Study protocol: improving response to malaria in the Amazon through identification of inter-community networks and human mobility in border regions of Ecuador, Peru and Brazil.

INTRODUCTION: Understanding human mobility's role in malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. METHODS: We measure community connectivity across the study area using a respondent driven sampling design among key informants who are at least 18 years of age. 45 initial communities will be selected: 10 in Brazil, 10 in Ecuador and 25 in Peru. Participants will be recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses will be ranked and the 2-3 most connected communities will then be selected and surveyed. This process will be repeated for a third round of data collection. Community network matrices will be linked with each country's malaria surveillance system to test the effects of mobility on disease risk. ETHICS AND DISSEMINATION: This study protocol has been approved by the institutional review boards of Duke University (USA), Universidad San Francisco de Quito (Ecuador), Universidad Peruana Cayetano Heredia (Peru) and Universidade Federal Minas Gerais (Brazil). Results will be disseminated in communities by the end of the study.

Network Profile: Improving Response to Malaria in the Amazon through Identification of Inter-Community Networks and Human Mobility in Border Regions of Ecuador, Peru, and Brazil.

Objectives: Understanding human mobility's role on malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. Design: A community-level network survey. Setting: We collect data on community connectivity along three river systems in the Amazon basin: the Pastaza river corridor spanning the Ecuador-Peru border; and the Amazon and Javari river corridors spanning the Brazil-Peru border. Participants: We interviewed key informants in Brazil, Ecuador, and Peru, including from indigenous communities: Shuar, Achuar, Shiwiar, Kichwa, Ticuna, and Yagua. Key informants are at least 18 years of age and are considered community leaders. Primary outcome: Weekly, community-level malaria incidence during the study period. Methods: We measure community connectivity across the study area using a respondent driven sampling design. Forty-five communities were initially selected: 10 in Brazil, 10 in Ecuador, and 25 in Peru. Participants were recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses were ranked and the 2-3 most connected communities were then selected and surveyed. This process was repeated for a third round of data collection. Community network matrices will be linked with eadch country's malaria surveillance system to test the effects of mobility on disease risk. Findings: To date, 586 key informants were surveyed from 126 communities along the Pastaza river corridor. Data collection along the Amazon and Javari river corridors is ongoing. Initial results indicate that network sampling is a superior method to delineate migration flows between communities. Conclusions: Our study provides measures of mobility and connectivity in rural settings where traditional approaches are insufficient, and will allow us to understand mobility's effect on malaria transmission.

Climate and sea surface trends in the Galapagos Islands.

The Galapagos Islands are a global hotspot of environmental change. However, despite their potentially major repercussions, little is known about current and expected changes in regional terrestrial climate variables and sea surface temperatures (SST). Here, by analysing existing meteorological observations and secondary datasets, we find that the Islands have warmed by about 0.6 °C since the early 1980s, while at the same time becoming drier. In fact, the onset of the wet season is currently delayed 20 days. This drying trend may reverse, however, given that future climate projections for the region suggest mean annual precipitation may increase between 20 and 70%. This would also be accompanied by more extreme wet and hot conditions. Further, we find that regional SST has increased by 1.2 °C over the last two decades. These changes will, in turn, translate into deterioration of marine ecosystems and coral, proliferation of invasive species, and damages to human water, food, and infrastructure systems. Future projections, however, may be overestimated due to the poor capacity of climatic models to capture Eastern-Pacific ENSO dynamics. Our findings emphasize the need to design resilient climate adaptation policies that will remain robust in the face of a wide range of uncertain and changing climatic futures.

Malaria Transmission and Spillover across the Peru-Ecuador Border: A Spatiotemporal Analysis.

Border regions have been implicated as important hot spots of malaria transmission, particularly in Latin America, where free movement rights mean that residents can cross borders using just a national ID. Additionally, rural livelihoods largely depend on short-term migrants traveling across borders via the Amazon's river networks to work in extractive industries, such as logging. As a result, there is likely considerable spillover across country borders, particularly along the border between Peru and Ecuador. This border region exhibits a steep gradient of transmission intensity, with Peru having a much higher incidence of malaria than Ecuador. In this paper, we integrate 13 years of weekly malaria surveillance data collected at the district level in Peru and the canton level in Ecuador, and leverage hierarchical Bayesian spatiotemporal regression models to identify the degree to which malaria transmission in Ecuador is influenced by transmission in Peru. We find that increased case incidence in Peruvian districts that border the Ecuadorian Amazon is associated with increased incidence in Ecuador. Our results highlight the importance of coordinated malaria control across borders.

Modeling asymptomatic infections and work-related human circulation as drivers of unstable malaria transmission in low-prevalence areas: A study in the Northern Peruvian Amazon.

