Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Aug 1.
Published in final edited form as: Glob Chang Biol. 2024 Aug;30(8):e17433. doi: 10.1111/gcb.17433

Infectious disease responses to human climate change adaptations

Georgia Titcomb 1, Johnny Uelmen 2,3, Mark Janko 4, Charles Nunn 2,4,5
PMCID: PMC11646313  NIHMSID: NIHMS2009143  PMID: 39171421

Abstract

Many recent studies have examined the impact of predicted changes in temperature and precipitation patterns on infectious diseases under different greenhouse gas emissions scenarios. But these emissions scenarios symbolize more than altered temperature and precipitation regimes; they also represent differing levels of change in energy, transportation, and food production at a global scale to reduce the effects of climate change. The ways humans respond to climate change, either through adaptation or mitigation, have underappreciated, yet hugely impactful effects on infectious disease transmission, often in complex and sometimes nonintuitive ways. Thus, in addition to investigating the direct effects of climate changes on infectious diseases, it is critical to consider how human preventative measures and adaptations to climate change will alter the environments and hosts that support pathogens. Here, we consider the ways that human responses to climate change will likely impact disease risk in both positive and negative ways. We evaluate the evidence for these impacts based on the available data, and identify research directions needed to address climate change while minimizing externalities associated with infectious disease, especially for vulnerable communities. We identify several different human adaptations to climate change that are likely to affect infectious disease risk independently of the effects of climate change itself. We categorize these changes into adaptation strategies to secure access to water, food, and shelter, and mitigation strategies to decrease greenhouse gas emissions. We recognize that adaptation strategies are more likely to have infectious disease consequences for under-resourced communities, and call attention to the need for socio-ecological studies to connect human behavioral responses to climate change and their impacts on infectious disease. Understanding these effects is crucial as climate change intensifies and the global community builds momentum to slow these changes and reduce their impacts on human health, economic productivity, and political stability.

Keywords: agricultural adaptation, climate change mitigation, food security, pathogen transmission, shared socioeconomic pathways, vector ecology, water supply and distribution

1 |. INTRODUCTION

Many studies have highlighted the considerable impacts of climate change on infectious diseases (Altizer et al., 2013; Lafferty, 2009; Patz, 1996; Rohr et al., 2011). Many of these studies have investigated how shifting temperatures and rainfall are reshaping the suitability of landscapes for host and pathogen or vector survival. In some cases, climate change reduces infectious disease hazards, and in others, it increases hazards. Translating these hazards into disease risk involves studies that quantify human exposure and vulnerabilities (Gibb et al., 2020). For example, one study showed that increases in the projected total area suitable for malaria transmission in Africa did not uniformly scale to increased human cases when population density and seasonality were considered, illustrating the importance of combining temperature changes with demographic and ecological factors (Ryan et al., 2015). Land-use changes and human movements are also known to have dramatic effects on infectious disease, potentially at a faster rate than climate changes (Gottdenker et al., 2014; Patz et al., 2004). We argue that when climate changes cause humans to alter land-use, water management, agriculture, energy systems, movement, and distribution, these combined changes may act synergistically, additively, or antagonistically to produce even larger impacts on infectious disease than climate change alone (Figure 1).

FIGURE 1.

FIGURE 1

Open in a new tab

Effects of climate change and human responses to climate change on infectious disease transmission. While climate changes are known to directly influence host, pathogen, and environmental components of the infectious disease triad, climate change may also significantly affect disease through a pathway mediated by human actions (dark gray arrow). These climate-induced human responses (illustrations) may act synergistically or in opposition to the direct effects of climate change on infectious disease.

In this vein, the Intergovernmental Panel on Climate Change (IPCC) has recognized how different global responses to climate change over the remainder of the twenty-first century will have vastly different impacts on greenhouse gas emissions (Pörtner et al., 2022). These shared socioeconomic pathways (SSPs) also characterize different outcomes in urbanization, GDP, and population size, all of which are likely to strongly influence public health, including infectious disease. These SSPs have been mapped onto two axes of varying climate change mitigation challenges (i.e., preventative steps) and climate change adaptation challenges (i.e., reactive steps). For example, a “Sustainability” pathway, SSP1, will minimize future socioeconomic challenges in both mitigation and adaptation, while a “Regional Rivalry” pathway, SSP3, in which nationalistic sentiments redirect resources to conflict, will result in high socioeconomic challenges for mitigation and adaptation. The SSP framework is relatively new (published in 2021) and has yet to be linked quantitatively to infectious disease risk: a Web of Science search for “shared socioeconomic pathways” returned 2009 papers, but “shared socioeconomic pathways” AND “infectious disease” returned only seven papers (April, 2024). Indeed, a separate systematic review of climate change adaptations and public health more broadly emphasized a dearth of standardized reporting across public health sectors, noting the likely bias in reports of climate mitigation actions that have a positive impact on human health, and an underreporting of actions with negative effects (Scheelbeek et al., 2021).

To investigate these questions, we systematically searched the literature for evidence of directional infectious disease outcomes for a range of climate change adaptations and mitigations. These adaptations included changes to water supply and distribution, food security, and shelter, while climate change mitigations included carbon sequestration, green transportation, diet shifts, and metal mining for batteries. Although the systematic search revealed some important and interesting papers, we were surprised to discover how few studies have quantitatively investigated these topics. We build on this search, along with other review articles, to synthesize the major ways that human climate change adaptations and mitigations are likely to affect infectious diseases, identifying potential future areas of concern and areas of greatest uncertainty (summarized in Table 1). To provide more informative predictions of future infectious disease risks and priorities, we call for a more intentional integration of SSPs in infectious disease research, which will require coalitions of social scientists, ecologists, epidemiologists, ecologists, and climate scientists, among others.

TABLE 1.

Summary of different human adaptations and mitigations to climate changes and their potential effects on infectious disease.

Adaptation (A) or mitigation (M) N papers N quantitative papers (%) N countries and regions Primary infectious diseases Primary transmission routes Mechanisms Major knowledge gaps Research status
(A) Dam construction 40 10 (21%) 16 Schistosomiasis Indirect Altered (often increased) intermediate snail hosts of trematodes; altered contact rates with water. Exploring variation in dam size and ecology (e.g., mega-dams vs provisional water sources) and resulting impacts on different infectious diseases.
Accounting for potentially synergistic or antagonistic effects of altered human behavior with dam construction.		 graphic file with name nihms-2009143-t0006.jpg
Malaria Vector Altered (often increased) vector habitat.
(A) Rainwater harvesting 24 1 (4%) 10 Enteric bacteria Indirect Increased suitable environment for growth and survival of water-borne pathogens. Pathogen and infection monitoring in under-resourced areas where access to filtration is lacking.
Controlled studies on incidence of disease in people with implementation of rainwater harvesting projects.		 graphic file with name nihms-2009143-t0007.jpg
(A) Movement and migration 31 2 (6%) 65 Arboviruses and malaria Vector Climate-induced food insecurity drives movement from rural to urban areas, altering vector-borne disease transmission patterns. Data on disease shifts in areas experiencing both climate out-migration and in-migration.
Understanding of the scale of different movements and their relationship to transmission patterns for different pathogens.
Coevolutionary relationships between climate-induced human activity shifts and mosquito feeding patterns.		 graphic file with name nihms-2009143-t0008.jpg
Diarrheal diseases Indirect Climate catastrophes drive individuals into living conditions that promote diarrheal disease transmission.
(A) Crop shifts 3 0 (0%) 1 Food and soil-borne pathogens Indirect Change in habitat for reservoir species or vectors, change in contact rates with infectious agents. Ecological and epidemiological data associated with sustainable crop shifts (vs agricultural expansion). graphic file with name nihms-2009143-t0009.jpg
(A) Livestock shifts Zero out of 3479 initial papers discussed how compositional shifts may alter disease Livestock-associated pathogens Direct Altered livestock composition: potential decreases in cattle pathogens and increase in pathogens of drought-tolerant species. Longitudinal prevalence data in humans and livestock in areas with shifting livestock assemblages.
Cultural and nutritional implications of livestock shifts in different communities as they relate to disease transmission.		 graphic file with name nihms-2009143-t0010.jpg
(M) Carbon sequestration and reforestation 4 1 (25%) 3 Gastrointestinal bacteria Indirect Tree planting alters environment in ways that both increase and decrease disease. Quantifying how reforestation “dilutes” or “amplifies” disease across transmission modes and depending on initial conditions. graphic file with name nihms-2009143-t0011.jpg
(M) Mining 2 0 (0%) 2 Gastrointestinal bacteria Indirect Increased human density, poor sanitation, and conversion of forest increases disease transmission rates. Infectious and noninfectious disease monitoring in communities affected by mines; health impacts on communities displaced by mines. graphic file with name nihms-2009143-t0012.jpg
(M) Green transportation 4 0 (0%) 2 Respiratory disease Direct Increased mass transit increases contact rates, but physical locomotion and telework reduces contacts. Quantitative studies on risks and mitigation steps to reduce disease transmission, especially in low-income communities. graphic file with name nihms-2009143-t0013.jpg
(M) Diet shifts 503 reviews of zoonotic poultry pathogens. Food-borne bacteria (Campylobacter, Salmonella, Escherichia coli and AMR genes) Food-borne Increased poultry consumption increases bacteria transmission and transfer of antimicrobial resistance (AMR) genes. Biosecurity capacity in new areas of increasing poultry production, especially in under-resourced areas.
Understanding of the potential role of CRISPR-edited poultry in viral evolution and its implication for domestic poultry in under-resourced areas.		 graphic file with name nihms-2009143-t0014.jpg
Avian influenza Direct Increased poultry consumption may increase bird density, leading to explosive outbreaks with greater potential for zoonotic transmission.
Open in a new tab

Note: For each topic, we illustrate the quantity and quality of prior research, identifying primary infectious diseases and transmission routes. We highlight major knowledge gaps associated with each topic and provide a visual coding of suggested research priorities (red being highest priority) based on the extent of prior research, likely future extensiveness of each adaptation or mitigation, and our perception of future infectious disease challenges for under-resourced communities.

