A method for estimating the deforestation timeline in rural settlements in a scenario of malaria transmission in frontier expansion in the Amazon Region

The Malaria Frontier Hypothesis (MFH) is the current model for predicting malaria emergence in the Brazilian Amazon. It has two important dimensions, ‘settlement time’ and ‘malaria incidence’, and its prediction are: malaria incidence peaks five years after the initiation of human settlement and declines towards zero after an estimated 10 years. Although MFH is currently accepted, it has been challenged recently. Herein, we described a novel method for estimating settlement timeline by using remote sensing technology integrated in an open-software geographic information system. Surprisingly, we found that of the majority of the rural settlements with high malaria incidence are more than 10 years old.

geographic information system; remote sensing technologies; malaria

Authorship

Roberto Cardoso Ilacqua

Faculdade de Medicina do ABC, Setor de Pós-Graduação, Pesquisa e Inovação, Santo André, SP, BrasilFaculdade de Medicina do ABCBrasilSanto André, SP, BrasilFaculdade de Medicina do ABC, Setor de Pós-Graduação, Pesquisa e Inovação, Santo André, SP, Brasil

Leonardo Suveges Moreira Chaves

Universidade de São Paulo, Faculdade de Saúde Pública, Departamento de Epidemiologia, São Paulo, SP, BrasilUniversidade de São PauloBrasilSão Paulo, SP, BrasilUniversidade de São Paulo, Faculdade de Saúde Pública, Departamento de Epidemiologia, São Paulo, SP, Brasil

Eduardo Sterlino Bergo

Secretaria de Estado da Saúde de São Paulo, Superintendência de Controle de Endemias, Araraquara, SP, BrasilSecretaria de Estado da Saúde de São PauloBrasilAraraquara, SP, BrasilSecretaria de Estado da Saúde de São Paulo, Superintendência de Controle de Endemias, Araraquara, SP, Brasil

Jan E Conn

The Wadsworth Center, New York State Department of Health, Slingerlands, NY, USAThe Wadsworth CenterUSASlingerlands, NY, USAThe Wadsworth Center, New York State Department of Health, Slingerlands, NY, USA

University at Albany, State University of New York, Department of Biomedical Sciences, School of Public Health, Albany, NY, USAState University of New YorkUSAAlbany, NY, USAUniversity at Albany, State University of New York, Department of Biomedical Sciences, School of Public Health, Albany, NY, USA

Maria Anice Mubeb Sallum

Gabriel Zorello Laporta ⁺+ Corresponding author: gabriel.laporta@fmabc.br

Universidade Federal do ABC, Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas, Santo André, SP, BrasilUniversidade Federal do ABCBrasilSanto André, SP, BrasilUniversidade Federal do ABC, Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas, Santo André, SP, Brasil

+ Corresponding author: gabriel.laporta@fmabc.br

AUTHORS’ CONTRIBUTION

MAMS, JEC and GZL - Original idea; ESB, MAMS, LSMC and GZL - landscapes selection; RCI and GZL - study design; RCI - data analysis and figures production; GZL - data analysis supervision and results interpretation; RCI, GZL, MAMS and JEC - scientific writing. All authors approved the final version.

SCIMAGO INSTITUTIONS RANKINGS

Figures | Tables

Figures (10)
Tables (1)

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Fig. 1
: study area. Sampled localities and landscapes in 2015-16, Amazon. The background is a RGB mosaic made from remote sensing images in 2011. Source: USGS, Landsat Project. Software: QGIS v. 2.16.2.

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Fig. 2
: image classification method. Supervised classification using a composite image of the locality of Acrelândia, in 2006. Source: USGS, Landsat Project. Software: QGIS v. 2.16.2.

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Fig. 3
: Cruzeiro do Sul-AC. Temporal evolution of loss of forest cover. Landscapes (L1-L6) are each 5-km2. Parasite index for malaria in April/15: L1, 70.6; L2, 52.5; L3, 450.8; L4, 111.1; L5, 138; L6, 138. Source: Ministry of Health. Software: Inkscape v. 0.48.2.

