Changing World: Vectors, Climate and Society

Abstract

A critical review of the impact of climate change on vector-borne diseases (VBDs) was carried out. The systems Chagas-Triatominae as hemimetabolous insect and leishmaniasis-Phlebotominae as holometabolous with terrestrial larval stages were taken as examples and both were considered for their diversity of vector species, reservoirs and socio-environmental settings of transmission. Results from the literature were discussed in relation to: a) the multiplicity of causes, targets and consequences from the perspective of multilevel eco-epidemiology; b) assumptions of linearity of predictive models rather than chaos and complexity of the systems involved; c) the assumption of species with fixed biological characteristics, rather than species that, under external pressure, can adapt and change their natural nidality, and pathogens that can change host species (Stockholm paradigm); d) consistency between stated objectives and conclusions. Most of the reviewed articles refer to the medium- and long-term impacts of climate change on the transmission of VBDs, and advocate for emission reductions. However, based on the conceptual and operational arguments discussed, recommendations are proposed to develop a strategy for preventive monitoring of VBDs, in line with public health needs in the short term and at the local level, transferable to programmes for timely decision-making.

Key words
Climate change; Eco-epidemiology; Predictive modeling; Social determinants; Vector borne diseases

INTRODUCTION

Cassandra’s syndrome and the silver of Potosi

Anthropogenic climate change is already a recordable, accelerating process, not a hypothesis. A vast literature has been published in recent decades using mathematical models to forecast the potential impact of climate change on vector-borne diseases (VBDs) epidemics and their vectors. For example, species distribution modeling techniques, which allow the identification of potential areas of species occurrence over space and time, often based on Pavlovsky’s concept of natural nidality (Pavlovsky 1966) with macroclimatic variables as proxies for complex changes comprising “vegetation, soil and favorable microclimate”. In turn, a significant number of descriptive articles retrospectively analyze the linear correlation between climate-environment and incidence, prevalence of VBDs or vector distribution and abundance, using climate change as a justification to extrapolate inferences, or to explain “differences that may be due to climate change”.

These publications, as a threat of dystopian apocalypse, have been functional to legitimate environmental advocacy, although they also reinforce the Cassandra syndrome of some researchers, feeling that they prophesy the end of civilization, but are not heard. However, the apparent failure to receive the warning this time may not stem from the subtle and cruel divine punishment that the Greeks imagined for the Trojan priestess. As with some weakly evidenced social and biological hypotheses, the need for urgent action is asserted, but the results rarely serve as a basis for developing implementation strategies that define the what, how, where and when of priority interventions (Leach & Scoones 2013, Montaner-Angoiti & Llobat 2023). Moreover, when many models anticipate a reduction in the risk area of VBDs (Moo-Llanes et al. 2017, Moo-Llanes 2016), they can result in an argument for climate change mitigation denialism.

The incoherence between objectives, results and conclusions at different spatio-temporal scales occurs in a social context where climate-environmental discursive centrality can lead to the use of risk as a tool for social manipulation, or on the contrary to an emancipatory catastrophism that consolidates collective interests (Beck 1992, 2014). Despite this, limited approaches with unlimited conclusions are repeated, undermining the arguments they purport to advocate, perhaps in part due to the Potosi silver ship effect. In a system of finite resources for science, issues such as climate change periodically arise to sensitize funders, and as when a fleet loaded with the ore from Cerro de Potosí, which accounted for 80% of the world’s silver between the 16th and 18th centuries, sailed, privateers from the seven seas would mobilize to try to board it, with the honest aim of sharing the scarce wealth between their ship’s crew and the crowns of their countries. As anthropologists conclude after their coexistence with biomedical academic tribes: “First, what comes out of my account is the remarkably unremarkable fact that the scientists are humans!” (Charlesworth et al. 1989), even if they don’t believe it.

In line with the previous paragraph regarding VBDs, prediction in the face of climate change, and navigation for resource search, most of the published papers deal with Culicidae-Aedes and arboviruses, periodic yellow fever outbreaks, and persistent malaria transmission, flagships for funding predictive risk modeling in health entomology. A Medline search on 01/02/2024 retrieved 838 titles for mosquito genera AND Climate Change and 1455 articles for their pathogens or their VBDs AND Climate Change, while 86 and 203 titles were obtained for Triatominae or Phlebotominae AND Climate Change and for their pathogens or American trypanosomiasis, Chagas disease, leishmaniasis AND Climate Change, respectively.

However, analyzing the impact of climate change on Triatominae and Phlebotominae is relevant because: a) they transmit neglected tropical diseases of high social cost; b) they show plasticity of adaptation to new environments and capacity for urbanization; c) Triatominae are hemimetabolous and Phlebotominae holometabolous with a terrestrial preimaginal cycle, not aquatic like Culicidae; d) their BVDs present multiplicity of vector species, reservoirs, and parasites in Leishmania spp. or lineages-Discrete Typing Units in Trypanosoma cruzi, and a wide range of clinical manifestations and socio-environmental scenarios of occurrence. Therefore, we will use the systems Chagas Disease (CD), Visceral Leishmaniasis (VL) and Cutaneous Leishmaniasis (CL) as case studies to problematize the dynamic processes associated with climate change and their predictive models, both in relation to the objectives stated by the authors, the complexity of causes, impact targets and indicators, as well as from the modeling assumptions and the possible operational implementation of the results.

Razors or matryoshkas

According to the theoretical framework of eco-epidemiology physical, biological and social determinants contribute to health events in a multidimensional and multiscale framework, a conceptual perspective illustrated as Chinese boxes or matryoshkas (Susser & Susser 1996a, b). The coincidence in time and space of multiple, qualitatively different risk factors, each with its own quantitative threshold, forms the eco-epidemiological momentum or perfect storm. To transform the balance of this confluence of probabilities into an event of effective transmission and collective outbreak, triggers, usually climatic and social, are required. Thus, while biological aspects allow for the possibility of transmission, physical and cultural aspects give it a definite probability. This statement, however, does not seem to fit with Ecological Niche Modeling (ENM) and the parsimony used as the basis for much of the predictions of VBDs transmission risk or vector spread, or even when assuming multi-causality there is an imbalance between data and assumptions depending on whether the factors are environmental, biomedical or socio-cultural (May 2004).

