An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Larval thermal characteristics of multiple ixodid ticks.

Temperature limits the geographic ranges of several tick species. Little is known about the thermal characteristics of these pests outside of a few studies on survival related to thermal tolerance. In this study, thermal tolerance limits, thermal preference, and the impact of temperature on activity levels and metabolic rate were examined in larvae for six species of ixodid ticks. Tolerance of low temperatures ranged from -15 to -24 °C with Dermacentor andersoni surviving the lowest temperatures. High temperature survival ranged from 41 to 47 °C, with Rhipicephalus sanguineus sensu lato having the highest upper lethal limit. Ixodes scapularis showed the lowest survival at both low and high temperatures. Thermal preference temperatures were tested from 0 to 41 °C. The majority of species preferred temperatures between 17 and 22 °C, while Dermacentor variabilis preferred significantly lower temperatures, near 12 °C. Overall activity was measured across a range of temperatures from 10 to 60 °C, and most tick species had the greatest activity near 30 °C. Metabolic rate was the greatest between 30 and 40 °C for all tick species and was relatively stable from 5 to 20 °C. The optimal temperature for tick larvae is likely near the thermal preference for each species, where oxygen consumption is low and activity occurs that will balance questing and conservation of nutrient reserves. In summary, tick species vary greatly in their thermal characteristics, and our results will be critical to predict distribution of these ectoparasites with changing climates.

Brown marmorated stink bug, Halyomorpha halys (Stål), genome: putative underpinnings of polyphagy, insecticide resistance potential and biology of a top worldwide pest.

BACKGROUND: Halyomorpha halys (Stål), the brown marmorated stink bug, is a highly invasive insect species due in part to its exceptionally high levels of polyphagy. This species is also a nuisance due to overwintering in human-made structures. It has caused significant agricultural losses in recent years along the Atlantic seaboard of North America and in continental Europe. Genomic resources will assist with determining the molecular basis for this species' feeding and habitat traits, defining potential targets for pest management strategies. RESULTS: Analysis of the 1.15-Gb draft genome assembly has identified a wide variety of genetic elements underpinning the biological characteristics of this formidable pest species, encompassing the roles of sensory functions, digestion, immunity, detoxification and development, all of which likely support H. halys' capacity for invasiveness. Many of the genes identified herein have potential for biomolecular pesticide applications. CONCLUSIONS: Availability of the H. halys genome sequence will be useful for the development of environmentally friendly biomolecular pesticides to be applied in concert with more traditional, synthetic chemical-based controls.

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions.

Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance time series based on "delta densities", and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on available data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory. Corresponding distributional forecasts cover more observed events than alternatives that treat the whole season as a unit, and improve upon multiple evaluation metrics when extracting key targets of interest to public health officials. Adaptively weighted ensembles integrate the results of multiple forecasting methods, such as delta density, using weights that can change from situation to situation. We treat selection of optimal weightings across forecasting methods as a separate estimation task, and describe an estimation procedure based on optimizing cross-validation performance. We consider some details of the data generation process, including data revisions and holiday effects, both in the construction of these forecasting methods and when performing retrospective evaluation. The delta density method and an adaptively weighted ensemble of other forecasting methods each improve significantly on the next best ensemble component when applied separately, and achieve even better cross-validated performance when used in conjunction. We submitted real-time forecasts based on these contributions as part of CDC's 2015/2016 FluSight Collaborative Comparison. Among the fourteen submissions that season, this system was ranked by CDC as the most accurate.

A human judgment approach to epidemiological forecasting.

Infectious diseases impose considerable burden on society, despite significant advances in technology and medicine over the past century. Advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic. Historically, such a capability has lagged for many reasons, including in particular the uncertainty in the current state of the system and in the understanding of the processes that drive epidemic trajectories. Presently we have access to data, models, and computational resources that enable the development of epidemiological forecasting systems. Indeed, several recent challenges hosted by the U.S. government have fostered an open and collaborative environment for the development of these technologies. The primary focus of these challenges has been to develop statistical and computational methods for epidemiological forecasting, but here we consider a serious alternative based on collective human judgment. We created the web-based "Epicast" forecasting system which collects and aggregates epidemic predictions made in real-time by human participants, and with these forecasts we ask two questions: how accurate is human judgment, and how do these forecasts compare to their more computational, data-driven alternatives? To address the former, we assess by a variety of metrics how accurately humans are able to predict influenza and chikungunya trajectories. As for the latter, we show that real-time, combined human predictions of the 2014-2015 and 2015-2016 U.S. flu seasons are often more accurate than the same predictions made by several statistical systems, especially for short-term targets. We conclude that there is valuable predictive power in collective human judgment, and we discuss the benefits and drawbacks of this approach.

Flexible Modeling of Epidemics with an Empirical Bayes Framework.

