Chagas disease vector control and Taylor's law

Background: Large spatial and temporal fluctuations in the population density of living organisms have profound consequences for biodiversity conservation, food production, pest control and disease control, especially vector-borne disease control. Chagas disease vector control based on insecticide spraying could benefit from improved concepts and methods to deal with spatial variations in vector population density. Methodology/Principal findings: We show that Taylor's law (TL) of fluctuation scaling describes accurately the mean and variance over space of relative abundance, by habitat, of four insect vectors of Chagas disease (Triatoma infestans, Triatoma guasayana, Triatoma garciabesi and Triatoma sordida) in 33,908 searches of people's dwellings and associated habitats in 79 field surveys in four districts in the Argentine Chaco region, before and after insecticide spraying. As TL predicts, the logarithm of the sample variance of bug relative abundance closely approximates a linear function of the logarithm of the sample mean of abundance in different habitats. Slopes of TL indicate spatial aggregation or variation in habitat suitability. Predictions of new mathematical models of the effect of vector control measures on TL agree overall with field data before and after community-wide spraying of insecticide. Conclusions/Significance: A spatial Taylor's law identifies key habitats with high average infestation and spatially highly variable infestation, providing a new instrument for the control and elimination of the vectors of a major human disease. Author summary: Chagas disease, or American trypanosomiasis, is mainly transmitted to humans by insects that dwell in human habitations and structures closely associated with human habitations, such as kitchen out-buildings, chicken coops, goat corrals, and grain storage bins. Widespread in the Americas, the disease causes chronic illness and often eventual death. No vaccines exist. Available drugs may cause undesirable adverse effects and do not prevent re-infection. Efforts at suppressing the disease have been directed at eliminating the principal insect vector species from human dwelling compounds. Effective insecticide spraying requires finding out where the insects are. Both the average and the variance of the relative number of insect vectors of each species in each habitat are relevant to control efforts. We demonstrate here that the spatial distribution of the insect vectors of Chagas disease obeys a previously unrecognized pattern, known in ecology as Taylor's law (TL): in different habitats, the variance of vector relative numbers is approximately a power function of the mean of vector relative numbers. TL identifies key habitats with high average infestation and highly variable infestation, providing a new instrument for the control and elimination of the vectors of a major human disease.</p

Human Trypanosoma cruzi infection in the Argentinean Chaco: risk factors and identification of households with infected children for treatment

Abstract Background Chagas disease is a neglected tropical disease (NTD). Cost-effective strategies for large-scale implementation of diagnosis and etiological treatment are urgently needed to comply with NTD control goals. We determined the seroprevalence of Trypanosoma cruzi infection and associated risk factors in a well-defined rural population of Pampa del Indio municipality including creole and indigenous (Qom) households and developed two indices to identify houses harboring infected children. Methods We serodiagnosed and administered a questionnaire to 1337 residents (48.2% of the listed population) in two sections of the municipality (named Areas II and IV) 6–9 years after deploying sustained vector control interventions. Multiple logistic regression models were used to evaluate the relationship between human infection and a priori selected predictors. Two risk indices were constructed based on environmental and serostatus variables, and we used spatial analysis to test whether households harboring T. cruzi-seropositive children were randomly distributed. Results The global seroprevalence of T. cruzi infection was 24.8%. Human infection was positively and significantly associated with exposure time to triatomines, the household number of seropositive co-inhabitants, maternal seropositivity for T. cruzi, recent residence at the current house and the presence of suitable walls for triatomine colonization in the domicile. The pre-intervention mean annual force of infection (FOI) was 1.23 per 100 person-years. Creoles from Area IV exhibited the highest seroprevalence and FOI; Qom people from both areas displayed intermediate ones and creoles from Area II the lowest. Three hotspots of infected children were spatially associated with hotspots of triatomine abundance at baseline and persistent house infestation. No child born after vector control interventions was T. cruzi seropositive except for one putative transplacental case. Two simple risk indices (based on self-reported inhabiting an infested house and suitable walls for triatomines or maternal serostatus) identified 97.3–98.6% of the households with at least one T. cruzi-seropositive child. Conclusions We showed strong heterogeneity in the seroprevalence of T. cruzi infection within and between ethnic groups inhabiting neighboring rural areas. Developed indices can be used for household risk stratification and to improve access of rural residents to serodiagnosis and treatment and may be easily transferred to primary healthcare personnel. Graphical Abstrac

Summary of the main activities and data collected in each of the study areas, Amamá (core and periphery), Olta, Figueroa, Pampa del Indio, Argentina.

