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Null models provide a critical baseline for the evaluation of predictive disease models. Many studies consider only the grand mean null model (i.e. R 2 ) when evaluating the predictive ability of a model, which is insufficient to convey the predictive power of a model. We evaluated ten null models for human cases of West Nile virus (WNV), a zoonotic mosquito-borne disease introduced to the United States in 1999. The Negative Binomial, Historical (i.e. using previous cases to predict future cases) and Always Absent null models were the strongest overall, and the majority of null models significantly outperformed the grand mean. The length of the training timeseries increased the performance of most null models in US counties where WNV cases were frequent, but improvements were similar for most null models, so relative scores remained unchanged. We argue that a combination of null models is needed to evaluate the forecasting performance of predictive models for infectious diseases and the grand mean is the lowest bar.

Analysis of a large grassland biodiversity dataset shows that increases in local land-use intensity cause biotic homogenization at landscape scale across microbial, plant and animal groups, both above- and belowground, that is largely independent of changes in local diversity. Land-use intensification and biological diversity Although much is known about the effect of land-use intensification on local species richness, effects at the landscape and regional level are more difficult to establish. Martin Gossner and colleagues assess the effect of land-use intensification on biological diversity across 105 grasslands in Germany, comprising more than 4,000 species belonging to 12 trophic groups. They find that even moderate increases in local land-use intensity cause biotic homogenization across grasslands in microbial, plant and animal groups, both above- and below-ground. The findings suggest that land-use intensification reduces biodiversity at the landscape scale in a wide range of species. Land-use intensification is a major driver of biodiversity loss 1 , 2 . Alongside reductions in local species diversity, biotic homogenization at larger spatial scales is of great concern for conservation. Biotic homogenization means a decrease in β -diversity (the compositional dissimilarity between sites). Most studies have investigated losses in local ( α )-diversity 1 , 3 and neglected biodiversity loss at larger spatial scales. Studies addressing β -diversity have focused on single or a few organism groups (for example, ref. 4 ), and it is thus unknown whether land-use intensification homogenizes communities at different trophic levels, above- and belowground. Here we show that even moderate increases in local land-use intensity (LUI) cause biotic homogenization across microbial, plant and animal groups, both above- and belowground, and that this is largely independent of changes in α -diversity. We analysed a unique grassland biodiversity dataset, with abundances of more than 4,000 species belonging to 12 trophic groups. LUI, and, in particular, high mowing intensity, had consistent effects on β -diversity across groups, causing a homogenization of soil microbial, fungal pathogen, plant and arthropod communities. These effects were nonlinear and the strongest declines in β -diversity occurred in the transition from extensively managed to intermediate intensity grassland. LUI tended to reduce local α -diversity in aboveground groups, whereas the α -diversity increased in belowground groups. Correlations between the β -diversity of different groups, particularly between plants and their consumers, became weaker at high LUI. This suggests a loss of specialist species and is further evidence for biotic homogenization. The consistently negative effects of LUI on landscape-scale biodiversity underscore the high value of extensively managed grasslands for conserving multitrophic biodiversity and ecosystem service provision. Indeed, biotic homogenization rather than local diversity loss could prove to be the most substantial consequence of land-use intensification.

We reviewed the literature on the role of temperature in transmission of zoonotic arboviruses. Vector competence is affected by both direct and indirect effects of temperature, and generally increases with increasing temperature, but results may vary by vector species, population, and viral strain. Temperature additionally has a significant influence on life history traits of vectors at both immature and adult life stages, and for important behaviors such as blood-feeding and mating. Similar to vector competence, temperature effects on life history traits can vary by species and population. Vector, host, and viral distributions are all affected by temperature, and are generally expected to change with increased temperatures predicted under climate change. Arboviruses are generally expected to shift poleward and to higher elevations under climate change, yet significant variability on fine geographic scales is likely. Temperature effects are generally unimodal, with increases in abundance up to an optimum, and then decreases at high temperatures. Improved vector distribution information could facilitate future distribution modeling. A wide variety of approaches have been used to model viral distributions, although most research has focused on the West Nile virus. Direct temperature effects are frequently observed, as are indirect effects, such as through droughts, where temperature interacts with rainfall. Thermal biology approaches hold much promise for syntheses across viruses, vectors, and hosts, yet future studies must consider the specificity of interactions and the dynamic nature of evolving biological systems.