Arguing about the differences between analytical approaches based on simplicity, linearity and parsimony versus those based on complexity and chaos is a controversy that belongs to the philosophy of science and requires an in-depth discussion, as well as avoiding the mistake of confusing these conceptual terms with their use in everyday language. However, for the purposes of this discussion, it will suffice to point out that the parsimony, derived from the principles attributed to Ockham’s razor, can be considered a rule of inductive elimination of hypotheses and postulates that do not provide an explanation of a phenomenon. As an ahistorical statement, it concludes that the rare rarely exists and must therefore be considered superfluous, non-existent, in order to increase the probability of describing a system. This reasoning compels linearity, rules out synergies, presents difficulties in dealing with new events or comparing their predictions, and simplifies outcomes by considering them as unique, when they may be as complex and multiple as their causes (Sober 1981, 1996). In relation to health issues, taking into account that individual health determinants differ from collective health determinants (Rose 1985), then health systems, epidemics and ecosystems are examples of irreducible complexity, chaotic, non-linear dynamics, with different subsystems operating at various spatio-temporal scales. Moreover, each level has its own attributes, connected in an interactive network with crisis points, feedback loops, unstable bifurcations oscillating between possible states, and events away from equilibrium (Bardosh et al. 2017, Gleick 1987, Kernick 2004, Waldrop 1992). The persistence of presenting changes in vector distribution or VBD epidemics due to climate change as a monocausal, unilinear process can be attributed to what Gaylord Simpson identified in evolutionary proponents of orthogenesis, “as the evidence that some scientists’ minds tend to move in straight lines not that evolution does” (Simpson 1967).

Another component of complexity, in addition to Chinese boxes, when not considered as black boxes, is that the boundary between levels-scales is a fluid interface, with pseudopodia, pores and conjugation channels, with layers moving at different speeds. This conception of space-time was described by the historian Fernand Braudel (1958) as phenomena of long, medium and short duration. The term duration here is understood as a social construct, decoupled from the linear time of the climate record, but modified by intense climatic events in a historical and contingent manner, as the same protracted interaction between dynamic levels organizes the system (Dietz et al. 2020, Lee 2018, Prigogine 1997). Applied to the eco-epidemiological momentum of VBDs, these conceptual frameworks allow us to investigate the mechanism of the triggers of imbalance that produce epidemics, which we identified above as climatic and social. It was precisely climatic events that gave rise to the development of chaos theory (Lorenz 1963), which manages to explain in the universe of complexity the irregularities of processes rather than of states (Gleick 1987), although in social systems the mathematical application of chaos theory is only able to analyze the simplest ones (Waldrop 1992). Social complexity is amplified as it is segmented by the perceptions, perspectives, expectations and narratives of all actors as individuals and as collectives, with their uncertainties, ignorance and ambiguities, from the community to the decision-makers and academics who advise or criticize them (Leach & Scoones 2013). However, on a national scale, the heterogeneity of qualitative social representations and practices and quantitative variables can validate each other, as is the case when characterizing heterogeneous bio-ecological scenarios with the presence and dispersion of T. cruzi in Mexico (Valdez-Tah et al. 2015). For these complex socio-cultural factors it will be another historian, Yuval Harari (2015), who introduces the concept of second-order chaos, systems that feedback on their own prediction, modifying the predicted results by knowing their previous forecast.

Second-order chaos was observed in leishmaniasis, when VL became urbanized in America, and national programs defined as a control measure the euthanasia of infected dogs, the main reservoirs of VL. Then, the prediction of disease risk, canine prevalence and mitigation expectation was socialized. Irrespective of the intended effectiveness of the culling intervention, reactive, dynamic and cross-cutting opinion groups were formed, which modified any projected impact. The community’s perception of low human incidence with high canine prevalence, so that a large number of animals had to be euthanized without any purpose, and with up to 74% of the population assigning morality to dogs (Giménez-Ayala et al. 2018), resulted in a loop of non-acceptance of program recommendations (Mastrangelo et al. 2018), and modification of the expected outcome. Another example, also related to canine VL, is the result of a prediction that, through a feedback loop of human behavior, generated a “methodological anomaly” and altered the expected outcome. It is common for case-control studies to be used to demonstrate that knowledge protects and so to promote communication campaigns; however, a study during an outbreak, far from substantiating this conclusion, demonstrated that ignorance protects and knowledge increases risk. This unexpected result is because the case-control design used did not take into account that those who had a dog suspected of having VL searched for information on the web before the epidemiological survey, while people without a suspected infected dog did not, thus ignoring basic aspects of the disease (López et al. 2016).

In relation to CD, mandatory pre-employment serological screening was proposed by law in some countries such as Argentina, conceived as an equity measure that will reduce the risk of chronic forms in the population, facilitating accessibility to diagnosis, monitoring and treatment, and disseminating information on the disease. Once it was applied in practice, many employers, without distinguishing between infection and disease, and anticipating additional social charges, excluded “positive” people from work using other justifications, as the text of the law prohibited such discrimination. Potential employees, in turn, foreseeing a possible reactive serology and stigmatization, accepted informal jobs, avoided health systems, or placed the vector at the center of the discourse, hiding the importance of vertical mother-to-child transmission outside endemic areas (Avaria et al. 2021, González-Salazar et al. 2022, Storino et al. 2002, Sanmartino et al. 2015, 2021), altering the projected results and forcing a change in the law. Similarly, a program to provide food in exchange for housing improvements, based on the risk of transmission of CD by household triatomines, once socialized, resulted in increased inequity, as those who were unable to self-finance a change in the structure of their homes did not receive food and claimed it as a collective, which led to coupling this initiative with housing and drinking water provision programs in order to achieve the projected goals (Salazar Paredes 1999).