2 |. WATER AND FOOD SECURITY

Climate change is impacting rainfall patterns globally: While there is a great deal of variation in projected precipitation totals across the globe, the IPCC’s sixth assessment report (2021) states with high confidence that precipitation extremes will increase everywhere, with more places experiencing greater drought frequency and severity (Pörtner et al., 2022). Many of the countries experiencing less rainfall lack the resources to address water shortages. Indeed, global aridification is projected to increase such that more than half the human population will live in water-stressed areas by 2100 (Huang et al., 2016), driving both individual-level and national efforts to conserve and even fight for this crucial resource. These changes in precipitation threaten water supplies for people, agriculture, and industry, thus motivating alterations to watersheds to better store available water. These adaptation efforts alter infectious disease risks in significant ways, and often with greater magnitudes and more quickly than the effects of climate change itself. These infectious disease risks will be most relevant for SSPs with high adaptation challenges, namely SSP2: “Middle of the Road,” SSP3: “Regional Rivalry,” and SSP4: “Inequality.” Here, we consider four major examples: rainfall harvesting, dam-building, agricultural adaptations, and shifts to drought-resistant livestock.

Rainwater harvesting is an early human adaptation to unpredictable climates dating back to the Holocene (Pandey et al., 2003) and has been increasingly adopted across the globe in both developed and developing countries (Amos et al., 2018). While rainwater storage provides increased water and food security, it can also provide new or more stable habitat for pathogens and vectors to proliferate in areas with high probability of human contact. For example, rainwater harvesting systems have been linked to increases in malaria (Yohannes & Haile, 2010) and to microbial and fecal contaminants (Ahmed et al., 2010; Celik et al., 2017; Karim, 2010; Lye, 2002). However, of the 24 papers that met our review criteria, only one was a quantitative study with a control group, while most of the literature focused on detecting pathogens present in stored water. No papers investigated the socio-ecological contexts or reasons for rainwater storage. Widespread adoption of rainwater systems in low-resource regions may increase infectious disease risk, particularly from enteric bacteria and protozoa (Figure 2), leading to calls for comprehensive risk-assessments and regulations in adopting rainwater harvesting systems (Boelee et al., 2013; Gwenzi et al., 2015; Hamilton et al., 2019). Thus, in addition to increased monitoring in under-resourced areas where filtration may be lacking, public communication efforts and increased water treatment capacity will be important complements to policies that encourage rainwater harvesting.

FIGURE 2.

FIGURE 2

Open in a new tab

Sankey diagram of infectious disease topics across climate change adaptations from a Web of Science search that resulted in 98 papers. Infectious agents (left) are connected to a topic if a paper discussed, implied, or quantitatively demonstrated a connection between the pathogen and a given climate change adaptation. Corresponding transmission modes (right) illustrate the dominant transmission modes relevant for each adaptation and underscore the importance of indirect and vector-borne transmission.

Dam construction can offset water insecurity due to climate change at larger scales than rainwater harvesting, and its effects on infectious diseases, particularly schistosomiasis, are well documented (Figure 3). Indeed, a large study estimated that dam construction has led to increases in schistosomiasis risk for up to 400 million people because dams disrupt the movement of shrimp that prey upon snails harboring intermediate trematode stages (Sokolow et al., 2017). Our review of 48 papers showed stronger quantitative evidence of causal relationships between dam construction and increased disease risks (Table 1), with the plurality of these papers focusing on schistosomiasis. Dam construction has also been linked to increased malaria transmission in previously water-limited areas (Ampadu et al., 2015; Kibret et al., 2017), as well as to onchocerciasis (Lakwo et al., 2020) and gastroenteritis (Teixeira et al., 1993). However, there are cases where dams have unexpectedly reduced infectious disease by eliminating vector habitat: for example, schistosomiasis prevalence in the lower Mekong valley dropped during and after construction of the Three Gorges Dam (Zhang et al., 2019), despite predictions that it would increase, and a dam in Sudan reduced black fly vectors of the nematode that causes onchocerciasis (Zarroug et al., 2014). There are still large knowledge gaps in understanding shifts in disease risk following dam construction, especially regarding the impacts of dam size, the timing of studies, and the additional behavioral impacts on humans and densities of reservoir hosts, like rats, that may increase following dam construction (Zhang et al., 2014). Given these diverse outcomes, investment in understanding the disease ecology of multiple pathogens is needed during planning stages in different geographic areas, along with capacity-building in ecological monitoring skills in low-income countries to monitor any changes that occur.

FIGURE 3.

FIGURE 3

Open in a new tab

Dam-building and schistosomiasis. Top map shows the global distribution of dams in 2023, provided by the Global Dam Tracker (Zhang & Gu, 2023). (Inset) Raw schistosomiasis prevalence values (maximum used when multiple species were recorded) were averaged across multiple surveys (1975–2016) within African countries and are shown with dams overlaid. Schistosomiasis survey data were obtained from the Global Atlas of Helminth Infections (London Applied & Spatial Epidemiology Research Group (LASER), 2023). Figure design based on Sokolow et al. (2017). Basemap: ArcGIS Pro©. Map lines delineate study areas and do not necessarily depict accepted national boundaries.

Given that approximately 70% of global water use is for agriculture (Food and Agriculture Organization & AQUASTAT data, 2020), climate changes that alter rainfall patterns also drive changes in irrigation systems. Indeed, global irrigation water demand is projected to increase by 20% by 2080, limiting agricultural productivity due to reduced water supply (El-Nashar & Elyamany, 2023; Esteve et al., 2015). Expanding irrigation networks can have notable effects on disease transmission: historical examples have included increases in lymphatic filariasis (Harb et al., 1993), malaria (Tyagi & Chaudhary, 1997), and schistosomiasis (Sokolow et al., 2017) when irrigation networks provided increased suitable habitat for vectors and intermediate hosts (Rohr et al., 2019). Thus, in areas with shifting irrigation systems, it will be important for public health officials to consider how water redistribution will affect host, pathogen, and vector ecology in a specific region. Similarly, studies considering how habitat modifications designed to protect against flooding may impact infectious diseases in both positive and negative ways are needed.

Amid ongoing climate changes, decreased agricultural productivity (Bajželj & Richards, 2014) is, and will continue to be, a major concern worldwide. Farmers at all scales are adapting to this issue in multiple ways, including expanding irrigation systems as discussed above (Yamba et al., 2019), shifting to new crops (Yamba et al., 2019), and increasing livestock investment (Jones & Thornton, 2009), all of which may have important consequences for infectious disease transmission. Climate-related declines in productivity may drive farmers to expand to new areas, although this is not often a feasible solution for small-scale farmers (Maja & Ayano, 2021). Such agricultural expansion is one of the major ways in which zoonotic spillovers occur, as it often leads to increased human–wildlife contact (Jones et al., 2013). Indeed, a recent review identified that >25% of all and >50% of zoonotic infectious diseases that emerged in humans since 1940 are associated with agriculture and predicted that future agricultural expansions will increase this risk (Rohr et al., 2019).

Another mitigation effort involves shifting to crops that are better adapted to altered climates. These changes may have both positive and negative effects on disease prevalence because they alter the ways in which people interact with the environment and are exposed to pathogens. However, we found almost no papers that considered this topic, so predictions about the impact of crop shifts are highly speculative. For example, farmers in China have shifted to and from rice production in relation to changing climatic suitability (Liu et al., 2015). This may change the ecology and population dynamics of rodents in ways that alter risk patterns of leptospirosis, a bacterial pathogen that commonly infects rice farmers. However, leptospirosis morbidity has generally declined in China over recent years, likely due to increased mechanical harvesting, which reduces human exposures (Zhang et al., 2020). Identifying crop shifts and technological innovations that increase productivity without increasing land-use, and which reduce human contact with vectors or infectious substrates will enable win–win solutions for climate change adaptation and public health.

