Correlations and Timeliness of COVID-19 Surveillance Data Sources and Indicators - United States, October 1, 2020-March 22, 2023.

When the U.S. COVID-19 public health emergency declaration expires on May 11, 2023, national reporting of certain categories of COVID-19 public health surveillance data will be transitioned to other data sources or will be discontinued; COVID-19 hospitalization data will be the only data source available at the county level (1). In anticipation of the transition, national COVID-19 surveillance data sources and indicators were evaluated for purposes of ongoing monitoring. The timeliness and correlations among surveillance indicators were analyzed to assess the usefulness of COVID-19-associated hospital admission rates as a primary indicator for monitoring COVID-19 trends, as well as the suitability of other replacement data sources. During April 2022-March 2023, COVID-19 hospital admission rates from the National Healthcare Safety Network (NHSN)† lagged 1 day behind case rates and 4 days behind percentages of positive test results and COVID-19 emergency department (ED) visits from the National Syndromic Surveillance Program (NSSP). In the same analysis, National Vital Statistics System (NVSS) trends in the percentage of deaths that were COVID-19-associated, which is tracked by date of death rather than by report date, were observable 13 days earlier than those from aggregate death count data, which will be discontinued (1). During October 2020-March 2023, strong correlations were observed between NVSS and aggregate death data (0.78) and between the percentage of positive SARS-CoV-2 test results from the National Respiratory and Enteric Viruses Surveillance System (NREVSS) and COVID-19 electronic laboratory reporting (CELR) (0.79), which will also be discontinued (1). Weekly COVID-19 Community Levels (CCLs) will be replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrated >99% concordance by county during February 2022-March 2023. COVID-19-associated hospital admission levels are a suitable primary metric for monitoring COVID-19 trends, the percentage of COVID-19 deaths is a timely disease severity indicator, and the percentages of positive SARS-CoV-2 test results from NREVSS and ED visits serve as early indicators for COVID-19 monitoring. Collectively, these surveillance data sources and indicators can support monitoring of the impact of COVID-19 and related prevention and control strategies as ongoing public health priorities.

Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021.

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

Considering indirect benefits is critical when evaluating SARS-CoV-2 vaccine candidates.

Significant progress has already been made in development and testing of SARS-CoV-2 vaccines, and Phase III clinical trials have begun for 6 novel vaccine candidates to date. These Phase III trials seek to demonstrate direct benefits of a vaccine on vaccine recipients. However, vaccination is also known to bring about indirect benefits to a population through the reduction of virus circulation. The indirect effects of SARS-CoV-2 vaccination can play a key role in reducing case counts and COVID-19 deaths. To illustrate this point, we show through simulation that a vaccine with strong indirect effects has the potential to reduce SARS-CoV-2 circulation and COVID-19 deaths to a greater extent than an alternative vaccine with stronger direct effects but weaker indirect effects. Protection via indirect effects may be of particular importance in the context of this virus, because elderly individuals are at an elevated risk of death but are also less likely to be directly protected by vaccination due to immune senescence. We therefore encourage ongoing data collection and model development aimed at evaluating the indirect effects of forthcoming SARS-CoV-2 vaccines.

Evolutionary consequences of feedbacks between within-host competition and disease control.

Lay Summary: Competition often occurs among diverse parasites within a single host, but control efforts could change its strength. We examined how the interplay between competition and control could shape the evolution of parasite traits like drug resistance and disease severity.

Stochasticity and Infectious Disease Dynamics: Density and Weather Effects on a Fungal Insect Pathogen.

In deterministic models of epidemics, there is a host abundance threshold above which the introduction of a few infected individuals leads to a severe epidemic. Studies of weather-driven animal pathogens often assume that abundance thresholds will be overwhelmed by weather-driven stochasticity, but tests of this assumption are lacking. We collected observational and experimental data for a fungal pathogen, Entomophaga maimaiga, that infects the gypsy moth, Lymantria dispar. We used an advanced statistical-computing algorithm to fit mechanistic models to our data, such that different models made different assumptions about the effects of host density and weather on E. maimaiga epizootics (epidemics in animals). We then used Akaike information criterion analysis to choose the best model. In the best model, epizootics are driven by a combination of weather and host density, and the model does an excellent job of explaining the data, whereas models that allow only for weather effects or only for density-dependent effects do a poor job of explaining the data. Density-dependent transmission in our best model produces a host density threshold, but this threshold is strongly blurred by the stochastic effects of weather. Our work shows that host-abundance thresholds may be important even if weather strongly affects transmission, suggesting that epidemiological models that allow for weather have an important role to play in understanding animal pathogens. The success of our model means that it could be useful for managing the gypsy moth, an important pest of hardwood forests in North America.

