult mosquito densities around high-risk regions are required. This would involve developing methods for either directly measuring counts in the field or robustly converting trap indices to estimates of such. Second, to allow prediction of future densities, mechanistic models are required for vector species of concern (such as that developed for the dengue mosquito Ae. aegypti in Australia; Kearney et al. 2009). With such developments, far more detailed and robust identification of at-risk host communities could be
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made, both for RRV and other vector-borne threats to human health, such as dengue fever and chikungunya virus (Crump et al. 2001, Derraik et al. 2010). Acknowledgments
This research was supported by funding from the New Zealand Ministry of Research, Science & Technology under contract C03X0801. The comments of three anonymous reviewers greatly improved the manuscript. Author Disclosure Statement
There are no competing interests. References
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district of NZ for antibodies to Ross River virus. NZ Vet J 2009; 57:116–120. Maguire T. Do Ross River and dengue viruses pose a threat to NZ? NZ Med J 1994; 107:448–450. Marshall ID, Miles JAR. Ross River virus and epidemic polyarthritis. Curr Top Vector Res 1979; 2:31–56. Ministry for Primary Industries, New Zealand [cited August 27, 2012] www.biosecurity.govt.nz New Zealand Government [cited August 27, 2012] www.dogsafety .govt.nz Robertson GJ, Doggett SL, Seeman O, Russell RC, et al. Ross River virus and its vectors in Sorell Municipal area, southeastern Tasmania, January to March 2002. Commun Dis Intell 2004; 28:261–266. Robertson J. Biology of the Ross River virus vector mosquito Ochlerotatus camptorhynchus in an urban environment. University of Western Australia, Perth; 2006. Rosen L, Gubler DJ, Bennett PH. Epidemic polyarthritis (Ross River) virus infection in the Cook Islands. Am J Trop Med Hyg 1981; 30:1294–1302. Russell RC. Arboviruses and their vectors in Australia: An update on the ecology and epidemiology of some mosquito-borne arboviruses. Rev Med Vet Entomol 1995; 83:141–158. Russell RC. Mosquito-borne arboviruses in Australia: The current scene and implications of climate change for human health. Int J Parasitol 1998; 28:955–969. Russell RC. Ross River virus: Ecology and distribution. Annu Rev Entomol 2002; 47:1–31. Russell RC. Mosquito-borne disease and climate change in Australia: Time for a reality check. Aust J Entomol 2009; 48:1–7. Shepherd J, Barron M, Pech R. The national possum model— involving more than 30 million individuals. Kararehe Kino 2011; 17:22–23. Statistics New Zealand [cited August 27, 2012] www.stats .govt.nz
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Tompkins DM, Dobson AP, Arneberg P, Begon ME, et al. Parasites and host population dynamics. In: Ecology of Wildlife Diseases (Hudson PJ, Grenfell BT, Hesterbeek H, Dobson AP, eds). Oxford: Oxford University Press, 2002; 45–62. Tompkins DM, Paterson R, Massey B, Gleeson DM. Whataroa virus four decades on: Emerging, persisting or fading out? J Royal Soc NZ 2010; 40: 1–9. Tong S, Dale P, Nicholls N, Mackenzie JS, et al. Climate variability, social and environmental factors, and Ross River virus transmission: Research development and future research needs. Environ Health Perspect 2008; 116:1591–1597. Watson TM, Kay BH. Is Aedes notoscriptus (Skues) an urban vector of Ross River virus in Southeast Queensland? Arbovirus Res Aust 1997; 7:305–307. Watson TM, Kay BH. Vector competence of Aedes notoscriptus (Diptera: Culicidae) for Ross River virus in Queensland, Australia. J Med Entomol 1998; 35:104–106. Watson TM, Saul A, Kay BH. Aedes notoscriptus (Diptera: Culicidae) survival and dispersal estimated by mark-releaserecapture in Brisbane, Queensland, Australia. J Med Entomol 2000;37:380–384. Weinstein P, Laird M, Calder L. Australian arboviruses: At what risk NZ? Aust NZ J Med 1995; 25:666–669. Werner AK, Goater S, Carver S, Robertson G, et al. Environmental drivers of Ross River virus in south-east Tasmania, Australia: Towards strengthening public health interventions. Epidemiol Infect 2012; 140:359–371.
