The 2023 Latin America report of the Lancet Countdown on health and climate change: the imperative for health-centred climate-resilient development.

In 2023, a series of climatological and political events unfolded, partly driving forward the global climate and health agenda while simultaneously exposing important disparities and vulnerabilities to climate-related events. On the policy front, a significant step forward was marked by the inaugural Health Day at COP28, acknowledging the profound impacts of climate change on health. However, the first-ever Global Stocktake showed an important gap between the current progress and the targets outlined in the Paris Agreement, underscoring the urgent need for further and decisive action. From a Latin American perspective, some questions arise: How do we achieve the change that is needed? How to address the vulnerabilities to climate change in a region with long-standing social inequities? How do we promote intersectoral collaboration to face a complex problem such as climate change? The debate is still ongoing, and in many instances, it is just starting. The renamed regional centre Lancet Countdown Latin America (previously named Lancet Countdown South America) expanded its geographical scope adding Mexico and five Central American countries: Costa Rica, El Salvador, Guatemala, Honduras, and Panama, as a response to the need for stronger collaboration in a region with significant social disparities, including research capacities and funding. The centre is an independent and multidisciplinary collaboration that tracks the links between health and climate change in Latin America, following the global Lancet Countdown's methodologies and five domains. The Lancet Countdown Latin America work hinges on the commitment of 23 regional academic institutions, United Nations agencies, and 34 researchers who generously contribute their time and expertise. Building from the first report, the 2023 report of the Lancet Countdown Latin America, presents 34 indicators that track the relationship between health and climate change up to 2022, aiming at providing evidence to public decision-making with the purpose of improving the health and wellbeing of Latin American populations and reducing social inequities through climate actions focusing on health. This report shows that Latin American populations continue to observe a growing exposure to changing climatic conditions. A warming trend has been observed across all countries in Latin America, with severe direct impacts. In 2022, people were exposed to ambient temperatures, on average, 0.38 °C higher than in 1986-2005, with Paraguay experiencing the highest anomaly (+1.9 °C), followed by Argentina (+1.2 °C) and Uruguay (+0.9 °C) (indicator 1.1.1). In 2013-2022, infants were exposed to 248% more heatwave days and people over 65 years old were exposed to 271% more heatwave days than in 1986-2005 (indicator 1.1.2). Also, compared to 1991-2000, in 2013-2022, there were 256 and 189 additional annual hours per person, during which ambient heat posed at least moderate and high risk of heat stress during light outdoor physical activity in Latin America, respectively (indicator 1.1.3). Finally, the region had a 140% increase in heat-related mortality from 2000-2009 to 2013-2022 (indicator 1.1.4). Changes in ecosystems have led to an increased risk of wildfires, exposing individuals to very or extremely high fire danger for more extended periods (indicator 1.2.1). Additionally, the transmission potential for dengue by Aedes aegypti mosquitoes has risen by 54% from 1951-1960 to 2013-2022 (indicator 1.3), which aligns with the recent outbreaks and increasing dengue cases observed across Latin America in recent months. Based on the 2023 report of the Lancet Countdown Latin America, there are three key messages that Latin America needs to further explore and advance for a health-centred climate-resilient development. Latin American countries require intersectoral public policies that simultaneously increase climate resilience, reduce social inequities, improve population health, and reduce greenhouse gas (GHG) emissions. The findings show that adaptation policies in Latin America remain weak, with a pressing need for robust vulnerability and adaptation (V&A) assessments to address climate risks effectively. Unfortunately, such assessments are scarce. Up to 2021, Brazil is the only country that has completed and officially reported a V&A to the 2021 Global Survey conducted by the World Health Organization (WHO). Argentina, Guatemala, and Panama have also conducted them, but they have not been reported (indicator 2.1.1). Similarly, efforts in developing and implementing Health National Adaptation Plans (HNAPs) are varied and limited in scope. Brazil, Chile, and Uruguay are the only countries that have an HNAP (indicator 2.1.2). Moreover, self-reported city-level climate change risk assessments are very limited in the region (indicator 2.1.3). The collaboration between meteorological