Climate changes have also motivated some farmers to shift livestock systems in favor of animals that are better adapted to local conditions. Indeed, a recent global analysis showed alarming increases in heat stress for livestock by 2090 across many regions of the world (Thornton et al., 2021). For example, recent droughts in East Africa have caused Borana pastoralists to shift to camel management, despite rich cultural and historical ties to cattle husbandry (Wako et al., 2017). Other studies have shown that this pattern in shifting livestock is common: In Kenya, camels increased 13% and sheep and goats, which are also well adapted to heat and water stress (Joy et al., 2020), increased 76% from 1980 to 2013, while cattle numbers fell by 25% (Ogutu et al., 2016) (Figure 4). Indeed, a recent modeling study spanning north sub-Saharan Africa showed that replacing cattle with camels and goats would lead to higher milk production while reducing water and food use, as well as a 7.9% reduction in carbon emissions (Rahimi et al., 2022).

FIGURE 4.

FIGURE 4

Open in a new tab

Shifts to drought-tolerant livestock. (a) Change in livestock numbers across Kenyan counties from 1980 to 2016 using data reported in Ogutu et al. (2016). Sheep and goats have more than doubled in many regions, and camels have increased by very large margins in several counties. Meanwhile, cattle numbers have generally declined across counties. (b) Annual total precipitation trends in Kenya over three time periods, indicating decreasing annual rainfall over time. Drawn using data retrieved from the Climate Change Knowledge Portal (World Bank Group, 2023). Basemap: ArcGIS Pro©. Map lines delineate study areas and do not necessarily depict accepted national boundaries.

Shifts to more drought-tolerant livestock are promising from both climate change adaptation and mitigation perspectives. Less appreciated, however, are the effects on infectious disease risk. For example, camels are known to be hosts of Middle East Respiratory Syndrome (MERS). While confirmed MERS cases have been confined to Saudi Arabia due to geographic variation in the virus’s genetics (Chu et al., 2018), high infection rates of camels in Africa, high contact rates among humans and camels, and evidence of prior MERS infections in humans working with camels (Mok et al., 2021) suggest that this could be a potentially important zoonotic disease system (Perlman & Zumla, 2021), especially in East Africa, where viruses may have similar zoonotic potential to those in the Arabian Peninsula (Chu et al., 2018).

Alternatively, reductions in cattle populations may reduce the impact of 45 known zoonotic pathogens of cattle (McDaniel et al., 2014), such as Mycobacterium bovis, the causative agent of cattle tuberculosis. Many of these pathogens are also capable of infecting alternative livestock: for example, Coxiella burnetii, the causative agent of Q Fever, infects cattle, but camels have recently been shown to be major reservoirs of the disease (Hussein et al., 2015). Unfortunately, information about the prevalence of these diseases in humans is currently lacking in regions with the highest risk because symptoms may be nonspecific. Increased public education and resources for tracking both disease prevalence and nutritional aspects of livestock shifts are especially needed in lower income regions where humans are adapting to the effects of climate change.

3 |. LIVING CONDITIONS

Climate change is likely to alter human movements across the world. Indeed, 1–3 billion people are predicted to fall outside of the “human niche” in the next half a century based on current population distributions (Xu et al., 2020), potentially driving people to relocate to areas with more reliable living conditions. While the motivations for human movements are complex and multifaceted (Boas et al., 2019), climate change will undeniably affect the locations where people live and support their families, from national to hyperlocal scales (e.g., communities and households). Over the next 30 years, more than 216 million people will likely seek opportunities to move within their countries in the face of sea level rise, drought, and climate catastrophes (Clement et al., 2021). Although the overall proportion of inhabitants who are predicted to move is relatively small (up to 3% of the total population over 30 years [Clement et al., 2021]), those movements are likely to be concentrated in certain regions, such as island and coastal communities threatened by rising sea levels, leading to much larger percent changes when considering the scales at which disease transmission occurs. For example, the United Nations estimated that in 2023 alone, approximately 8.9 million people in the Sahel region will be displaced by climate change and climate-induced conflict (Sahel Predictive Analytics Project, 2022).

While many climate-related factors are likely to drive human geographic mobility a common pattern is for people to migrate from rural to urban, high-density settings (Rigaud et al., 2018). Moving to an urban setting has both positive and negative effects on infectious diseases, which heavily depends on sociodemographic background. For example, increased urbanization, independent of population density, is linked to reductions in the burden of several infectious diseases (Wood et al., 2017), but increases in noninfectious diseases such as cardiovascular disease (Yusuf et al., 2001) and diabetes (Goryakin et al., 2017). However, these patterns may be reversed for those living in urban poverty (Neiderud, 2015). In terms of mosquito-borne disease, urbanization has been shown to shift mosquito communities toward species that cause the greatest concerns for human health and which are uniquely well-adapted to anthropogenic environments (Perrin et al., 2022). Air pollution in urban areas may also increase susceptibility to airborne human diseases (Domingo & Rovira, 2020). While zoonotic diseases are less common in large urban centers, when land conversion and expansion occurs, there is a larger risk of an outbreak that spreads (Neiderud, 2015), and the urban landscape itself can foster unique transmission pathways among humans and synanthropic species like rats and mice (Combs et al., 2022). Indeed, one recent study creatively used a human transportation network to predict the spatial distribution of a range of zoonotic diseases in China, identifying specific high-risk regions under various climate change scenarios (Cao et al., 2023). To help address these concerns, more research is needed on why people move, where they move, and ways to make urban settings healthier across all sociodemographic backgrounds.

Migrations are extreme responses to climate changes, but relatively small shifts in human behavior may also have meaningful impacts on infectious disease transmission. For example, one study showed that citizens of Laredo, Texas, USA, had substantially lower seroprevalence of dengue than their peers across the US–Mexico border in Nuevo Laredo, Mexico. This difference existed despite Laredo having three times as many mosquitoes as Nuevo Laredo, and was driven by greater availability of air conditioning in Laredo (Reiter et al., 2003).

Heat wave frequency, intensity, duration, and length of heat wave season have increased steadily over the past several decades (United States Environmental Protection Agency, 2021), with the northern hemisphere summer of 2023 being the hottest on record (Fox et al., 2023). Human behavioral adaptations to increasing temperatures commonly involve staying indoors where air conditioning is available (Esplin et al., 2019), which is likely to reduce contact with pathogens, particularly vectors. However, poor air conditioner maintenance may increase risk of certain infectious diseases. Access to air conditioning is also becoming increasingly inequitable across the globe: As temperatures rise, demand for air conditioning is expected to rise substantially, but only the wealthiest families are likely to afford it (Davis et al., 2021). From a health perspective, air conditioning both reduces heat-related mortalities and exposures to vectors and pathogens, as seen for dengue virus. Alternatively, temporal shifts in behavior, like shifting outdoor activities to later in the evening in response to heat (Fan et al., 2023), may alter contact rates with vectors and reservoir hosts in both positive and negative ways, depending on the vector species in question (Wilke et al., 2023). Thus, we advocate that future models of vector distributions and pathogen risk under different climate change scenarios not only consider variable vector tolerance to heat (Dennington et al., 2024), but also consider how human behavior may change in response to the same climate conditions.

Together, it is clear that future research needs to more quantitatively connect climate drivers with human movements on multiple scales. These movements, ranging from hyperlocal behavioral changes to large geographic changes in human distributions, will have wide-ranging impacts on disease, yet we have little evidence to inform policy or allocation of resources. Developing that evidence will require multidisciplinary research involving climate and environmental scientists, public health scientists, and social scientists to develop studies that can improve our understanding of the different pathways through which climate change leads to migration and its impacts on infectious disease risk. For example, our review suggested multiple indirect paths, such as climate-induced food insecurity leading to rural–urban migration, as well as direct paths, such as climate catastrophes resulting in forced displacement. The literature revealed that the effects of these pathways on infectious diseases are heterogeneous and context-dependent, meaning global findings are unlikely to emerge (Schwerdtle et al., 2020). This is not surprising given the varying effects that climate change will have across the globe, and the varying migration strategies available to different populations. Nevertheless, it is critical to recognize that the future research agenda will need to consider the context and scale of different movements and how they relate to transmission patterns for different pathogens.

4 |. CLIMATE MITIGATION EFFECTS ON INFECTIOUS DISEASE RISK

In addition to adaptations designed to secure water, food, and shelter, human responses to climate change also include coordinated efforts to reduce emissions that contribute to worsening climate shifts. These coordinated efforts are essential to meeting emissions goals, but, due to the need to implement them quickly and at scale, they may also have implications for infectious (and noninfectious) disease transmission. The Intergovernmental Panel on Climate Change (IPCC) has emphasized the urgency of climate action: global greenhouse gas emissions must be halved by 2030 to limit warming to +1.5°C (Levin et al., 2023). To accomplish this, broad changes are required across agriculture, energy, transportation, and industry sectors. Successful shifts to broadscale climate mitigation are not only represented by SSP1: “Sustainability,” but also SSP4: “Inequality” when poorer individuals cannot easily adapt to climate change. The disease ramifications of climate mitigation will similarly depend on how under-resourced sectors are supported.