Address correspondence to: Daniel M. Tompkins Landcare Research Private Bag 193 Dunedin New Zealand E-mail: [email protected]
Exploring the Potential for Ross River Virus Emergence in New Zealand Daniel M. Tompkins1 and David Slaney 2,3
Abstract
Ross River virus (RRV) is an exotic vector-borne disease considered highly likely to emerge as a future human health issue in New Zealand, with its range expansion from Australia being driven by exotic mosquito introduction and improving conditions for mosquito breeding. We investigated our ability to assess the potential for such emergence using deterministic modeling and making preliminary predictions based on currently available evidence. Although data on actual mosquito densities (as opposed to indices) were identified as a need for predictions to be made with greater confidence, this approach generated a contrasting prediction to current opinion. Only limited potential for RRV emergence in New Zealand was predicted, with outbreaks in the human population more likely of concern in urban areas (mainly should major exotic vectors of the virus establish). The mechanistic nature of the model also allowed the understanding that if such outbreaks do occur, they will most likely be driven by virus amplification in dense human populations (as opposed to the spillover infection from wildlife common in Australia). With implications for biosecurity and health care resource allocation, modeling approaches such as that employed here have much to offer both for disease emergence prediction and surveillance strategy design. Key Words:
Disease emergence—Deterministic modeling—Exotic incursion—Mosquito—Polyarthritis.
Introduction
N
ew Zealand lacks many of the vector-borne diseases in Australia and the Pacific Nations, and only a single mosquito-borne virus circulating in the country has been isolated to date (Tompkins et al. 2010). However, there is the threat of future expansions of such disease-causing agents and their vectors into the country. Ross River virus (RRV) has been identified by multiple authors as one of the most threatening such agents (Maguire 1994, Weinstein et al. 1995, Crump et al. 2001, Kelly-Hope et al. 2002, Derraik and Calisher 2004). RRV is widely endemic in Australia where it is the most common mosquito-borne pathogen causing human disease (Bi et al. 2009, Russell 2009). RRV can be transmitted without substantial involvement of nonhuman vertebrates in virus amplification; however, it generally persists in wildlife reservoir hosts (principally macropods; Harley et al. 2001, Russell 2002). In tropical regions of Australia, spillover of infection to humans occurs annually during the wet season. In
1 2 3
the more temperate southern regions, human infection occurs relatively infrequently outside of epidemics. With variable environmental conditions, and vector and wildlife reservoir communities throughout the country, disease epidemiology varies regionally and even locally (Kelly-Hope et al. 2004, Jacups et al. 2008, Tong et al. 2008). Although RRV outbreaks are yet to occur in New Zealand, five factors are believed to make them likely in the future. First, RRV persists in Tasmania (Robertson et al. 2004, Werner et al. 2012), which has a similar climate envelope to parts of New Zealand (Maguire 1994, Weinstein et al. 1995). Second, the threat of RRV as an emerging disease has been demonstrated elsewhere in recent decades. In 1979, an infected Australian tourist introduced RRV to Fiji, leading to a major epidemic of polyarthritis in the Pacific area where as many as half a million people were infected (Marshall and Miles 1979, Rosen et al. 1981). Third, multiple human viremic cases of RRV are imported into New Zealand every year (Maguire 1994, Kelly-Hope et al. 2002). Fourth, the introduced brushtail possum Trichosurus vulpecula, of which
Landcare Research, Dunedin, New Zealand. Institute of Environmental Science & Research, Porirua, New Zealand. Barbara Hardy Institute, University of South Australia, Adelaide, South Australia, Australia.
