A method for estimating the deforestation timeline in rural settlements in a scenario of malaria transmission in frontier expansion in the Amazon Region
Abstract: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 tec… Show more
“…The proposed unimodal relationship between forest cover and malaria emergence indicates that 30% to 70% of the remaining forest cover represents a landscape scenario that can encompass the ecological and environmental conditions that can favour peak transmission of malaria [24, 36, 47]. This risky scenario can occur either in newly colonized or old settlements [34]. For instance, the landscapes shown in Fig.…”
Section: Discussionmentioning
confidence: 99%
“…For instance, the landscapes shown in Fig. 7 started colonization in the 1970s [34] and currently have high levels of transmission, with an estimated malaria incidence of 45–100 cases per day and a P. vivax reproduction number of 3.3–16.8 [48]. Fig.…”
Section: Discussionmentioning
confidence: 99%
“…7 Satellite imagery composite . Landscape (5-km 2 ) in where malaria transmission level [48] and the deforestation timeline [34] were estimated. CZS Cruzeiro do Sul, ML Mancio Lima, GUA Guarani-landscape studied by Lana et al [49].…”
Section: Discussionmentioning
confidence: 99%
“…CZS Cruzeiro do Sul, ML Mancio Lima, GUA Guarani-landscape studied by Lana et al [49]. The satellite imagery composite was made by using the protocol developed by Ilacqua et al [34] with QGis v. 2.18.14 (QGis Community, https://qgis.org) and SCP plugin v. 5.4.2 (Luca Congedo, Italy). Legend: blue, ground waters; dark green, forest vegetation; light green, crops, shrubs or secondary vegetation; pink, exposed or urban soil.…”
Section: Discussionmentioning
confidence: 99%
“…Fragmented landscapes with approximately 30–70% forest cover have the forest fringe effect, which maximizes the abundance of the main malarial vector ( Anopheles darlingi ) in the Amazon [24]. Malaria transmission can be sustained in any given landscape with the forest fringe effect [34]. Landscapes near natural conservation units (e.g., federal forests and indigenous reserves) are generally represented by settlements with intermediate forest cover and present a high risk of malaria incidence [35].…”
Background
Plasmodium falciparum
malaria is a threat to public health, but
Plasmodium vivax
malaria is most prevalent in Latin America, where the incidence rate has been increasing since 2016, particularly in Venezuela and Brazil. The Brazilian Amazon reported 193,000 cases in 2017, which were mostly confirmed as
P. vivax
(~ 90%). Herein, the relationships among malaria incidence rates and the proportion of accumulated deforestation were contrasted using data from the states of Acre and Rondônia in the south-western Brazilian Amazon. The main purpose is to test the hypothesis that the observed difference in incidence rates is associated with the proportion of accumulated deforestation.
Methods
An ecological study using spatial and temporal models for mapping and modelling malaria risk was performed. The municipalities of Acre and Rondônia were the spatial units of analysis, whereas month and year were the temporal units. The number of reported malaria cases from 2009 until 2015 were used to calculate the incidence rate per 1000 people at risk. Accumulated deforestation was calculated using publicly available satellite images. Geographically weighted regression was applied to provide a local model of the spatial heterogeneity of incidence rates. Time-series dynamic regression was applied to test the correlation of incidence rates and accumulated deforestation, adjusted by climate and socioeconomic factors.
Results
The malaria incidence rate declined in Rondônia but remained stable in Acre. There was a high and positive correlation between the decline in malaria and higher proportions of accumulated deforestation in Rondônia. Geographically weighted regression showed a complex relationship. As deforestation increased, malaria incidence also increased in Acre, while as deforestation increased, malaria incidence decreased in Rondônia. Time-series dynamic regression showed a positive association between malaria incidence and precipitation and accumulated deforestation, whereas the association was negative with the human development index in the westernmost areas of Acre.
Conclusion
Landscape modification caused by accumulated deforestation is an important driver of malaria incidence in the Brazilian Amazon. However, this relationship is not linearly correlated because it depends on the overall proportion of the land covered by forest. For regions that are partially degraded, forest cover becomes a less representative component in the landscape, causing the abovementioned non-linear relationship. In such a scenario, accumulated deforestation can lead to a decline in malaria incidence.