Thus, the systems that comprise VBDs must be understood in their chaos dynamics as complex, context-dependent, adaptive systems, in a sum of progressive complexity ranging from the physical laws of matter to biological, environmental and socio-cultural scenarios (Gell-Mann 1994), where epidemics are an expression of disequilibrium. Nassim Taleb (2014) proposes that stationary states increase the fragility of systems, while “anti-fragile” events, such as climate change, fortify the system by overcompensation, in our case vector plasticity, environmental recovery, resilient and preventive behaviors. The author concludes about predictive models: “This reflects one of the main problems of modern times: making predictions about the future based on a narrow view of the past”.

The time machine, climate and vectors

Zoologist HG Wells in War of the Worlds kills the space-travelling aliens with a bacterial infection, but when his character travels in The Time Machine from 1895 into the future, prior to final universe destruction, he finds a stratified but disease-free humanity, with bacteria and fungi extinct, and only recovers for his present a pair of dried white flowers, not considered himself as a possible infectious vector, the cause of the latest apocalypse of immune debilitated humans. Why this difference between moving in space or time? The author, formed as a scientist in the rationalism that was one of the conceptual foundations of mathematical modeling, says through the traveler to the future: “There is no difference between Time and any of the three dimensions of Space, except that our consciousness moves along it”. In this sense Wells, as a social critic, with the invading Martians perhaps tried to reflect on the colonialism of his present, while with the transtemporal journey he set out to warn us of our tendency to self-destruction. With a similar consciousness-based aim, many justify publications on predictive models of climate dynamics and vector distribution projected far into the future, although as the protagonist in The Time Machine also says: “Very simple was my explanation, and plausible enough-as most wrong theories are!” (Wells 1994). However, modeling the present or future distribution of species or diseases is always complex, given the interrelationship of factors that modulate it, which requires caution both in the selection of parameters for its construction and in interpreting its results.

The same inference used to model vector distribution in the present results in a first challenge and difficulty, when trying to integrate spatial statistical models based on climate or environmental proxies, and biological explanatory models of nidality based on numerous theoretical assumptions (Ready 2008), with spatial presence data biases depending on effort and accessibility to sampling sites (Carvalho et al. 2017, Glidden et al. 2023), or with results coming from different trapping protocols. It has been proposed that a specific design for vectors and climate change should consider: a) standardized sampling with adequate spatial coverage, frequency and duration, discriminating between trend and short-term variation; b) monitoring at known distribution limits to observe early changes, not only at sites of high abundance or risk; c) records of spatial contraction, changes in abundance and seasonal dynamics; d) integration with non-climatic factors that also change over time (Kovats et al. 2001). This last point, which reiterates the need to think about multi-causality, is critical when inferring the impact of climate change on health events. For example, a serological study of canine VL was conducted in two sites 13 years apart and in one of them the seroprevalence increased eight-fold. This increase was attributed to a difference in mean annual temperature of 1°C at that site, which was discussed in the context of climate change, but other possible differential variables between the two sites were not assessed (Dereure et al. 2009).

To describe current or past nidality for future projections, several approaches based mainly on the ENM concept and Machine Learning have been used, with species occurrence records, climate-BIOCLIM variables, topography and different environmental classification methodologies. A non-exhaustive list of analytical strategies includes Maximum Entropy Approach-MaxEnt, Generalized Linear Models-GLMs, Generalized Boosted Model-GBM, Genetic Algorithm for Rule Set Production-GARP, General Additive Models-GAMs, Generalized Additive Mixed Models-GAMMs, Artificial Neural Networks-ANNs, Classification Tree Analysis-CTA, Flexible Discriminant Analysis-FDA, Multivariate Adaptive Regression Splines-MARS, Random Forest-RF, Surface Range Envelope-SRE, and Support Vector Machine-SVM. Some publications show the results of a single analytical strategy, but the presentation of the contrasted result of several models or them integrated in a consensus model is considered preferable (Carvalho et al. 2015, 2017, Chalghaf et al. 2018, Garrido et al. 2019, González-Salazar et al. 2022, Koch et al. 2017, Moo-Llanes et al. 2019). Then, from such models made with historical data, the range of ENM results opens up again in the fan of predictive simulations, with the climate models of the Intergovernmental Panel on Climate Change-IPCC, Representative Concentration Pathways-RCPs, and Integrated Assessment Modeling-IAM, describing possible future scenarios with their own probability, scales and multiple variables. The multiplication of analytical methods generates variations in results, but without clarifying the true sources of uncertainty — just as a layperson, when hearing different violins play the same piece, notices the sound varies, but can’t discern whether the differences come from the instrument, the bow, or the musician’s technique. The core problem persists: all models share the same intrinsic data limitations, just as all violins are bound by the composition they perform.

The disparate response of closely related species to the models, in turn, demonstrates the need for species-specific analyses and simulations, as different environmental suitability and associated behaviors may be part of the speciation process, with changes in vectorial capacity. Nyssomyia intermedia and N. neivai, very close species and both vectors of Leishmania braziliensis, according to predictive models the former will reduce its current spatial distribution, while the latter will disperse southwards in Brazil and Argentina (McIntyre et al. 2017), while in triatomines an equivalent situation is seen with Triatoma gerstaeckeri and T. sanguisuga in North America (Garza et al. 2014). A similar need for specific discrimination occurs when species are implicitly grouped together by modeling disease distribution with different sympatric vectors or in broad regions that include more than one vector (Moo-Llanes 2016), or when analyzing genetically and ethologically distinct cryptic complexes, such as Lutzomyia longipalpis, the main vector in the Americas of L. infantum, the pathogen that causes VL (Peterson et al. 2017).