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there are an estimated 30 million in the wild in New Zealand (Shepherd et al. 2011), has been implicated as a reservoir host of the virus in Australia (Boyd et al. 2001, Kay et al. 2007). Finally, six mosquito species that have shown at least some degree of RRV vector competence in laboratory trials currently occur in New Zealand (Holder et al. 1999): The introduced Aedes notoscriptus, Ae. australis, and Culex quinquefasciatus, and the native Ae. antipodeus, Cx. pervigilans, and Opifex fuscus (Maguire 1994, Doggett and Russell 1997, Watson and Kay 1998, Kramer et al. 2011). Ae. notoscriptus has been identified as a likely vector of the virus in urban Australia (Russell 1995, 1998, 2002, Watson and Kay 1997, Lindsay et al. 1998), whereas Ae. australis has been implicated in transmission in Tasmania (Hearnden et al. 1999). Additionally the exotic Ae. camptorhynchus, recently declared eradicated in New Zealand following its first detection in 1998 (Bader and Williams 2011), is the main vector of RRV in southern Australia (Harley et al. 2001, Russell 2002). With a likely reservoir species widespread and abundant (as well as isolated populations of captive and wild wallabies), and viremic human cases of RRV imported to New Zealand annually, the most likely bottleneck for current RRV transmission within the country is believed to be of an insufficient vectorial capacity (Kelly-Hope et al. 2002). Hence, two nonexclusive mechanisms considered likely to cause RRV emergence in New Zealand are improving conditions for mosquito breeding (caused by factors such as land use and climate change; Derraik and Slaney 2007) and further incursions and spread of major vectors (Derraik and Calisher 2004). Given the need to allocate constrained biosecurity and health care resources to where they provide the greatest benefit in any country, formal predictive exercises need to be carried out for any potential future threat, such as RRV to New Zealand, to guide decisions regarding preventive management and incursion responses. For RRV in Australia, and indeed for vector-borne diseases in general (e.g., Hales et al. 2002), such exercises generally involve correlational modeling of outbreak data against environmental variables (e.g., Werner et al. 2012). However such an approach, at least for vector-borne agents such as RRV where vector and wildlife reservoir communities are highly variable, has proven nontransferable between regions (Kelly-Hope et al. 2004, Jacups et al. 2008, Tong et al. 2008). As an alternative to correlational modeling, here we explore the use of deterministic models to investigate the potential for RRV emergence in New Zealand. Such models have been constructed previously for RRV in Australia to explore ecological questions (Glass 2005, Carver et al. 2009). However, although the understanding of the mechanisms that are implicit in deterministic modeling makes it a potentially more valid approach than correlational work for investigating disease emergence, such a benefit comes at the cost of increased information requirements. For example, while correlational models of infectious diseases can be based on general environmental data (e.g., Hales et al. 2002, Werner et al. 2012), deterministic models require rate information for specific mechanisms (Tompkins et al. 2002). Hence, our first aim was to use model construction and parameterization as an objective process for highlighting knowledge gaps regarding the assessment of RRV emergence potential in New Zealand. We then make preliminary predictions based on currently
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available data to explore the worth of filling those gaps, and inform on the utility of this approach for exploring disease emergence in general. Materials and Methods Model structure
Our model is based on that of Carver et al. (2009), formulated for investigating ecological influences on RRV dynamics in southwest Western Australia (see reference for base model details). The original model took the form of a deterministic ‘‘Susceptible, Exposed, Infected, and Resistant’’ model, constructed to simulate RRV epidemics where local transmission briefly occurs (triggered by a pulse of mosquito abundance following favorable environmental conditions) followed by local extinction of the virus. Such an approach is also relevant to New Zealand, where RRV occurrence in the foreseeable future will most likely be as similar highly seasonal epidemics rather than as endemic (Kelly-Hope et al. 2004). However, our model differs in two key respects: Humans are specifically included as hosts, and the model is extended to allow multiple vectors. Bites from each vector type were apportioned among the different host types according to the relative abundance of each host type weighted by a ‘‘relative bite factor.’’ Acknowledging that mosquitoes may prefer certain hosts over others ( Johansen et al. 2009), and that certain hosts are more accessible to mosquitoes than others (Kay et al. 2007), these factors were calculated from published blood meal identification and host abundance data and scored relative to humans. Where data were available on the same host type from different mosquitoes, no significant difference in ‘‘relative bite factor’’ was observed (data not presented for brevity). These factors were thus assumed to be invariable host traits. Vector and host categorization
