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
Authors: Janko Mark M. , Araujo Andrea L. , Ascencio Edson J. , Guedes Gilvan R. , Vasco Luis E. , Santos Reinaldo A. , Damasceno Camila P. , Medrano Perla G. , Chacón-Uscamaita Pamela R. , Gunderson Annika K. , O’Malley Sara , Kansara Prakrut H. , Narvaez Manuel B. , Coombes Carolina S. , Pizzitutti Francesco , Salmon-Mulanovich Gabriela , Zaitchik Benjamin F. , Mena Carlos F. , Lescano Andres G. , Barbieri Alisson F. , Pan William K. 
Published: November 2023

AbstractObjectivesUnderstanding 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.DesignA community-level network surveySettingWe 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.ParticipantsWe 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 outcomeWeekly, community-level malaria incidence during the study period.MethodsWe 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.FindingsTo 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.ConclusionsOur 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.Strengths and LimitationsStrength: Community networks are unmeasured in rural areas of the Amazon, but have been shown to capture human mobility in other regions of the world.Strength: Our design captures social, economic, and human wellbeing connectivity and migration in key indigenous communities along the Peru-Ecuador border as well as in the most important confluence for the Amazon River located in the Brazil-Peru-Colombia tri-country intersection.Strength: Our design quantifies cross-border human mobility between communities, as well as the magnitude, timing, duration, and reason for mobility, which provides actionable information for malaria control and elimination programs in the regionLimitation: Migration decisions occur at individual and household levels that are coupled with environmental change and seasonality, meaning that our measures of community mobility may not be stable over time and we may be subject to ecological fallacy by inferring individual risk from community networks.Limitation: Our study relies on passive surveillance to test the community network/human mobility link with malaria. However, there exist cases that are asymptomatic, unreported (i.e., treated with traditional medicines), or that occur in our community network but are reported elsewhere. The extent of these cases can significantly increase uncertainty.FundingThis work was supported by the US National Institutes of Health (R01 AI51056; William K. Pan, PI) and by a grant from the Duke Climate and Health Initiative (William Pan, PI). PRC-U was supported by CONCYTEC through the PROCIENCIA program under the call entitled “Science, Technology and Innovation Thesis and Internships” according to the contract PE501081617-2022. AGL, CSC, EJA and PRC-U were sponsored by Emerge, the Emerging Diseases Epidemiology Research Training grant D43 TW007393 awarded by the Fogarty International Center of the US National Institutes of Health.Competing InterestsWe declare no conflicts