In vectors of T. cruzi, predictive models have been applied for Mepraia spp. distribution (Garrido et al. 2019), for Dipetalogaster maximus (Flores-López et al. 2022), for vectors and cases in Chile (Tapia-Garay et al. 2018), for vulnerability according to linear population growth and future distribution of five vectors (Ceccarelli & Rabinovich 2015), for changes in infection strength of Rhodnius prolixus in Venezuela with tropical climate and T. infestans in Argentina with temperate climate (Medone et al. 2015), showing a general trend of slight to moderate decrease. In the case of Leishmania spp. the spatial scales used in modeling range from macroscale to focal scale. As an example of the former, the American continent was modelled, where changes were predicted for some species such as N. whitmani, which would spread to southeastern Brazil (Peterson & Shaw 2003). The focal scale for leishmaniasis was applied, for example in Turkey, predicting an increase in VL cases due to proximity to current transmission sites (Artun 2019), and forecasting spatial expansion, reduction or altitudinal change of Phebotomus sergenti-L. tropica in Morocco (Daoudi et al. 2022), of CL in Palestine (Amro et al. 2022), and of CL and VL in Iran (Trajer 2021). Changes in altitude with range reduction have also been predicted in Colombia for CL (Altamiranda-Saavedra et al. 2020), for the VL vectors L. longipalpis and L. evansi (González et al. 2014), for vectors and parasites in North America (Carmona-Castro et al. 2018), for L amazonensis together with dispersal in seven countries of the Amazon area (Carvalho et al. 2015), for CL vectors in Turkey (Kavur 2019), and in CD for P. geniculatus (Vivas et al. 2021).

Other authors analyzed the impact on the current distribution of vectors of long-term climatic changes from the past to the present, such as glaciations and interglacial periods, rather than from the present to the future. They hypothesized climatic refugia and relict populations that explain the current discontinuity, and would act as potential source populations for dispersal, or speciation centers in Mepraia Chile as vector of T. cruzi (Campos-Soto et al. 2022), or in L. infantum and its vectors in Central Europe (Beasley et al. 2022, Aspöck et al. 2008). These retrospective investigations show that, despite niche conservationism (Galvis-Martinez et al. 2023), the geographical range and genetic diversity favor the plasticity of vector response to climate change. Precisely, for the phylogenetic past of the genus Triatoma, it is suggested that niche evolutionism is modulated by climate change scenarios, human movements and expansion of human transport networks (Ibarra-Cerdeña et al. 2014). However, even considering only the impact of temperature, humidity or precipitation variables, the targets of impact on insects and reservoirs may be multiple and with different temporal lags.

What Is It Like to Be a Bug?

Thomas Nagel (1974), with the question, What Is It Like to Be a Bat? summarized the problem of self-awareness, and the impossibility of being in another with a universe constructed from a different sensory perception. Nagel exemplifies this perceptual distance with insectivorous bats, but the otherness is undoubtedly greater with hematophagous insects, which can feed on these bats. Without attempting to discuss the awareness of a triatomine or phlebotomine sandfly, projections of their distribution over time usually consider only some aspects of the adult niche, without considering other targets of the biology of these vectors that may be impacted by climate.

Some elements of complexity that should be considered in models of insect and climate-extreme event response refer to different levels of aggregation, from the individual to the community. In relation to the individual, climate may modulate the metabolic rate of physiological, developmental and reproductive processes, the population may be affected in terms of abundance, growth rate, phenology and voltinism, and in the community, climate may impact on interspecific relationships, structure, diversity, synchronization, competition or predation. Processes involved in these changes may include thermoregulation, acclimatization, ontogenetic variation, adaptive evolution and modifications of complex ecological networks (Ma et al. 2021, Hughes 2000).

Depending on their intensity, duration and frequency, extreme climatic events have different impacts at each developmental stage and even at later stages. Thus, thermal fluctuations supported by Lepidoptera eggs resulted in differences in body mass, intermolt periods, and when those eggs reach adult stage in longevity and fecundity (Xing et al. 2014). In the same way, temperature variation, even in non-extreme ranges, modifies fertility and mortality in P. geniculatus (Vivas et al. 2021) and in R. prolixus impacts on metabolism, food to reserve conversion in the same stage and in the stages following exposure, as well as in life cycle, mortality, fertility and hatching success (Loshouarn & Guarneri 2024, Tamayo et al. 2018). For Phlebotominae the difference in temperature tolerance of the different intraspecific lineages in P. sergenti would lead to different patterns of subspecific dispersal in the face of climate change (Merino-Espinosa et al. 2016), pressing the speciation processes of complex or cryptic species, although the integral homeostatic response must be considered, as the expression of different genes have different responses to bioclimatic and metabolic variables (Bordbar & Parvizi 2021).

As insect vectors of pathogens, the impact of climate change on transmission must also be considered. In Aedes aegypti the daily temperature range affects parameters of dengue virus vectorial capacity, such as extrinsic incubation period, mortality rate, biting rate/day, probability of human infection and probability of transmission by biting (Liu-Helmersson et al. 2014). In R. prolixus temperature modulates parasite multiplication, urine parasite concentration (transmission route) and urine volume (Loshouarn & Guarneri 2024, Tamayo et al. 2018), as well as in Meccus pallidipennis it varied parasite counts (González-Rete et al. 2021). Predictive models of P. sergenti also highlighted the difference between future climatic suitability for the vector and potential constraints for parasite development (Fischer et al. 2010), or the greater potential for dispersal at latitude and altitude in parasite-vector systems with wide spatial distribution and genetic diversity such as L. braziliensis or L. infantum versus “mountain” species adapted to low temperatures and low vector metabolism such as L. peruviana (Hlavacova et al. 2013). Thus, temperature-modulated changes in vectorial capacity would be mediated by enzymatic activity and changes in the immune capacity of the gut, where parasites are hosted (González-Rete et al. 2019), but also by differences in thermal tolerance between parasite strains (González-Rete et al. 2021, Tamayo et al. 2018). A parallel target for both insect metabolism and pathogen success are the impact of climate changes on obligate or facultative symbiont lineages, which may also favor tolerance and speciation processes or dispersal and colonization of their hosts (Deng et al. 2021), linked to changes in the pattern of human migrations, land use, and interaction with wild animals (Báez et al. 2019, Jiménez-Cortés et al. 2018).