Given the range of potential RRV vectors in New Zealand, either already present or with a possibility of incursion, mosquitoes were modeled as belonging to either one of two categories: (1) minor vectors (combining Ae. notoscriptus, Ae. antipodeus, Ae. australis, Cx. pervigilans, Cx. quinquefasciatus, and O. fuscus) or (2) major vectors (combining Ae. camptorhynchus, Ae. vigilax, and Cx. annulirostris; the latter two are the other main vectors of RRV in Australia; Harley et al. 2001, Russell 2002). Major and minor vector parameters were set to those for Ae. camptorhynchus and Ae. notoscriptus, respectively, being the species in the two categories for which most information regarding their role in RRV transmission is available. All vectors categorized as major perform at similarly high levels in laboratory competence trials (Harley et al. 2001), whereas those categorized as minor all perform significantly worse (Maguire 1994, Doggett and Russell 1997, Watson and Kay 1998, Kramer et al. 2011). Given the range of potential RRV hosts in New Zealand, relevant ecological, clinical, and immunological characteristics were used to combine hosts into meaningful functional groups with respect to RRV dynamics (Table 1). The ‘‘deadend hosts’’ category, comprising cats and birds, contains hosts in which viremia in the wild (if any) is considered too low to lead to the subsequent transmission of infection to
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Table 1. Parameter Estimates for Ross River Virus Infection Dynamics in the Six Host Categories Employed in the New Zealand Model Major vector transmission probabilities Parameter Symbol Humans Possums Macropods Minor hosts Dogs Other dead-end hosts
Minor vector transmission probabilities
Relative bite factor
Mosquito to host
Host to mosquito
Mosquito to host
Host to mosquito
Rate at which exposed hosts become infectious
a 1 2 4 6 6 1
TMH 0.5 0.3 1 0.75 0 0
THM 0.5 0.55 0.5 0.55 0 0
TMH 0.07 0.04 0.13 0.1 0 0
THM 0.5 0.55 0.5 0.55 0 0
r 0.13 1 1 1 0 0
Host recovery rate c 0.25 1 0.17 0.4 0 0
See text for the source of the minor vector transmission probability estimates. All rates are per day. Relative bite factors are calculated from blood meal identification and host abundance data and scored relative to humans. Information was obtained from Harley et al. (2001), Choi et al. (2002), Carver et al. (2009), and Johansen et al. (2009). Other model parameters are as Carver et al. (2009).
feeding mosquitoes, and ‘‘relative bite factors’’ are similarly low. Although a host species in which viremia is considered similarly low, dogs were modeled separately because their relative bite factor is much higher and they thus may act as an important sink of infection (Kay et al. 2007). The minor hosts category, comprising sheep, horses, cattle, deer, rabbits, and pigs, contains hosts in which viremia occurs, but the levels and duration are similar and consistently lower than those in the recognized primary hosts of the virus in Australia. Interestingly, hosts in this category have relative bite factors much higher than humans. This is well recognized and linked to them (and also dogs) being relatively sedentary and exposed outdoors (Kay et al. 2007). The host categories employed here align with the generally accepted conclusion of Kay and Aaskov (1989) that ‘‘marsupials are more competent hosts than placental mammals which, in turn, are more effective amplifiers than birds.’’ Estimates of RRV transmission probabilities (i.e., the probability that a bite from an ‘‘Infected’’ class mosquito will infect a ‘‘Susceptible’’ class host, or the probability that biting an Infected class host would infect a Susceptible class mosquito) in the wild are only available for Ae. camptorhynchus (Carver et al. 2009). However, for mosquito-to hosttransmission, laboratory trials have been carried out for Ae. notoscriptus. Hence, we use a comparison of these trials with similar trials for Ae. camptorhynchus to obtain a factor by which to adjust the wild estimates for Ae. camptorhynchus to give meaningful estimates of the same for Ae. notoscriptus. Laboratory trials with Ae. camptorhynchus can achieve 100% RRV transmission to infant mice (Ballard and Marshall 1986); however, similar trials with Ae. notoscriptus can only achieve 13% (Watson and Kay 1998). Hence, transmission probabilities for RRV to hosts in the wild by the minor vector Ae. notoscriptus were estimated as being only 13% of those for the major vector Ae. camptorhynchus (Table 1). In contrast, because laboratory RRV infectious doses for Ae. notoscriptus are similar to those for Ae. camptorynchus (Harley et al. 2001), host-to-mosquito transmission probabilities for Ae. notoscriptus were assumed to be the same as those estimated for Ae. camptorhynchus. No data are available on RRV transmission probabilities to and from humans. These values were thus initially assumed to equal 0.5 with respect to major