and health sectors remains insufficient, with only Argentina, Brazil, Colombia, and Guatemala self-reporting some level of integration (indicator 2.2.1), hindering comprehensive responses to climate-related health risks in the region. Additionally, despite the urgent need for action, there has been minimal progress in increasing urban greenspaces across the region since 2015, with only Colombia, Nicaragua, and Venezuela showing slight improvements (indicator 2.2.2). Compounding these challenges is the decrease in funding for climate change adaptation projects in Latin America, as evidenced by the 16% drop in funds allocated by the Green Climate Fund (GCF) in 2022 compared to 2021. Alarmingly, none of the funds approved in 2022 were directed toward climate change and health projects, highlighting a critical gap in addressing health-related climate risks (indicator 2.2.3). From a vulnerability perspective, the Mosquito Risk Index (MoRI) indicates an overall decrease in severe mosquito-borne disease risk in the region due to improvements in water, sanitation, and hygiene (WASH) (indicator 2.3.1). Brazil and Paraguay were the only countries that showed an increase in this indicator. It is worth noting that significant temporal variation within and between countries still persists, suggesting inadequate preparedness for climate-related changes. Overall, population health is not solely determined by the health sector, nor are climate policies a sole responsibility of the environmental sector. More and stronger intersectoral collaboration is needed to pave development pathways that consider solid adaptation to climate change, greater reductions of GHG emissions, and that increase social equity and population health. These policies involve sectors such as finance, transport, energy, housing, health, and agriculture, requiring institutional structures and policy instruments that allow long-term intersectoral collaboration. Latin American countries need to accelerate an energy transition that prioritises people's health and wellbeing, reduces energy poverty and air pollution, and maximises health and economic gains. In Latin America, there is a notable disparity in energy transition, with electricity generation from coal increasing by an average of 2.6% from 1991-2000 to 2011-2020, posing a challenge to efforts aimed at phasing out coal (indicator 3.1.1). However, this percentage increase is conservative as it may not include all the fossil fuels for thermoelectric electricity generation, especially during climate-related events and when hydropower is affected (Panel 4). Yet, renewable energy sources have been growing, increasing by an average of 5.7% during the same period. Access to clean fuels for cooking remains a concern, with 46.3% of the rural population in Central America and 23.3% in South America lacking access to clean fuels in 2022 (indicator 3.1.2). It is crucial to highlight the concerning overreliance on fossil fuels, particularly liquefied petroleum gas (LPG), as a primary cooking fuel. A significant majority of Latin American populations, approximately 74.6%, rely on LPG for cooking. Transitioning to cleaner heating and cooking alternatives could also have a health benefit by reducing household air pollution-related mortality. Fossil fuels continue to dominate road transport energy in Latin America, accounting for 96%, although some South American countries are increasing the use of biofuels (indicator 3.1.3). Premature mortality attributable to fossil-fuel-derived PM2.5 has shown varied trends across countries, increasing by 3.9% from 2005 to 2020 across Latin America, which corresponds to 123.5 premature deaths per million people (indicator 3.2.1). The Latin American countries with the highest premature mortality rate attributable to PM2.5 in 2020 were Chile, Peru, Brazil, Colombia, Mexico, and Paraguay. Of the total premature deaths attributable to PM2.5 in 2020, 19.1% was from transport, 12.3% from households, 11.6% from industry, and 11% from agriculture. From emission and capture of GHG perspective, commodity-driven deforestation and expansion of agricultural land remain major contributors to tree cover loss in the region, accounting for around 80% of the total loss (indicator 3.3). Additionally, animal-based food production in Latin America contributes 85% to agricultural CO2 equivalent emissions, with Argentina, Brazil, Panama, Paraguay, and Uruguay ranking highest in per capita emissions (indicator 3.4.1). From a health perspective, in 2020, approximately 870,000 deaths were associated with imbalanced diets, of which 155,000 (18%) were linked to high intake of red and processed meat and dairy products (indicator 3.4.2). Energy transition in Latin America is still in its infancy, and as a result, millions of people are currently exposed to dangerous levels of air pollution and energy poverty (i.e., lack of access to essential energy sources or services). As shown in this report, the levels of air pollution, outdoors