What Is It Like to Be a Bat? The initial question serves also to shift the focus away from vectors and their pathogens, and to think of hosts and reservoirs as targets for the impact of climate change on VBDs, modifying their competence, capacity, dispersal, abundance, dynamics, or population structure. However, as with vectors, we do not have an exhaustive inventory of species by region, with relative weighting of their importance in transmission. For zoonotic leishmaniasis, potential reservoirs were estimated according to biogeography, phylogenetic distance and sampling effort biases, with climate and agricultural-rural land use being the strongest predictors of change (Glidden et al. 2023), while for vectors the importance of sampling effort biases was also highlighted, and predictors to be a vector were phylogeny, environment and vegetation cover, climate, generalism and synanthropy (Vadmal et al. 2023). Thus, sampling effort and design, according to available resources and accessibility of sites, is a strong determinant of the quality of niche modeling, and may generate geographical imbalances on information about the BVDs of interest, as it happens with the records from the United States (Beasley et al. 2022, de Almeida et al. 2021, Nepal et al. 2024), or from Europe (Maia 2024) in relation to other sites of high endemicity and greater public health impact of CD and leishmaniasis.

Joint modeling of vectors or incidence and reservoirs predicted a doubling of the current exposed population in North America by 2080 (González et al. 2010). Moreover, it explains the causality of temporal lags between climate events and CL incidence in Iran (Bozorg-Omid et al. 2023, Charrahy et al. 2022), or in Tunisia by the impact of rainfall in one year on the rodent trophic cascade, and the consequent effect in the following year on the size of the reservoir population and the abundance of blood food for the vectors (Talmoudi et al. 2017), lag reaching two years in North Africa (Bounoua et al. 2013). Temporal lags between rainfall and abundance of CL agent vectors were also observed in Argentina, possibly associated with an increase in phlebotomine sandfly breeding areas (Salomón et al. 2004). In CD, the potential impact of climate change may also present a delayed impact, but in space, when considering dynamic scenarios of reservoir meta-communities (Ibarra-Cerdeña et al. 2017).

In turn, predictive models that only consider changes in mean daily temperature neglect the periods and times of activity and rest of reservoirs and vectors. For example, by neglecting the mechanisms of nocturnal orientation, since predictive models of changes in cloudiness in the nocturnal and diurnal period present different speed and intensity in different geographical areas (Cox et al. 2020). Other health variables to consider include the impact of changes in the annual climate curve on the length of the transmission period of VBDs such as leishmaniasis (Purse et al. 2017, Trajer 2021), and the coupling or decoupling of population curves between vectors and reservoirs (Hughes 2000).

However, changes in the risk of human infection are not only determined by the climatic and biological component, but also by exposure behaviors, which, as well as events such as sampling biases, migrations and land-use change, point to the “chaotic” impact that other components of the vector-disease system can have on its distribution and intensity, synergistic with the forecasted climatic variation.

Thinking outside the entomological box

“Climate change is always already social; the social does not need to be added to it, just to be revealed” assert Szerszynski & Urry (2010). Disciplinary biases not only reflect the researcher’s perception of risk and its historical context, they also contribute to reifying the problem by naturalizing it. This partial observation of the present, and its complacent or apocalyptic long-term predictions, are very difficult to translate into concrete action, especially in the most vulnerable regions and communities with urgent priorities and finite resources. The shortcomings of the unilinear ENM-climate change projections logic are evident in reviews of VBDs and epidemic events (López et al. 2018), or when analyzing hierarchically the drivers associated with emerging pathogens where climate ranks tenth after other mostly social factors (Daszak et al. 2001, Woolhouse & Gowtage-Sequeria 2005).

Returning to Occam’s razor and second-order chaos, Reproductive Number predictive models of CD discursively assume that non-climatic events, such as the lack of control programs, will modify the estimated transmission risk, but by including experimental vector parameters such as biting rate as a function of temperature, they do not consider changes in shelter quality, vector accessibility to the host and host exposure habits (Ayala et al. 2019). Similarly, T. cruzi infection predicted as a risk to 2050, by the vector D. maximus and current human density, was framed as a warning to beach tourism (Flores-López et al. 2022), without considering potential coastal modification, and population trends, both local and of tourists, or refers to human demographic trends without incorporating it as a time-space variable in the modeling of vectors and parasites in North America (Carmona-Castro et al. 2018).

On the other hand, when predicting T. cruzi presence and transmission is complexed by trends in land use-land cover, domiciliation, urbanization and socio-economic conditions, the results of predicting spatial expansion vary significantly depending on the projection used (González-Salazar et al. 2022). For leishmaniasis, the impact on human exposure or susceptibility of spatially and temporally focused anthropogenic events, as war and social unrest, migration, immunosuppression caused by medical procedures, malnutrition or co-infections, and recreational practices was also highlighted (Shaw 2007). For Europe, agent-based models indicate that reduced human movement would significantly reduce CL transmission (Tabasi et al. 2020), associated with migratory routes (Tunalı & Özbilgin 2023) that increased immunosuppressive conditions and anthropogenic environmental changes (Maia 2024); as well as in the case of canine VL its spread may be favored by the “miserable dog market” of sick animals taking advantage of the empathy of foreign tourists (Aspöck et al. 2008).

