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Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract 

Predicting pathogen emergence and spillover risk requires understanding the determinants of a pathogens' host range and the traits involved in host competence. While host competence is often considered a fixed species-specific trait, it may be variable if pathogens diversify across hosts. Balancing selection can lead to maintenance of pathogen polymorphisms (multiple-niche-polymorphism; MNP). The causative agent of Lyme disease, Borrelia burgdorferi ( Bb ), provides a model to study the evolution of host adaptation, as some Bb strains defined by their outer surface protein C ( ospC ) genotype, are widespread in white-footed mice and others are associated with non-rodent vertebrates (e.g. birds). To identify the mechanisms underlying potential strain × host adaptation, we infected American robins and white-footed mice, with three Bb strains of different ospC genotypes. Bb burdens varied by strain in a host-dependent fashion, and strain persistence in hosts largely corresponded to Bb survival at early infection stages and with transmission to larvae (i.e. fitness). Early survival phenotypes are associated with cell adhesion, complement evasion and/or inflammatory and antibody-mediated removal of Bb, suggesting directional selective pressure for host adaptation and the potential role of MNP in maintaining OspC diversity. Our findings will guide future investigations to inform eco-evolutionary models of host adaptation for microparasites. 

Pathogens possess the ability to adapt and survive in some host species but not in others–an ecological trait known as host tropism. Transmitted through ticks and carried mainly by mammals and birds, the Lyme disease (LD) bacterium is a well-suited model to study such tropism. Three main causative agents of LD, Borrelia burgdorferi , B . afzelii , and B . garinii , vary in host ranges through mechanisms eluding characterization. By feeding ticks infected with different Borrelia species, utilizing feeding chambers and live mice and quail, we found species-level differences in bacterial transmission. These differences localize on the tick blood meal, and specifically complement, a defense in vertebrate blood, and a polymorphic bacterial protein, CspA, which inactivates complement by binding to a host complement inhibitor, Factor H (FH). CspA selectively confers bacterial transmission to vertebrates that produce FH capable of allele-specific recognition. CspA is the only member of the Pfam54 gene family to exhibit host-specific FH-binding. Phylogenetic analyses revealed convergent evolution as the driver of such uniqueness, and that FH-binding likely emerged during the last glacial maximum. Our results identify a determinant of host tropism in Lyme disease infection, thus defining an evolutionary mechanism that shapes host-pathogen associations.