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic's behavior, policy makers can design and implement more effective countermeasures. This past year, the Centers for Disease Control and Prevention hosted the "Predict the Influenza Season Challenge", with the task of predicting key epidemiological measures for the 2013-2014 U.S. influenza season with the help of digital surveillance data. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, and the season onset, duration, peak time, and peak height, with and without using Google Flu Trends data. Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, tailoring these models to certain types of surveillance data can be challenging, and overly complex models with many parameters can compromise forecasting ability. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to some other diseases with seasonal epidemics. This method produces a complete posterior distribution over epidemic curves, rather than, for example, solely point predictions of forecasting targets. We report prospective influenza-like-illness forecasts made for the 2013-2014 U.S. influenza season, and compare the framework's cross-validated prediction error on historical data to that of a variety of simpler baseline predictors.

Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016.

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015-2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

Comparative genomic analysis of six Glossina genomes, vectors of African trypanosomes.

BACKGROUND: Tsetse flies (Glossina sp.) are the vectors of human and animal trypanosomiasis throughout sub-Saharan Africa. Tsetse flies are distinguished from other Diptera by unique adaptations, including lactation and the birthing of live young (obligate viviparity), a vertebrate blood-specific diet by both sexes, and obligate bacterial symbiosis. This work describes the comparative analysis of six Glossina genomes representing three sub-genera: Morsitans (G. morsitans morsitans, G. pallidipes, G. austeni), Palpalis (G. palpalis, G. fuscipes), and Fusca (G. brevipalpis) which represent different habitats, host preferences, and vectorial capacity. RESULTS: Genomic analyses validate established evolutionary relationships and sub-genera. Syntenic analysis of Glossina relative to Drosophila melanogaster shows reduced structural conservation across the sex-linked X chromosome. Sex-linked scaffolds show increased rates of female-specific gene expression and lower evolutionary rates relative to autosome associated genes. Tsetse-specific genes are enriched in protease, odorant-binding, and helicase activities. Lactation-associated genes are conserved across all Glossina species while male seminal proteins are rapidly evolving. Olfactory and gustatory genes are reduced across the genus relative to other insects. Vision-associated Rhodopsin genes show conservation of motion detection/tracking functions and variance in the Rhodopsin detecting colors in the blue wavelength ranges. CONCLUSIONS: Expanded genomic discoveries reveal the genetics underlying Glossina biology and provide a rich body of knowledge for basic science and disease control. They also provide insight into the evolutionary biology underlying novel adaptations and are relevant to applied aspects of vector control such as trap design and discovery of novel pest and disease control strategies.

Low and high thermal tolerance characteristics for unfed larvae of the winter tick Dermacentor albipictus (Acari: Ixodidae) with special reference to moose.

We report that larvae of the winter tick Dermacentor albipictus, the only stage that will quest for a host, can tolerate short-term cold shock down to -25°C and short-term heat shock as high as 46°C. Unlike a three host-tick, larvae of D. albipictus have no preconditioning response to low or high temperature exposure by rapid cold hardening (RCH) or heat hardening, and poor ability to acclimate to low and high temperature extremes. Thermal tolerance limits were not improved as the result of larval clustering, and there was only a minimal effect due to changes in photoperiod. These larvae are freeze intolerant and die at higher temperatures (-5 to -10°C) from contact with ice by inoculative freezing. In absence of cold-associated resistance mechanisms, winter survival requires that larvae procure a host before the first snow cover. Their low and high temperature tolerance, however, is a key survival element that adapts them for off-host periods during summer, which in the arctic could allow for northern expansion.

Results from the second year of a collaborative effort to forecast influenza seasons in the United States.

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from <0.01 to 0.41, peak week ranged from 0.08 to 0.49, and peak intensity ranged from <0.01 to 0.17. The scores for predictions of ILI 1-4 weeks in advance ranged from 0.02-0.38 and was highest 1 week ahead. Forecast skill varied by HHS region. Forecasts can predict epidemic characteristics that inform public health actions. CDC, state and local health officials, and researchers are working together to improve forecasts.

Dehydration and starvation yield energetic consequences that affect survival of the American dog tick.

Ticks are obligate hematophagous arthropods, but may have to endure extended time (1-2years) between feedings. During these off-host periods, ticks must contend with a multitude of environmental stresses including prolonged or repeated exposure to desiccating conditions. In this study, we measured the energetic consequences of single and repeated bouts of dehydration of American dog ticks, Dermacentor variabilis, and examined the impact of energy reserves on tick survival during dehydration. Recently molted ticks exposed to a single period at 0% relative humidity (RH) for 5d lost ∼26% of their body water and showed 1.3- and 1.7-fold reductions in protein and lipid, respectively. These reduced energy reserves coincided with increased O2 consumption in dehydrated ticks. Exposure to repeated cycles of dehydration (0% RH, 48h) and rehydration (100% RH, 24h) also reduced energy reserves; however, ticks were able to fully recover their body water after 12 cycles of dehydration/rehydration and endured >20 cycles. Starvation of ticks, in the absence of dehydration, for 18 or 36weeks resulted in the loss of ∼20-40% of protein and 60% of lipid reserves. When ticks were exposed to continuous dehydration at 0% RH, their survival after 18weeks of starvation was only minimally impacted; however, individuals starved for 36weeks succumbed to dehydration much more rapidly than recently fed ticks. Both single and repeated dehydration exposures resulted in substantial energetic costs and ticks with limited energy reserves were more susceptible to dehydration-induced mortality, indicating that adequate energy reserves are critical for tolerance to dehydration stress and long-term success of ticks.