“…The proposed unimodal relationship between forest cover and malaria emergence indicates that 30% to 70% of the remaining forest cover represents a landscape scenario that can encompass the ecological and environmental conditions that can favour peak transmission of malaria [24, 36, 47]. This risky scenario can occur either in newly colonized or old settlements [34]. For instance, the landscapes shown in Fig.…”
Section: Discussionmentioning
confidence: 99%
“…For instance, the landscapes shown in Fig. 7 started colonization in the 1970s [34] and currently have high levels of transmission, with an estimated malaria incidence of 45–100 cases per day and a P. vivax reproduction number of 3.3–16.8 [48]. Fig.…”
Section: Discussionmentioning
confidence: 99%
“…7 Satellite imagery composite . Landscape (5-km 2 ) in where malaria transmission level [48] and the deforestation timeline [34] were estimated. CZS Cruzeiro do Sul, ML Mancio Lima, GUA Guarani-landscape studied by Lana et al [49].…”
Section: Discussionmentioning
confidence: 99%
“…CZS Cruzeiro do Sul, ML Mancio Lima, GUA Guarani-landscape studied by Lana et al [49]. The satellite imagery composite was made by using the protocol developed by Ilacqua et al [34] with QGis v. 2.18.14 (QGis Community, https://qgis.org) and SCP plugin v. 5.4.2 (Luca Congedo, Italy). Legend: blue, ground waters; dark green, forest vegetation; light green, crops, shrubs or secondary vegetation; pink, exposed or urban soil.…”
Section: Discussionmentioning
confidence: 99%
“…Fragmented landscapes with approximately 30–70% forest cover have the forest fringe effect, which maximizes the abundance of the main malarial vector ( Anopheles darlingi ) in the Amazon [24]. Malaria transmission can be sustained in any given landscape with the forest fringe effect [34]. Landscapes near natural conservation units (e.g., federal forests and indigenous reserves) are generally represented by settlements with intermediate forest cover and present a high risk of malaria incidence [35].…”
Background
Plasmodium falciparum
malaria is a threat to public health, but
Plasmodium vivax
malaria is most prevalent in Latin America, where the incidence rate has been increasing since 2016, particularly in Venezuela and Brazil. The Brazilian Amazon reported 193,000 cases in 2017, which were mostly confirmed as
P. vivax
(~ 90%). Herein, the relationships among malaria incidence rates and the proportion of accumulated deforestation were contrasted using data from the states of Acre and Rondônia in the south-western Brazilian Amazon. The main purpose is to test the hypothesis that the observed difference in incidence rates is associated with the proportion of accumulated deforestation.
Methods
An ecological study using spatial and temporal models for mapping and modelling malaria risk was performed. The municipalities of Acre and Rondônia were the spatial units of analysis, whereas month and year were the temporal units. The number of reported malaria cases from 2009 until 2015 were used to calculate the incidence rate per 1000 people at risk. Accumulated deforestation was calculated using publicly available satellite images. Geographically weighted regression was applied to provide a local model of the spatial heterogeneity of incidence rates. Time-series dynamic regression was applied to test the correlation of incidence rates and accumulated deforestation, adjusted by climate and socioeconomic factors.
Results
The malaria incidence rate declined in Rondônia but remained stable in Acre. There was a high and positive correlation between the decline in malaria and higher proportions of accumulated deforestation in Rondônia. Geographically weighted regression showed a complex relationship. As deforestation increased, malaria incidence also increased in Acre, while as deforestation increased, malaria incidence decreased in Rondônia. Time-series dynamic regression showed a positive association between malaria incidence and precipitation and accumulated deforestation, whereas the association was negative with the human development index in the westernmost areas of Acre.
Conclusion
Landscape modification caused by accumulated deforestation is an important driver of malaria incidence in the Brazilian Amazon. However, this relationship is not linearly correlated because it depends on the overall proportion of the land covered by forest. For regions that are partially degraded, forest cover becomes a less representative component in the landscape, causing the abovementioned non-linear relationship. In such a scenario, accumulated deforestation can lead to a decline in malaria incidence.
The relationship between deforestation and malaria is a spatiotemporal process of variation in Plasmodium incidence in human-dominated Amazonian rural environments. The present study aimed to assess the underlying mechanisms of malarial exposure risk at a fine scale in 5-km2 sites across the Brazilian Amazon, using field-collected data with a longitudinal spatiotemporally structured approach. Anopheline mosquitoes were sampled from 80 sites to investigate the Plasmodium infection rate in mosquito communities and to estimate the malaria exposure risk in rural landscapes. The remaining amount of forest cover (accumulated deforestation) and the deforestation timeline were estimated in each site to represent the main parameters of both the frontier malaria hypothesis and an alternate scenario, the deforestation-malaria hypothesis, proposed herein. The maximum frequency of pathogenic sites occurred at the intermediate forest cover level (50% of accumulated deforestation) at two temporal deforestation peaks, e.g., 10 and 35 years after the beginning of the organization of a settlement. The incidence density of infected anophelines in sites where the original forest cover decreased by more than 50% in the first 25 years of settlement development was at least twice as high as the incidence density calculated for the other sites studied (adjusted incidence density ratio = 2.25; 95% CI, 1.38–3.68; p = 0.001). The results of this study support the frontier malaria as a unifying hypothesis for explaining malaria emergence and for designing specific control interventions in the Brazilian Amazon.
As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on forest-going populations, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases in confirmed malaria case incidence in Lao People's Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest-going populations on malaria transmission in the GMS.
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