1. Contributions to the field statement
Arboviruses present a significant public health risk to the Australian population.
Both the many indigenous arboviruses and imported cases of major global pathogens
contribute to this burden. Effective surveillance measures, which involve monitoring
for mosquitoes responsible for transmission, the signs and symptoms of disease in
humans, and a range of environmental and climactic factors, are essential to detect
and respond early to local outbreaks. This is particularly crucial in regional Australia,
a vast area that is underserved but is now becoming a focal point for economic and
social development. As this transformation progresses, there will be increased human
interaction with native reservoir animal hosts and vector mosquitoes, creating a potential
scenario for a higher prevalence of neglected indigenous arbovirus infections. Additionally,
the impact of climate change in the tropical north of the country is predicted to
lead to a population boom of arbovirus-transmitting mosquitoes, further exacerbating
the situation. Hence, it is imperative to maintain diligent attention to vector monitoring
and control efforts. Integrating artificial intelligence to rapidly process large
volumes of data should enhance surveillance by improving data analysis, prediction,
and decision-making. More accurate and quicker detection of arboviral disease outbreaks
will enable proactive and effective public health responses.
2. Introduction: the public health problem
Viruses that are transmitted between vertebrate hosts by biting, blood-feeding arthropods
(primarily mosquitoes and ticks) are called arthropod-borne viruses or, for short,
arboviruses. The transmission of arboviruses to humans poses a significant and accelerating
global public health risk (Madewell, 2020). It is estimated that 3.9 billion people,
approaching half of the world's population is at risk (World Health Organization,
2022), leading to hundreds of millions of symptomatic infections, a disease burden
of tens of thousands of deaths and up to 5 million disability-adjusted life years
lost annually (Labeaud et al., 2011). Notable examples of pathogens include dengue
(DENV), chikungunya, yellow fever, Japanese encephalitis (JEV), West Nile, Zika and
Mayaro viruses. For several of these, humans serve as the primary reservoir host,
with many causing pandemics over the last few decades (Mayer et al., 2017). Mild infection
is typically associated with influenza-like symptoms such as fever, headache, muscle
or joint pain, and/or a skin rash. Less commonly, severe infection is characterised
by rapid onset of haemorrhagic fever (with internal bleeding) or life-threatening
shock syndrome (with circulatory collapse). Signs and symptoms of encephalitis include
confusion, tremors, seizures, paralysis, and loss of consciousness (Labeaud et al.,
2011; Mayer et al., 2017).
The distribution of an arbovirus is restricted by the territory of its mosquito vector(s)
of transmission, which tends to be limited to tropical and subtropical zones. Yet,
due to the effects of climate change (involving rainfall patterns) the geographical
range of common vectors may be predicted to expand in future (Madewell, 2020). Thus,
locations that at present are currently not affected should not be complacent that
they will always remain free of arboviruses. The dramatic emergence and re-emergence
of arboviral diseases has been greatly exacerbated by a combination of global meteorological,
demographic, and societal changes, principally increasing rates and levels of climate
change, urbanisation, globalisation, and international mobility (Bellone et al., 2023).
These environmental and anthropogenic factors have facilitated viral etiological agents
to break out of their natural ecological zones to become established in novel geographical
sites where susceptible arthropod vectors and human hosts provide conditions supportive
to their causing epidemics (Madewell, 2020).
3. The usual suspects
DENV is an arbovirus of global concern but for which local outbreaks in Australia
are restricted to Queensland, where the vector mosquito Aedes aegypti is established
(Beebe et al., 2009). Community acquired infections have been reported only from urban
areas in the northeast of the state, where the vector is most abundant. However, historical
data show that much of Australia has previously sustained both the virus and the vector
mosquito (Russell et al., 2009). Factors such as increased DENV activity in neighbouring
countries like Indonesia and Papua New Guinea, plus the growing human population of
northern Australia contribute to the risk of DENV transmission (Gyawali et al., 2016a).
Climate change projections also suggest potential rises in dengue incidence and distribution
associated with increasing temperatures (Williams et al., 2014). This also applies
to JEV, the recent and rapid emergence of which in several states is a cause for concern
(Williams et al., 2022).
Imported cases of DENV and other arboviruses, including JEV, also pose a risk to public
health in Australia. With increased global travel and trade, there is a potential
for the introduction of arboviruses through infected travellers or imported vectors
(Mackenzie and Williams, 2009). The spread of arboviruses to regions without established
vectors, such as Ae. aegypti and Ae. albopictus, can occur through international air
and sea ports (Gyawali et al., 2016a). Therefore, surveillance and control measures
at ports of entry are crucial to prevent the importation and establishment of arboviruses
in Australia. The most recent national report, for 2016, shows 2,227 notifications
of DENV, of which 31 were locally acquired and the remainder travel-related, mostly
tourists visiting Bali (Australian Government Department of Health, 2021).