Urbanization, as mentioned above, is a potential causal process for epidemics and changes in vector dynamics, diversity and distribution. It is related to climate change in an additive, synergistic or complementary way, as a focal phenomenon in the peripheral space, or as transmission in the urban environment by selection pressure. In triatomines, urban records, in addition to visitation at the wild-household interface and urban edge, were usually the result of passive transport along with human goods or luggage. However, relatively recent records indicate urban adaptation of T. cruzi infected or potentially infected vectors associated with socio-economic conditions, household and peridomestic structures and domestic animals from Mexico to Argentina (Carbajal-De-la-fuente et al. 2022, González-Salazar et al. 2022, Ochoa-Martínez et al. 2023, Segovia et al. 2023). The relationship of housing as a social phenomenon linked to CD had already been characterized by Briceño-León (1990) in rural areas, but urbanization begins to show greater spatial stratification according to age and socio-environmental quality of the domestic space (Levy et al. 2014), attraction to public lighting (Pacheco-Tucuch et al. 2012), and to rururban practices such as hunting and ingestion of game meat (Sangenis et al. 2016).

In CL the edge effect generated by deforestation and environmental degradation has implied the colonization and focal increase of vector populations, in areas of human exposure in Argentina, Brazil, Mexico and Morocco (Canché-Pool et al. 2022, Da Costa et al. 2018, Fernández et al. 2020, Gálvez et al. 2010, Quintana et al. 2010). In the CL endemic area of Argentina, a significant increase in annual cumulative precipitation in the 1880-1980 time series led to a change in the profitability of land use, as forest extraction sites began to be exploited for agricultural crops, and a consequent shift in exposure from isolated deforesters to populations settled in the interface. Thus, in the mid-1980s, there was a 300% increase in CL cases, concentrated in time and space; epidemic outbreaks replicated with deforestation processes on both sides of the Argentina-Bolivia border (Mollinedo et al. 2020). Focal changes due to the synergistic effect of deforestation and climate change can also have distant and long-term effects, such as the “aerial rivers” generated by deforestation of the Amazon on hydrological regimes in La Plata basin (Ruv et al. 2023).

Also related to the combination of environmental degradation and local climate change was reported the spread of CL in Iran in the period 1991-2021. associated with the decreasing trend of the standardized drought index and land reform (Bamorovat et al. 2024). These synergistic events generated socio-environmental changes in housing and agricultural patterns and practices, leading to the spread of transmission to areas with no previous knowledge of the disease, with the consequent stigma on new cases.

For VL, the process of vector urbanization has been described in the Americas and Morocco (Kahime et al. 2017, Salomón et al. 2015). In geographic areas where minimum temperature may be a constraint, such as in Europe, urban heat islands can be a refuge during winter for vector source populations (Bede-Fazekas & Trájer 2015). At the micro-scale, climate change with an increase in frequency and intensity of high temperature-humidity nights may increase inequity of risk, as while one social stratum and their pets will overcome discomfort indoors with air conditioning (contributing disproportionately to the energy crisis), another stratum will be exposed to infection while trying to alleviate the heat on the veranda, at the time and season of peak vector activity (Fuenzalida et al. 2011, Santini et al. 2010).

Thus, considered from a socio-economic and psychosocial perspective, although social conflicts, land use changes and climate change are key factors (Marou et al. 2024, Nogueira de Brito et al. 2024), the unequal distribution of wealth is the primary factor, the necessary enzyme for them to be expressed in their cruelest version. Vulnerable targets in fragile populations, which are socially determined disease enhancers, are multiple. The physical conditions of housing or shelter, already described, in the case of forced displacement may have an additional somatization of stress due to discrimination or xenophobia. The quality and location of housing may be related to overcrowding, lack of sanitation and contact with polluting sources, unhealthy handling of domestic animals, and difficulty of access to diagnosis and treatment. In turn, many of these enhancers may weaken the immune system, which aggravates the infection, hinders serological diagnosis and compromises the therapeutic response. To this must be added, as a compounding effect of climate-related and non-climate-related impoverishment and migration of climate refugees, the inaccessibility of quality health care due to the direct and indirect costs of travel to the health center, lost working hours and the psychosocial cost of stigmatization (Choi et al. 2023, Devakumar et al. 2022, Grifferty et al. 2021, Messaoudene et al. 2023, Selvarajah et al. 2022).

Stockholm paradigm and Oran fact

Urbanization, more specifically the emergence of “urban” hotspots, has social and biological aspects, in addition to those mentioned above, that merit discussion because they overlap with and are exacerbated by the climate change phenomenon and discourse. For example, fluctuations in land values in residential areas push vulnerable communities into peripheral or flood-prone areas without title deeds, while the wealthier population promotes a return to nature and intra-urban green spaces, and can prioritize their demands to policy makers. Moreover, these officials tend to intervene against urban VBDs with insecticides even if these measures are not recommended or effective. Both trends can generate chaotic stress inputs, modifying projections, and the perception of global risk associated with climate change at the local scale.

From the biological perspective, the increase in urban focal risk was associated above with the edge effect in scenarios of metapopulation dynamics. For the Phlebotominae-CL system, this phenomenon has been described in the Americas from linear deforestation due to agricultural expansion in Oran, Argentina (Quintana et al. 2010), due to urbanization in secondary vegetation (Fernández et al. 2020, Neves et al. 2023, Moraes et al. 2020), deforestation during informal land occupation with subsistence farms (Salomón et al. 2009), and by contiguity to the established domestic area and urban green spaces (De Oliveira Lavitschka et al. 2018, Manteca-Acosta et al. 2023, Quintana et al. 2019), with the possibility of generating peridomestic colonization from wild source populations (Salomón et al. 2004). The increase and concentration of vectors and likelihood of transmission due to the existence of edge-ecotones or Oran fact, occurs without an increase in permanent food sources in the intervened area, although the effect increases when population growth and change of agricultural practices reduce wildlife habitats, increase their interaction with humans and domestic animals, and also increase the concentration of food sources for hematophagous, as in India (Singh & Gajadhar 2014). Therefore, it has been proposed to include the variable of interfaces in predictive distribution models (Vadmal et al. 2023), considering also changes in wind speed and orientation, due to macro-climatic and micro-landscape changes, determinants for the olfactory clues and flight capacity of the vectors (Saadene et al. 2023).

