Drivers and Consequences of Viral Zoonoses: Public Health and Economic Perspectives

Simple Summary

Zoonotic diseases cause around 2.5 billion illnesses and 2.7 million deaths each year, making up most new infectious threats. Outbreaks like Coronavirus disease 2019, Ebola, and avian flu strain health systems and cost trillions of dollars, while rural and low-income communities are hit hardest. Tropical regions are at highest risk due to land use change, climate shifts, and wildlife contact. This review stresses the urgent need for practical, equitable One Health approaches to prevent future pandemics.

Abstract

Viral zoonoses or viral pathogens transmitted from animals to humans—constitute a rapidly intensifying global health and economic challenge. They are responsible for an estimated 2.5 billion illnesses and 2.7 million deaths annually, representing nearly 60% of all infectious diseases and 75% of newly emerging infections. Recent outbreaks, including Coronavirus disease 2019 (COVID-19), Ebola, Nipah, and avian influenza, underscore their capacity to overwhelm health systems, with COVID-19 alone projected to reduce global Gross Domestic Product by USD 22 trillion by 2025 and impose annual healthcare costs of USD 2–3 trillion. Beyond mortality and morbidity, zoonotic events disrupt trade, depress rural livelihoods, and inflict agricultural losses exceeding USD 100 billion per outbreak, with impacts disproportionately borne by low- and middle-income countries. Hotspot regions across tropical North and South America, Asia, and Central Africa remain especially vulnerable due to accelerating land use change, climate variability, and intensified wildlife–human interfaces. While the Global One Health Index highlights high regional heterogeneity, with sub-Saharan Africa scoring lowest, a critical gap persists between the conceptual strength of One Health and its operationalization in resource-limited settings. This review synthesizes evidence on drivers, clinical manifestations, and socioeconomic burdens of viral zoonoses, while highlighting novel perspectives on equity gaps, co-infection dynamics, and limitations of global preparedness initiatives. We argue that current strategies remain over-reliant on donor-driven agendas and insufficiently integrated across sectors. Addressing future zoonotic threats requires prioritizing surveillance in high-risk geographies, integrating epidemiological and economic data for preparedness planning, and supporting context sensitive One Health approaches that confront political, financial, and structural barriers to implementation.

1. Introduction

In the recent decades, zoonosis has been predominantly caused by the emergence of different viruses, accounting for approximately 60–75% of the new pathogens identified over the recent decades [1,2]. Predictive model-based studies have suggested tropical regions of North America, South America, Asia and Central Africa are at higher risk of zoonotic viral emergence [3]. Zoonotic viruses having a high mutation rate, a rapid replication cycle, and an ability to adapt to rapid environmental changes poses a major threat in crossing species barriers and infecting human populations [4,5]. While zoonotic transmission is an ancient phenomenon, evidenced by historical scourges like plague and influenza pandemics, the frequency of emerging zoonotic viral events appears to have accelerated dramatically in the last few decades [6]. Deforestation, agricultural expansion, mining, and urbanization relentlessly fragment wildlife habitats resulting in an increased probability of human interaction with wildlife reservoirs. This increased interface dramatically heightens opportunities for pathogen exchange as evidenced by the emergence of Nipah virus in Malaysia due to fruit bat migration into pig farms established near deforested areas [7,8]. Similarly, Ebola virus outbreaks are often traced back to human incursion into forested areas or contact with infected bushmeat [9]. Intensive farming facilitates viral mutation and reassortment, producing highly infective strains capable of efficient human-to-human transmission [10,11]. Climate change due to different anthropogenic activities is a global threat; alterations in temperature, precipitation patterns, and ecological zones influence the shift in geographic distribution and abundance of the reservoir hosts and associated viruses [12,13]. Additionally, exponential population growth, poverty, inadequate sanitation, and cultural practices, including wildlife consumption, further augment the risks of human exposure to the zoonotic pathogens [14,15,16]. The consequences of these outbreaks extend far beyond immediate morbidity and mortality, inflicting major economic loss due to the enormous costs of healthcare burden, trade and travel disruptions, and lessened productivity [17]. While this review focuses on viral zoonoses, it is essential to clarify that zoonotic diseases arise from a broad spectrum of pathogens including bacteria (e.g., Brucella spp., Salmonella spp., Mycobacterium bovis), parasites (e.g., Toxoplasma gondii, Echinococcus spp.), and fungi (e.g., Cryptococcus spp.), which collectively contribute significantly to global morbidity and mortality. Despite significant advances in research, our ability to predict and prevent the zoonotic spillover event remains limited. Surveillance at the human–animal–environment interface is often fragmented and under-resourced, particularly in high biodiversity regions where the risk of exposure is the highest [18]. The development of vaccines and therapeutics often faces challenges due to viral diversity and adaptability during the outbreak [19]. A robust, multifaceted, and proactive framework for the detection and prevention of zoonotic viral outbreaks is critical for mitigating future pandemics. The concept of “One Health” of the US Centers for Disease Control and Prevention (CDC) recognizes the intricate linkages between human, animal, and environmental health and calls for an integrated, collaborative, multisectoral, and transdisciplinary approach at the local, regional, national, and global levels to prevent such disease outbreaks in the future [20] A recent study has reported that the median Global One Health Index-zoonoses score was found to be the highest in the North American region (82.76) and lowest in sub-Saharan Africa (58.76) suggesting the need of better implementation of the One Health policy across the world [21]. This literature review delves into the details of the recently emerged zoonotic outbreaks, examining their origin, mechanism of transmission, clinical syndromes, and impacts on public health and economy. Furthermore, the study critically analyzes the plausible drivers behind such spillover events and multifaceted mitigation strategies required for the prevention and control of such events in the future.

