Introduction
Vector-borne diseases (VBDs) have been spreading geographically, which is largely attributed to climate change [
1‐
4]. As new areas become suitable for vectors and pathogens, the threat to unexposed populations grows [
5]. Japanese encephalitis virus (JEV) is a zoonotic disease transmitted by mosquitoes endemic to Southeast Asia and the Western Pacific [
6]. In 2022, Australia experienced a geographic expansion of JEV, spreading across four states and 80 piggeries [
7] representing the first time transmission of JEV has occurred beyond the Torres Starit and Cape York in the far northeast of Australia. Climate conditions elsewhere have been associated with the spread of JEV [
8‐
10].
Models using climate change projections provide insights into the potential future range of vectors and associated diseases [
11]. By gauging the habitat suitability of vectors in response to climate change, it is possible to predict vector expansion [
12]. Identifying regions where the climate might suit pathogens, vectors, and reservoir hosts, can help with advanced preparation and advocacy [
11]. Nevertheless, modelling VBDs can be challenging due to the complex interactions between the vector, pathogen, hosts, and environmental conditions [
13]. Moreover, vectors can have different requirements in different geographic areas due to localised factors [
14‐
16].
Among predictive models for VBDs, the Environmental Niche Models (ENMs) are popular. They estimate areas suitable for pathogen transmission by mapping the geographic distribution of vectors [
17,
18]. A distinct advantage of ENMs is their ability to function without needing data on the interactions between the environment, vectors, hosts, and pathogens [
17]. Yet, their efficacy is contingent upon the availability and accuracy of presence/absence records of vectors or pathogens, potentially constraining their results [
17].
While software exists for developing an ENM [
19], there is a lack of evidence that local and state governments in Victoria, Australia use such tools for their risk assessment [
20]. Predominantly, their risk assessments draw on historical data and past incidents, which can limit the prediction of disease emergence [
20].
A geographic information system (GIS) can also be useful to VBD modelling efforts. It assimilates diverse environmental variables within a geographically delineated grid of pixels, thereby pinpointing the environmental suitability for a given species and predicting its potential distribution [
21]. Modelling the VBD risk with GIS is relevant to target efforts in specific locations [
22]. When coupled with multi-criteria decision analysis (MCDA) [
23,
24], it creates a spatial multicriteria decision analysis that encompasses geographically specific alternatives and aids spatial decision-making [
23]. Applying such an approach to VBD modelling offers actionable insights based on ranking alternatives and sensitivity analysis within targeted locations [
23]. Integrating spatiotemporal disease modelling can help to identify and understand variations over time and space while considering environmental and sociodemographic factors [
25].
The analytical hierarchy process (AHP) is a decision-making tool that breaks down complex problems into interrelated decisional elements and arranges them into a hierarchical structure [
23,
26]. Several applications of the AHP and other MCDA in healthcare were discussed previously as a solid method to use in decision-making in this field [
27]. Additionally, geospatial technologies have been previously used by decision-makers to map vulnerable and high-risk locations and to support public health decisions, such as the case of COVID-19 response programs launched by the WHO since 2020 [
28].
The combination of GIS and AHP in modelling for VBDs has successfully mapped high-risk areas in a number of previous studies [
29‐
35]. Such a GIS-AHP-based model can support local government efforts in assessing disease emergence risk, especially by identifying areas where the disease may be established due to climatic shifts. Furthermore, this approach can catalyse effective public health policy formulation, medication and vaccination distribution, business intelligence insights, and healthcare infrastructure planning [
25]. It does so by illuminating patterns, demarcating high-risk zones, and identifying communities susceptible to infectious diseases [
25].
The 2022 outbreak of JEV took an unexpected turn, reaching the southern regions of Australia where the disease’s presence was previously unanticipated [
36]. The prediction of VBDs in Australia’s temperate climates remains an area requiring further investigation. Tools designed to predict potential outbreaks – both temporally and spatially—are essential for enhancing preparedness and implementing effective mitigation strategies [
37].
Our objective is to develop a GIS-AHP model designed to identify potential JEV risk zones in Victoria, especially considering the implications of climate change. Beyond its immediate application, this model holds promise as a versatile framework to determine the risk of other VBDs and as a decision support tool for local policymakers, public health authorities, land-use planners, and scholars.
Discussion
In recent years, the modelling of VBDs has advanced, employing spatial and temporal trend analysis to forewarn of potential future vector and disease expansions, particularly within various climate change scenarios [
11,
17] GIS has established itself as a vital tool in VBD modelling, facilitating the mapping of areas vulnerable to vector species proliferation and territory expansion, as delineated in various studies [
53‐
55].
The synergistic approach of integrating GIS with an AHP analysis has demonstrated substantial efficacy in forecasting disease risk and demarcating high-risk areas [
29‐
35]. Furthermore, GIS serves as a precise tool in risk mapping, capable of pinpointing risk areas at a micro level, hence proving indispensable in identifying vulnerable localities [
53,
54].