and indoors, are a significant problem in the wholeregion, with marked disparities between urban and rural areas. In 2022, Peru, Chile, Mexico, Guatemala, Colombia, El Salvador, Brazil, Uruguay, Honduras, Panama, and Nicaragua were in the top 100 most polluted countries globally. Transitioning to cleaner sources of energy, phasing out fossil fuels, and promoting better energy efficiency in the industrial and housing sectors are not only climate mitigation measures but also huge health and economic opportunities for more prosperous and healthy societies. Latin American countries need to increase climate finance through permanent fiscal commitments and multilateral development banks to pave climate-resilient development pathways. Climate change poses significant economic costs, with investments in mitigation and adaptation measures progressing slowly. In 2022, economic losses due to weather-related extreme events in Latin America were US$15.6 billion -an amount mainly driven by floods and landslides in Brazil-representing 0.28% of Latin America's Gross Domestic Product (GDP) (indicator 4.1.1). In contrast to high-income countries, most of these losses lack insurance coverage, imposing a substantial financial strain on affected families and governments. Heat-related mortality among individuals aged 65 and older in Latin America reached alarming levels, with losses exceeding the equivalent of the average income of 451,000 people annually (indicator 4.1.2). Moreover, the total potential income loss due to heat-related labour capacity reduction amounted to 1.34% of regional GDP, disproportionately affecting the agriculture and construction sectors (indicator 4.1.3). Additionally, the economic toll of premature mortality from air pollution was substantial, equivalent to a significant portion of regional GDP (0.61%) (indicator 4.1.4). On a positive note, clean energy investments in the region increased in 2022, surpassing fossil fuel investments. However, in 2020, all countries reviewed continued to offer net-negative carbon prices, revealing fossil fuel subsidies totalling US$23 billion. Venezuela had the highest net subsidies relative to current health expenditure (123%), followed by Argentina (10.5%), Bolivia (10.3%), Ecuador (8.3%), and Chile (5.6%) (indicator 4.2.1). Fossil fuel-based energy is today more expensive than renewable energy. Fossil fuel burning drives climate change and damages the environment on which people depend, and air pollution derived from the burning of fossil fuels causes seven million premature deaths each year worldwide, along with a substantial burden of disease. Transitioning to sustainable, zero-emission energy sources, fostering healthier food systems, and expediting adaptation efforts promise not only environmental benefits but also significant economic gains. However, to implement mitigation and adaptation policies that also improve social wellbeing and prosperity, stronger and solid financial systems are needed. Climate finance in Latin American countries is scarce and strongly depends on political cycles, which threatens adequate responses to the current and future challenges. Progress on the climate agenda is lagging behind the urgent pace required. While engagement with the intersection of health and climate change is increasing, government involvement remains inadequate. Newspaper coverage of health and climate change has been on the rise, peaking in 2022, yet the proportion of climate change articles discussing health has declined over time (indicator 5.1). Although there has been significant growth in the number of scientific papers focusing on Latin America, it still represents less than 4% of global publications on the subject (indicator 5.3). And, while health was mentioned by most Latin American countries at the UN General Debate in 2022, only a few addressed the intersection of health and climate change, indicating a lack of awareness at the governmental level (indicator 5.4). The 2023 Lancet Countdown Latin America report underscores the cascading and compounding health impacts of anthropogenic climate change, marked by increased exposure to heatwaves, wildfires, and vector-borne diseases. Specifically, for Latin America, the report emphasises three critical messages: the urgent action to implement intersectoral public policies that enhance climate resilience across the region; the pressing need to prioritise an energy transition that focuses on health co-benefits and wellbeing, and lastly, that need for increasing climate finance by committing to sustained fiscal efforts and engaging with multilateral development banks. By understanding the problems, addressing the gaps, and taking decisive action, Latin America can navigate the challenges of climate change, fostering a more sustainable and resilient future for its population. Spanish and Portuguese translated versions of this Summary can be found in Appendix B and C, respectively. The full translated report in Spanish is available in Appendix D.