2. Key Factors Driving Zoonotic Disease Emergence

2.1. Land Use Change and Habitat Encroachment

Land use change is defined as the anthropogenic conversion of natural ecosystems for alternative uses like agriculture, urban settlements, or infrastructure, and represents the primary cause of global habitat encroachment, leading to profound and often irreversible ecological degradation [22]. This transformation fundamentally fragments and diminishes natural habitats, directly encroaching upon the territories vital for the survival of numerous species. Agricultural expansion is responsible for more than 90% of tropical deforestation, as forests and grasslands are cleared for croplands and pastures to meet the rising need of food, fiber, and biofuel [23]. Analyses reveal that approximately 31% of disease outbreaks including Ebola, Nipah, and severe acute respiratory syndrome (SARS) are linked to land use changes, driven by a 300% increase in agricultural land expansion over the last 50 years [24]. A seminal statistical model found that the probability of Ebola virus spillover events in West and Central Africa increases significantly in areas with recent deforestation, where the loss of just 5–10% of forest cover can double the risk of outbreak occurrence [9]. Similarly, in Southeast Asia, the intensification of pig farming and fruit production in the deforested areas has been directly linked to Nipah virus emergence as fruit bats are compelled to forage in agricultural settings, contaminating date palm sap and pig feed [25]. The expansion of oil palm plantations in Southeast Asia has been associated with repeated outbreaks of Nipah virus in Malaysia and Bangladesh [7]. Meanwhile, the deforestation rate surged by over 50% in the Amazon during the early 2020s compared to the previous decade, which has been correlated with increased incidence of malaria [26]. Simultaneously, rapid urbanization and associated infrastructural developments carve through landscapes, creating insurmountable barriers for wildlife movement [27,28]. The rapid, unplanned growth of cities in sub-Saharan Africa and Asia without adequate sanitation promotes the transmission of rodent-borne viruses like Lassa fever. Statistical models incorporating satellite-derived data on nighttime lights and vegetation loss have successfully predicted areas at high risk for zoonotic emergence, highlighting the power of geospatial analysis in quantifying this relationship [3]. The consequence of this pervasive encroachment is widespread habitat loss and fragmentation that reduces core habitat areas and results in isolated wildlife populations divided into smaller, vulnerable patches less capable of supporting genetic diversity [28]. Furthermore, encroachment escalates human–wildlife conflict as species are forced into closer proximity with human settlements in search of food or space [29]. Alarmingly, habitat encroachment facilitates zoonotic disease emergence by increasing interaction between wildlife reservoirs, domestic animals, and human populations, resulting in increased chances of pathogen spillover [3,25]. Spatial epidemiological models predict that regions undergoing the fastest land conversion, particularly in sub-Saharan Africa, South Asia, and Latin America, will remain hotspots for novel zoonotic viruses, with a projected 40% increase in spillover risks by 2070 [3,13].

2.2. Agricultural Intensification and Livestock Production

The relentless drive to meet the demand of global consumption of food resources through agricultural intensification, particularly within the livestock sector, has fundamentally reshaped animal husbandry practices, creating a potent crucible for the emergence of zoonotic diseases [30]. Between 2001 and 2018, nearly 386 million hectares of forest land were lost, which has been strongly correlated with the recent outbreaks of zoonotic viruses such as Ebola, Nipah, and severe acute respiratory coronavirus 2 (SARS-CoV-2) [31,32]. The Food and Agricultural Association estimates that over 40% of land is globally utilized for agriculture, with a rapid shift toward livestock farming; this creates closer contact zones between humans, domesticated animals, and wildlife reservoirs [33]. Data from the World Health Organization (WHO) suggests that intensive livestock production is implicated in more than 25% of zoonotic viral outbreaks in the past three decades [34]. Large, densely distributed populations of genetically similar livestock organisms function as ideal reservoirs for the zoonotic pathogens [35]. High stocking density dramatically increases the interaction with the infected individual facilitating rapid pathogen transmission within a group; this constant circulation provides ample opportunity for the different pathogens, especially viruses, to replicate, rapidly mutate, and evolve, potentially enhancing the ability to infect humans [13,25]. Genetic homogeneity of the livestock organisms causes lesser resistance to the pathogens leading to explosive outbreaks and sustained viral load within the environment [36]. Furthermore, the physiological stress induced by confinement, rapid growth demands, transportation, and suboptimal welfare conditions suppresses the immune system of the livestock animals, rendering them more susceptible to zoonotic infections [37]. The expansion of feed crop cultivation and grazing land to support intensive livestock operations drives deforestation, habitat fragmentation, and encroachment into previously undisturbed ecosystems leading to increased spillover events [3]. During the Nipah virus outbreak in Malaysia, habitats of fruit bats were disrupted by deforestation, causing viral transmission to pigs, and subsequently to humans [7]. Livestock production acts as a significant source of environmental contamination where large quantities of manure and wastewater contain high concentrations of pathogens [38]. Furthermore, globalized trade networks amplify the pathways for pathogens to spread far beyond the point of origin, transforming local outbreaks into international threats [11,38]. The intensification of livestock production requires a large workforce, which increases the frequency of human–animal interactions at the farm level, significantly increasing the probability of zoonotic spillover events [39,40].