4. The less usual suspects
The threat presented by emerging indigenous arboviruses in Australia is arguably undervalued
(Gyawali et al., 2016b). More than 75 arboviruses have been identified that are unique
to the continent. While several are recognised to cause disease in humans, information
on the potential human pathogenicity of most of these indigenous viruses is negligible
(Gyawali et al., 2017a). Ross River (RRV) and Barmah Forest (BFV) viruses trigger
an often debilitating and sometimes chronic type of arthritis that affects several
joints at once. Murray Valley encephalitis (MVEV) and West Nile Kunjin strain (KUNV)
viruses cause inflammation of the brain.
One of the key arboviruses of concern is RRV, which is endemic and enzootic in the
country and Papua New Guinea (Kuleshov et al., 2022). The major vector in inland areas
is the freshwater-breeding Culex annulirostris, whereas Ochlerotatus vigilax and O.
camptorhynchus transmit in brackish coastal waters. RRV infection in humans can cause
peripheral polyarthralgia or arthritis, with disease notifications averaging 5,000
per year in Australia since the start of this century. Yet, there is considerable
annual fluctuation of confirmed case reports; for instance, 9,555 notifications in
2015 but 3,677 in the following year (Australian Government Department of Health,
2021).
As with RRV, human infections with BFV have been reported from all states and territories
in Australia. Moreover, serological surveys indicate that this is a widespread phenomenon.
Clinical manifestations often include fever, rash, chronic fatigue and polyarthritis.
BFV is transmitted primarily by Cx. annulirostris and Aedes funereus in inland and
in coastal regions, respectively. The reported incidence is usually close to 1,000
cases per annum since routine testing by immunoassay antibody detection became widely
available (Gyawali and Taylor-Robinson, 2017).
MVEV is endemic in northern Australia, with sporadic outbreaks occurring (Broom et
al., 2003). The virus is transmitted primarily by Cx. annulirostris mosquitoes, and
its activity is influenced by rainfall and flooding. Other emerging arboviruses, such
as KUNV, have been detected in this and other ornithophilic mosquitoes and pose a
potential public health threat (Broom et al., 2003). The presence of competent vectors
and the potential for virus introduction through travel and trade increase the risk
of emerging indigenous arboviruses in Australia (Mackenzie and Williams, 2009).
Other Australian arboviruses, such as Alfuy, Edge Hill, Gan Gan, Kokobera, Sindbis
and Stratford, are also associated with human disease (Gyawali et al., 2019). However,
they appear to cause predominantly mild symptoms and a major outbreak has not yet
been reported. While the epidemiology of these neglected viruses is poorly understood,
they are likely maintained in zoonotic cycles rather than by human-to-human transmission.
Hence, they are harboured by apathogenic, persistent infections in native Australian
reservoir mammals (such as kangaroos and wallabies) and birds (including herons and
egrets) (e.g., Gyawali et al., 2020), with occasional spillover into humans.
5. Need for improved early detection
For many years it was speculated that infection with arboviruses may be a cause of
febrile illness in Australia, as elsewhere in the world. This was confirmed with the
discovery of the now frequently diagnosed RRV in 1959 and BFV in 1974. Yet, even after
identification of these viruses it took almost 15 years for routine laboratory tests
(involving detection of virus-specific immunoglobulin (Ig) M and IgG) to diagnose
infection to become available (Gyawali et al., 2017a). While paired serology of RRV
and BBV is considered clinical best practise, it requires careful interpretation considering
the high rates of false positive and negative results, plus the long-term persistence
of IgM in some individuals. Incorrect interpretation risks misdiagnosis and therefore
inappropriate patient treatment (Gyawali et al., 2017b).
Compounding this problem of inaccurate viral infection case reporting is the fact
that more than half of so-called undifferentiated fevers (those with non-specific
symptoms) in Australia still go undiagnosed (Gyawali et al., 2017a). In many instances
this is because the treating physician may consider the cost of testing is not justified
or the causative agent is novel, not known to cause human disease or no routine diagnostic
test is available. In such cases, an association could be assumed but not proved between
arboviruses and feverish illness. Hence, establishing a robust surveillance system
would enable the early warning of an infection outbreak. Developing accurate diagnostic
tools would aid early diagnosis and correct treatment of febrile primary care patients.
Unforeseen climatic and environmental variations, such as the increased incidence
of cyclones, heavy rainfall, and resultant intensified flooding associated with outbreaks
of RRV (Tall et al., 2014) and MVEV (Selvey et al., 2014), have been occurring of
late with disconcerting regularity, potentially effectuating an ecological change
for Australian arboviruses (Young, 2018). The projected future climatic suitability
of Northern Australia for competent vector mosquito species needs to be evaluated.
In this context, improved epidemiological surveillance of prevailing environmental
conditions, mosquito vector species and reservoir host animals, should be considered
a public health priority.
6. A proposed solution
In order to prepare effectively for the emergence of an arbovirus outbreak of public
health concern, both globally (Weaver and Reisen, 2010), and in particular in regional
Australia (Gyawali and Taylor-Robinson, 2017), key surveillance measures are essential.
These include the following five actions:
Vector surveillance: monitoring and mapping the distribution and abundance of mosquito
vectors is crucial. This involves regular trapping and identification of vector species,
as well as testing them for the presence of arboviruses. Vector surveillance helps
identify areas at risk and informs targeted control measures.