The application of GIS goes beyond mapping; it functions as a crucial component of spatial decision support systems, aiding decision-makers in developing informed strategies, especially concerning public health preparedness against emerging infectious diseases [
23,
24]. Along these lines, our study conceptualised and executed a GIS-AHP model to discern areas within Victoria, Australia, susceptible to the emergence of JEV. By assimilating historical climate data and recent 2022 data (the year marking the emergence of JEV in Australia), we aspire to contribute a robust tool in the evolving domain of public health preparedness.
The integration of expert insights into the AHP enables the formulation of a consensus regarding the influence of various criteria. Notwithstanding, some disparities were noted in the weights assigned to certain criteria by different participants (Table
2). This variance could be attributed to the diverse professional backgrounds of the participants and possibly signifies gaps in current knowledge or data paucity about VBDs. Despite these disparities, a consensus on the consolidated weight values for each criterion was achieved among the majority of participants.
Climate variables significantly influence mosquito proliferation, thereby increasing the risk of disease emergence [
56]. Mosquito abundance is cumulatively influenced by both seasonality and previous weather conditions [
14]. Furthermore, temperature acts as a critical determinant governing vector and pathogen survival, their geographic distribution, and the transmission dynamics of the disease [
38,
57]. Nonetheless, the influence of temperature on disease transmission exhibits a non-linear pattern, as it experiences daily, monthly, and yearly fluctuations, accompanied by a lag phase between the optimal temperature and the actual disease transmission [
38]. This study did not incorporate considerations of time lags and temperature fluctuations in assessing disease emergence risk, but it is postulated that a one-month time lag may be anticipated for JEV incidence [
10].
Our model facilitates the extraction of individual climate condition data layers, which can subsequently be overlaid to generate a comprehensive climate suitability map (Fig.
5). This mapping process elucidates the specific conditions driving climate suitability and helps unravel the interactions of all pertinent conditions. The AHP analysis also aids in navigating the complexities associated with the amalgamation of these conditions. Notably, minimum temperature emerged as a strong predictor of JEV incidence, as supported by other studies [
10,
58]. During 2022, the minimum temperature was responsible for the greatest increase in suitability, compared to historical trends. Many LGAs reported minimum temperatures exceeding the average by 3.65
0C in January 2022 [
59], creating conditions for JEV transmission during that period.
During January and March of 2022, a noticeable increase in precipitation-driven suitability was observed, with more LGAs exhibiting medium to high suitability compared to the historical data, a consequence of above-average precipitation in certain regions. An overall increase in climate suitability for JEV transmission was predominantly noted in these months, which could be attributed to a simultaneous increase in both minimum temperature and precipitation suitability. This contrasted with the maximum temperature suitability, which remained relatively consistent with historical data, indicating no significant changes that might affect JEV emergence in the 2021–22 period.
When analysing the consolidated weights assigned to different climate conditions, it becomes evident that the summer season of 2021–22 marked heightened suitability, primarily driven by increased precipitation and minimum temperature levels. These elements were recognised as pivotal factors influencing JEV transmission, as corroborated by expert consensus.
Nevertheless, it is crucial to underscore that this study was limited by the omission of certain influential factors such as geographic barriers, elevation, and anthropogenic modifications to the landscape. Consequently, the projected climate suitability areas might encompass a larger region than the actual potential habitat of the mosquito vectors [
21]. Additionally, the chosen variables considered in this model may not encapsulate the complete array of factors dictating the spatial distribution of these vectors, signifying that areas identified as climatically suitable may not invariably harbor the vector population [
21]. Hence, even though an area might exhibit climate suitability, it is not certain that the vector will occupy that location. It is also pertinent to mention that host availability remains a requisite determinant in assessing disease risk, an aspect only partially incorporated in this study by considering the piggeries’ location. The inclusion of other hosts population distribution and dynamics should be addressed in future research, and it represents a limitation of the current study. This project primarily focused on delineating climate prerequisites conducive to vector and virus transmission.
In assessing the potential emergence of diseases, this study integrated several risk factors such as climate variables, proximity to piggeries and wetlands, and human population density. By combining these risk factors, we managed to pinpoint regions within the state that were at a heightened risk for disease emergence. In particular, an increase in JEV incidence was observed in January and March of 2022, compared to historical data. This revealed that piggeries located in the north-central-south regions in the state were susceptible to JEV emergence, a trend significantly influenced by climate factors. It is however important to note that this does not equate to a definite presence of the disease in all piggeries within the specified regions. Instead, the model aims to identify areas potentially at risk. In this respect, evidence of the model’s predictive powers was demonstrated by accurate identifications of 7 out of 8 LGAs that reported positive cases of JEV in piggeries during the summer of 2022, representing a high sensitivity. One drawback, however, is the low specificity of the model since more LGAs were categorised as high-risk than the number of LGAs with confirmed cases during the JEV outbreak. This might be due to the lack of other hosts’ availability and dynamics data, and aspects of the disease’s ecology that is still not well understood.