2.3. Biodiversity Loss

Ecosystems with naturally rich biodiversity are the potential hotspots for the emergence of novel zoonotic pathogens posing risks to human health [41,42]. This view stems from the idea that greater species diversity results in a wider range of pathogens, which elevates the likelihood of zoonotic spillover events [43]. However, recent studies have claimed that greater biodiversity can actually suppress the spread of zoonotic diseases once they are established [36,44]. Together, these seemingly contradictory insights suggest that biodiversity loss might increase human vulnerability to existing pathogens while also lowering the chances of novel infections. The “total host diversity” model includes a broad geographic area where comparisons are made among multiple regions with different biodiversity. A seminal study by Jones et al. identified the most probable locations of emergence of the zoonotic diseases reported between 1940 and 2005, concluding that wildlife host species richness is a significant predictor for the emergence of zoonotic diseases. The study also observed that high human population density increased the probability of zoonotic spillover events by 75 to 90% [41]. The “zoonotic host diversity” model proposes that the host species which are more closely related to humans are more likely to become the source of zoonotic pathogens for any spillover event. A study by Pedersen and Davies reported a hotspot for probable zoonotic outbreaks in central and western Africa because of the broad geographic overlap between humans and primates, two evolutionarily closely related species [45]. In the “zoonotic host diversity and abundance” model, both the diversity and the abundance of the wild animals are considered to be important parameters for predicting potential sources of zoonotic outbreaks [46]. Using a database of approximately 800 zoonotic pathogens, ungulates and carnivores were identified as the major sources of zoonotic pathogens [47]. Furthermore, studies by Dobson and Calisher et al. highlighted the role of bats as an important reservoir to host the zoonotic pathogens [48]. Several recent investigations reported rodents as one of the major threats associated with zoonotic outbreaks [49,50]. On the contrary, studies by Gibb et al. and Johnson et al. laid the groundwork for a revised perspective on how biodiversity and its alterations influence the emergence and spread of zoonotic diseases [49,51]. In the recent years, the phenomenon of the “dilution effect,” which proposes that multiple species existing in diverse communities dilute the impact of host species, reducing the chances of zoonotic outbreaks, has been extensively debated and reviewed [36].

The accelerating erosion of global biodiversity predominantly driven by anthropogenic activities is a critical factor of emerging zoonotic viral infections. Ecosystems with high biodiversity reduce the prevalence of zoonotic pathogens by the dilution effect [36,44]. However, biodiversity loss is often reported to increase the number of species acting as competent reservoirs for pathogens. These resilient species thrive in degraded habitats and simplified landscapes where they experience reduced predation and competition, leading to population explosions significantly increasing the prevalence and transmission of zoonotic pathogens [51,52]. Habitat destruction and fragmentation often act as the primary drivers of biodiversity loss, forcing wildlife species into smaller habitat patches. This compression increases the population density within these patches, augmenting inter-species viral transmission events and promoting viral evolution through co-infection and recombination [25]. Simultaneously, these altered landscapes bring wildlife reservoirs into unprecedented contact with human-dominated areas. Deforestation due to agriculture, logging, mining, or human settlement create ecotones between natural habitats and human-modified landscapes which become hotspots for zoonotic transmission [53]. The spillover of the Hendra virus in Australia was associated with habitat loss and fragmentation altering bat foraging behavior and bringing them closer to equine populations [54]. Diverse ecosystems are generally more resistant to invasive species and environmental fluctuations. The loss of biodiversity undermines the overall stability and resilience of ecosystems, making them more susceptible to perturbations that can trigger zoonotic spillover events [55]. Importantly, the loss of genetic diversity within species is a critical component of overall biodiversity, contributing to increased susceptibility of disease outbreaks.

