TriatoScore is a single-figure measure of Chagas disease entomological risk that (i) covers both native and non-native triatomine bug species, (ii) tackles the issue of locally incomplete vector-occurrence records, and (iii) is designed to support decision-making at the spatial scale most relevant to decentralized control-surveillance systems. Our approach leverages the best available knowledge on the ecology-behavior and distribution-biogeography of individual triatomine bug species to compute local (e.g., municipal) TriatoScores, which can in turn be used to stratify and map entomological risk over larger spatial units. When based on standardized TriatoScores, risk stratification becomes a fundamentally dynamic exercise—changes in local vector faunas (or in our knowledge about them) are automatically accounted for as local spatial units are reassigned to risk strata relative to the recalculated average risk across all units in the region of interest. Although the most basic version of TriatoScore uses only vector data, integrating epidemiological, demographic, environmental, or operational information is straightforward. TriatoScore may hence become a useful addition to the Chagas disease vector control-surveillance toolbox.
Our approach draws primarily on the hierarchical working classification of Chagas disease vectors put forward by one of us with the suggestion that it was not only simple and biologically sound, but also potentially useful  see also . Here, we illustrate how this idea can be put to work in practice. At the highest level of the hierarchy , two triatomine bug species recorded in Bahia are non-native to the state. Triatoma infestans is the most dangerous domestic vector of Chagas disease [7, 11, 12, 19, 20, 24, 25], and was hence given the highest “species relevance score” (Table 1). Triatoma rubrofasciata is strongly associated with rats of the genus Rattus, among which it transmits Trypanosoma conorhini; although it can also support T. cruzi infections, this originally Asian species has limited relevance as a vector of Chagas disease [17, 24,25,26, 53], and its “species relevance score” is therefore much lower (Table 1). Importantly, these non-native species can and should be targeted for local elimination, and this critical operational consideration sets them apart from the species that are locally native [12, 20, 39]. Native species were given “species relevance scores” ranging from 4 for those known to often breed inside and/or around houses (T. juazeirensis/brasiliensis, T. pseudomaculata, T. sordida, T. lenti/bahiensis and P. megistus) to 1 for those that have only rarely been found invading human dwellings (e.g., Rhodnius domesticus or Parabelminus yurupucu) (see Table 1 and [7, 11, 12, 14,15,16,17, 19,20,21, 24,25,26,27,28,29,30,31,32,33,34, 39]). While the values we chose for scoring are admittedly arbitrary, they reflect our best knowledge about the epidemiological relevance of each species—and, most importantly, the scores’ relative sizes reflect the species’ relative relevance . In practice, the initial step of our approach thus entails eliciting expert opinion on the relevance of each triatomine bug species known to be present in the region of interest, with special attention paid to relative relevance.
The second general requirement was to map each species’ occurrence at the scale of municipalities. We used vector-presence records generated by local surveillance systems as our main data source, yet as many as 152 municipalities (36.5%) did not produce any record over the period 2006–2019—and the tally remains at 125 (or 30%) when considering the two decades since 1999 (Additional file 1: Table S8). This, of course, could not be taken to mean that triatomines do not occur (and enter houses) in any of those municipalities; rather, the absence of records almost certainly springs from the imperfect functioning of entomological surveillance [13,14,15, 21]. To fill in the spurious blanks in the species-by-municipality matrices and maps, we complemented this dataset with records from the literature, including (i) actual occurrence records that we could map to a municipality, (ii) species distribution maps derived from ecological niche models, and (iii) the best available knowledge about the ecoregional biogeography of each species (Table 2) [17, 27,28,29,30,31,32,33,34, 40,41,42,43,44,45].
This mapping procedure was straightforward for most species, but somewhat challenging for a few (see Table 2 and its footnotes). First, some species are local endemics restricted to a specific subarea within an ecoregion. For example, T. sherlocki seems to be endemic to the Jacaré-Verde basin and the ranges that bound it [17, 30,31,32,33, 40] (Fig. 1), and T. melanica does not seem to extend into the Cerrado to the northwest of Bahia [17, 30,31,32,33, 40, 45] (see Table 2 and Additional file 2: Figures S13, S17). Second, we found that some species occur in municipalities with supposedly unsuitable ecoregional ecologies. For example, T. tibiamaculata, R. neglectus, R. domesticus and Pa. yurupucu have been recorded in municipalities with 100% of the land classified as Caatinga (Table 2). Most such cases are readily explained by the relatively low spatial resolution of available ecoregional classifications—suitable habitat is likely present in small patches not captured by our coarse-scale ecoregion map (Fig. 1). To reflect this “marginal occurrence”, we assigned a 0.001 value to the “weighted presence” of T. tibiamaculata, P. lutzi, R. domesticus and Pa. yurupucu in municipalities with this kind of mismatch between records and ecoregions (see Table 2 and [17, 34]). The case of R. neglectus appears to be different—local populations of this species, which is primarily from the Cerrado [17, 24, 25, 34, 42, 43], seem to have adapted to drier Caatinga environments in northern-central Bahia [32, 33, 42] (see Table 2 and Additional file 2: Figure S11). Finally, although P. megistus is primarily a moist-forest species, wild populations are also common in drier ecoregions including the Cerrado and Caatinga, where they occupy gallery forests and other moister-habitat patches [17, 29]; again, our ecoregional assessment does not capture such fine-scale environmental heterogeneity. The full set of species-specific “weighted presence” maps is presented in Additional file 2: Figures S3–S24, and the data used to build them are available in Additional file 1: Tables S1–S7.