For the agricultural sector, greenhouse gas (GHG) emissions must be reduced by 22% (relative to 2017) by 2030 (Boehm et al., 2022). Reduced production and consumption of ruminant meat is one commonly recommended shift (Boehm et al., 2022). While reduced consumption of ruminant meat is best replaced by nonanimal protein sources, poultry consumption has been recommended as a good alternative to methane-producing beef (Rose et al., 2019). Indeed, in the United States, production of broiler chickens has more than doubled since 1980, while beef production has remained relatively stable (United States Department of Agriculture, 2024). This trajectory creates numerous concerns about disease transmission, especially when production increases are the result of increased animal population size and density. From our review of 503 papers on zoonotic poultry pathogens, the plurality (31%) focused on avian influenza, and many identified high risks for individuals directly involved in bird handling. Indeed, the 2021–2022 outbreak of highly pathogenic avian flu (HPAI) led to the death of more than 130 million domestic and wild birds globally, and, in March 2024, the virus began spreading among dairy cattle in the United States. While human infections have been rare, they have major pandemic potential if the virus evolves more effective human–human transmission (Yamaji et al., 2020). Increased poultry production and associated disease risks also vary greatly by economic context. Poorer small-holder farms often lack sufficient biosecurity measures to mitigate HPAI risks (as well as significant food-borne bacterial risks). Thus, recommendations for GHG-reducing agricultural policies should also consider disease risks that may arise with changing animal densities and distribution of cropland, as well as the available disease-prevention infrastructure.

To reach CO2 levels similar to those in 1988, humans must remove 500 billion tons of CO2 from the atmosphere. Fortunately, natural processes store approximately 15 billion tons of CO2 each year, and reforestation efforts are the most common practice for aiding in carbon sequestration (Friedlingstein et al., 2022). Our review of the literature showed that despite large global reforestation efforts, we lack controlled studies quantifying how carbon sequestration efforts change infectious disease risk. In a few promising examples, active carbon sequestration efforts through composting, reforestation, and other tree planting practices reduced environmental pathogens and risks to human health (Ersson & King, 2019; Sun et al., 2009). Another study demonstrated that green remediation efforts increased overall microbial diversity, including potentially pathogenic Clostridium species, in one American neighborhood (Pearson et al., 2019). Given the variable direction of infectious disease responses to potentially increased biodiversity (Halliday & Rohr, 2019) through reforestation, future studies should identify optimal reforestation efforts that decrease or do not change disease exposure in people.

Achieving climate goals also requires fundamental shifts in the energy production and travel sectors, which together are responsible for 28.7% of greenhouse gas emissions. According to the State of Climate Action (Boehm et al., 2022), by 2050, nearly all electricity needs must be supplied by zero-carbon sources (currently 36%), and by 2030, 75%–90% of all car sales need to be electric vehicles (currently <10%). Private car use needs to decline, and public transportation needs to at least double. The scale and rapidity of these necessary changes may have both positive and negative infectious disease consequences. For energy production, a transition from coal mining to renewable energy is a highly effective shift to meet climate goals. Depending on the renewable technology, this transition is likely to have significant benefits for noninfectious diseases, including reduced risk of black lung, a debilitating and deadly condition that affects approximately 200,000 miners per year (Li et al., 2022; Vos et al., 2020). In addition, the lung damage associated with mining increases the risk of respiratory viruses (Ghio, 2014).

In the transportation sector, transitioning to zero-carbon energy involves precious metal mining, especially of lithium, nickel, and cobalt. To meet demand, the International Energy Agency estimates that at least 117 new mines must be built by 2030 (International Energy Agency, 2022). While efforts for global sustainable mining are underway, serious public health and human rights concerns have been raised in the Democratic Republic of the Congo, which has more cobalt reserves than the rest of the world combined (Figure 5) (Siddharth, 2023). Little is known about the health impacts of cobalt, nickel, and lithium mining on workers in these mines. To address these issues, researchers and humanitarians have called for increased transparency, research, and efforts to combat worker exploitation and improve health (Bamana et al., 2021; Landrigan et al., 2022; Shanks, 2022). This scenario illustrates SSP4—“Inequality,” in which climate mitigation actions by wealthier groups may exacerbate health and adaptation challenges for poorer groups.

FIGURE 5.

FIGURE 5

Open in a new tab

Mining operations at a global scale. (a) Active coal and metal mining operations between 2000 and 2021 (white circles), overlaid by the global distribution of 2019 gross domestic product (GDP, United States Dollar) by country (choropleth display, quintile distribution). Mines that primarily produce lithium, cobalt, and nickel, the metals required for battery production (including electric vehicles), are indicated by red circles. Data retrieved from Jasansky et al. (2022) and Feenstra et al. (2015) for mining and GDP, respectively. (b) Satellite imagery of a large-scale mine in Kolwezi, Democratic Republic of the Congo, where mining activities overlap with residential areas. Large-scale industrial mines impose very different environmental and health impacts compared to (c) artisanal mining operations, like the Kaji gold mine in the Democratic Republic of the Congo. Satellite image (b) taken on May 7th, 2023, Google Earth; photo credit (c): Enough Project (CC-BY-NC-ND). Map lines delineate study areas and do not necessarily depict accepted national boundaries.

Mining increases risk of other infectious diseases, especially when new areas are cleared and when people live and work in tight quarters and unsanitary conditions. Land-clearing and increased human activity in previously intact landscapes generates considerable risks of zoonotic disease transmission and heightened pandemic potential. Extractive industries have been connected to infectious disease risks when they involve intense land conversion and poor working conditions. For example, historical gold mining in Australia was associated with outbreaks of dysentery, malaria, and typhoid (Shanks, 2022): Today, similar patterns in gold mines in Africa (Knoblauch et al., 2014) and the Amazon (Douine et al., 2020) are thought to have led to increased malaria transmission. However, the literature on this topic is lacking, and we have little data as to whether metal mining for batteries imposes similar health risks as gold mines, emphasizing the importance of better public health infrastructure and transparency around mining communities. However, the resources and development that mining operations can bring to a region have the potential to reduce infectious and noninfectious disease risks when companies consider the importance of worker health and well-being (Viliani et al., 2017).

Climate change will also require people to change their mobility patterns and how they travel, especially through greater adoption of public transportation. To meet emissions goals, public transportation coverage (distance per person) needs to double by 2030 in the world’s 50 highest GHG-emitting cities (Boehm et al., 2022). However, public transportation is also known to increase contact rates, leading to greater theoretical outbreak potential (Gkiotsalitis & Cats, 2021; Xu et al., 2013). For example, public transit has been associated with a sixfold higher risk of acute respiratory infection (Troko et al., 2011) and was associated with higher COVID-19 incidence at the beginning of the pandemic in the United States (Thomas et al., 2022). However, due to the logistical challenges in identifying transmission events, there are few studies that quantitatively link public transit to increased disease transmission, and this is particularly important in low-resource areas where infectious disease risk is often elevated. It is clear that infectious disease risks associated with public transportation can be mitigated with additional public health measures, such as mask-wearing (Ku et al., 2021). In addition, GHG reducing modifications to travel such as walking, bike commuting, or travel reductions will likely contribute to net reductions in infectious disease by reducing human–human contact rates, as well as by improving cardiovascular health (Mailloux et al., 2021).

5 |. CONCLUSION

Most research on the links between climate change and health has focused on the direct effects of climate on pathogens, hosts, and the environments in which they transmit. Here, we argue for the importance of quantifying how human responses to climate change will modify these risks. By responding to climate change, humans also alter the disease triad that connects host, parasite, and the environment. These changes occur across a wide range of human responses at the individual, population, and global level, involving access to water, food, and shelter, and to reduce carbon emissions to prevent worsening climatic conditions. Importantly, human adaptations to climate change that lower risk of disease are likely to be disproportionately adopted by wealthier individuals. Together, these changes represent a profound shift that will have broad-ranging effects on infectious diseases, some of which may be more pronounced than the direct effects of climate change on infectious disease. We identified a number of important research directions to move this understanding forward and to broaden appreciation of the many direct and indirect ways that climate change impacts health.

Below, we provide Broad recommendations to address knowledge gaps outlined in Table 1:

  • Increased collaboration among climate and environmental scientists, social scientists and infectious disease specialists to more deeply understand how human behavioral changes and economic growth can be integrated into infectious disease models. For example, the NASA Socioeconomic Data and Applications Center has recently released a spatial dataset of population and migration projections that could be used for future infectious disease risk mapping (CUNY Institute for Demographic Research-CIDR-City University of New York et al., 2022).

  • Consistent integration of infectious diseases into the SSP frame-work. Where possible, future infectious disease projections should distinguish between hazard and risk and to consider variable outcomes under different SSPs.