vectors (and adjusted as above for minor vectors), but later varied across the range of values observed for non–dead-end hosts to assess the robustness of model predictions to such variation (data not shown; impact on predictions is reported in the Discussion section). Model simulation
There is a global lack of absolute count data for adult mosquito populations; the general practice is to use numbers caught per trap-night as an index. These data are easily incorporated into correlational models, but they are insufficient for mechanistic approaches like that adopted here. Thus, we treat mosquito density as a variable, assessing the potential for RRV outbreaks across mosquito pulses of different densities with either only the minor vector category included (i.e., the current situation) or both the minor and major vector categories included (i.e., the situation if further incursions and spread of known major vectors occurs). Mosquito densities from 0 to 1 m - 2 were explored in 0.01-m - 2 steps, with the upper limit being the same order of magnitude as the maximum local density of 2.6 m - 2 estimated from a 1-ha area in Western Australia by Carver et al. (2009). The model was simulated on a daily basis at a spatial scale of 1 km2. This is similar to the scale employed by Carver et al. (2009) and is representative of the average dispersal range of the mosquito species involved (Watson et al. 2000, Robertson 2006, Jardine 2007). Simulations were initiated with a single Exposed class human being present in an otherwise Susceptible host community, and run for 100 days; RRV emergence in New Zealand being seeded by an infected traveler is considered the most likely route of future incursion. Because the potential combinations of different RRV hosts present across the country are countless, simulations were conducted for six common scenarios chosen to span the potential range (Table 2), two of which were simulated in the presence and absence of a low-density macropod population to investigate the effect of wallaby population presence on predicted dynamics. The proportion of the human population infected during the simulated outbreak was the default predicted variable (equivalent to the proportion in the Recovered class
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Table 2. Representative Host Densities (km - 2) for the Six Host Community Scenarios Simulated Using the New Zealand Ross River Virus Model
Scenario Humans Possums Macropods Minor hosts Dogs Other dead-end hosts
Highdensity urban
Lowdensity urban
Highdensity pastoral
Lowdensity pastoral
Broadleaf/ podocarp forest
Beech forest/ plantation
500 0 0 0 0 200
250 10 0 0 50 400
10 100 0 (100) 1000 5 600
1 200 0 (100) 1000 1 200
1 1000 0 10 0 200
1 200 0 10 0 200
Note that high- versus low-density refers to the human population as per table values. Numbers in brackets denote densities used only in a few specific simulations (see main text for details). Densities based on information in King (2006), MacLeod et al. (2006, 2009), Ministry for Primary Industries (2012), New Zealand Government (2012), and Statistics New Zealand (2012).
at the end of each simulation). For those scenarios in which humans are not present or are at very low density, the proportion of the possum population infected during the simulated outbreak became the predicted variable, with the probability that a human visitor to the area would be infected also evaluated. Results
The model simulated RRV outbreaks with infectious humans (or possums) present for approximately 3 months, a timescale similar to that observed in outbreaks in Southern Australia (Fig. 1; Jardine et al. 2008). The general characteristics of its predictions were as expected. First, major vectors were more efficient than minor vectors, causing outbreaks (and resulting in higher proportions of hosts being infected) at lower vector densities. Second, the presence of dead-end hosts inhibited outbreaks and reduced proportions
FIG. 1. Outbreak of Ross River virus (RRV) infection in the human population, generated by the model for the highdensity urban scenario, at minor and major vector densities of 0.5 m - 2 (i.e., one minor and one major vector every 2 m2). The y axis shows number of people in each of the four model disease classes; the x axis shows days since the introduction of a single Exposed class individual into the population.