Quantifying climatic and socioeconomic drivers of urban malaria in Surat, India: a statistical spatiotemporal modelling study.

BACKGROUND: Cities are becoming increasingly important habitats for mosquito vectors of disease. The pronounced heterogeneity of urban landscapes challenges our understanding of the effects of climate and socioeconomic factors on mosquito-borne disease dynamics at different spatiotemporal scales. Here, we quantify the impact of climatic and socioeconomic factors on urban malaria risk, using an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, northwest India. METHODS: We analysed 10 years of monthly P falciparum cases resolved at three nested spatial resolutions (seven zones, 32 units, and 478 worker units) with a Bayesian hierarchical mixed model that incorporates the effects of population density, poverty, relative humidity, and temperature, in addition to random effects (structured and unstructured). To reduce dimensionality and avoid correlation of covariates, socioeconomic variables from survey data were summarised into main axes of variation using principal component analysis. With model selection, we identified the main drivers of spatiotemporal variation in malaria incidence rates at each of the three spatial resolutions. We also compared observations to model-fitted cases by quantifying the percentage of predictions within five discrete levels of malaria risk. FINDINGS: The spatial variation of urban malaria cases was stationary over time, whereby locations with high and low yearly cases remained largely consistent across years. Local socioeconomic variation could be summarised with three principal components accounting for approximately 80% of the variance. The model that incorporated local temperature and relative humidity together with two of these principal components, largely representing population density and poverty, best explained monthly malaria patterns in models formulated at the three different spatial scales. As model resolution increased, the effect size of humidity decreased, whereas those of temperature and the principal component associated with population density increased. Model predictions accurately captured aggregated total monthly cases for the city; in space-time, they more closely matched observations at the intermediate scale, with around 57% of units estimated to fall in the observed category on average across years. The mean absolute error was lower at the intermediate level, showing that this is the best aggregation level to predict the space-time dynamics of malaria incidence rates across the city with the selected model. INTERPRETATION: This statistical modelling framework provides a basis for development of a climate-driven early warning system for urban malaria for the units of Surat, including spatially explicit prediction of malaria risk several weeks to months in advance. Results indicate environmental and socioeconomic covariates for which further measurement at high resolution should lead to model improvement. Advanced warning combined with local surveillance and knowledge of disease hotspots within the city could inform targeted intervention as part of urban malaria elimination efforts. FUNDING: US National Institutes of Health.

A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia.

BACKGROUND: Snakebite envenoming is a neglected tropical disease affecting deprived populations, and its burden is underestimated in some regions where patients prefer using traditional medicine, case reporting systems are deficient, or health systems are inaccessible to at-risk populations. Thus, the development of strategies to optimize disease management is a major challenge. We propose a framework that can be used to estimate total snakebite incidence at a fine political scale. METHODOLOGY/PRINCIPAL FINDINGS: First, we generated fine-scale snakebite risk maps based on the distribution of venomous snakes in Colombia. We then used a generalized mixed-effect model that estimates total snakebite incidence based on risk maps, poverty, and travel time to the nearest medical center. Finally, we calibrated our model with snakebite data in Colombia from 2010 to 2019 using the Markov-chain-Monte-Carlo algorithm. Our results suggest that 10.19% of total snakebite cases (532.26 yearly envenomings) are not reported and these snakebite victims do not seek medical attention, and that populations in the Orinoco and Amazonian regions are the most at-risk and show the highest percentage of underreporting. We also found that variables such as precipitation of the driest month and mean temperature of the warmest quarter influences the suitability of environments for venomous snakes rather than absolute temperature or rainfall. CONCLUSIONS/SIGNIFICANCE: Our framework permits snakebite underreporting to be estimated using data on snakebite incidence and surveillance, presence locations for the most medically significant venomous snake species, and openly available information on population size, poverty, climate, land cover, roads, and the locations of medical centers. Thus, our algorithm could be used in other countries to estimate total snakebite incidence and improve disease management strategies; however, this framework does not serve as a replacement for a surveillance system, which should be made a priority in countries facing similar public health challenges.