TriatoScore values were overall higher in municipalities dominated by dry-to-semiarid ecoregions, lower in municipalities where moister forests dominate, and intermediate in municipalities dominated by seasonally dry savanna-grassland (Fig. 4). The higher entomological risk in the Caatinga and Atlantic dry forest reflects both a particularly high triatomine species richness (Table 2) and the fact that many of those species are often found infesting or invading houses (Table 1) [7, 11, 12, 15, 17, 19, 24,25,26,27,28,29,30,31,32,33,34, 40,41,42,43,44,45]. While the eastern portion of the Cerrado that covers western Bahia is also fairly species-rich (Table 2), at least three of the 11 triatomine bug species occurring there (Panstrongylus diasi, Cavernicola pilosa and Psammolestes tertius) are seldom found in or around houses (Table 1) [7, 11, 12, 17, 24,25,26,27,28,29,30,31,32,33,34, 40,41,42,43,44,45]. In Bahia, the species-rich Caatinga, Atlantic dry forest and Cerrado meet along the São Francisco River valley (Fig. 1), and TriatoScore mapping revealed a pattern of higher entomological risk in that region (Fig. 3). In contrast, the Campos Rupestres montane savannas of the Serra da Mangabeira (Fig. 1) are home to just three triatomine bug species (Table 2) [17, 27,28,29,30,31,32,33], and TriatoScore values were accordingly low in ten municipalities (with ~ 10% to ~ 50% of territory corresponding to Campos Rupestres) located along a southeast-northwest diagonal, narrow strip in the center of the state (Figs. 1, 3). Finally, 6 of the 10 triatomine bug species known to occur in the moister coastal ecoregions do not seem able to stably infest houses (Tables 1, 2) [7, 11, 12, 17, 24,25,26,27,28,29,30,31,32,33,34]. Therefore, TriatoScore values are particularly low along the central and southern coast of Bahia (Fig. 3). Geospatial analyses confirmed these patterns by showing (i) a clear-cut hotspot of higher entomological risk in municipalities along the São Francisco valley and on the ranges that bound it (particularly to the east and north), (ii) a clear-cut coldspot of lower entomological risk in municipalities along the central-southern coast, and (iii) two separate areas of nonsignificant clustering of TriatoScore values: (a) the Cerrado-dominated western region and (b) the Caatinga-dominated region east of the central uplands (the Serra do Angelim-Chapada Diamantina-Serra da Mangabeira-Serra do Espinhaço complex) plus the overall drier northern coast (Figs. 1, 3).
One particularly attractive feature of the TriatoScore approach is that it is fundamentally dynamic. This can be illustrated with a hypothetical example. Suppose that in some year in the near future (i) T. infestans infestation foci are discovered in two municipalities where the species was historically present but from where it was not reported since 2006 (say, Abaré and Anagé), and (ii) intensive but negative searches strongly suggest that T. rubrofasciata is no longer present in two municipalities where it was recorded in the past (say, Glória and Jussiape). In Additional file 1: Table S9 we show how these changes can swiftly be incorporated into an exercise of entomological-risk assessment and stratification—by typing the new “species relevance score” values into the appropriate cells (here, “10.0” in cells B4 and B15, and “0.0” in cells C141 and C224; highlighted in red font in Additional file 1: Table S9), TriatoScores are automatically updated and standardized and risk strata automatically recalculated (see columns Z to AC in Additional file 1: Table S9). To provide a real-life (if retrospective) example of TriatoScore’s flexibility, we examined how the historical elimination of non-native T. infestans from 125 municipalities [27,28,29,30,31,32,33] changed entomological-risk patterns across Bahia. We found that TriatoScores were reduced by an average of 26.5% (range, 20.4–43.5%) in those municipalities; declines were steeper in municipalities where the “baseline” entomological risk brought about by native vectors is lower (see Additional file 1: Table S10). A further example (this time prospective) is our evaluation of “baseline” risk—what would be the patterns of Chagas disease entomological risk if the two non-native vector species still found in the state were finally eliminated (Figs. 2, 3; Additional file 1: Tables S3, S6, S10). In general, thus, our approach can swiftly incorporate new data on local-scale vector-species distribution. Note also that to quickly identify municipalities where risk is much higher or much lower than average one just needs to tinker with the threshold used to define risk strata. For example, a ±1.5 SD criterion highlights 14 municipalities with very high, and 20 with very low, entomological risk (Additional file 1: Table S11).
Finally, we again draw attention to the fact that the basic version of TriatoScore we have presented uses only vector data, yet Chagas disease transmission risk depends on a constellation of social, cultural, economic, demographic, ecological, environmental, political, and operational determinants [7, 23, 24, 39, 54,55,56]. For example, and as Carlos Chagas vividly described in his 1909 paper , poor-quality housing sets the stage for frequent contact between vectors and humans . It would therefore be of interest to develop a more elaborate version of TriatoScore in which metrics describing further potential determinants of transmission could be incorporated (see, e.g., ). As we have shown with our computation of municipal “TriatoScore-plus” values, this is straightforward enough—all that is needed is a set of weights, one for each putative determinant, derived from measurements taken at the scale of interest (Fig. 6). We chose to illustrate this procedure with a municipality-level measure of housing conditions, but many other possibilities could merit consideration . A non-exhaustive list of examples might include local-scale measures of (i) the frequencies of dwelling infestation with triatomines or of T. cruzi infection in humans or vectors; (ii) the levels of poverty or human development; (iii) the patterns and dynamics of land-use change and deforestation; (iv) the demographics of urban/rural populations; or (v) whether control-surveillance systems are adequately funded, staffed, and operated in each municipality.