of hosts being infected, due to wasted bites by infected mosquitoes on these hosts causing parasite dilution. Finally, lower host population sizes led to higher proportions of hosts being infected due to the frequency dependent nature of mosquito to host transmission. The relationship between the proportion of the human population infected during the simulated outbreak and mosquito density, with either only minor vectors present or both minor and major vectors present, is shown in Figure 2 for the three host community scenarios with appreciable human populations. Outbreak size is consistently low with only minor vectors present. It is not until a relatively high mosquito density of 0.35 m - 2 that the proportion infected reaches even 0.01 for any scenario (the high-density urban one). Even at the highest mosquito density simulated, only 0.06 of the human population is predicted to be infected during the outbreak. With both minor and major vectors present, outbreak size is markedly higher for all three scenarios (Fig. 2). This is particularly the case for the high-density urban scenario, where the proportion of the human population infected during the outbreak reaches 0.01 at minor and major vector densities of only 0.05 m - 2, with half infected at 0.35 m - 2. For the low-density urban and high-density pastoral (without macropods) scenarios, the minor and major vector densities required for 0.01 infected were 0.10 m - 2 and 0.24 m - 2, respectively. Including macropods in the last scenario had little influence on infection dynamics (data not shown). The relationship between the proportion of the possum population infected during the simulated outbreak and mosquito density, with either only minor vectors present or both minor and major vectors present, is shown in Figure 3 for the three scenarios without appreciable human populations. As for the previous three scenarios, outbreak size is low with only minor vectors present for the low-density pastoral (without macropods) and broadleaf/podocarp forest scenarios, with the proportion infected not reaching even 0.01. However, for the beech forest/plantation scenario, 0.01 infected is reached at a mosquito density of 0.22 m - 2 (the lowest for any scenario, with just minor vectors present), with the proportion infected reaching 0.23 at the highest mosquito density simulated. At this point, there is a similar probability of a single human visitor being infected. With both minor and major vectors present, outbreak size is again markedly higher for all three scenarios (Fig. 3). This
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FIG. 2. Proportion of the human population infected by Ross River virus (RRV) during the simulated outbreak, after the introduction of a single Exposed class individual. Model predictions for the high-density urban, low-density urban, and high-density pastoral (without macropods) scenarios are shown, across a range of mosquito densities with either only minor vectors present or both minor and major vectors present. is particularly the case for the beech forest/plantation scenario, where the proportion of the possum population infected during the outbreak reaches 0.01 at minor and major vector densities of only 0.09 m - 2, with half infected at 0.20 m - 2, and all infected at 0.71 m - 2 (at which density there is again a similar probability of a single human visitor being infected). For both the low-density pastoral (without macropods) and broadleaf/podocarp forest scenarios, the minor and major vector densities required for 0.01 infected were 0.21 m - 2. As was observed previously for the highdensity pastoral scenario, including macropods in the low-
density pastoral scenario had little influence on infection dynamics. Discussion
Although we were able to obtain data from New Zealand and Australia to parameterize our RRV model, the process of model construction highlighted our lack of absolute adult mosquito count data as a key knowledge gap. Thus, we interpret our model output in relation to mosquito density thresholds. While the magnitude of mosquito outbreak
FIG. 3. Proportion of the possum population infected by Ross River virus (RRV) during the simulated outbreak, after the introduction of a single Exposed class person. Model predictions for the low-density pastoral (without macropods), hardwood/podocarp forest, and beech forest/plantation scenarios are shown, across a range of mosquito densities with either only minor vectors present or both minor and major vectors present.
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densities explored (up to 1 m - 2) was based on maximum local densities estimated from Western Australia, averages there are an order of magnitude lower than maximum local densities (Carver et al. 2009). In addition, trap catch indices for Cx. pervigilans (New Zealand’s most common mosquito, occurring in the widest range of habitats including highly modified ones; Holder et al. 1999) tend to be an order of magnitude lower than what can be observed in Australia (Leisnham et al. 2007). Densities up to 0.1 m - 2 are thus reasonable estimates of what can be achieved by the mosquitoes currently established in New Zealand (i.e., the minor vectors for RRV) under contemporary conditions in their optimal habitats, with greater densities potentially achievable should conditions for mosquito breeding improve. For the major vectors of RRV (all of which are exotic species not currently established in New Zealand), densities of up to 0.1 m - 2 are the maximum considered likely should incursion occur, as current conditions in Australia are markedly more conducive to the development of such species than most parts of New Zealand in the foreseeable future (de Wet et al. 2005). According to these thresholds, the potential for RRV emergence in New Zealand is predicted to be less than current opinion suggests, being highly variable across the scenarios simulated. With only minor vectors present, appreciable outbreaks could occur in the high-density urban and beech forest/plantation host community scenarios, and then only should conditions for mosquito breeding improve. With both minor and major vectors present, appreciable outbreaks may occur under contemporary conditions in the high-density