Bridging landscape ecology and urban science to respond to the rising threat of mosquito-borne diseases.

The prevalence of diseases borne by mosquitoes, particularly in the genus Aedes, is rising worldwide. This has been attributed, in part, to the dramatic rates of contemporary urbanization. While Aedes-borne disease risk varies within and between cities, few investigations use urban science-based approaches to examine how city structure and function contribute to vector or pathogen introduction and maintenance. Here, we integrate theories from complex adaptive systems, landscape ecology and urban geography to develop an urban systems framework for understanding Aedes-borne diseases. The framework establishes that cities comprise hierarchically structured patches of different land uses and characteristics. Properties of the patches (that is, composition) determine localized disease risk, while configuration and connectivity drive emergent patterns of pathogen spread. Complexity is added by incorporating individual and collective human social structures, considering how feedbacks among social actors and with the landscape drive risk and transmission. We discuss how these concepts apply to case studies of Aedes-borne disease from around the world. Ultimately, the framework strengthens existing theoretical and mixed qualitative-quantitative approaches, and advances considerations of how interventions including urban planning (for example, piped water provisioning) and emerging vector control strategies (for example, Wolbachia-infected mosquitoes) can be implemented to prevent and control the rising threat of Aedes-borne diseases.

Estimating and forecasting the burden and spread of Colombia's SARS-CoV2 first wave.

Following the rapid dissemination of COVID-19 cases in Colombia in 2020, large-scale non-pharmaceutical interventions (NPIs) were implemented as national emergencies in most of the country's municipalities, starting with a lockdown on March 20th, 2020. Recently, approaches that combine movement data (measured as the number of commuters between units), metapopulation models to describe disease dynamics subdividing the population into Susceptible-Exposed-Asymptomatic-Infected-Recovered-Diseased and statistical inference algorithms have been pointed as a practical approach to both nowcast and forecast the number of cases and deaths. We used an iterated filtering (IF) framework to estimate the model transmission parameters using the reported data across 281 municipalities from March to late October in locations with more than 50 reported deaths and cases in Colombia. Since the model is high dimensional (6 state variables in every municipality), inference on those parameters is highly non-trivial, so we used an Ensemble-Adjustment-Kalman-Filter (EAKF) to estimate time variable system states and parameters. Our results show the model's ability to capture the characteristics of the outbreak in the country and provide estimates of the epidemiological parameters in time at the national level. Importantly, these estimates could become the base for planning future interventions as well as evaluating the impact of NPIs on the effective reproduction number ([Formula: see text]) and the critical epidemiological parameters, such as the contact rate or the reporting rate. However, our forecast presents some inconsistency as it overestimates the deaths for some locations as Medellín. Nevertheless, our approach demonstrates that real-time, publicly available ensemble forecasts can provide short-term predictions of reported COVID-19 deaths in Colombia. Therefore, this model can be used as a forecasting tool to evaluate disease dynamics and aid policymakers in infectious outbreak management and control.

Genomic epidemiological models describe pathogen evolution across fitness valleys.