2.4. Climate Change

Climate change interferes with the complex ecology of zoonotic viruses resulting in significantly increased risk of pathogen spillover events. Changing temperature and precipitation patterns alter species distributions, forcing wildlife to migrate to new areas in search of suitable habitats, potentially introducing pathogens to native host populations in closer proximity with humans and domestic animals [56]. Expansion of the geographical distribution of several mosquito vectors into different regions of Europe and North America has been reported due to global warming [57,58]. Furthermore, heavy rainfall creates abundant breeding sites for the mosquitoes, amplifying vector populations and zoonotic transmission cycles [59]. Prolonged droughts may also lead to aggregation of wildlife animals around scarce water sources, drastically increasing the potential of pathogen transmission in humans, as observed in the Nipah virus outbreak due to contaminated date palm sap accessed by thirsty bats [60]. Climate change also exacerbates habitat loss and fragmentation through wildfires and changing vegetation patterns, forcing wildlife into human-dominated landscapes in search of food and shelter, thereby increasing direct and indirect contact interfaces [61]. Moreover, warmer temperatures can accelerate the replication rates of the zoonotic pathogens within the hosts leading to faster transmission cycles and larger outbreaks [62]. The cumulative effect of climate-driven changes is profound destabilization of the delicate ecological balances responsible for the emergence of various zoonotic diseases, transforming global disease landscape and posing an escalating threat to global health security. Climate change models are increasingly being applied to predict the emergence, re-emergence, and geographical redistribution of zoonotic diseases by simulating how climatic variables influence pathogen, reservoir host, vector, and human interactions. Such models typically incorporate temperature, precipitation, humidity, extreme weather events, and other environmental variables to project changes in suitability for disease spillover and transmission [63]. These predictive models are often developed using different statistical models and machine learning that can capture non-linear climate–disease relationships. A University College London-led model of Lassa fever in West Africa used known outbreak locations (1967–2012), changing land use, crop yield, temperature and rainfall, and future projections of population growth to predict that the number of people affected by Lassa fever might double by 2070 due to climate change and increased human population [64]. A recent study from China used a hybrid “long short-term memory neural networks” model with time-series decomposition to forecast incidence of schistosomiasis, echinococcosis, and leptospirosis, obtaining closer fits to observed data than simpler models [65]. Another example is human brucellosis in China, where “seasonal autoregressive integrated moving average exogenous (SARIMAX)” models incorporating climatic and socioenvironmental data (sunlight, humidity, etc.) produced better predictions than regressions alone and showed spatial and seasonal variation in outbreak risk [66]. Mechanistic and “Earth-system” style coupled models further aim to combine vector biology, host reservoir dynamics, human population movement, land cover, and climatic projections to map risk under various Representative Concentration Pathway (RCP) or Shared Socioeconomic Pathway (SSP) scenarios. Such models can forecast shifts in vector ranges, emergence of new spillover hotspots, or intensification of transmission in areas already endemic. Effective mitigation demands breaking transmission cycles through targeted vector control (chemical, biological, and habitat modification), eliminating or immunizing vertebrate hosts via vaccination campaigns and livestock management, and deploying integrated One Health interventions that combine surveillance networks, vaccination, and ecological stewardship to prevent resurgence and emergence in both endemic and newly vulnerable areas. The CDC One Health framework and recent artificial intelligence (AI)-powered risk mapping initiatives have substantiated the value of these strategies in anticipating and containing outbreak risks, making vector management and host immunization central to future zoonosis prevention [65,66].

2.5. Wildlife Trade and Bushmeat Consumption

The wildlife trade, both legal and illegal, alongside the consumption of bushmeat (the meat of wild animals), are significant drivers in the emergence and transmission of zoonotic diseases. The risk stems from multiple phases, including hunting, trapping, butchering, transporting, selling, and consumption of wild animals. Each stage of the supply chain increases opportunities for pathogens to jump species barriers, with notable diseases such as human immunodeficiency virus (HIV), Ebola, SARS, and mpox all having origins or amplification through wildlife trade or bushmeat practices [67,68,69]. Urban wet markets where live or butchered wild animals are sold are particularly at high risk, since a diverse range of animal species are confined in close quarters, heightening the potential for cross-species viral transmission [67,68,70]. Pathogens such as Leptospira, Rickettsia, and various viruses have been detected in animals sampled from wildlife trade locations, representing substantial infection risk to humans who handle or consume bushmeat [69]. In many regions, bushmeat is a crucial source of dietary protein and livelihood, especially in rural or impoverished communities. However, increased demand, often fueled by urban populations viewing bushmeat as a luxury, leads to higher hunting pressure, biodiversity loss, and frequent contact between humans and novel pathogens [71,72]. Additionally, illegally transported wildlife may harbor parasites and viruses, spreading diseases to new geographic regions and even livestock, as witnessed with outbreaks like avian influenza and SARS [67,68]. Thus, regulating wildlife trade and bushmeat consumption is a complex public health issue, requiring a balance between disease prevention, biodiversity preservation, and the food security needs of vulnerable populations [68].

Regulatory failures and enforcement challenges against wildlife trade represent major obstacles to biodiversity conservation, public health, and sustainable livelihoods. Fukushima et al. observed that the Convention on International Trade in Endangered Species (CITES) often suffers from gaps in listed species, lack of reliable data on population status, and inconsistent domestic implementation, which undermines the legal basis for regulation [73]. Many countries lack sufficient trained personnel to detect, investigate, and prosecute wildlife crime effectively. There is often insufficient funding, equipment, logistics, or support infrastructure, which undermines both detection and the legal process. In East Africa, bushmeat enforcement has been hampered by lack of affordable, rapid forensic tools and trained staff to identify species from meat samples [74]. Similarly, in the Save Valley Conservancy in Zimbabwe, hunters reported that penalties for illegal bushmeat hunting were so small that they did not dissuade continued exploitation [75]. Additionally, socioeconomic drivers create enforcement challenges since many communities depend on bushmeat for income, for cultural practices, or in response to food insecurity. When regulations fail to take these needs into account by providing alternative livelihoods, they are often ignored [75]. Furthermore, there is also the problem of continuous effective monitoring for cross-border trade. Smuggled bushmeat or wildlife products are often mislabeled and transported in small consignments; there is also increasing trade via digital platforms and hidden supply chains [73]. Addressing these challenges will require more robust legal reform, investment in capacity, community-based approaches that balance livelihoods and conservation, stronger international coordination, and upgraded detection and forensic methods.