  • More basic science on the effects of climate adaptations and mitigations to improve disease predictions. Controlled studies, and ideally experiments where ethical and possible, are required to ascertain the effects of human climate change adaptations on infectious diseases across a wide range of transmission modes, but especially for indirectly transmitted and vector-borne pathogens that strongly depend on the environment.

  • Building human infrastructure in ecological monitoring to connect environmental changes to different mechanisms of infectious disease risk and transmission. For example, establishing vector monitoring programs may enable communities to act quickly in communicating risk to residents or by implementing effective interventions. This will require developing partnerships with national, regional, and local ministries of health and environment to effectively link environmental monitoring and infectious diseases surveillance programs to guide decision-making.

  • Increased focus on how individual climate adaptation responses, which do not have the same scrutiny and process as governmental actions, impact public health. Individual actions to secure food, water, and shelter may be less likely to consider the multiple facets of infectious disease risk. For example, supporting community health centers as nexuses of information sharing may be an effective way to promote climate adaptations while minimizing infectious disease risks.

  • Greater advocacy for climate mitigation efforts that consider their far-reaching disease implications. For example, promoting carbon sequestration practices that reduce pathogen exposure risk, or oversight of mining operations to support worker health. Mapping these mitigation efforts onto the SSP framework will help to identify actions that fall into an SSP1 pathway of “Sustainability,” versus an SSP4 pathway of “Inequality.”

Literature search details

We conducted a Web of Science search following PRISMA guidelines (SI I). Search terms included each topic, followed by “AND (infectious disease* OR zoono* OR pathogen* OR parasit*) AND (human OR people).” Papers were assessed for any positive, negative, or neutral link between each topic (dam construction, crop shifts, rainwater harvesting, mining, migration, carbon sequestration, and public transit) and human infectious diseases. Searches on poultry and transit returned >5000 papers, so searches were restricted to review topics only. We further restricted the 3479 results for livestock shifts to those with “shift” in the abstract. Following screening of 3485 papers (6964 including all livestock), 108 papers met initial review criteria of being relevant to each adaptation (n = 98) or mitigation (n = 10) and discussing a human infectious disease; of which only 14 were quantitative studies with a control or reference group.

Supplementary Material

Supinfo

ACKNOWLEDGEMENTS

This work was supported by Colorado State University, the Triangle Center for Evolutionary Medicine (TriCEM) and an NIH Climate Change and Health Supplement to an award from the NIH-NSF-NIFA Ecology and Evolution of Infectious Disease program (R01-TW011493).

Funding information

National Institutes of Health, Grant/Award Number: R01-TW011493

Footnotes

CONFLICT OF INTEREST STATEMENT

The authors declare no competing interests.

SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available from Zenodo at https://doi.org/10.5281/zenodo.13314361 (Titcomb et al., 2024). Dam data were provided by the Global Dam Tracker (GDAT), available at: https://zenodo.org/records/7616852. Global gross domestic product values were provided by the Penn World Table, available at: https://doi.org/10.15141/S5Q94M.

