Measuring the contribution of human mobility to malaria persistence

Abstract

Background To achieve malaria elimination, it is important to determine the role of human mobility in parasite transmission maintenance. The Alto Juruá basin (Brazil) exhibits one of the largest vivax and falciparum malaria prevalence in the Amazon. The goal of this study was to estimate the contribution of human commutes to malaria persistence in this region, using data from an origin-destination survey. Methods Data from an origin-destination survey were used to describe the intensity and motivation for commutations between rural and urban areas in two Alto Juruá basin (Brazil) municipalities, Mâncio Lima and Rodrigues Alves. The relative time-person spent in each locality per household was estimated. A logistic model was developed to estimate the effect of commuting on the probability of contracting malaria for a certain residence zone inhabitant commuting to another zone. Results The main results suggest that the assessed population is not very mobile. A total of $$96\%$$ 96 % households reported spending over $$90\%$$ 90 % of their annual person-hour in areas within the same residence zone. Study and work were the most prevalent commuting motivations, calculated at $$40.5\%$$ 40.5 % and $$29.5\%$$ 29.5 % respectively. Spending person-hours in urban Rodrigues Alves conferred relative protection to urban Mâncio Lima residents. The opposite effect was observed for those spending time in rural areas of both municipalities. Conclusion Residence area is a stronger determinant for contracting malaria than commuting zones in the Alto Juruá region. As these municipalities are a hotspot for Plasmodium transmission, understanding the main local human fluxes is essential for planning control strategies, since the probability of contracting malaria is dependent on the transmission intensity of both the origin and the displacement area. The natural conditions for the circulation of certain pathogens, such as Plasmodium spp., combined with the Amazon human mobility pattern indicate the need for disease control perspective changes. Therefore, intersectoral public policies should become the basis for health mitigation actions.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

“…Spatial covariates are entered into the model in the same way as nonspatial covariates, but aim to account for connectivity within the model. Spatial covariates included the observed incidence in connected regions [ 21 – 30 ], the number of people moving between regions [ 20 , 31 – 35 ], the distance between regions [ 31 , 35 – 37 ], coordinates of the centroid of a region [ 38 – 40 ], the number of time spent commuting between regions [ 41 ] and spatial eigenvectors created using spatial filtering [ 42 – 44 ]. Spatial filtering creates spatial covariates by decomposing Moran's I (a measure of spatial correlation) into an eigenvector per region/observation [ 45 ].…”

“…Although there were nine distinct statistical models, all of them used one of three methods to account for spatial connectivity: inclusion of spatial covariates as fixed effects, localized regression models or the inclusion of a spatially structured random effect or random field. incidence in connected regions [21][22][23][24][25][26][27][28][29][30], the number of people moving between regions [20,[31][32][33][34][35], the distance between regions [31,[35][36][37], coordinates of the centroid of a region [38][39][40], the number of time spent commuting between regions [41] and spatial eigenvectors created using spatial filtering [42][43][44]. Spatial filtering creates spatial covariates by decomposing Moran's I (a measure of spatial correlation) into an eigenvector per region/observation [45].…”

“…royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20210096 incidence in connected regions [21][22][23][24][25][26][27][28][29][30], the number of people moving between regions [20,[31][32][33][34][35], the distance between regions [31,[35][36][37], coordinates of the centroid of a region [38][39][40], the number of time spent commuting between regions [41] and spatial eigenvectors created using spatial filtering [42][43][44]. Spatial filtering creates spatial covariates by decomposing Moran's I (a measure of spatial correlation) into an eigenvector per region/observation [45].…”

Reaching the malaria elimination goal in Brazil: a spatial analysis and time-series study

“…The basic reproduction number of P. vivax in agricultural settlements in Cruzeiro do Sul can be as high as 10 (one person infected by P. vivax can cause up to 10 new infections) [ 29 ]. The genomic signature of P. vivax populations in Mâncio Lima is characterized by high levels of inbreeding at local distances [ 30 ], which means the influence of people mobility is not so high on malaria transmission [ 31 ], and thus local of residence is still the major determinant for contracting malaria [ 32 ]. Living in precarious housing in the peripheries of the town’s urban centre is consistent with substantial transmission foci [ 33 ].…”

Epidemiology, Biodiversity, and Technological Trajectories in the Brazilian Amazon: From Malaria to COVID-19

“…Malaria is the main cause of disease burden in all three, although more intense in TT2. Individual risk factors include working within or close to the forest, living at the border of the forest, being an immigrant from a non-endemic area, living in houses made of wood and lacking nets and scarce access to treatment ( 52 , 83 ). American cutaneous leishmaniasis is concentrated in TT3 (and TT4) municipalities, characterized by the presence of large livestock herds.…”