West Nile virus (WNV; Flaviviridae: Flavivirus) is a widely distributed arthropod-borne virus that has negatively affected human health and animal populations. WNV infection rates of mosquitoes and human cases have been shown to be correlated with climate. However, previous studies have been conducted at a variety of spatial and temporal scales, and the scale-dependence of these relationships has been understudied. We tested the hypothesis that climate variables are important to understand these relationships at all spatial scales. We analyzed the influence of climate on WNV infection rate of mosquitoes and number of human cases in New York and Connecticut using Random Forests, a machine learning technique. During model development, 66 climate-related variables based on temperature, precipitation and soil moisture were tested for predictive skill. We also included 20-21 non-climatic variables to account for known environmental effects (e.g., land cover and human population), surveillance related information (e.g., relative mosquito abundance), and to assess the potential explanatory power of other relevant factors (e.g., presence of wastewater treatment plants). Random forest models were used to identify the most important climate variables for explaining spatial-temporal variation in mosquito infection rates (abbreviated as MLE). The results of the cross-validation support our hypothesis that climate variables improve the predictive skill for MLE at county- and trap-scales and for human cases at the county-scale. Of the climate-related variables selected, mean minimum temperature from July-September was selected in all analyses, and soil moisture was selected for the mosquito county-scale analysis. Models demonstrated predictive skill, but still over- and under-estimated WNV MLE and numbers of human cases. Models at fine spatial scales had lower absolute errors but had greater errors relative to the mean infection rates.

West Nile virus (WNV) primarily infects birds and mosquitoes but has also caused over 2,000 human deaths, and >50,000 reported human cases in the United States. Expected numbers of WNV neuroinvasive cases for the present were described for the Northeastern United States, using a negative binomial model. Changes in temperature‐based suitability for WNV due to climate change were examined for the next decade using a temperature‐trait model. WNV suitability was generally expected to increase over the next decade due to changes in temperature, but the changes in suitability were generally small. Many, but not all, populous counties in the northeast are already near peak suitability. Several years in a row of low case numbers is consistent with a negative binomial, and should not be interpreted as a change in disease dynamics. Public health budgets need to be prepared for the expected infrequent years with higher‐than‐average cases. Low‐population counties that have not yet had a case are expected to have similar probabilities of having a new case as nearby low‐population counties with cases, as these absences are consistent with a single statistical distribution and random chance. Plain Language Summary West Nile virus (WNV) is a virus spread to humans by mosquitoes that had previously bitten an infected animal, usually a bird. We described the chance of one human case over the next one or next 5 years, and the chance of five human cases in any single year over the next 5 years. These chances were broadly similar, and highlight known locations with high numbers of cases of WNV. We also looked at how WNV suitability is expected to change in the near future due to climate change using established methods. We found that suitability for West Nile is expected to increase over most of the northeast, but decrease in some locations. Some locations identified as suitable based on temperature have had relatively few cases. Key Points Small increases in temperature‐based suitability for West Nile virus (WNV) are expected over most of the northeastern US in the next decade Years with high and low cases are consistent with the same underlying statistical distribution Temperature‐based suitability for WNV in some of the most populous counties is expected to decrease under climate change

Recently emerged SARS-CoV-2 variants have greater potential than earlier variants to cause vaccine breakthrough infections. During emergence of the Delta and Omicron variants, a matched case–control analysis used a viral genomic sequence dataset linked with demographic and vaccination information from New York, USA, to examine associations between virus lineage and patient vaccination status, patient age, vaccine type, and time since vaccination. Case-patients were persons infected with the emerging virus lineage, and controls were persons infected with any other virus lineage. Infections in fully vaccinated and boosted persons were significantly associated with the Omicron lineage. Odds of infection with Omicron relative to Delta generally decreased with increasing patient age. A similar pattern was observed with vaccination status during Delta emergence but was not significant. Vaccines offered less protection against Omicron, thereby increasing the number of potential hosts for emerging variants.