BACKGROUND: Despite relatively successful control campaigns, malaria remains a relevant public health problem in the Peruvian Amazon. Several studies suggest that malaria persistence in the area can be connected with a high prevalence of asymptomatic infections, which were subsequently shown to be connected with work-related exposure in areas of hyperendemic transmission. In this study, we tested the hypothesis that the infection reservoir represented by asymptomatic carriers in the northern Peruvian Amazon, combined with circular human movement to and from hyperendemic working areas, can capture the observed hypoendemic malaria transmission. METHODS: We designed a set of agent-based models that represent local-scale malaria transmission in a typical riverine community in the northern Peruvian Amazon. The models include asymptomatic individuals as well as a full representation of human movements within the community and between the community and external hyperendemic working places. Several theoretical scenarios are explored to verify if and how malaria clinical immunity prevalence and human work-related movements influence the malaria morbidity registered in the community. RESULTS: Agent-based simulations suggest that malaria incidence observed through passive case detection can be reproduced as exclusively generated by the asymptomatic infection reservoir. Scenarios analysis also show that, even if asymptomatic infections are completely eliminated, human movements to and from hyperendemic working areas generate a flow of imported cases that is enough to permit the persistence of transmission in the community. Simulation results were verified over a wide range of clinical immunity prevalence values and over a wide range of percentages of people working in remote hyperendemic areas. This context of unstable malaria transmission is observed to be vulnerable to severe outbreaks. CONCLUSIONS: Asymptomatic malaria infection and occupational circular human movement to hyperendemic transmission areas are designated by agent-based models as possible exclusive causes of residual hypoendemic malaria transmission observed in the Peruvian Amazon. Control strategies are proposed to decrease asymptomatic infection prevalence and to block transmission from asymptomatic individuals to the malaria susceptible population.

Out of the net: An agent-based model to study human movements influence on local-scale malaria transmission.

Though malaria control initiatives have markedly reduced malaria prevalence in recent decades, global eradication is far from actuality. Recent studies show that environmental and social heterogeneities in low-transmission settings have an increased weight in shaping malaria micro-epidemiology. New integrated and more localized control strategies should be developed and tested. Here we present a set of agent-based models designed to study the influence of local scale human movements on local scale malaria transmission in a typical Amazon environment, where malaria is transmission is low and strongly connected with seasonal riverine flooding. The agent-based simulations show that the overall malaria incidence is essentially not influenced by local scale human movements. In contrast, the locations of malaria high risk spatial hotspots heavily depend on human movements because simulated malaria hotspots are mainly centered on farms, were laborers work during the day. The agent-based models are then used to test the effectiveness of two different malaria control strategies both designed to reduce local scale malaria incidence by targeting hotspots. The first control scenario consists in treat against mosquito bites people that, during the simulation, enter at least once inside hotspots revealed considering the actual sites where human individuals were infected. The second scenario involves the treatment of people entering in hotspots calculated assuming that the infection sites of every infected individual is located in the household where the individual lives. Simulations show that both considered scenarios perform better in controlling malaria than a randomized treatment, although targeting household hotspots shows slightly better performance.

Interactions of social, terrestrial, and marine sub-systems in the Galapagos Islands, Ecuador.

Galapagos is often cited as an example of the conflicts that are emerging between resource conservation and economic development in island ecosystems, as the pressures associated with tourism threaten nature, including the iconic and emblematic species, unique terrestrial landscapes, and special marine environments. In this paper, two projects are described that rely upon dynamic systems models and agent-based models to examine human-environment interactions. We use a theoretical context rooted in complexity theory to guide the development of our models that are linked to social-ecological dynamics. The goal of this paper is to describe key elements, relationships, and processes to inform and enhance our understanding of human-environment interactions in the Galapagos Islands of Ecuador. By formalizing our knowledge of how systems operate and the manner in which key elements are linked in coupled human-natural systems, we specify rules, relationships, and rates of exchange between social and ecological features derived through statistical functions and/or functions specified in theory or practice. The processes described in our models also have practical applications in that they emphasize how political policies generate different human responses and model outcomes, many detrimental to the social-ecological sustainability of the Galapagos Islands.

A validated agent-based model to study the spatial and temporal heterogeneities of malaria incidence in the rainforest environment.