2.6. Human Demographics, Behavior, and Globalization

Rapid global population growth, urbanization, and changing human behaviors have dramatically increased the interface between humans, wildlife, and livestock, driving the rise of zoonotic diseases. High human population density, expansion into natural habitats, and increased mobility frequently expose people to potential animal reservoirs and vectors for disease [76,77,78,79]. Urbanization, while associated with improved sanitation and healthcare, can concentrate people and vectors (like rodents or mosquitoes), enabling the rapid spread of infectious agents. The loss of biodiversity due to habitat modification often leaves behind species more prone to hosting and transmitting zoonotic pathogens, such as rodents and bats, resulting in greater human exposure to emerging infectious diseases [76,80]. Urbanization statistics reveals that nearly 68% of the global population will live in cities by 2050, expanding peri-urban areas where humans, livestock, and wildlife increasingly intersect [81]. Studies indicate that peri-urban sprawl in tropical regions is associated with a 1.5- to 2-fold increase in vector-borne zoonoses such as dengue and chikungunya, which are exacerbated by land clearing and changes in water use [82,83]. The role of globalization cannot be overstated. Massive growth in international travel, trade of animals and goods, and human migration facilitate the rapid and sometimes unwitting transportation of pathogens across borders. Direct human contact with animals is increasingly reported in diverse contexts, extending to urban areas, tourist sites, live animal exhibits, peri-urban recreational spaces, and travel-related exposures. For example, peri-urban sprawl has been associated with increased incidence of vector-borne zoonoses such as dengue and chikungunya, which are worsened by land clearing and water management changes. Additionally, travelers to wildlife habitats, wet markets, petting zoos, or regions with endemic animal reservoirs can encounter novel pathogens during leisure or occupational activities, as documented during the spread of the SARS, Nipah, and Ebola viruses [76,77,79,80]. For instance, travelers can become vectors, carrying infections acquired abroad back to their home countries, as seen with diseases like COVID-19 and influenza. [76]. The volume of globally traded food including meat from wild and domestic animals introduces additional risk, allowing diseases to spread far beyond their regions of origin. Refugee movements and urbanization also mean more people living in conditions of overcrowding and poor sanitation, which can amplify outbreaks. Human behavior, including dietary choices (e.g., bushmeat consumption), animal husbandry, and cultural practices around animals, strongly shape patterns of zoonotic spillover and transmission [76,84]. In many low- and middle-income countries, rapid rural-to-urban migration leads to the expansion of informal settlements or slums, where infrastructure for water supply, sanitation, waste management, and housing is inadequate resulting in ideal habitats for synanthropic animal species which acts as reservoirs of zoonotic pathogens [76,78].

To give a stratified insight, zoonotic transmission occurs through several routes, including direct contact (such as animal bites or exposure to secretions), which accounts for about 29% of emerging zoonotic infectious diseases, posing notable risks to livestock handlers and wildlife workers. Vector-borne pathways—including mosquitoes, ticks, and fleas—are responsible for 42% of all zoonotic pathogens, facilitating the spread of viruses like West Nile virus and Rift Valley fever. Inhalation or airborne exposure contributes to 36% of zoonotic events and is critical in outbreaks of hantavirus and Q fever. Ingestion of contaminated food or water (oral transmission) drives another 40% of cases, underpinning the burden from pathogens such as Salmonella and Cryptosporidium. Urban outbreaks of rodent-borne diseases like leptospirosis highlight rising risks in rapidly growing cities with inadequate sanitation, while epidemics of dengue, Chikungunya, and West Nile virus reflect shifting vector distributions linked to urbanization and climate change [85]. The various drivers of zoonosis spillover are depicted in Figure 1.

Consequently, public health efforts to control zoonotic viral threats must account for demographic shifts, behavioral changes, and the realities of an interconnected, rapidly changing world, necessitating interdisciplinary and internationally coordinated responses [76,77,78,79,80].

3. Impact on Public Health

3.1. Direct Burden of Disease

Zoonotic diseases represent approximately 60–75% of all emerging infectious diseases globally, accounting for an estimated 2.5 billion cases of human illness and about 2.7 million deaths annually worldwide [70,86,87]. The clinical spectrum is broad, ranging from mild diseases such as Chikungunya fever to severe, often fatal infections including Ebola virus disease, Crimean–Congo Hemorrhagic Fever, and Middle East respiratory syndrome (MERS). For instance, the 2014–2016 Ebola outbreak in West Africa caused over 28,000 cases and around 11,000 deaths, with case fatality rates as high as 90% in some outbreaks. During the 2014–2016 Ebola outbreak, up to 20–30% of suspected Ebola cases in Sierra Leone were ultimately malaria, complicating diagnosis and clinical management. Similarly, COVID-19–HIV co-infection in sub-Saharan Africa was associated with a nearly two-fold higher risk of severe outcomes and mortality, while tuberculosis co-infection disrupted routine tuberculosis (TB) detection and treatment programs. These interactions highlight the need for integrated surveillance and dual diagnostic strategies in endemic areas [86,88,89]. The COVID-19 pandemic, caused by the zoonotic coronavirus SARS-CoV-2, has resulted in more than 6 million deaths globally since its emergence in 2019 [62]. Vulnerable populations, including rural communities, lower-middle income regions, agricultural workers, children, the elderly, and immunocompromised individuals, suffer disproportionately from zoonotic diseases due to their closer contact with animal reservoirs and limited access to healthcare. These infections exert severe stress on health systems by disrupting routine healthcare services and immunization programs, particularly in resource-limited settings [90,91].