Background The Myxococcales are well known for their predatory and developmental social processes, and for the molecular complexity of regulation of these processes. Many species within this order have unusually large genomes compared to other bacteria, and their genomes have many genes that are unique to one specific sequenced species or strain. Here, we describe RNAseq based transcriptome analysis of the FruA regulon of Myxococcus xanthus and a comparative RNAseq analysis of two Myxococcus species, M. xanthus and Myxococcus stipitatus , as they respond to starvation and begin forming fruiting bodies. Results We show that both species have large numbers of genes that are developmentally regulated, with over half the genome showing statistically significant changes in expression during development in each species. We also included a non-fruiting mutant of M. xanthus that is missing the transcriptional regulator FruA to identify the direct and indirect FruA regulon and to identify transcriptional changes that are specific to fruiting and not just the starvation response. We then identified Interpro gene ontologies and COG annotations that are significantly up- or down-regulated during development in each species. Our analyses support previous data for M. xanthus showing developmental upregulation of signal transduction genes, and downregulation of genes related to cell-cycle, translation, metabolism, and in some cases, DNA replication. Gene expression in M. stipitatus follows similar trends. Although not all specific genes show similar regulation patterns in both species, many critical developmental genes in M. xanthus have conserved expression patterns in M. stipitatus , and some groups of otherwise unstudied orthologous genes share expression patterns. Conclusions By identifying the FruA regulon and identifying genes that are similarly and uniquely regulated in two different species, this work provides a more complete picture of transcription during Myxococcus development . We also provide an R script to allow other scientists to mine our data for genes whose expression patterns match a user-selected gene of interest.

Large-scale studies that investigate longitudinal changes in SARS-CoV-2 antibody reactivity in newborn infants are limited. Infants acquire maternal IgG antibodies that decay after birth; if infected, they produce infant-derived IgG, IgA, and IgM antibodies. The New York State Newborn Screening Program (NYS NSP) collects dried blood spots (DBS) from infants at birth and follow-up specimens from a select group of infants, many of whom are premature. We tested 100,318 remnant DBS from 50,036 infants with repeat specimens received between November 2019 and November 2021 for SARS-CoV-2 IgG antibodies; 9611 infants were IgG reactive at birth and 630 seroconverted for SARS-CoV-2 IgG. The first infant seroconversion occurred in March 2020. Infants antibody-reactive at birth were less likely to have low or very low birthweight or be from a multiple birth; infants with repeat specimens were less likely to be reactive at birth than those with single specimens. Antibody decay occurred in a non-linear process with initial rapid decay followed by slower decay (half-life of 22–23 days for 0–30 days after birth, 37–38 days for > 30 days after birth). Seroconversion was confirmed by retesting IgG seroconverting infants for detectable IgA and IgM SARS-CoV-2 antibodies.

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m–km, days–weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

The responses of species and populations to changes in the environment (e.g. changes in climate and land use) are often complex and difficult to predict. We have created the SpatialDemography model (R package: spatialdemography). The model is a spatially explicit, stage-structured, matrix-based metacommunity model, with the potential for modeling species’ and populations’ potential responses to environmental heterogeneity and change. The SpatialDemography model assumes a cellular landscape populated by organisms with four life stages: a mobile dispersing stage, two sessile non-reproductive stages, and a reproductive adult stage. Individuals are assumed to originate at the center of a given cell and disperse according to a specified dispersal kernel (e.g. log-normal). All adult individuals are capable of producing offspring. The model approach and framework are described in the context of a hypothetical example with multiple competing species in a four cell landscape. In this example simulation, both spatial location and species interactions were important for understanding population dynamics. SpatialDemography can be applied to questions where an understanding of transient and long-term demographic responses to spatiotemporal changes is desired. It is primarily applicable to metapopulations and metacommunities of organisms with early dispersal and sessile adults (i.e. modular organisms such as plants and some marine organisms). SpatialDemography differs from other population models in that it is spatially explicit, can incorporate biotic interactions, and is implemented in R.