Environmental surveillance: monitoring environmental factors, such as temperature,
rainfall, and humidity, can provide insights into vector breeding and arbovirus transmission
dynamics. This information helps predict and anticipate outbreaks, enabling timely
interventions.
Animal surveillance: monitoring arboviral infections in animal populations, particularly
in sentinel species, can serve as an early warning system for human outbreaks. Animals,
such as marsupials and water birds, can act as reservoir hosts or environmental indicators
of arbovirus activity.
Disease surveillance: active surveillance for human cases of arboviral infections
is vital. This involves monitoring and reporting suspected cases, conducting microbiology
laboratory testing for confirmation, and analysing epidemiological data to identify
trends and patterns. Early detection and reporting of cases allow for prompt public
health responses.
Syndromic surveillance: implementing surveillance systems that monitor specific clinical
symptoms or syndromes associated with arboviral infections can provide early indications
of outbreaks. Health indicators that are discernible before confirmed diagnosis include
monitoring febrile illnesses, neurological symptoms, and other relevant clinical presentations.
7. A novel approach
Artificial intelligence (AI) can play a prominent role in enhancing arbovirus surveillance
at scales ranging from local to global. AI algorithms can analyze large volumes of
data, including environmental, epidemiological, and entomological data. Integrating
human, pathogen, vector, and climatic variables from various existing surveillance
sources into a unified system can enhance pattern recognition and generate probabilistic
risk models for outbreak spread and severity (Pley et al., 2021). This allows epidemiologists
to detect patterns, predict outbreaks, and inform targeted interventions more accurately
using such high-throughput techniques as metatranscriptomic sequencing (Batovska et
al., 2022). Moreover, AI can automate data processing, improve data integration, assist
in modelling, and provide real-time monitoring and analysis of multiple variables.
This enables public health authorities to identify areas at high risk, to allocate
resources more efficiently and thereby to make more proactive and effective responses
(Batovska et al., 2019; Pley et al., 2021).
In the context of arbovirus surveillance, AI can assist in the identification and
tracking of mosquito vectors, processes that are crucial for understanding the transmission
dynamics of arboviruses. By analysing data on mosquito populations, AI algorithms
can identify trends and patterns that may indicate increased virus activity or the
novel emergence of a virus in a location (Ramírez et al., 2018). This information
can then be used to guide control and preventive measures. For example, AI can analyze
satellite imagery and climate data, mosquito surveillance data, and human case data
to reveal vector habitats, identify high-risk areas for disease outbreaks, and predict
disease transmission dynamics (Kurucz et al., 2022). AI algorithms can also analyze
social media and internet search data for disease outbreaks and public concerns to
help to develop early warning systems and decision support tools (Batovska et al.,
2022). Additionally, AI can assist in data integration and modelling to improve disease
forecasting and inform targeted interventions by public health authorities.
Research conducted in Kenya has demonstrated the effectiveness of mosquito-based arbovirus
surveillance in diverse ecological zones (Ochieng et al., 2013). Similarly, in Burkina
Faso, AI has been employed to enhance surveillance during dengue outbreaks, leading
to improved understanding of the burden of arboviral diseases (Sanou et al., 2018).
These experiences highlight the potential of AI in strengthening surveillance and
response measures for arbovirus disease outbreaks globally. Therefore, the use of
AI in arbovirus surveillance is not limited to Australia. Yet, this affluent developed
nation is particularly suited to integrating AI into outbreak preparedness procedures
(van den Hurk et al., 2012). It has the infrastructure and resources to leverage AI
better than most countries, while also having plenty to gain by mitigating a neglected
public health threat, especially in rural and regional locations that are relatively
underserved.
8. Conclusion
Arboviruses pose a significant public health threat to the population of Australia.
There is a risk of emerging indigenous arboviruses, while imported cases also contribute
to the burden. Surveillance measures, including monitoring vectors, diseases, and
environmental factors, are crucial for early detection and response to outbreaks.
This is particularly impactful in regional Australia, a historically underinvested
region that is set to become a focus of economic and social development. This will
increasingly bring humans into close contact with native reservoir hosts and vector
mosquitoes. Such a convergence of factors could trigger an increased prevalence of
infection with neglected indigenous arboviruses. Moreover, the escalating rate and
effects of climate change that are observed in the tropical north of the country will
likely drive a population boom of arbovirus-transmitting mosquitoes. As a commensurate
response, continuing assiduous attention to vector monitoring and control is required.
It is anticipated that the integration of artificial intelligence to process large
volumes of data rapidly will enhance surveillance efforts by improving data analysis,
prediction, and decision-making, ultimately leading to improved accuracy in detecting
arboviral disease outbreaks and enabling more proactive and effective public health
responses. The lessons learned from this Australian experience can help to better
prepare government agencies in other nations to adopt AI technology in their enhanced
surveillance efforts. In particular, this applies to low- and lower-middle income
countries in tropical and subtropical zones where the rising incidence of arboviral
diseases is a major public health concern.
Author contributions
AWT-R: Conceptualisation, Formal analysis, Investigation, Writing—original draft,
Writing—review and editing.