At the edges, the Oran fact in turn enhances the Stockholm paradigm (Hoberg & Brooks 2015, Brooks et al. 2019), by generating adaptive pressure to some species with the capacity to colonize the urban environment, or wild-equivalent urban microhabitats, and facilitating their emergent dispersal in cities, giving pathogens the opportunity to exploit new vector or reservoir species, or to exploit their population dominance in the new habitat. The L. longipalpis-VL system in the Americas, together with tropicalization processes, environmental modification by development works (dams, roads) and the migration associated with them, began its urban dispersal in the mid-1980s from the northern to southern Brazil, reaching Paraguay and Argentina in 2000 and Uruguay in 2008 (Salomón et al. 2015). The parasite, human hosts and canine reservoirs would disperse along trunk roads through transit, and the insect vectors along green corridors and secondary roads between towns, depending on climatic and environmental suitability (Sevá et al. 2017). The joint consideration of Oran fact and Stockholm paradigm points out again the limitation of unilinear predictive models, which consider the climate variable but the populations involved static, without adaptive capacity.

Many vectors, like most insects, have reproductive strategies based on their abundance and gene pool, relatively short life cycles, small dimensions, rapid individual and population growth, assimilable to what was characterized as r-strategies, with a greater capacity to adapt to unstable environments and generate microevolutionary changes. Precisely, adaptive plasticity, such as acclimation and thermolerance ranges, has been pointed out as a challenge for the distribution prediction of T. infestans and R. prolixus (Belliard et al. 2019, Clavijo-Baquet et al. 2021). In relation to L. longipalpis and urban dispersal, selection of hormonal phenotypes with different dispersal capacity (Casanova et al. 2015), and haplogroup diversity with hybridization between “urbanized dispersive” and “rural-sylvatic resident” populations have been observed, giving rise to possible new capacities for adaptation to the local environment, vectorial competition and seasonal patterning (Pech-May et al. 2018).

Given the reproductive capacity of the vectors, the phenomenon of exaption, which allows an under-represented species or subspecies to be very successful in the creation of a new habitat or climatic scenario, must also be considered as an effect of plasticity. Among sister species of Rhodnius, which inhabit palms, the most tolerant to low humidity and dehydration of their eggs, was pre-adapted to relatively dry anthropic habitats (Brito et al. 2019), while in T. dimidiata there is a predilection for shelters with high relative humidity (Badel-Mogollón et al. 2017).

The invention of fermentation, chance and necessity

The limitations of the analytical strategies associated with the prediction of the impact of climate change on the VBDs, as described in the sections above and summarized in Figure 1, have been previously pointed out numerous times by different authors. However, perhaps it is the current perception of complexity that now allows us to problematize the conceptual issues in order to generate concrete actions. These alerts come from different times and sources, some in parallel and others confluent, like many of the scientific-technological advances of mankind. Let us take fermentation as an example, unlike roasting or boiling as a method associated with food, it is a complex process, requiring substrates, inoculums, temperature, time and sequential procedures, yet it emerged in a “polyphyletic” way in Asia, Africa, Europe and Americas from different grains and fruits, possibly discovered by chance and necessity (to get drunk), to paraphrase Jacques Monod (1972). Nobody invented it, but after it was explained by Pasteur, the results were optimized on the basis of evidence-based protocols. Similarly, although without the alcoholic component, what is presented in this paper, on climate change modeling and vector dispersal, does not claim originality. Criticisms of effective framing of predictions were generated simultaneously, not by accumulating objections to normal science, paradigm or episteme shifts, but by changes in the set of ideas that characterize a socio-cultural moment, the “collective of thought” in which science is immersed according to Fleck (2022).

Figure 1
Major factors in the changing world, including climate change, that from an eco-epidemiological perspective interact to increase the likelihood of changes in distribution, transmission intensity or epidemic outbreaks of vector-borne diseases

This context which, as mentioned, led to the conceptualization of eco-epidemiology as a theoretical framework and One Health as an operational guidelines, must therefore incorporate accelerated social, environmental and climatic changes as causal, target and consequences, in a non-linear, interconnected and tandem fashions, with complex dynamics between overlapping scales, with fluid interfaces, even though some multi-scale analyses maintain the concept of rigid-walled Chinese boxes (Butler & Harley 2010). However, as it is noted, these are not such novel ideas, as Descartes (1637) in the Discourse on Method, told us about such complexity by assuming that, if a thing that might seem, with some sort of reason, very imperfect if it were alone in the world, it is still very perfect if it is considered as a part of the universe as a whole.

In addition to biological changes in nidality and environment and long-term climate trends, the most analyzed in predictive modeling, the literature indicates the incorporation of the following topics: 1) Short-term climatic factors, extraordinary climatic events such as floods, hurricanes, droughts; 2) Socio-environmental factors, changes in land use and physic-chemical cycles, habitat degradation; 3) Socio-economic factors, agricultural expansion or resource exploitation projects, development works with environmental modification, migration, and urbanization; trade and tourism; 4) Socio-cultural factors, political context, population growth and land occupation, habits, practices and risk perceptions in new exposure interfaces, conflicts, forced displacement and deficiencies in food security, structural inequities in monitoring and access to health; 5) Epidemiological and biological factors, resistance to therapeutic drugs and insecticides, immunosuppressive co-infections, and food source diversion (Bardosh et al. 2017, Ceccato et al. 2018, Nava et al. 2017, Rosa et al. 2004, de Souza et al. 2021). The synergistic effect of these factors should be evaluated, as the concurrent impact of extreme weather events and displacement extensively described for epidemics, including VBDs (Dayrit et al. 2018, Salomón et al. 2006, Watson et al. 2007).

