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Abstract BackgroundEffectively controlling heartworm disease—a major parasitic disease threatening animal health in the US and globally—requires understanding the local ecology of mosquito vectors involved in transmission. However, the key vector species in a given region are often unknown and challenging to identify. Here we investigate (i) the key vector species associated with transmission of the parasite,Dirofilaria immitis, in California and (ii) the climate and land cover drivers of vector presence. MethodsTo identify key mosquito vectors involved in transmission, we incorporated long-term, finely resolved mosquito surveillance data and dog heartworm case data in a statistical modeling approach (fixed-effects regression) that rigorously controls for other unobserved drivers of heartworm cases. We then used a flexible machine learning approach (gradient boosted machines) to identify the climate and land cover variables associated with the presence of each species. ResultsWe found significant, regionally specific, positive associations between dog heartworm cases and the abundance of four vector species:Aedes aegypti(Central California),Ae. albopictus(Southern California),Ae. sierrensis(Central California), andCuliseta incidens(Northern and Central California). The proportion of developed land cover was one of the most important ecological variables predicting the presence or absence of the putative vector species. ConclusionOur results implicate three previously under-recognized vectors of dog heartworm transmission in California and indicate the land cover types in which each putative vector species is commonly found. Efforts to target these species could prioritize surveillance in these land cover types (e.g. near human dwellings in less urbanized settings forAe. albopictusandCs. incidens) but further investigation on the natural infection prevalence and host-biting rates of these species, as well as the other local vectors, is needed. Graphical Abstract
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Smoothed counting of 0–1 points in polyhedra
Abstract Given a system of linear equations in an ‐vector of 0–1 variables, we compute the expectation of , where is a vector of independent Bernoulli random variables and are constants. The algorithm runs in quasi‐polynomial time under some sparseness condition on the matrix of the system. The result is based on the absence of the zeros of the analytic continuation of the expectation for complex probabilities, which can also be interpreted as the absence of a phase transition in the Ising model with a sufficiently strong external field. We discuss applications to perfect matchings in hypergraphs and randomized rounding in discrete optimization.
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- Award ID(s):
- 1855428
- PAR ID:
- 10418803
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Random Structures & Algorithms
- Volume:
- 63
- Issue:
- 1
- ISSN:
- 1042-9832
- Page Range / eLocation ID:
- p. 27-60
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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