BACKGROUND: The Amazon environment has been exposed in the last decades to radical changes that have been accompanied by a remarkable rise of both Plasmodium falciparum and Plasmodium vivax malaria. The malaria transmission process is highly influenced by factors such as spatial and temporal heterogeneities of the environment and individual-based characteristics of mosquitoes and humans populations. All these determinant factors can be simulated effectively trough agent-based models. METHODS: This paper presents a validated agent-based model of local-scale malaria transmission. The model reproduces the environment of a typical riverine village in the northern Peruvian Amazon, where the malaria transmission is highly seasonal and apparently associated with flooding of large areas caused by the neighbouring river. Agents representing humans, mosquitoes and the two species of Plasmodium (P. falciparum and P. vivax) are simulated in a spatially explicit representation of the environment around the village. The model environment includes: climate, people houses positions and elevation. A representation of changes in the mosquito breeding areas extension caused by the river flooding is also included in the simulation environment. RESULTS: A calibration process was carried out to reproduce the variations of the malaria monthly incidence over a period of 3 years. The calibrated model is also able to reproduce the spatial heterogeneities of local scale malaria transmission. A "what if" eradication strategy scenario is proposed: if the mosquito breeding sites are eliminated through mosquito larva habitat management in a buffer area extended at least 200 m around the village, the malaria transmission is eradicated from the village. CONCLUSIONS: The use of agent-based models can reproduce effectively the spatiotemporal variations of the malaria transmission in a low endemicity environment dominated by river floodings like in the Amazon.

Land Use Change on Household Farms in the Ecuadorian Amazon: Design and Implementation of an Agent-Based Model.

This paper describes the design and implementation of an Agent-Based Model (ABM) used to simulate land use change on household farms in the Northern Ecuadorian Amazon (NEA). The ABM simulates decision-making processes at the household level that is examined through a longitudinal, socio-economic and demographic survey that was conducted in 1990 and 1999. Geographic Information Systems (GIS) are used to establish spatial relationships between farms and their environment, while classified Landsat Thematic Mapper (TM) imagery is used to set initial land use/land cover conditions for the spatial simulation, assess from-to land use/land cover change patterns, and describe trajectories of land use change at the farm and landscape levels. Results from prior studies in the NEA provide insights into the key social and ecological variables, describe human behavioral functions, and examine population-environment interactions that are linked to deforestation and agricultural extensification, population migration, and demographic change. Within the architecture of the model, agents are classified as active or passive. The model comprises four modules, i.e., initialization, demography, agriculture, and migration that operate individually, but are linked through key household processes. The main outputs of the model include a spatially-explicit representation of the land use/land cover on survey and non-survey farms and at the landscape level for each annual time-step, as well as simulated socio-economic and demographic characteristics of households and communities. The work describes the design and implementation of the model and how population-environment interactions can be addressed in a frontier setting. The paper contributes to land change science by examining important pattern-process relations, advocating a spatial modeling approach that is capable of synthesizing fundamental relationships at the farm level, and links people and environment in complex ways.

Contrasting colonist and indigenous impacts on amazonian forests.

To examine differences in land use and environmental impacts between colonist and indigenous populations in the northern Ecuadorian Amazon, we combined data from household surveys and remotely sensed imagery that was collected from 778 colonist households in 64 colonization sectors, and 499 households from five indigenous groups in 36 communities. Overall, measures of deforestation and forest fragmentation were significantly greater for colonists than indigenous peoples. On average, colonist households had approximately double the area in agriculture and cash crops and 5.5 times the area in pasture as indigenous households. Nevertheless, substantial variation in land-use patterns existed among the five indigenous groups in measures such as cattle ownership and use of hired agricultural labor. These findings support the potential conservation value of indigenous lands while cautioning against uniform policies that homogenize indigenous ethnic groups.

Socioeconomic drivers of deforestation in the Northern Ecuadorian Amazon.

Investigations of land use/land cover (LULC) change and forest management are limited by a lack of understanding of how socioeconomic factors affect land use. This lack also constrains the predictions of future deforestation, which is especially important in the Amazon basin, where large tracts of natural forest are being converted to managed uses. Research presented in this article was conducted to address this lack of understanding. Its objectives are (a) to quantify deforestation in the Northern Ecuadorian Amazon (NEA) during the periods 1986-1996 and 1996-2002; and (b) to determine the significance and magnitude of the effects of socioeconomic factors on deforestation rates at both the parroquia (parish) and finca (farm) levels. Annual deforestation rates were quantified via satellite image processing and geographic information systems. Linear spatial lag regression analyses were then used to explore relationships between socioeconomic factors and deforestation. Socioeconomic factors were obtained, at the finca level, from a detailed household survey carried out in 1990 and 1999, and at the parroquia level from data in the 1990 and 2001 Ecuadorian censuses of population. We found that the average annual deforestation rate was 2.5% and 1.8%/year for 1986-1996 and 1996-2002, respectively. At the parroquia level, variables representing demographic factors (i.e., population density) and accessibility factors (i.e., road density), among others, were found to be significantly related to deforestation. At the farm level, the factors related to deforestation were household size, distance by road to main cities, education, and hired labor. The findings of this research demonstrate both the severity of deforestation in the Northern Ecuadorian Amazon and the array of factors affecting deforestation in the tropics.