The biosocial perspective, in turn, should incorporate resilience, participation, community-based adaptation and social justice (Bardosh et al. 2017), a multi-causality that highlights the need to reduce inequality in wealth distribution as a key intervention (Aagaard-Hansen & Chaignat 2010). In this sense, a multi-causal intervention strategy approach has been proposed for CD (Ventura-Garcia et al. 2013), and multi-causal surveillance for malaria by the World Health Organization by monitoring vulnerability, seasonal climate prediction, environment, and sentinel cases (DaSilva et al. 2004).

The non-linear functions related to VBDs systems were highlighted by many authors previously, as in human movements in epidemics (Gu et al. 2024), vector-borne diseases, predator-prey interaction in zoonotic ecosystems (Eilersen et al. 2020, Stiefs et al. 2009), vector-host relationships in regimes with different “environmental noise” (Son & Denu 2021), vector population dynamics in complex networks (Wang & Yang 2020) incorporating seasonality (Thornley & France 2016), in host demography (McLennan-Smith & Mercer 2014) and also into suggestions for control (Pedro et al. 2014). Therefore, given the connectivity between spatio-temporal scales, any global climate change will act as a chaos factor on the local determinants of VBDs, in different ways and at different rates in each determinant (Sutherst 2004).

About the criticism of the models’ objectives, previous articles have also pointed out that, in a global context of systematic reduction of resources for collective health systems or their prioritization due to emerging contingencies, the focus on the risk associated with long-term climate change, and its degree of uncertainty, make it difficult to discuss more urgent needs. These authors highlighted the need to develop immediate monitoring strategies, tools for real-time decision-making, short-term climate risk management, as well as to ensure the sustainability of monitoring programs, increase community resilience and promote interventions on the contingent socio-cultural determinants that produce and reproduce inequalities (Bardosh et al. 2017, Campbell-Lendrum et al. 2015, Gorla 2021).

The information accumulated on the strictly entomological component indicates that models that contribute to strategies for monitoring changes in vector distribution, due to or enhanced by climate change, should consider metapopulation dynamics at edges (Berger et al. 2014, Quintana et al. 2010), in environmental suitability buffers (Costa et al. 2014), and the least-cost routes (Fischer et al. 2011). In relation to time scales and extreme events, historical series showed association of ENSO indices or precipitation with Trypanosoma infection (Báez et al. 2019), and CL or its vectors in the Americas (Altamiranda-Saavedra et al. 2020, Ávila-Jiménez et al. 2024, Chaves et al. 2014, Mendes et al. 2016). Thus, instrumental models for early warning can define lags with sufficient time to implement mitigation or control interventions (da Silva et al. 2021), adapted to risk according to gender and age (Adegboye et al. 2019).

Opening remarks

Most of the articles analyzed and discussed in this review refer to the impacts of climate change in the medium and long term on the transmission of VBDs, and advocate changes in direct or indirect anthropogenic activities that affect the global climate. However, the arguments put forward can also serve to propose, by way of conclusion, a conceptual and instrumental framework for preventive surveillance of VBDs in the short term, in accordance with public health needs at the local level. In this way, it should contemplate both changes in intensity, frequency and duration of extraordinary climatic events, as well as confluent and synergistic biological and social changes. To this end, as starting points, the considerations to be taken into account to develop a transferable strategy that will allow programmatic decisions to be made on a time and space scale appropriate to the specific event to be monitored and controlled are presented:

1) The multidisciplinary and multiscale approach of eco-epidemiology and multisectoral operational One Health. Integrating the multiplicity of causes, impact targets and consequences associated with physical-climatic, biological, environmental and social variables that converge in the eco-epidemiological momentum.

2) The irreducible complexity of non-linear chaos systems with feedback loops; review the simplifications of the assumptions made and the parameters estimated for predictive modeling of the impact of climate change on VBDs. Factors that may contribute to chaos should be spatially defined, as geopolitical jurisdictional boundaries may result in different socio-cultural behaviours, health systems and health strategies.

3) The need for specific designs to assess the impact of climate change on BVDs and the inputs to models, with appropriate time-spatial scales, sampling effort and designs, including extreme weather events according to their intensity, frequency and duration.

4) The consistency between objectives, analytical strategy and inferences, so as not to undermine advocacy efforts; discriminate whether results will be used as arguments by environmentalists to act on the drivers of climate change, by entomologists, physicians and epidemiologists to support the need to fund their research, by sanitarians to sustain and intensify surveillance systems, or by all of them to generate operational public health responses, holistic monitoring, early warning, prevention, mitigation and control effective strategies.

5) The biological plasticity of the species, and the cultural human capacity for damage, resilience and repair, as not only climate varies during climate change. Let us learn from the wrong prediction about the end of leishmaniasis due to deforestation and the interruption of CD transmission with population urbanization, ignoring the synergy of the Stockholm paradigm and Oran fact.

One last thought remains, made by the historian Marc Bloch in 1940, in the face of the “strange defeat” on a supposedly impregnable war front: “I don’t believe that the fault was, in the strict sense of the word, in not foreseeing enough. On the contrary, the forecasts were made in too much detail. But each time they applied only to a small number of eventualities (...) history is, in essence, the science of change. It knows and teaches that no two events are ever exactly alike, because the conditions never coincide exactly” (Bloch 1990).

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Publication Dates

• Publication in this collection
  14 Nov 2025
• Date of issue
  2025

History

• Received
  09 Oct 2024
• Accepted
  27 June 2025