Genomics is fundamentally changing epidemiological research. However, systematically exploring hypotheses in pathogen evolution requires new modeling tools. Models intertwining pathogen epidemiology and genomic evolution can help understand processes such as the emergence of novel pathogen genotypes with higher transmissibility or resistance to treatment. In this work, we present Opqua, a flexible simulation framework that explicitly links epidemiology to sequence evolution and selection. We use Opqua to study determinants of evolution across fitness valleys. We confirm that competition can limit evolution in high-transmission environments and find that low transmission, host mobility, and complex pathogen life cycles facilitate reaching new adaptive peaks through population bottlenecks and decoupling of selective pressures. The results show the potential of genomic epidemiological modeling as a tool in infectious disease research.

Disentangling snakebite dynamics in Colombia: How does rainfall and temperature drive snakebite temporal patterns?

The role of climate driving zoonotic diseases' population dynamics has typically been addressed via retrospective analyses of national aggregated incidence records. A central question in epidemiology has been whether seasonal and interannual cycles are driven by climate variation or generated by socioeconomic factors. Here, we use compartmental models to quantify the role of rainfall and temperature in the dynamics of snakebite, which is one of the primary neglected tropical diseases. We took advantage of space-time datasets of snakebite incidence, rainfall, and temperature for Colombia and combined it with stochastic compartmental models and iterated filtering methods to show the role of rainfall-driven seasonality modulating the encounter frequency with venomous snakes. Then we identified six zones with different rainfall patterns to demonstrate that the relationship between rainfall and snakebite incidence was heterogeneous in space. We show that rainfall only drives snakebite incidence in regions with marked dry seasons, where rainfall becomes the limiting resource, while temperature does not modulate snakebite incidence. In addition, the encounter frequency differs between regions, and it is higher in regions where Bothrops atrox can be found. Our results show how the heterogeneous spatial distribution of snakebite risk seasonality in the country may be related to important traits of venomous snakes' natural history.

How disease risk awareness modulates transmission: coupling infectious disease models with behavioural dynamics.

Epidemiological models often assume that individuals do not change their behaviour or that those aspects are implicitly incorporated in parameters in the models. Typically, these assumptions are included in the contact rate between infectious and susceptible individuals. However, adaptive behaviours are expected to emerge and play an important role in the transmission dynamics across populations. Here, we propose a theoretical framework to couple transmission dynamics with behavioural dynamics due to infection awareness. We modelled the dynamics of social behaviour using a game theory framework, which is then coupled with an epidemiological model that captures the disease dynamics by assuming that individuals are aware of the actual epidemiological state to reduce their contacts. Results from the mechanistic model show that as individuals increase their awareness, the steady-state value of the final fraction of infected individuals in a susceptible-infected-susceptible (SIS) model decreases. We also incorporate theoretical contact networks, having the awareness parameter dependent on global or local contacts. Results show that even when individuals increase their awareness of the disease, the spatial structure itself defines the steady state.

Deciphering the introduction and transmission of SARS-CoV-2 in the Colombian Amazon Basin.

BACKGROUND: The SARS-CoV-2 pandemic has forced health authorities across the world to take important decisions to curtail its spread. Genomic epidemiology has emerged as a valuable tool to understand introductions and spread of the virus in a specific geographic location. METHODOLOGY/PRINCIPAL FINDINGS: Here, we report the sequences of 59 SARS-CoV-2 samples from inhabitants of the Colombian Amazonas department. The viral genomes were distributed in two robust clusters within the distinct GISAID clades GH and G. Spatial-temporal analyses revealed two independent introductions of SARS-CoV-2 in the region, one around April 1, 2020 associated with a local transmission, and one around April 2, 2020 associated with other South American genomes (Uruguay and Brazil). We also identified ten lineages circulating in the Amazonas department including the P.1 variant of concern (VOC). CONCLUSIONS/SIGNIFICANCE: This study represents the first genomic epidemiology investigation of SARS-CoV-2 in one of the territories with the highest report of indigenous communities of the country. Such findings are essential to decipher viral transmission, inform on global spread and to direct implementation of infection prevention and control measures for these vulnerable populations, especially, due to the recent circulation of one of the variants of concern (P.1) associated with major transmissibility and possible reinfections.