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Fig. 4
: Mâncio Lima-AC. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in May/15: L1, 152.5; L2, 152.5; L3, 356.2; L4, 32.1; L5, 32.1; L6, 32.1. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

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Fig. 5
: Lábrea-AM. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in July/15: L1, 447.9; L2, 447.9; L3, 173.5; L4, 173.5; L5, 447.9; L6, 447.9. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

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Fig. 6
: Acrelândia-AC. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in August/15: L1, 116.7; L2, 116.7; L3, 116.7; L4, 116.7; L5, 26.1; L6, 26.1. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

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Fig. 7
: Machadinho d’Oeste-RO. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in October/15: L1, 185.2; L2, 150; L3, 150; L4, 150; L5, 230.8; L6, 185.2. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

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Fig. 8
: Pacajá-PA. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in April/16: L1, 53.6; L2, 53.6; L3, 53.6; L4, 16.3; L5, 26.5; L6, 53.6. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

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Fig. 9
: Humaitá-AM. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in July/16: L1, 191.8; L2, 31.6; L3, 277.8; L4, 31.6; L5, 31.6; L6, 31.6. Source: Ministry of Health. Software: Inkscape v. 0.48.2.

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Fig. 10
: Itacoatiara-AM. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in November/16: L1-L6, > 50. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

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TABLE
Contingency matrix 2 by 2 with Parasite Index for Malaria vs. Settlement Time in landscape (n = 48)

Fig. 1 : study area. Sampled localities and landscapes in 2015-16, Amazon. The background is a RGB mosaic made from remote sensing images in 2011. Source: USGS, Landsat Project. Software: QGIS v. 2.16.2.

Fig. 2 : image classification method. Supervised classification using a composite image of the locality of Acrelândia, in 2006. Source: USGS, Landsat Project. Software: QGIS v. 2.16.2.

Fig. 3 : Cruzeiro do Sul-AC. Temporal evolution of loss of forest cover. Landscapes (L1-L6) are each 5-km2. Parasite index for malaria in April/15: L1, 70.6; L2, 52.5; L3, 450.8; L4, 111.1; L5, 138; L6, 138. Source: Ministry of Health. Software: Inkscape v. 0.48.2.

Fig. 4 : Mâncio Lima-AC. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in May/15: L1, 152.5; L2, 152.5; L3, 356.2; L4, 32.1; L5, 32.1; L6, 32.1. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

Fig. 5 : Lábrea-AM. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in July/15: L1, 447.9; L2, 447.9; L3, 173.5; L4, 173.5; L5, 447.9; L6, 447.9. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

Fig. 6 : Acrelândia-AC. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in August/15: L1, 116.7; L2, 116.7; L3, 116.7; L4, 116.7; L5, 26.1; L6, 26.1. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

Fig. 7 : Machadinho d’Oeste-RO. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in October/15: L1, 185.2; L2, 150; L3, 150; L4, 150; L5, 230.8; L6, 185.2. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

Fig. 8 : Pacajá-PA. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in April/16: L1, 53.6; L2, 53.6; L3, 53.6; L4, 16.3; L5, 26.5; L6, 53.6. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

Fig. 9 : Humaitá-AM. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in July/16: L1, 191.8; L2, 31.6; L3, 277.8; L4, 31.6; L5, 31.6; L6, 31.6. Source: Ministry of Health. Software: Inkscape v. 0.48.2.

Fig. 10 : Itacoatiara-AM. Temporal evolution of loss of forest cover. Each landscape (L1-L6) is 5-km2. Parasite index for malaria in November/16: L1-L6, > 50. Source: Ministry of Health. Software: Inkscape v. 0.48.2. Red borders for pairs of location and time represent when and where the threshold of forest coverage < 90% was reached.

TABLE Contingency matrix 2 by 2 with Parasite Index for Malaria vs. Settlement Time in landscape (n = 48)

Settlement time	Parasite index for malaria

	≥ 50 cases per 1,000	< 50 cases per 1,000
New (≤ 10 years)	8	0
Old (> 10 years)	29	11

Fisher’s exact test for count data: alternative hypothesis, new settlements are associated with higher malaria incidence (malaria frontier hypothesis). Decision: accept the null hypothesis (there is no association); p-value = 0.1704.

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A method for estimating the deforestation timeline in rural settlements in a scenario of malaria transmission in frontier expansion in the Amazon Region

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[1] + Corresponding author: gabriel.laporta@fmabc.br