scholarly journals Spatially Explicit Coupled Map Lattice Simulation of Malaria Transmission in the Brazilian Amazon

Author(s):  
Anthony E. Kiszewski ◽  
Marcia Castro ◽  
Sarah McGough
2020 ◽  
Vol 5 (4) ◽  
pp. 161
Author(s):  
Elerson Matos Rocha ◽  
Ricardo de Melo Katak ◽  
Juan Campos de Oliveira ◽  
Maisa da Silva Araujo ◽  
Bianca Cechetto Carlos ◽  
...  

In Brazil, malaria transmission is mostly confined to the Amazon, where substantial progress has been made towards disease control in the past decade. Vector control has been historically considered a fundamental part of the main malaria control programs implemented in Brazil. However, the conventional vector-control tools have been insufficient to control or eliminate local vector populations due to the complexity of the Amazonian rainforest environment and ecological features of malaria vector species in the Amazon, especially Anopheles darlingi. Malaria elimination in Brazil and worldwide eradication will require a combination of conventional and new approaches that takes into account the regional specificities of vector populations and malaria transmission dynamics. Here we present an overview on both conventional and novel promising vector-focused tools to curb malaria transmission in the Brazilian Amazon. If well designed and employed, vector-based approaches may improve the implementation of malaria-control programs, particularly in remote or difficult-to-access areas and in regions where existing interventions have been unable to eliminate disease transmission. However, much effort still has to be put into research expanding the knowledge of neotropical malaria vectors to set the steppingstones for the optimization of conventional and development of innovative vector-control tools.


2018 ◽  
Vol 1 (11) ◽  
pp. 657-664 ◽  
Author(s):  
Jon Strand ◽  
Britaldo Soares-Filho ◽  
Marcos Heil Costa ◽  
Ubirajara Oliveira ◽  
Sonia Carvalho Ribeiro ◽  
...  

2007 ◽  
Vol 47 (3) ◽  
pp. 541-567 ◽  
Author(s):  
Eugenio Y. Arima ◽  
Cynthia S. Simmons ◽  
Robert T. Walker ◽  
Mark A. Cochrane

Author(s):  
J. F. Mas ◽  
B. Soares-Filho ◽  
H. Rodrigues

Spatially explicit land use / land cover (LUCC) models aim at simulating the patterns of change on the landscape. In order to simulate landscape structure, the simulation procedures of most computational LUCC models use a cellular automata to replicate the land use / cover patches. Generally, model evaluation is based on assessing the location of the simulated changes in comparison to the true locations but landscapes metrics can also be used to assess landscape structure. As model complexity increases, the need to improve calibration and assessment techniques also increases. In this study, we applied a genetic algorithm tool to optimize cellular automata’s parameters to simulate deforestation in a region of the Brazilian Amazon. We found that the genetic algorithm was able to calibrate the model to simulate more realistic landscape in term of connectivity. Results show also that more realistic simulated landscapes are often obtained at the expense of the location coincidence. However, when considering processes such as the fragmentation impacts on biodiversity, the simulation of more realistic landscape structure should be preferred to spatial coincidence performance.


Author(s):  
Andrew J. MacDonald ◽  
Erin A. Mordecai

Identifying the effects of environmental change on the transmission of vectorborne and zoonotic diseases is of fundamental importance in the face of rapid global change. Causal inference approaches, including instrumental variable (IV) estimation, hold promise in disentangling plausibly causal relationships from observational data in these complex systems. Valle and Zorello Laporta recently critiqued the application of such approaches in our recent study of the effects of deforestation on malaria transmission in the Brazilian Amazon on the grounds that key statistical assumptions were not met. Here, we respond to this critique by 1) deriving the IV estimator to clarify the assumptions that Valle and Zorello Laporta conflate and misrepresent in their critique, 2) discussing these key assumptions as they relate to our original study and how our original approach reasonably satisfies the assumptions, and 3) presenting model results using alternative instrumental variables that can be argued more strongly satisfy key assumptions, illustrating that our results and original conclusion—that deforestation drives malaria transmission—remain unchanged.


PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0172330 ◽  
Author(s):  
Jussara Rafael Angelo ◽  
Tony Hiroshi Katsuragawa ◽  
Paulo Chagastelles Sabroza ◽  
Lino Augusto Sander de Carvalho ◽  
Luiz Hildebrando Pereira da Silva ◽  
...  

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