Estimating snakebite incidence from mathematical models: A test in Costa Rica.

BACKGROUND: Snakebite envenoming is a neglected public health challenge that affects mostly economically deprived communities who inhabit tropical regions. In these regions, snakebite incidence data is not always reliable, and access to health care is scare and heterogeneous. Thus, addressing the problem of snakebite effectively requires an understanding of how spatial heterogeneity in snakebite is associated with human demographics and snakes' distribution. Here, we use a mathematical model to address the determinants of spatial heterogeneity in snakebite and we estimate snakebite incidence in a tropical country such as Costa Rica. METHODS AND FINDINGS: We combined a mathematical model that follows the law of mass action, where the incidence is proportional to the exposed human population and the venomous snake population, with a spatiotemporal dataset of snakebite incidence (Data from year 1990 to 2007 for 193 districts) in Costa Rica. This country harbors one of the most dangerous venomous snakes, which is the Terciopelo (Bothrops asper, Garman, 1884). We estimated B. asper distribution using a maximum entropy algorithm, and its abundance was estimated based on field data. Then, the model was adjusted to the data using a lineal regression with the reported incidence. We found a significant positive correlation (R2 = 0.66, p-value < 0.01) between our estimation and the reported incidence, suggesting the model has a good performance in estimating snakebite incidence. CONCLUSIONS: Our model underscores the importance of the synergistic effect of exposed population size and snake abundance on snakebite incidence. By combining information from venomous snakes' natural history with census data from rural populations, we were able to estimate snakebite incidence in Costa Rica. The model was able to fit the incidence data at fine administrative scale (district level), which is fundamental for the implementation and planning of management strategies oriented to reduce snakebite burden.

Development, environmental degradation, and disease spread in the Brazilian Amazon.

The Amazon is Brazil's greatest natural resource and invaluable to the rest of the world as a buffer against climate change. The recent election of Brazil's president brought disputes over development plans for the region back into the spotlight. Historically, the development model for the Amazon has focused on exploitation of natural resources, resulting in environmental degradation, particularly deforestation. Although considerable attention has focused on the long-term global cost of "losing the Amazon," too little attention has focused on the emergence and reemergence of vector-borne diseases that directly impact the local population, with spillover effects to other neighboring areas. We discuss the impact of Amazon development models on human health, with a focus on vector-borne disease risk. We outline policy actions that could mitigate these negative impacts while creating opportunities for environmentally sensitive economic activities.

The rise and fall of malaria under land-use change in frontier regions.

Land-use change is the main force behind ecological and social change in many countries around the globe; it is primarily driven by resource needs and external economic incentives. Concomitantly, transformations of the land are the main drivers for the emergence and re-emergence of malaria. An understanding of malaria population dynamics in transforming landscapes is lacking, despite its relevance for developmental and public health policies. We develop a mathematical model that couples malaria epidemiology with the socio-economic and demographic processes that occur in a landscape undergoing land-use change. This allows us to identify different types of malaria dynamics that can arise in early stages of this transformation. In particular, we show that an increase in transmission followed by either a decline, or a further enhancement, of risk is a common outcome. This increase results from the asymmetry between the relatively fast ecological changes in transformed landscapes, and the slower pace of investment in malaria protection. These results underscore the importance of reducing ecological risk, while providing services and economic opportunities to early migrants for longer periods. Consideration of these socio-ecological processes and, more importantly, the temporal scale on which they act, is critical to avoid potential bifurcations that lead to long-lasting endemic malaria.

Population Density, Climate Variables and Poverty Synergistically Structure Spatial Risk in Urban Malaria in India.

