Search

Additional file 8. Quality assessment of the selected papers.

Additional file 1. PECO search framework.

Additional file 2. Descriptive system of cost components.

Additional file 4. Studies excluded after full-text review and reasons for exclusion.

Additional file 6. Funding sources of studies included in the analysis.

Additional file 3. Items included in the quality assessment of the included articles.

Additional file 5. List of included references.

Malaria remains endemic in 17 countries in the Americas, where 723,000 cases were reported in 2019. The majority (> 90%) of the regional malaria burden is found within the Amazon Basin, which includes nine countries and territories in South America. Locally generated evidence is critical to provide information to public health decision makers upon which the design of efficient and regionally directed malaria control and elimination programs can be built. Plasmodium vivax is the predominant malaria parasite in the Amazon Basin. This parasite species appears to be more resilient to malaria control strategies worldwide. Asymptomatic Plasmodium infections constitute a potentially infectious reservoir that is typically missed by routine microscopy-based surveillance and often remains untreated. The primary Amazonian malaria vector, Nyssorhynchus (formerly Anopheles) darlingi, has changed its behavior to feed and rest predominantly outdoors, reducing the efficiency of core vector control measures such as indoor residual spraying and distribution of long-lasting insecticide-treated bed nets. We review public health implications of recent field-based research carried out by the Amazonia International Center of Excellence in Malaria Research in Peru and Brazil. We discuss the relative role of traditional and novel tools and strategies for better malaria control and elimination across the Amazon, including improved diagnostic methods, new anti-relapse medicines, and biological larvicides, and emphasize the need to integrate research and public health policymaking.

Additional file 1 : Figure S1. Histogram of the number of fatalities per conflict event using data from August 1, 2018 to July 26, 2019. Figure S2. Ratio of conflict events during the outbreak vs before the outbreak for health zones in Ituri and North Kivu that did not have transmission, those health zones with transmission and all other regions in the Democratic Republic of the Congo. Data come from two different sources (Uppsala and Armed Conflict Location & Event Data). Figure S3. a) Correlation of number of conflicts before the outbreak (1989 – July 31, 2018) and during the outbreak for each of the affected health zones. Gray line shows the regression line and shaded area is 95% confidence interval. b) Correlation between number fatalities before and after the conflict using the same time intervals than in a). Data are shown on the log scale. Gray line shows the regression line and shaded area is 95% confidence interval. Figure S4. Number of conflict events in the first half of the outbreak (August 2018 – January 2019) vs. second half of the outbreak (February 2019 – July 2019). Each dot represents one health zone in North Kivu and Ituri. Figure S5. Number of conflict events before the outbreak normalized by year (2017) and capita and during the outbreak normalized by year and capita. Figure S6. Number of conflicts per 10,000 vs. number of cases per 10,000 (Pearson’s r = 0.33, 95% CI: 0.05–0.57, p-value

Additional file 1: Additional results and sensitivity analysis.

Additional file 2. In-depth larval sampling protocol, larval model results, correlation tests of variables included in region-specific models, and analysis of seasonality.

Additional file 1. Preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) checklist.

Outbreaks of vector-borne diseases (VBDs) impose a heavy burden on vulnerable populations. Despite recent progress in eradication and control, malaria remains the most prevalent VBD. Integrative approaches that take into account environmental, socioeconomic, demographic, biological, cultural, and political factors contributing to malaria risk and vulnerability are needed to effectively reduce malaria burden. Although the focus on malaria risk has increasingly gained ground, little emphasis has been given to develop quantitative methods for assessing malaria risk including malaria vulnerability in a spatial explicit manner. Building on a conceptual risk and vulnerability framework, we propose a spatial explicit approach for modeling relative levels of malaria risk - as a function of hazard, exposure, and vulnerability - in the United Republic of Tanzania. A logistic regression model was employed to identify a final set of risk factors and their contribution to malaria endemicity based on multidisciplinary geospatial information. We utilized a Geographic Information System for the construction and visualization of a malaria vulnerability index and its integration into a spatially explicit malaria risk map. The spatial pattern of malaria risk was very heterogeneous across the country. Malaria risk was higher in Mainland areas than in Zanzibar, which is a result of differences in both malaria entomological inoculation rate and prevailing vulnerabilities. Areas of high malaria risk were identified in the southeastern part of the country, as well as in two distinct “hotspots” in the northwestern part of the country bordering Lake Victoria, while concentrations of high malaria vulnerability seem to occur in the northwestern, western, and southeastern parts of the mainland. Results were visualized using both 10x10 km2 grids and subnational administrative units. The presented approach makes an important contribution toward a decision support tool. By decomposing malaria risk into its components, the approach offers evidence on which factors could be targeted for reducing malaria risk and vulnerability to the disease. Ultimately, results offer relevant information for place-based intervention planning and more effective spatial allocation of resources.