REFERENCES

  1. Ahmed W, Vieritz A, Goonetilleke A, & Gardner T (2010). Health risk from the use of roof-harvested rainwater in Southeast Queensland, Australia, as potable or nonpotable water, determined using quantitative microbial risk assessment. Applied and Environmental Microbiology, 76(22), 7382–7391. 10.1128/AEM.00944-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Altizer S, Ostfeld RS, Johnson PTJ, Kutz S, & Harvell CD (2013). Climate change and infectious diseases: From evidence to a predictive framework. Science (New York, N.Y.), 341(6145), 514–519. 10.1126/science.1239401 [DOI] [PubMed] [Google Scholar]
  3. Amos CC, Rahman A, & Gathenya JM (2018). Economic analysis of rainwater harvesting systems comparing developing and developed countries: A case study of Australia and Kenya. Journal of Cleaner Production, 172, 196–207. 10.1016/j.jclepro.2017.10.114 [DOI] [Google Scholar]
  4. Ampadu B, Awinbire B, Musah A, Zango M, Abanyie S, Ampofo S, & Akurugu B (2015). Assessing the impact of a dam on the livelihood of surrounding communities: A case study of Vea Dam in the Upper East Region of Ghana. Journal of Environment and Earth Science, 5(4), 20–25. [Google Scholar]
  5. Bajželj B, & Richards KS (2014). The positive feedback loop between the impacts of climate change and agricultural expansion and relocation. Landscape, 3(3), 898–916. 10.3390/land3030898 [DOI] [Google Scholar]
  6. Bamana G, Miller JD, Young SL, & Dunn JB (2021). Addressing the social life cycle inventory analysis data gap: Insights from a case study of cobalt mining in the Democratic Republic of the Congo. One Earth, 4(12), 1704–1714. 10.1016/j.oneear.2021.11.007 [DOI] [Google Scholar]
  7. Boas I, Farbotko C, Adams H, Sterly H, Bush S, van der Geest K, Wiegel H, Ashraf H, Baldwin A, Bettini G, Blondin S, de Bruijn M, Durand-Delacre D, Fröhlich C, Gioli G, Guaita L, Hut E, Jarawura FX, Lamers M, … Hulme M (2019). Climate migration myths. Nature Climate Change, 9(12), 901–903. 10.1038/s41558-019-0633-3 [DOI] [Google Scholar]
  8. Boehm S, Jeffery L, Levin K, Hecke J, Schumer C, Fyson C, Majid A, Jaeger J, Nilsson A, Naimoli S, Thwaites J, Cassidy E, Waite R, Wilson R, Castellanos S, Singh N, Lee A, & Geiges A (2022). State of climate action 2022. World Resources Institute. 10.46830/wrirpt.22.00028 [DOI] [Google Scholar]
  9. Boelee E, Yohannes M, Poda J-N, McCartney M, Cecchi P, Kibret S, Hagos F, & Laamrani H (2013). Options for water storage and rainwater harvesting to improve health and resilience against climate change in Africa. Regional Environmental Change, 13(3), 509–519. 10.1007/s10113-012-0287-4 [DOI] [Google Scholar]
  10. Cao B, Bai C, Wu K, La T, Su Y, Che L, Zhang M, Lu Y, Gao P, Yang J, Xue Y, & Li G (2023). Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate-correlated risk shifts of main zoonotic diseases in China. Global Change Biology, 29(13), 3723–3746. 10.1111/gcb.16708 [DOI] [PubMed] [Google Scholar]
  11. Celik I, Tamimi LMA, Al-Khatib IA, & Apul DS (2017). Management of rainwater harvesting and its impact on the health of people in the Middle East: Case study from Yatta town, Palestine. Environmental Monitoring and Assessment, 189(6), 271. 10.1007/s10661-017-5970-y [DOI] [PubMed] [Google Scholar]
  12. Chu DKW, Hui KPY, Perera RAPM, Miguel E, Niemeyer D, Zhao J, Channappanavar R, Dudas G, Oladipo JO, Traoré A, Fassi-Fihri O, Ali A, Demissié GF, Muth D, Chan MCW, Nicholls JM, Meyerholz DK, Kuranga SA, Mamo G, … Peiris M (2018). MERS coronaviruses from camels in Africa exhibit region-dependent genetic diversity. Proceedings of the National Academy of Sciences of the United States of America, 115(12), 3144–3149. 10.1073/pnas.1718769115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Clement V, Rigaud KK, de Sherbinin A, Jones B, Adamo S, Schewe J, Sadiq N, & Shabahat E (2021). Groundswell part 2: Acting on internal climate migration. World Bank. http://hdl.handle.net/10986/36248 [Google Scholar]
  14. Combs MA, Kache PA, VanAcker MC, Gregory N, Plimpton LD, Tufts DM, Fernandez MP, & Diuk-Wasser MA (2022). Socio-ecological drivers of multiple zoonotic hazards in highly urbanized cities. Global Change Biology, 28(5), 1705–1724. 10.1111/gcb.16033 [DOI] [PubMed] [Google Scholar]
  15. CUNY Institute For Demographic Research-CIDR-City University Of New York, Center For International Earth Science Information Network-CIESIN-Columbia University, & The World Bank. (2022). Groundswell spatial population and migration projections at one-eighth degree according to SSPs and RCPs, 2010–2050 [dataset]. NASA Socioeconomic Data and Applications Center (SEDAC). 10.7927/C5KQ-FB78 [DOI] [Google Scholar]
  16. Davis L, Gertler P, Jarvis S, & Wolfram C (2021). Air conditioning and global inequality. Global Environmental Change, 69, 102299. 10.1016/j.gloenvcha.2021.102299 [DOI] [Google Scholar]
  17. Dennington NL, Grossman MK, Ware-Gilmore F, Teeple JL, Johnson LR, Shocket MS, McGraw EA, & Thomas MB (2024). Phenotypic adaptation to temperature in the mosquito vector, Aedes aegypti. Global Change Biology, 30(1), e17041. 10.1111/gcb.17041 [DOI] [PubMed] [Google Scholar]
  18. Domingo JL, & Rovira J (2020). Effects of air pollutants on the transmission and severity of respiratory viral infections. Environmental Research, 187, 109650. 10.1016/j.envres.2020.109650 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Douine M, Lambert Y, Musset L, Hiwat H, Blume LR, Marchesini P, Moresco GG, Cox H, Sanchez JF, Villegas L, de Santi VP, Sanna A, Vreden S, & Suarez-Mutis M (2020). Malaria in gold miners in the Guianas and the Amazon: Current knowledge and challenges. Current Tropical Medicine Reports, 7(2), 37–47. 10.1007/s40475-020-00202-5 [DOI] [Google Scholar]
  20. El-Nashar W, & Elyamany A (2023). Adapting irrigation strategies to mitigate climate change impacts: A value engineering approach. Water Resources Management, 37(6), 2369–2386. 10.1007/s11269-022-03353-4 [DOI] [Google Scholar]
  21. Ersson O, & King K (2019). The Kailash Ecovillage project converting human excreta into organic foodstuffs and sanitized compost using new international building codes for compost toilet and urine diversion systems. Blue-Green Systems, 1(1), 33–54. 10.2166/bgs.2019.192 [DOI] [Google Scholar]
  22. Esplin ED, Marlon JR, Leiserowitz A, & Howe PD (2019). “Can You Take the Heat?” Heat-induced health symptoms are associated with protective behaviors. Weather, Climate, and Society, 11(2), 401–417. 10.1175/WCAS-D-18-0035.1 [DOI] [Google Scholar]
  23. Esteve P, Varela-Ortega C, Blanco-Gutiérrez I, & Downing TE (2015). A hydro-economic model for the assessment of climate change impacts and adaptation in irrigated agriculture. Ecological Economics, 120, 49–58. 10.1016/j.ecolecon.2015.09.017 [DOI] [Google Scholar]
  24. Fan Y, Wang J, Obradovich N, & Zheng S (2023). Intraday adaptation to extreme temperatures in outdoor activity. Scientific Reports, 13(1), 1. 10.1038/s41598-022-26928-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Feenstra R, Inklaar R, & Timmer M (2015). The next generation of the Penn World Table. American Economic Review, 105(10), 3150–3182. 10.1257/aer.20130954 [DOI] [Google Scholar]
  26. Food and Agriculture Organization, & AQUASTAT data. (2020). Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal). World Bank Open Data. https://data.worldbank.org [Google Scholar]
  27. Fox K, Keck A, & Richmond J (2023). NASA announces summer 2023 hottest on record. NASA. http://www.nasa.gov/press-release/nasa-announces-summer-2023-hottest-on-record [Google Scholar]
  28. Friedlingstein P, O’Sullivan M, Jones MW, Andrew RM, Gregor L, Hauck J, Le Quéré C, Luijkx IT, Olsen A, Peters GP, Peters W, Pongratz J, Schwingshackl C, Sitch S, Canadell JG, Ciais P, Jackson RB, Alin SR, Alkama R, … Zheng B (2022). Global carbon budget 2022. Earth System Science Data, 14(11), 4811–4900. 10.5194/essd-14-4811-2022 [DOI] [Google Scholar]
  29. Ghio AJ (2014). Particle exposures and infections. Infection, 42(3), 459–467. 10.1007/s15010-014-0592-6 [DOI] [PubMed] [Google Scholar]
  30. Gibb R, Franklinos LHV, Redding DW, & Jones KE (2020). Ecosystem perspectives are needed to manage zoonotic risks in a changing climate. BMJ, 371, m3389. 10.1136/bmj.m3389 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Gkiotsalitis K, & Cats O (2021). Public transport planning adaptation under the COVID-19 pandemic crisis: Literature review of research needs and directions. Transport Reviews, 41(3), 374–392. 10.1080/01441647.2020.1857886 [DOI] [Google Scholar]
  32. Goryakin Y, Rocco L, & Suhrcke M (2017). The contribution of urbanization to non-communicable diseases: Evidence from 173 countries from 1980 to 2008. Economics and Human Biology, 26, 151–163. 10.1016/j.ehb.2017.03.004 [DOI] [PubMed] [Google Scholar]
  33. Gottdenker NL, Streicker DG, Faust CL, & Carroll CR (2014). Anthropogenic land use change and infectious diseases: A review of the evidence. EcoHealth, 11(4), 619–632. 10.1007/s10393-014-0941-z [DOI] [PubMed] [Google Scholar]
  34. Gwenzi W, Dunjana N, Pisa C, Tauro T, & Nyamadzawo G (2015). Water quality and public health risks associated with roof rainwater harvesting systems for potable supply: Review and perspectives. Sustainability of Water Quality and Ecology, 6, 107–118. 10.1016/j.swaqe.2015.01.006 [DOI] [Google Scholar]
  35. Halliday FW, & Rohr JR (2019). Measuring the shape of the biodiversity–disease relationship across systems reveals new findings and key gaps. Nature Communications, 10(1), 5032. 10.1038/s41467-019-13049-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hamilton K, Reyneke B, Waso M, Clements T, Ndlovu T, Khan W, DiGiovanni K, Rakestraw E, Montalto F, Haas CN, & Ahmed W (2019). A global review of the microbiological quality and potential health risks associated with roof-harvested rainwater tanks. NPJ Clean Water, 2(1), 1. 10.1038/s41545-019-0030-5 [DOI] [Google Scholar]
  37. Harb M, Faris R, Gad AM, Hafez ON, Ramzy R, & Buck AA (1993). The resurgence of lymphatic filariasis in the Nile delta. Bulletin of the World Health Organization, 71(1), 49–54. [PMC free article] [PubMed] [Google Scholar]