Combined Effects of Natural Enemies and Competition for Resources on a Forest Defoliator: A Theoretical and Empirical Analysis.

Explanations for the dynamics of insect outbreaks often focus on natural enemies, on the grounds that parasitoid and pathogen attack rates are high during outbreaks. While natural enemy models can successfully reproduce outbreak cycles, experiments have repeatedly demonstrated the importance of resource quality and abundance. Experiments, however, are rarely invoked in modeling studies. Here we combine mechanistic models, observational data, and field experiments to quantify the roles of parasitoid attacks and resource competition on the jack pine budworm, Choristoneura pinus. By fitting models to a combination of observational and experimental data, we show that parasitoid attacks are the main source of larval budworm mortality at low and intermediate budworm densities but that resource competition is the main source of mortality at high densities. Our results further show that the effects of resource competition become more severe with increasing host tree age and that the effects of parasitoids are moderated by strong competition between parasitoids for hosts. Allowing for these effects in a model of insect outbreaks leads to realistic outbreak cycles, while a host-parasitoid model without resource competition produces an unrealistic stable equilibrium. The effects of resource competition are modulated by tree age, which in turn depends on fire regimes. Our model therefore suggests that increases in fire frequency due to climate change may interact in complex ways with budworm outbreaks. Our work shows that resource competition can be as important as natural enemies in modulating insect outbreaks, while demonstrating the usefulness of high-performance computing in experimental field ecology.

Causes and Consequences of Spatial Within-Host Viral Spread.

The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Here, with a focus on influenza A viruses (IAVs), we first review experimental studies that have shed light on the mechanisms and spatial dynamics of viral spread within hosts. These studies provide strong empirical evidence for highly localized IAV spread within hosts. Since mathematical and computational within-host models have been increasingly used to gain a quantitative understanding of observed viral dynamic patterns, we then review the (relatively few) computational modeling studies that have shed light on possible factors that structure the dynamics of spatial within-host IAV spread. These factors include the dispersal distance of virions, the localization of the immune response, and heterogeneity in host cell phenotypes across the respiratory tract. While informative, we find in these studies a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature on range expansions to provide informed theoretical expectations. We find that factors such as the type of density dependence, the frequency of long-distance dispersal, specific life history characteristics, and the extent of spatial heterogeneity are critical factors affecting the speed of population spread and the genetic composition of spatially expanding populations. For each factor that we identified in the theoretical literature, we draw parallels to its analog in viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host IAV spread and the potential for within-host spatial considerations to inform the development of disease control strategies.

Mutations in Caenorhabditis briggsae identify new genes important for limiting the response to EGF signaling during vulval development.

Studies of vulval development in the nematode C. elegans have identified many genes that are involved in cell division and differentiation processes. Some of these encode components of conserved signal transduction pathways mediated by EGF, Notch, and Wnt. To understand how developmental mechanisms change during evolution, we are doing a comparative analysis of vulva formation in C. briggsae, a species that is closely related to C. elegans. Here, we report 14 mutations in 7 Multivulva (Muv) genes in C. briggsae that inhibit inappropriate division of vulval precursors. We have developed a new efficient and cost-effective gene mapping method to localize Muv mutations to small genetic intervals on chromosomes, thus facilitating cloning and functional studies. We demonstrate the utility of our method by determining molecular identities of three of the Muv genes that include orthologs of Cel-lin-1 (ETS) and Cel-lin-31 (Winged-Helix) of the EGF-Ras pathway and Cel-pry-1 (Axin), of the Wnt pathway. The remaining four genes reside in regions that lack orthologs of known C. elegans Muv genes. Inhibitor studies demonstrate that the Muv phenotype of all four new genes is dependent on the activity of the EGF pathway kinase, MEK. One of these, Cbr-lin(gu167), shows modest increase in the expression of Cbr-lin-3/EGF compared to wild type. These results argue that while Cbr-lin(gu167) may act upstream of Cbr-lin-3/EGF, the other three genes influence the EGF pathway downstream or in parallel to Cbr-lin-3. Overall, our findings demonstrate that the genetic program underlying a conserved developmental process includes both conserved and divergent functional contributions.