Table 1. Zoonotic viral outbreaks and impact on mortality *.

Virus (Disease)	Source (Animal)	Key Features of Virus	Total Cases	Fatality	Year of Major Outbreaks	References
SARS-CoV-2 (COVID-19)	Likely bats, possibly pangolins	Highly transmissible coronavirus; respiratory illness	~778.5 million	~7.1 million confirmed; estimated 19–36 million excess deaths globally (as of Sept 2025)	2019	[92,93]
Nipah Virus	Fruit bats, pigs	Paramyxovirus; severe encephalitis/respiratory symptoms	~700 cases (major outbreaks)	300–400 deaths (case fatality rate 40–70%)	Major spillovers ongoing, e.g., 2018–2023	[94,95]
Ebola Virus	Fruit bats, primates	Filovirus; hemorrhagic fever, person-to-person transmission	~34,000 cases (2014–2016)	~11,300 deaths (case fatality up to 90%)	2014 (West Africa), 2018–2020 (RDC)	[95,96]
MERS-CoV	Dromedary camels	Coronavirus; severe respiratory illness	~2600 cases	~900 deaths (case fatality ~34%)	2012	[92,93,97,98]
H1N1 Influenza (Swine Flu)	Swine	Influenza A virus, high transmissibility, often mild symptoms	>1.4 billion estimated (2009)	~284,500 deaths globally (estimated)	2009	[94,95]
Avian Influenza (H5N1/H7N9)	Poultry/wild birds	Influenza A; respiratory symptoms, occasional human-to-human	~900 human cases	~455 deaths (case fatality for H5N1 up to 60%)	H5N1: 2003; H7N9: 2013	[95,99]
Chikungunya	Non-human primates, mosquitoes	Alphavirus; fever, joint pain, transmitted by Aedes mosquitoes	>1 million cases (global)	<1% mortality (low)	2005–2006 (Indian Ocean), 2013–2014 (Americas)	[95,100]
Mpox	Rodents, primates	Orthopoxvirus; rash, fever	~87,000 cases (2022 outbreak)	~890 deaths globally (case fatality varies by clade)	2022 (globally)	[94,95,101]
Rift Valley Fever	Livestock (cattle, sheep, goats, camels)	Phlebovirus; fever, hemorrhagic symptoms, mosquito-borne	~20,000 cases	~500 deaths (case fatality around 1–10%)	2007 (Sudan, Kenya), 2010 (South Africa)	[89,95]
Crimean-Congo Hemorrhagic Fever	Livestock, ticks	Nairovirus; hemorrhagic fever, tick-borne	~1000 cases (varies)	100–400 deaths (case fatality rate 10–40%)	Ongoing, notable outbreaks: 2015 (India), 2016–2018 (Pakistan)	[95]
SARS-CoV	Himalayan palm civets, raccoon dogs	Flu-like symptoms with high fever, myalgia, dry non-productive dyspnea, and lymphopaenia	~8000 cases (2002–2003)	~774 deaths (case fatality ~10%)	2002	[102]
West Nile Virus	Wild birds, mosquitoes	Flavivirus; fever, neurological symptoms, mosquito-borne	~3 million infections estimated (U.S.)	~2100 deaths (mostly neuroinvasive cases)	1999 (The United States of America), ongoing	[93,95]

* Severe acute respiratory coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Democratic Republic of Congo (RDC); Middle East Respiratory Syndrome Coronavirus (MERS-CoV); Severe acute respiratory coronavirus (SARS-CoV).

consolidates zoonotic viral spillover features, case fatalities, and animal sources from the past decade highlighting the mortality threats.

3.2. Socioeconomic and Demographic Inequities

Socioeconomic disparities seriously influence zoonotic disease risks and outcomes. Poor housing, poor sanitation, and dependence on livestock or bushmeat increase exposure and complicate control efforts—especially in informal urban settlements and rural areas [70,88]. For example, in parts of the Democratic Republic of Congo (RDC), poverty and weak health infrastructure have hampered efforts to contain recurring Ebola outbreaks [86,103]. Children and marginalized groups face higher infection rates and mortality, exacerbating health inequities. Inequities related to gender, forced displacement, and vaccine accessibility are critical in shaping zoonotic disease burdens. For instance, women are disproportionately affected due to caregiving roles and higher exposure in informal agricultural and market activities. Displaced populations (e.g., refugees) are often at heightened risk due to overcrowded settlements and disrupted health infrastructure, as evidenced by Ebola flare-ups in conflict-affected regions of the DRC. Furthermore, vaccine inequities are stark, as observed during the emergent pandemic COVID-19: by mid-2021, less than 2% of Africa’s population was vaccinated compared to over 50% in North America and Europe, underscoring systemic imbalances in access [86,103]. Urban overcrowding can intensify transmission of rodent- and vector-borne zoonoses like leptospirosis and dengue [81,86].