The sensitivity analysis showed a pronounced correlation between high climate suitability, such as that witnessed in January of 2022, and the relative proximity to piggeries, highlighting a notable sensitivity to alterations in output data (Fig.
10). This underscores the importance of incorporating any amplifying host locations to precisely delineate areas vulnerable to increased disease emergence risks under conditions of high climate suitability. A similar trend was observed in relation to the proximity to wetlands, where a discernible sensitivity to changes was noted in scenarios exhibiting high climate suitability (Fig.
10).
For the enhancement of this model, incorporating data about the habitats of feral pigs and other potential hosts would be a prudent step. This further emphasises the necessity to incorporate climate change and climate change projections to ascertain the climate suitability for the emergence of diseases, thereby fostering a more robust and predictive approach to managing and mitigating the risks associated with infectious diseases.
Commercial piggeries are critical nodes in the propagation of JEV [
60]. The vaccination of pigs presents logistical challenges as a preventative measure against JEV, given their early slaughter age of 6–8 months. Consequently, the strategic position of piggeries emerges as a viable strategy to curb JEV transmission to humans [
61]. This model can provide a useful tool to aid in the planning or relocation of piggeries by delineating areas with high climate suitability and potential disease transmission risks. Given that Victoria ranks as Australia’s second-largest pork producer, with the industry witnessing rapid growth (doubling from 2018–19 to 2019–2020) [
62], prudent planning for piggery locations becomes important, especially in light of the recent emergence of JEV in the state.
Incorporating both spatial and temporal analyses with an AHP enables the capture of certain complex facets that drive VBDs [
14,
63]. This synthesis offers substantial value in bolstering decision-making processes aimed at enhancing preparedness and formulating adaptative strategies to combat VBDs. However, it should be noted that this model does not encompass all aspects integral to the transmission cycle of VBDs. It serves as a supportive tool in steering planning and surveillance initiatives and should ideally be utilised in conjunction with other predictive models to facilitate a more comprehensive analysis. In that way, it is possible to craft a robust predictive network that can more accurately anticipate disease spread patterns, thereby enabling proactive strategies in disease management and prevention.
In this model, facets such as the adaptation and evolution of vectors and pathogens were not considered, a potential limitation noted previously [
17]. Consequently, the projected risk in some areas could potentially be higher since host and vector data were not included in the analysis. Although human population density was factored into the study, given its relevance to assessing the risk of emergence in human populations, this does not negate the potential emergence risk within host populations in the areas identified as high-risk by the model.
The benchmarks for optimal virus temperatures were derived and obtained from the previous research [
38]. Due to the absence of specific data pertaining to the transmission dynamics of JEV by
Cx. annulirostris, we approximated the transmission values utilising data from the closely related MVEV. It is important to note that there could be variations in the optimal temperature thresholds for disease transmission, and these might be subject to fluctuations based on different climatic regions [
10]. Nonetheless, we anticipate these variations to exert minimal influence on the model’s overall outcome and its intended purpose. In addition, this study only considered the climate prerequisites necessary for vector transmission, potentially resulting in a biased outcome. This stems from the fact that individual species involved in the disease transmission cycle may necessitate distinct environmental conditions [
17]. Moving forward, a more encompassing approach that integrates a wider spectrum of environmental variables and species-specific data can potentially facilitate a more nuanced and accurate predictive model, enhancing our preparedness and response strategies in managing VBDs.
Conclusions
The results obtained from the GIS-AHP-based model developed in this research successfully delineated areas with elevated risk of JEV emergence during the summer of 2022, highlighting the central role of climate change in escalating the risk associated with the disease. This underscores the potential utility of this model as a first step to support the prediction of the emergence and patterns of other VBDs by integrating pertinent risk factors and climate projections into the analysis framework. Further work is needed to ensure more specific predictions by incorporating more variables into the analysis.
By employing this model, a significant stride is made in enhancing the decision-making capabilities of various stakeholders including policymakers, public health authorities, land-use planners, and academic researchers focused on VBDs. Furthermore, the model can serve as a starting point to facilitate more comprehensive local risk assessments, and consequently, fortifying public health preparedness strategies against VBDs.
Looking forward, refining this model to encompass a broader spectrum of variables such as geographic barriers, elevation, human modifications to the landscape, and the environmental requirements of each species involved in the disease cycle, would pave the way for more nuanced and accurate risk predictions. Moreover, incorporating data on host and vector distributions can further augment the predictive capacity of this model.
To summarise, this model signifies a first yet promising step in developing a resource aimed at streamlining localised risk assessments and fostering an environment of proactive response and preparedness against emerging threats of VBDs under climate change. Its adaptable nature aligns well with the evolving landscape of vector-borne disease epidemiology, offering a foundation structure that can be modified and enhanced to specific diseases and locations as required.
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