BACKGROUND: The world is rapidly becoming urban with the global population living in cities projected to double by 2050. This increase in urbanization poses new challenges for the spread and control of communicable diseases such as malaria. In particular, urban environments create highly heterogeneous socio-economic and environmental conditions that can affect the transmission of vector-borne diseases dependent on human water storage and waste water management. Interestingly India, as opposed to Africa, harbors a mosquito vector, Anopheles stephensi, which thrives in the man-made environments of cities and acts as the vector for both Plasmodium vivax and Plasmodium falciparum, making the malaria problem a truly urban phenomenon. Here we address the role and determinants of within-city spatial heterogeneity in the incidence patterns of vivax malaria, and then draw comparisons with results for falciparum malaria. METHODOLOGY/PRINCIPAL FINDINGS: Statistical analyses and a phenomenological transmission model are applied to an extensive spatio-temporal dataset on cases of Plasmodium vivax in the city of Ahmedabad (Gujarat, India) that spans 12 years monthly at the level of wards. A spatial pattern in malaria incidence is described that is largely stationary in time for this parasite. Malaria risk is then shown to be associated with socioeconomic indicators and environmental parameters, temperature and humidity. In a more dynamical perspective, an Inhomogeneous Markov Chain Model is used to predict vivax malaria risk. Models that account for climate factors, socioeconomic level and population size show the highest predictive skill. A comparison to the transmission dynamics of falciparum malaria reinforces the conclusion that the spatio-temporal patterns of risk are strongly driven by extrinsic factors. CONCLUSION/SIGNIFICANCE: Climate forcing and socio-economic heterogeneity act synergistically at local scales on the population dynamics of urban malaria in this city. The stationarity of malaria risk patterns provides a basis for more targeted intervention, such as vector control, based on transmission 'hotspots'. This is especially relevant for P. vivax, a more resilient parasite than P. falciparum, due to its ability to relapse and the operational shortcomings of delivering a "radical cure".

Climate forcing and infectious disease transmission in urban landscapes: integrating demographic and socioeconomic heterogeneity.

Urbanization and climate change are the two major environmental challenges of the 21st century. The dramatic expansion of cities around the world creates new conditions for the spread, surveillance, and control of infectious diseases. In particular, urban growth generates pronounced spatial heterogeneity within cities, which can modulate the effect of climate factors at local spatial scales in large urban environments. Importantly, the interaction between environmental forcing and socioeconomic heterogeneity at local scales remains an open area in infectious disease dynamics, especially for urban landscapes of the developing world. A quantitative and conceptual framework on urban health with a focus on infectious diseases would benefit from integrating aspects of climate forcing, population density, and level of wealth. In this paper, we review what is known about these drivers acting independently and jointly on urban infectious diseases; we then outline elements that are missing and would contribute to building such a framework.

Temperature and population density determine reservoir regions of seasonal persistence in highland malaria.

A better understanding of malaria persistence in highly seasonal environments such as highlands and desert fringes requires identifying the factors behind the spatial reservoir of the pathogen in the low season. In these 'unstable' malaria regions, such reservoirs play a critical role by allowing persistence during the low transmission season and therefore, between seasonal outbreaks. In the highlands of East Africa, the most populated epidemic regions in Africa, temperature is expected to be intimately connected to where in space the disease is able to persist because of pronounced altitudinal gradients. Here, we explore other environmental and demographic factors that may contribute to malaria's highland reservoir. We use an extensive spatio-temporal dataset of confirmed monthly Plasmodium falciparum cases from 1995 to 2005 that finely resolves space in an Ethiopian highland. With a Bayesian approach for parameter estimation and a generalized linear mixed model that includes a spatially structured random effect, we demonstrate that population density is important to disease persistence during the low transmission season. This population effect is not accounted for in typical models for the transmission dynamics of the disease, but is consistent in part with a more complex functional form of the force of infection proposed by theory for vector-borne infections, only during the low season as we discuss. As malaria risk usually decreases in more urban environments with increased human densities, the opposite counterintuitive finding identifies novel control targets during the low transmission season in African highlands.