3.3. Surveillance and Health System Challenges

Timely surveillance and response remain insufficient in many high-risk countries, delaying outbreak detection and prevention efforts. The COVID-19 pandemic highlighted vulnerabilities in even the most advanced health infrastructures, leading to a global mortality toll exceeding 6 million deaths and causing annual healthcare expenditures upwards of USD 2–3 trillion [67,90,104]. In resource-limited regions, surveillance gaps and delays in outbreak reporting are striking, with diagnostic capacity often lacking or fragmented. For example, sub-Saharan Africa’s median time to detect emerging zoonotic threats can exceed three months, allowing unchecked spread across borders and vulnerable communities. The pandemic era also accelerated antimicrobial resistance (AMR), which is largely driven by increased and often inappropriate antibiotic usage in both human and animal sectors—hospital-onset infections involving multidrug-resistant bacteria such as methicillin-resistant Staphylococcus aureus and Klebsiella increased by 20% during the pandemic period and remained above pre-pandemic levels in 2022. Globally, an estimated 1.27 million deaths are directly attributed to antimicrobial-resistant infections annually, with up to 10 million projected by 2050 without decisive intervention. In livestock, rising AMR has resulted in USD 3–4 billion in lost productivity and culling costs. These compounding factors make multidrug-resistant pathogens a growing threat to both human and veterinary health and highlight the necessity for substantially enhanced international and local surveillance programs to preempt future zoonotic crises [34,105].

3.4. Indirect Health and Social Impacts

Beyond direct infection, zoonotic diseases cause widespread social disruption, stigma, and mental health burdens. Quarantine measures often limit access to routine medical care, resulting in increased indirect mortality from diseases like malaria, HIV, and TB [106]. The psychological toll, including heightened anxiety and social isolation seen during COVID-19, adds to the overall health burden. These effects may outlast the outbreaks themselves, weakening community health resilience [70,90]. Figure 2 highlights the global areas affected by zoonotic viruses.

4. Impact on the Economy

4.1. Direct and Indirect Economic Losses

Zoonotic diseases impose enormous economic costs globally, estimated at around USD 2–3 trillion annually in healthcare costs, productivity losses, and broader societal disruption. The COVID-19 pandemic alone is projected to reduce global GDP by approximately USD 22 trillion by 2025 [86]. Major outbreaks like SARS in 2003 caused roughly USD 50 billion in global losses primarily through medical care and collapsed tourism [15,61,79]. The 2014–2016 Ebola outbreak led to economic damages over USD 2.2 billion in West African countries due to labor losses and business closures [70]. The Rift Valley fever occurred in East Africa, where the 2006–2007 outbreak cost Kenya, Somalia, and Tanzania an estimated USD 32 million in direct livestock losses and USD 60 million in trade bans [89,95]. Similarly, repeated Nipah outbreaks in Bangladesh and India (2001–2019) caused direct agricultural losses through pig culling and indirect effects such as reduced date palm sap consumption, amounting to millions in community-level livelihood disruptions [94,95]. Agricultural losses are significant in zoonoses affecting livestock, such as avian influenza, with estimated impacts surpassing USD 100 billion globally. Micro and small enterprises are particularly vulnerable, with income losses up to 90% reported during outbreaks like Ebola. However, it should be mentioned in this context that, while model-based estimates (e.g., the projected USD 22 trillion GDP loss from COVID-19 by 2025) provide long-term insights, they often exceed directly observed losses due to assumptions on productivity, trade, and consumer confidence. In contrast, observed figures (e.g., USD 50 billion in losses from SARS in 2003, USD 2.2 billion from Ebola in 2014–2016) represent immediate measured impacts [86].

4.2. Trade, Travel, and Global Instability

Outbreaks disrupt global trade and travel through necessary containment measures, causing sharp declines in tourism, transport, and retail sectors. For example, air freight capacity plummeted by 94% during the COVID-19 pandemic, severely impacting supply chains worldwide [86]. Export bans on animals and animal products in response to zoonotic outbreaks further damage agriculture-based economies [85,107]. These disruptions contribute to global economic instability, food insecurity, and rising market uncertainty.

4.3. Long-Term Systemic Impacts

Ongoing zoonotic threats force continuous investments in health systems, veterinary surveillance, and emergency preparedness. Such investments—though crucial—strain budgets, especially in low- and middle-income countries, diverting resources from broader development goals [86,104]. Sierra Leone redirected nearly 30% of its national health budget to the Ebola response in 2014–2015, causing routine immunization coverage to drop by 25%. Similarly, during COVID-19, India’s diversion of resources to pandemic control delayed tuberculosis detection by an estimated 25–30%, leading to a projected 400,000 excess TB deaths globally in 2020–2021. These cases underscore the “double burden” low- and medium-income countries face when scarce resources are reallocated to outbreak emergencies at the cost of other essential health services. Frequent outbreaks erode human capital and community resilience, perpetuating poverty cycles and vulnerability to future epidemics. Rising food prices and disrupted livelihoods disproportionately affect marginalized populations, creating systemic socioeconomic challenges [70].

5. Mitigation Strategies: Synergy Between Awareness and Scientific Advancement

Mitigating zoonotic outbreaks demands a robust, multipronged approach centered on the ‘One Health’ framework, which integrates human, animal, and environmental health sectors in a coordinated, multisectoral response at all governance levels [108]. Several success stories illustrate the effectiveness of multisector mitigation strategies against zoonotic threats. Global rabies deaths have fallen by more than 95% in Latin America following mass dog vaccination campaigns, demonstrating that sustained animal immunization drastically curtails human cases and healthcare costs. In China, human H7N9 influenza cases dropped to zero after the introduction of bivalent poultry vaccines across markets, validating animal-targeted interventions for spillover prevention. Rift Valley fever outbreaks in Africa have been controlled by systematic livestock vaccination, eliminating ruminants as amplifying hosts and preventing epidemic transmission. Additionally, One Health preparedness networks in the United States of America enabled rapid H1N1 pandemic response by linking veterinary, laboratory, and human health sectors, reducing disease burden and optimizing resources [109].