urban, low-density urban, and beech forest/plantation scenarios. For the beech forest/plantation scenario, outbreaks are enabled by virus amplification in the high-density possum population present; for the urban scenarios, outbreaks are enabled by virus amplification in the high-density human populations present. Current RRV outbreak potential in New Zealand is likely to be low. Although minor vector densities over 0.1 m - 2 were able to drive appreciable outbreaks in some scenarios, RRV is currently absent in the country. In addition, although major vector densities over 0.1 m - 2 were able to drive appreciable outbreaks in pastoral scenarios, antibodies to RRV were never detected in cattle in farmland where populations of the exotic Ae. camptorhynchus were present prior to eradication (McFadden et al. 2009). Hence, although having absolute mosquito count data would undoubtedly improve confidence in model predictions, we consider our mosquito density thresholds to be realistic. Our predictions with regard to these thresholds held with varying RRV transmission probabilities to and from humans. Higher probabilities could also lead to RRV outbreaks with only minor vectors present in the lowdensity urban scenario (and then only should conditions for mosquito breeding improve). This prediction was the only increase in disease emergence potential observed (simulations not presented for brevity). However, this was at the level of virus transmission observed in marsupials, the hosts in which RRV evolved, and is thus unlikely to occur in placental mammals such as humans (Harley et al. 2001, Russell 2002). Finally, with (1) all general mosquito parameters (such as those relating to vector competence, biting rates and transmission success) based on Australian estimates, (2) all minor vectors assumed to share the same mosquito-specific parameters as Ae. notoscriptus (implicated as playing a key
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role in RRV transmission in certain parts of Australia (Russell 1995, 1998, 2002, Watson and Kay 1997, Lindsay et al. 1998), rather than being supported only by laboratory competence trials as are others), and (3) exotic mosquitoes able to reach similar densities in New Zealand as do native mosquitoes, we have aimed to err on the side of caution to overestimate risk in our modeling approach. Thus, the prediction that actual RRV outbreak potential in New Zealand is less than current opinion suggests stands. With little in the way of human populations to be infected in scenarios with high possum densities (Table 2), this study predicts that RRV outbreaks in humans in New Zealand are likely to be of concern in urban areas, and then mainly should exotic major exotic vectors of the virus should be established. Within this scenario, environmental conditions are considered to be currently suitable for exotic major vectors of Ross River virus (C. annulirostris, Ae. vigilax, and/or Ae. camptorhynchus) in parts of Auckland, Hamilton, Gisborne, Palmerston North, and possibly also Christchurch (de Wet et al. 2005). This study thus justifies the efforts made to eradicate Ae. camptorhynchus, the main vector of RRV in Southern Australia, from New Zealand following its detected incursion in 1998. In addition, arguments based on vectorial competence have recently been put forward in support of calls for extensive surveillance for exotic vector-borne diseases and mosquito vectors to be undertaken on a regular basis across New Zealand (Kramer et al. 2011). Our approach, however, shows that variation in host competence is just as important and likely acts to limit the potential for disease emergence. As a result, should human RRV outbreaks happen in New Zealand, they will most likely be driven by virus amplification in dense human populations, as occurred in the Pacific Nations outbreak of late last century, as opposed to the spillover infection from wildlife common in Australia. However, our results for scenarios with high possum densities do indicate that model extension (through the inclusion of host and vector reproductive and seasonal parameters) should be used to explore the potential for RRV to become endemic in such host communities in the longer term. With predictions indicating that the potential for RRV emergence in New Zealand is less than current opinion suggests, and the mechanistic insight afforded, this study demonstrates how deterministic modeling can provide an additional approach for both disease emergence prediction and surveillance strategy design. The costs associated with the greater information needs of such approaches would likely be more than offset by the savings from targeted biosecurity and health care resource allocation. Thus, addressing key knowledge gaps impacting our ability to conduct these approaches in a robust manner, such as the lack of absolute count data for mosquito populations (with respect to vector borne diseases), would be highly beneficial. Two research directions to fill this specific gap can be identified. First, data on current adult mosquito densities around high-risk regions are required. This would involve developing methods for either directly measuring counts in the field or robustly converting trap indices to estimates of such. Second, to allow prediction of future densities, mechanistic models are required for vector species of concern (such as that developed for the dengue mosquito Ae. aegypti in Australia; Kearney et al. 2009). With such developments, far more detailed and robust identification of at-risk host communities could be
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made, both for RRV and other vector-borne threats to human health, such as dengue fever and chikungunya virus (Crump et al. 2001, Derraik et al. 2010). Acknowledgments
This research was supported by funding from the New Zealand Ministry of Research, Science & Technology under contract C03X0801. The comments of three anonymous reviewers greatly improved the manuscript. Author Disclosure Statement
There are no competing interests. References
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Address correspondence to: Daniel M. Tompkins Landcare Research Private Bag 193 Dunedin New Zealand E-mail: [email protected]