  38. Huang J, Yu H, Guan X, Wang G, & Guo R (2016). Accelerated dryland expansion under climate change. Nature Climate Change, 6(2), 166–171. 10.1038/nclimate2837 [DOI] [Google Scholar]
  39. Hussein MF, Alshaikh MA, Al-Jumaah RS, GarelNabi A, Al-Khalifa I, & Mohammed OB (2015). The Arabian camel (Camelus dromedarius) as a major reservoir of Q fever in Saudi Arabia. Comparative Clinical Pathology, 24(4), 887–892. 10.1007/s00580-014-2002-y [DOI] [Google Scholar]
  40. International Energy Agency. (2022). Global electric vehicle outlook 2022. International Energy Agency. https://www.iea.org/reports/global-ev-outlook-2022 [Google Scholar]
  41. Jasansky S, Lieber M, Giljum S, & Maus V (2022). Open database on global coal and metal mine production (1.0.3) [dataset]. Zenodo. 10.5281/zenodo.7369478 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Jones BA, Grace D, Kock R, Alonso S, Rushton J, Said MY, McKeever D, Mutua F, Young J, McDermott J, & Pfeiffer DU (2013). Zoonosis emergence linked to agricultural intensification and environmental change. Proceedings of the National Academy of Sciences, 110(21), 8399–8404. 10.1073/pnas.1208059110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Jones PG, & Thornton PK (2009). Croppers to livestock keepers: Livelihood transitions to 2050 in Africa due to climate change. Environmental Science and Policy, 12(4), 427–437. 10.1016/j.envsci.2008.08.006 [DOI] [Google Scholar]
  44. Joy A, Dunshea FR, Leury BJ, Clarke IJ, DiGiacomo K, & Chauhan SS (2020). Resilience of small ruminants to climate change and increased environmental temperature: A review. Animals: An Open Access Journal from MDPI, 10(5), 867. 10.3390/ani10050867 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Karim MR (2010). Microbial contamination and associated health burden of rainwater harvesting in Bangladesh. Water Science and Technology, 61(8), 2129–2135. 10.2166/wst.2010.031 [DOI] [PubMed] [Google Scholar]
  46. Kibret S, Wilson GG, Ryder D, Tekie H, & Petros B (2017). The influence of dams on malaria transmission in sub-Saharan Africa. EcoHealth, 14(2), 408–419. 10.1007/s10393-015-1029-0 [DOI] [PubMed] [Google Scholar]
  47. Knoblauch AM, Winkler MS, Archer C, Divall MJ, Owuor M, Yapo RM, Yao PA, & Utzinger J (2014). The epidemiology of malaria and anaemia in the Bonikro mining area, central Côte d’Ivoire. Malaria Journal, 13(1), 194. 10.1186/1475-2875-13-194 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Ku D, Yeon C, Lee S, Lee K, Hwang K, Li YC, & Wong SC (2021). Safe traveling in public transport amid COVID-19. Science Advances, 7(43), eabg3691. 10.1126/sciadv.abg3691 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Lafferty KD (2009). The ecology of climate change and infectious diseases. Ecology, 90(4), 888–900. 10.1890/08-0079.1 [DOI] [PubMed] [Google Scholar]
  50. Lakwo TL, Raimon S, Tionga M, Siewe Fodjo JN, Alinda P, Sebit WJ, Carter JY, & Colebunders R (2020). The role of the Maridi Dam in causing an onchocerciasis-associated epilepsy epidemic in Maridi, South Sudan: An epidemiological, sociological, and entomological study. Pathogens, 9(4), 315. 10.3390/pathogens9040315 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Landrigan P, Bose-O’Reilly S, Elbel J, Nordberg G, Lucchini R, Bartrem C, Grandjean P, Mergler D, Moyo D, Nemery B, von Braun M, & Nowak D (2022). Reducing disease and death from Artisanal and Small-Scale Mining (ASM)—The urgent need for responsible mining in the context of growing global demand for minerals and metals for climate change mitigation. Environmental Health, 21, 78. 10.1186/s12940-022-00877-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Levin K, Fransen T, Schumer C, Davis C, & Boehm S (2023). What does “net-zero emissions” mean? 8 common questions, answered. World Resources Institute. https://www.wri.org/insights/net-zero-ghg-emissions-questions-answered [Google Scholar]
  53. Li J, Yin P, Wang H, Wang L, You J, Liu J, Liu Y, Wang W, Zhang X, Niu P, & Zhou M (2022). The burden of pneumoconiosis in China: An analysis from the Global Burden of Disease Study 2019. BMC Public Health, 22, 1114. 10.1186/s12889-022-13541-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Liu Z, Yang P, Tang H, Wu W, Zhang L, Yu Q, & Li Z (2015). Shifts in the extent and location of rice cropping areas match the climate change pattern in China during 1980–2010. Regional Environmental Change, 15(5), 919–929. 10.1007/s10113-014-0677-x [DOI] [Google Scholar]
  55. London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global atlas of helminth infections: STH and schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0 [Google Scholar]
  56. Lye DJ (2002). Health risks associated with consumption of untreated water from household roof catchment systems1. JAWRA Journal of the American Water Resources Association, 38(5), 1301–1306. 10.1111/j.1752-1688.2002.tb04349.x [DOI] [Google Scholar]
  57. Mailloux NA, Henegan CP, Lsoto D, Patterson KP, West PC, Foley JA, & Patz JA (2021). Climate solutions double as health interventions. International Journal of Environmental Research and Public Health, 18(24), 24. 10.3390/ijerph182413339 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Maja MM, & Ayano SF (2021). The impact of population growth on natural resources and farmers’ capacity to adapt to climate change in low-income countries. Earth Systems and Environment, 5(2), 271–283. 10.1007/s41748-021-00209-6 [DOI] [Google Scholar]
  59. McDaniel CJ, Cardwell DM, Moeller RB, & Gray GC (2014). Humans and cattle: A review of bovine zoonoses. Vector Borne and Zoonotic Diseases, 14(1), 1–19. 10.1089/vbz.2012.1164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Mok CKP, Zhu A, Zhao J, Lau EHY, Wang J, Chen Z, Zhuang Z, Wang Y, Alshukairi AN, Baharoon SA, Wang W, Tan W, Liang W, Oladipo JO, Perera RAPM, Kuranga SA, Peiris M, & Zhao J (2021). T-cell responses to MERS coronavirus infection in people with occupational exposure to dromedary camels in Nigeria: An observational cohort study. The Lancet. Infectious Diseases, 21(3), 385–395. 10.1016/S1473-3099(20)30599-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Neiderud C-J (2015). How urbanization affects the epidemiology of emerging infectious diseases. Infection Ecology & Epidemiology, 5, 27060. 10.3402/iee.v5.27060 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Ogutu JO, Piepho H-P, Said MY, Ojwang GO, Njino LW, Kifugo SC, & Wargute PW (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PLoS One, 11(9), e0163249. 10.1371/journal.pone.0163249 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Pandey DN, Gupta AK, & Anderson DM (2003). Rainwater harvesting as an adaptation to climate change. Current Science, 85(1), 46–59. [Google Scholar]
  64. Patz JA (1996). Global climate change and emerging infectious diseases. JAMA: The Journal of the American Medical Association, 275(3), 217. 10.1001/jama.1996.03530270057032 [DOI] [PubMed] [Google Scholar]
  65. Patz JA, Daszak P, Tabor GM, Aguirre AA, Pearl M, Epstein J, Wolfe ND, Kilpatrick AM, Foufopoulos J, Molyneux DH, Bradley DJ, & Members of the Working Group on Land Use Change and Disease Emergence. (2004). Unhealthy landscapes: Policy recommendations on land use change and infectious disease emergence. Environmental Health Perspectives, 112(10), 1092–1098. 10.1289/ehp.6877 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Pearson AL, Rzotkiewicz A, Pechal JL, Schmidt CJ, Jordan HR, Zwickle A, & Benbow ME (2019). Initial evidence of the relationships between the human postmortem microbiome and neighborhood blight and greening efforts. Annals of the American Association of Geographers, 109(3), 958–978. 10.1080/24694452.2018.1519407 [DOI] [Google Scholar]
  67. Perlman S, & Zumla A (2021). MERS-CoV in Africa—An enigma with relevance to COVID-19. The Lancet Infectious Diseases, 21(3), 303–305. 10.1016/S1473-3099(20)30578-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Perrin A, Glaizot O, & Christe P (2022). Worldwide impacts of landscape anthropization on mosquito abundance and diversity: A meta-analysis. Global Change Biology, 28(23), 6857–6871. 10.1111/gcb.16406 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Pörtner H-O, Roberts DC, Tignor M, Poloczanska ES, Mintenbeck K, Alegría A, Craig M, Langsdorf S, Löschke S, Möller V, Okem A, & Rama B (2022). Climate change 2022: Impacts, adaptation, and vulnerability (Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change). IPCC. 10.1017/9781009325844 [DOI] [Google Scholar]
  70. Rahimi J, Fillol E, Mutua JY, Cinardi G, Robinson TP, Notenbaert AMO, Ericksen PJ, Graham MW, & Butterbach-Bahl K (2022). A shift from cattle to camel and goat farming can sustain milk production with lower inputs and emissions in north sub-Saharan Africa’s drylands. Nature Food, 3(7), 523–531. 10.1038/s43016-022-00543-6 [DOI] [PubMed] [Google Scholar]
  71. Reiter P, Lathrop S, Bunning M, Biggerstaff B, Singer D, Tiwari T, Baber L, Amador M, Thirion J, Hayes J, Seca C, Mendez J, Ramirez B, Robinson J, Rawlings J, Vorndam V, Waterman S, Gubler D, Clark G, & Hayes E (2003). Texas lifestyle limits transmission of dengue virus. Emerging Infectious Diseases, 9(1), 86–89. 10.3201/eid0901.020220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Rigaud KK, de Sherbinin A, Jones B, Bergmann J, Clement V, Ober K, Schewe J, Adamo S, McCusker B, Heuser S, & Midgley A (2018). Groundswell: Preparing for internal climate migration. World Bank. http://hdl.handle.net/10986/29461 [Google Scholar]