Early detection remains foundational, relying on sophisticated surveillance systems that monitor human syndromic data, wildlife, livestock at wildlife-domestic animal interfaces, and high-risk settings like animal markets, as demonstrated by the PREDICT project [110,111]. Advances in molecular techniques, including next-generation sequencing and metagenomics, have accelerated viral identification and biosurveillance, producing expansive viral genomic libraries essential for rapid diagnostics and outbreak response [112]. To prevent/tackle healthcare catastrophes, multinational authorities and scientific bodies have collaborated for surveillance and outbreak preparedness (

Table 2. Global collaborations surveillance network to mitigate outbreaks/epidemics.

Name	Unique Features	Governing Body	Link
CEPI (Coalition for Epidemic Preparedness)	Major global initiative for rapid vaccine R&D and response; focuses on viral threats including Disease X, COVID-19 *, Ebola, Lassa, and Nipah	CEPI Board, global health partners	https://cepi.net/priority-pathogens (31 August 2025)
MOOD * Epidemic Intelligence Platform	Integrates cross-sector epidemic data (animal and human viruses); spatial modeling and early warning for viral outbreaks in Europe	Horizon 2020 EU * project, public health/animal health agencies	https://mood-h2020.eu/mood-platform/ (31 August 2025)
Infectious Diseases Clinical Research Consortium (IDCRC)	United States-based, conducts rapid clinical trials (especially for viral pathogens); supports vaccine, antiviral, and trial development	National Institute of Allergy and Infectious Disease (NIAID)	https://idcrc.org (31 August 2025)
One Health Epidemic Preparedness Platform	Integrates human, animal, and environmental health data for viral epidemic warning and response; “One Health” approach	Cross-sector scientific consortium	https://sorveglianza-98a22.web.app/ (31 August 2025) [113]

* Coronavirus disease 2019 (COVID-19); Monitoring Outbreaks for Disease Surveillance in a data science context (MOOD); European Union (EU).

).

Computational advances, particularly AI and machine learning, have transformed predictive modeling of spillover events by integrating ecological, epidemiological, and genetic data to produce high-resolution risk maps, optimizing allocation of surveillance and preventive resources [12,114]. Strengthening laboratory capacity with polymerase chain reaction (PCR) and portable genome sequencing technologies in identified hotspots enables rapid confirmation and transmission tracking, crucial for timely public health interventions [115]. Prevention strategies emphasize reducing human exposure to high-risk reservoirs through enhanced biosecurity, community education, and habitat conservation, as biodiversity loss correlates with increased zoonotic risk by forcing wildlife–human proximity [44,51]. Despite advances by global initiatives like CEPI, PREDICT, and MOOD, persistent structural challenges hinder their full effectiveness. CEPI, for example, has raised approximately USD 760 million since its inception but remains well below its USD 3.5–4 billion target critical for rapid vaccine development. These programs often rely heavily on donor priorities, creating sustainability issues and misalignments with local health needs. Political obstacles such as delayed data sharing, weak multisector coordination, and poor integration into national systems impede impact. Notably, PREDICT’s extensive viral genome databases yielded limited actionable surveillance locally. Without stable domestic funding, robust community engagement, and stronger political commitment, One Health strategies risk stagnation. Robust public health infrastructure, infection control protocols, and broad-spectrum antiviral developments further bolster outbreak preparedness and response capabilities [116]. Finally, global coordination through frameworks such as WHO’s International Health Regulations and the Global Virome Project facilitates real-time data sharing and collective action to prevent pandemics. Together, these integrated strategies—fusing advanced diagnostics, computational modeling, environmental stewardship, and One Health collaboration—formulate a comprehensive defense against zoonotic viral threats, safeguarding health and economic stability globally. Although One Health provides a valuable framework for mitigation, its implementation faces significant barriers: poor intersectoral coordination (health, veterinary, and environmental agencies often operate in silos), limited domestic funding (with most programs being donor-driven), and sociopolitical challenges (e.g., resistance from local communities to wildlife market regulations). Incorporating these barriers highlights the need for context-sensitive approaches rather than a purely aspirational model [114,116,117,118].

6. Conclusions

Zoonotic viral pandemics arise from complex ecological, social, and economic interactions that disproportionately burden vulnerable populations, particularly in low- and middle-income countries. Our review highlights three critical gaps: insufficient surveillance in biodiversity hotspots, inadequate integration of epidemiological and economic data, and systemic inequities linked to gender, displacement, and vaccine access. The economic toll of zoonoses extends beyond global crises like COVID-19 to regional outbreaks such as Rift Valley fever in East Africa and Nipah virus in South Asia, where trade bans and agricultural losses destabilize rural livelihoods. Long-term systemic impacts include diversion of scarce health resources from endemic diseases like TB and malaria, amplifying co-infection risks. While the One Health framework provides a unifying approach, its implementation faces barriers of intersectoral fragmentation, limited domestic funding, and political resistance. Future preparedness must prioritize hotspot surveillance, strengthen economic-epidemiological integration, and support context-sensitive One Health strategies that build local capacity and resilience.