Improved annotation of the insect vector of citrus greening disease: biocuration by a diverse genomics community.

Database URL: https://citrusgreening.org/.

Cold hardiness and influences of hibernaculum conditions on overwintering survival of American dog tick larvae.

Understanding how ticks survive the multitude of stresses experienced during winter is integral to predicting population dynamics and transmission of tick-borne pathogens. The American dog tick (Dermacentor variabilis), a predominant vector of Rocky Mountain spotted fever, overwinters in any of its post-egg life stages. In this study, we characterized the cold tolerance of larval D. variabilis and examined the effect of various acclimatory conditions on cold hardiness. Cold-shock survival during 2h exposure to various subzero temperatures was assessed and a lower lethal temperature of -20°C and a 50% mortality temperature near -16°C was established. Larvae exposed to -5°C showed high survival (∼70%) after 14 d and near 50% for up to 56d at -5°C. Larvae cycled between supra- and subzero temperatures showed better long-term survival than at constant -5°C. The temperature of crystallization (Tc) was ∼-23°C and no larvae survived freezing after reaching their Tc. Larvae exposed to inoculative freezing survived brief, mild treatments (70% survival of -5°C for 2h) but survival was reduced compared to larvae cooled in the absence of ice. Reduced photophase, rapid cold hardening, dehydration, and long-term thermal acclimation all improved larval cold hardiness to varying degrees. Survival data were compared to measurements of hibernacula conditions, and we conclude that larvae face little threat from cold-induced mortality but inoculative freezing does pose a risk, and the geographic distribution of D. variabilis is only minimally influenced by the ability of larvae to survive low temperature exposure.

Computational Characterization of Transient Strain-Transcending Immunity against Influenza A.

The enigmatic observation that the rapidly evolving influenza A (H3N2) virus exhibits, at any given time, a limited standing genetic diversity has been an impetus for much research. One of the first generative computational models to successfully recapitulate this pattern of consistently constrained diversity posits the existence of a strong and short-lived strain-transcending immunity. Building on that model, we explored a much broader set of scenarios (parameterizations) of a transient strain-transcending immunity, ran long-term simulations of each such scenario, and assessed its plausibility with respect to a set of known or estimated influenza empirical measures. We evaluated simulated outcomes using a variety of measures, both epidemiological (annual attack rate, epidemic duration, reproductive number, and peak weekly incidence), and evolutionary (pairwise antigenic diversity, fixation rate, most recent common ancestor, and kappa, which quantifies the potential for antigenic evolution). Taking cumulative support from all these measures, we show which parameterizations of strain-transcending immunity are plausible with respect to the set of empirically derived target values. We conclude that strain-transcending immunity which is milder and longer lasting than previously suggested is more congruent with the observed short- and long-term behavior of influenza.

Compensatory mutations in predicted metal transporters modulate auxin conjugate responsiveness in Arabidopsis.

Levels of the phytohormone indole-3-acetic acid (IAA) can be altered by the formation and hydrolysis of IAA conjugates. The isolation and characterization of Arabidopsis thaliana mutants with reduced IAA-conjugate sensitivity and wild-type IAA responses is advancing the understanding of auxin homeostasis by uncovering the factors needed for conjugate metabolism. For example, the discovery that the IAA-Ala-resistant mutant iar1 is defective in a protein in the ZIP family of metal transporters uncovered a link between metal homeostasis and IAA-conjugate sensitivity. To uncover additional factors impacting auxin conjugate metabolism, we conducted a genetic modifier screen and isolated extragenic mutations that restored IAA-amino acid conjugate sensitivity to the iar1 mutant. One of these suppressor mutants is defective in a putative cation diffusion facilitator, MTP5 (At3g12100; formerly known as MTPc2). Loss of MTP5 function restored IAA conjugate sensitivity to iar1 but not to mutants defective in IAA-amino acid conjugate amidohydrolases. Our results are consistent with a model in which MTP5 and IAR1 transport metals in an antagonistic fashion to regulate metal homeostasis within the subcellular compartment in which the IAA-conjugate amidohydrolases reside, and support previous suggestions that the ion composition in this compartment influences hydrolase activity.