  73. Rohr JR, Barrett CB, Civitello DJ, Craft ME, Delius B, DeLeo GA, Hudson PJ, Jouanard N, Nguyen KH, Ostfeld RS, Remais JV, Riveau G, Sokolow SH, & Tilman D (2019). Emerging human infectious diseases and the links to global food production. Nature Sustainability, 2(6), 445–456. 10.1038/s41893-019-0293-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Rohr JR, Dobson AP, Johnson PTJ, Kilpatrick AM, Paull SH, Raffel TR, Ruiz-Moreno D, & Thomas MB (2011). Frontiers in climate change-disease research. Trends in Ecology & Evolution, 26(6), 270–277. 10.1016/j.tree.2011.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Rose D, Heller MC, Willits-Smith AM, & Meyer RJ (2019). Carbon footprint of self-selected US diets: Nutritional, demographic, and behavioral correlates. The American Journal of Clinical Nutrition, 109(3), 526–534. 10.1093/ajcn/nqy327 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Ryan SJ, McNally A, Johnson LR, Mordecai EA, Ben-Horin T, Paaijmans K, & Lafferty KD (2015). Mapping physiological suitability limits for malaria in Africa under climate change. Vector-Borne and Zoonotic Diseases, 15(12), 718–725. 10.1089/vbz.2015.1822 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Sahel Predictive Analytics Project, A. (2022). Moving from reaction to action—Anticipating vulnerability hotspots in the Sahel [Synthesis report]. United Nations. https://www.unhcr.org/media/moving-reaction-action-anticipating-vulnerability-hotspots-sahel-0 [Google Scholar]
  78. Scheelbeek PFD, Dangour AD, Jarmul S, Turner G, Sietsma AJ, Minx JC, Callaghan M, Ajibade I, Austin SE, Biesbroek R, Bowen KJ, Chen T, Davis K, Ensor T, Ford JD, Galappaththi EK, Joe ET, Musah-Surugu IJ, Alverio GN, … Berrang-Ford L (2021). The effects on public health of climate change adaptation responses: A systematic review of evidence from low- and middle-income countries. Environmental Research Letters, 16(7), 073001. 10.1088/1748-9326/ac092c [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Schwerdtle PN, McMichael C, Mank I, Sauerborn R, Danquah I, & Bowen KJ (2020). Health and migration in the context of a changing climate: A systematic literature assessment. Environmental Research Letters, 15(10), 103006. 10.1088/1748-9326/ab9ece [DOI] [Google Scholar]
  80. Shanks GD (2022). Historical gold mining infectious disease epidemics. Internal Medicine Journal, 52(8), 1441–1445. 10.1111/imj.15881 [DOI] [PubMed] [Google Scholar]
  81. Siddharth K (2023). Cobalt red: How the blood of the Congo powers our lives (1st ed.). St. Martin’s Press. [Google Scholar]
  82. Sokolow SH, Jones IJ, Jocque M, La D, Cords O, Knight A, Lund A, Wood CL, Lafferty KD, Hoover CM, Collender PA, Remais JV, Lopez-Carr D, Fisk J, Kuris AM, & de Leo GA (2017). Nearly 400 million people are at higher risk of schistosomiasis because dams block the migration of snail-eating river prawns. Philosophical Transactions of the Royal Society, B: Biological Sciences, 372(1722), 20160127. 10.1098/rstb.2016.0127 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Sun Q, Zhang J, Zhou J, Wu L, & Shan Q (2009). Effect of poplar forest on snail control in Dongting Lake Area. In 2009 3rd international conference on bioinformatics and biomedical engineering (pp. 1–5). 10.1109/ICBBE.2009.5162710 [DOI] [Google Scholar]
  84. Thomas MM, Mohammadi N, & Taylor JE (2022). Investigating the association between mass transit adoption and COVID-19 infections in US metropolitan areas. The Science of the Total Environment, 811, 152284. 10.1016/j.scitotenv.2021.152284 [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Thornton P, Nelson G, Mayberry D, & Herrero M (2021). Increases in extreme heat stress in domesticated livestock species during the twenty-first century. Global Change Biology, 27(22), 5762–5772. 10.1111/gcb.15825 [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Teixeira M. da G., Costa Mda C, de Carvalho VL, Pereira Mdos S, & Hage E (1993). Gastroenteritis epidemic in the area of the Itaparica Dam, Bahia, Brazil. Bulletin of the Pan American Health Organization, 27(3), 244–253. [PubMed] [Google Scholar]
  87. Titcomb G, Uelmen J, Janko M, & Nunn C (2024). Dataset for: Infectious disease responses to human climate change adaptations [Data set]. Global Change Biology, Zenodo. 10.5281/zenodo.1331436 [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Troko J, Myles P, Gibson J, Hashim A, Enstone J, Kingdon S, Packham C, Amin S, Hayward A, & Van-Tam JN (2011). Is public transport a risk factor for acute respiratory infection? BMC Infectious Diseases, 11(1), 16. 10.1186/1471-2334-11-16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Tyagi BK, & Chaudhary RC (1997). Outbreak of falciparum malaria in the Thar Desert (India), with particular emphasis on physiographic changes brought about by extensive canalization and their impact on vector density and dissemination. Journal of Arid Environments, 36(3), 541–555. 10.1006/jare.1996.0188 [DOI] [Google Scholar]
  90. United States Department of Agriculture. (2024). Economic Research Service meat statistics tables, historical [dataset]. https://www.ers.usda.gov/webdocs/DataFiles/104360/MeatStatsRecent.xlsx?v=5091.9 [Google Scholar]
  91. United States Environmental Protection Agency. (2021). Climate change indicators: Heat waves [Reports and assessments]. Climate Change Indicators. https://www.epa.gov/climate-indicators/climate-change-indicators-heat-waves [Google Scholar]
  92. Viliani F, Edelstein M, Buckley E, Llamas A, & Dar O (2017). Mining and emerging infectious diseases: Results of the Infectious Disease Risk Assessment and Management (IDRAM) initiative pilot. The Extractive Industries and Society, 4(2), 251–259. 10.1016/j.exis.2016.08.009 [DOI] [Google Scholar]
  93. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abolhassani H, Aboyans V, Abrams EM, Guimarà L, Abreu LG, Abrigo MRM, Jamal Abu-Raddad L, … Murray CJL (2020). Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396, 1135–1139. 10.1016/S0140-6736(20)30925-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Wako G, Tadesse M, & Angassa A (2017). Camel management as an adaptive strategy to climate change by pastoralists in Southern Ethiopia. Ecological Processes, 6(1), 26. 10.1186/s13717-017-0093-5 [DOI] [Google Scholar]
  95. Wilke ABB, Mhlanga A, Kummer AG, Vasquez C, Moreno M, Petrie WD, Rodriguez A, Vitek C, Hamer GL, Mutebi J-P, & Ajelli M (2023). Diel activity patterns of vector mosquito species in the urban environment: Implications for vector control strategies. PLoS Neglected Tropical Diseases, 17(1), e0011074. 10.1371/journal.pntd.0011074 [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Wood CL, Mcinturff A, Young HS, Kim D, & Lafferty KD (2017). Human infectious disease burdens decrease with urbanization but not with biodiversity. Philosophical Transactions B, 372, 20160122. 10.1098/rstb.2016.0122 [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. World Bank Group. (2023). Climate data & projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections [Google Scholar]
  98. Xu C, Kohler TA, Lenton TM, Svenning J-C, & Scheffer M (2020). Future of the human climate niche. Proceedings of the National Academy of Sciences, 117(21), 11350–11355. 10.1073/pnas.1910114117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Xu F, Connell McCluskey C, & Cressman R (2013). Spatial spread of an epidemic through public transportation systems with a hub. Mathematical Biosciences, 246(1), 164–175. 10.1016/j.mbs.2013.08.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Yamaji R, Saad MD, Davis CT, Swayne DE, Wang D, Wong FYK, McCauley JW, Peiris JSM, Webby RJ, Fouchier RAM, Kawaoka Y, & Zhang W (2020). Pandemic potential of highly pathogenic avian influenza clade 2.3.4.4 A(H5) viruses. Reviews in Medical Virology, 30(3), e2099. 10.1002/rmv.2099 [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Yamba S, Appiah DO, & Siaw LP (2019). Smallholder farmers’ perceptions and adaptive response to climate variability and climate change in southern rural Ghana. Cogent Social Science, 5(1), 1646626. 10.1080/23311886.2019.1646626 [DOI] [Google Scholar]
  102. Yohannes M, & Haile M (2010). The potential of in situ rain water harvesting for water resources conservation on malaria transmission in Tigray, northern Ethiopia. Momona Ethiopian Journal of Science, 2(2), 2. 10.4314/mejs.v2i2.57675 [DOI] [Google Scholar]
  103. Yusuf S, Reddy S, Ôunpuu S, & Anand S (2001). Global burden of cardiovascular diseases. Circulation, 104(22), 2746–2753. 10.1161/hc4601.099487 [DOI] [PubMed] [Google Scholar]
  104. Zarroug IM, Elaagip AH, Abuelmaali SA, Mohamed HA, ElMubarak WA, Hashim K, Deran TCM, Aziz N, & Higazi TB (2014). The impact of Merowe Dam on Simulium hamedense vector of onchocerciasis in Abu Hamed focus—Northern Sudan. Parasites & Vectors, 7, 168. 10.1186/1756-3305-7-168 [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. Zhang AT, & Gu VX (2023). Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information. Scientific Data, 10(1), 1. 10.1038/s41597-023-02008-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Zhang H, Zhang C, Zhu Y, Mehmood K, Liu J, McDonough SP, Tang Z, & Chang Y-F (2020). Leptospirosis trends in China, 2007–2018: A retrospective observational study. Transboundary and Emerging Diseases, 67(3), 1119–1128. 10.1111/tbed.13437 [DOI] [PubMed] [Google Scholar]
  107. Zhang S, Wang T, Zhou Y, Cao Z, Zhang G, Wang N, & Jiang Q (2019). Influence of the Three Gorges Dam on schistosomiasis control in the middle and lower reaches of the Yangtze River. Global Health Journal, 3(1), 9–15. 10.1016/j.glohj.2019.03.003 [DOI] [Google Scholar]
  108. Zhang X, Peng L, Liu W, Fan L, Zhang Q, Chen G, He P, Wu R, Liu A, Yang Y, Yu X, & Yun L (2014). Response of primary vectors and related diseases to impoundment by the Three Gorges Dam. Tropical Medicine & International Health: TM & IH, 19(4), 440–449. 10.1111/tmi.12272 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supinfo
NIHMS2009143-supplement-Supinfo.docx (49.1KB, docx)

Data Availability Statement

The data that support the findings of this study are openly available from Zenodo at https://doi.org/10.5281/zenodo.13314361 (Titcomb et al., 2024). Dam data were provided by the Global Dam Tracker (GDAT), available at: https://zenodo.org/records/7616852. Global gross domestic product values were provided by the Penn World Table, available at: https://doi.org/10.15141/S5Q94M.

RESOURCES