<p>Summary of the main activities and data collected in each of the study areas, Amamá (core and periphery), Olta, Figueroa, Pampa del Indio, Argentina.</p

Linear regression estimates of the parameters of Taylor's law (TL) log₁₀ <i>v</i> = <i>a</i> + <i>b</i> log₁₀ <i>m</i>, with <i>m</i> = sample mean, <i>v</i> = sample variance of the relative abundance of triatomines collected in domestic and peridomestic habitats from four field studies, separately for each vector species, intervention status, and survey (83 cases).

<p>The Note below the table defines column headings and explains entries where necessary.</p

Comparisons of TL slope <i>b</i> by <i>Triatoma</i> species (based on point estimates and standard errors in Table 2, summarizing results in S3 Table).

<p>(A) "Cell count" is the number of pairwise comparisons. For example, there were 9 values of <i>b</i> for <i>T</i>. <i>sordida</i> and 31 values of <i>b</i> for <i>T</i>. <i>infestans</i>, so there were 9×8/2 = 36 distinct intraspecific comparisons of <i>b</i> for <i>T</i>. <i>sordida</i>, 31×30/2 = 465 intraspecific comparisons of <i>b</i> for <i>T</i>. <i>infestans</i>, and 9×31 = 279 interspecific comparisons of <i>b</i> for <i>T</i>. <i>sordida</i> versus <i>T</i>. <i>infestans</i>. (B) "<i>P</i><0.01 count" is the number of these comparisons that had <i>P</i> < 0.01 according to the Welch's test (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006092#pntd.0006092.s004" target="_blank">S3 Table</a>). (C) "%<i>P</i><0.01" is the percentage of comparisons with <i>P</i> < 0.01. For example, for the comparisons of <i>b</i> for <i>T</i>. <i>sordida</i> versus <i>T</i>. <i>infestans</i>, 3.2% = 9/279. If differences in <i>b</i> were due to sampling fluctuations alone and there were no intra- or inter-specific differences in the underlying values of <i>b</i>, then "%<i>P</i><0.01" = (<i>P</i><0.01 count)/(Cell count) should approximate 1.0%.</p

Chagas disease vector control and Taylor's law - Fig 3

<p>For <i>T</i>. <i>infestans</i> in the Amamá core under sustained vector surveillance and control, in (A) surveys 1 (October 1993) to 7 (May 1997) and (B) surveys 8 (November 1997) to 13 (October 2002), TL described the relationship between <i>y</i> = log₁₀ <i>v</i> and <i>x</i> = log₁₀ <i>m</i> of the relative abundance of <i>T</i>. <i>infestans</i>. Each point represents the mean and variance of bug abundance for one habitat at one survey. The solid straight lines are fitted by least-squares regression to the data from each survey separately. (C) Values of the parameters <i>a</i> (solid gray line) and <i>b</i> (dashed black line) of TL at each of the 13 surveys. (D) Slope <i>b</i> as a function of the range (maximum log₁₀ mean minus minimum log₁₀ mean) of the number of bugs. The greater the range, the smaller the variability in <i>b</i>. Key as in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006092#pntd.0006092.g002" target="_blank">Fig 2</a>.</p

Map of the study areas, illustrated by the example of the Amamá study area, to be read counterclockwise from the upper left corner.

<p>(A) Gran Chaco region of northwest Argentina and neighboring countries, including the four study areas (Amamá, Olta, Figueroa, and Pampa del Indio). (B) Amamá study area core (Amamá village, Trinidad, Mercedes, Villa Matilde and Pampa Pozo). (C) Amamá village, showing individual sites (open circles). (D) One house compound in Amamá village, showing individual buildings.</p

Chagas disease vector control and Taylor's law - Fig 6

<p>In Figueroa [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006092#pntd.0006092.ref034" target="_blank">34</a>], TL described the relationship between <i>y</i> = log₁₀ <i>v</i> and <i>x</i> = log₁₀ <i>m</i> of the relative abundance of <i>T</i>. <i>infestans</i> (A), <i>T</i>. <i>guasayana</i> (B), and <i>T</i>. <i>garciabesi</i> (C) in 10 (peri)domestic habitats with positive mean abundance surveyed just before community-wide spraying with insecticides in October 2003 and during follow-up monitoring surveys of house/habitat infestations in which reinfested houses were selectively re-sprayed with insecticides in March and October 2004 and March 2005. Each point represents the sample mean and sample variance of bug abundance for one habitat on a specified date. The solid straight lines are fitted by least-squares regression to the data from each survey separately. Blue (solid circle), orange (diamond with white dot), red (circle with white dot), and green (square with white dot) points and lines represent October 2003, March and October 2004 and March 2005, respectively. Key to habitats as in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006092#pntd.0006092.g002" target="_blank">Fig 2</a>.</p