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Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021–22 and the 5th most accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change. This manuscript evaluates forecasts of laboratory-confirmed influenza hospital admissions, a new target for influenza forecasting in the United States. Across two influenza seasons, the FluSight ensemble is robust compared to submitted models. [ABSTRACT FROM AUTHOR]

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections. The US COVID-19 Scenario Modeling Hub produced medium to long term projections based on different epidemic scenarios. In this study, the authors evaluate 14 rounds of projections by comparing them to the epidemic trajectories that occurred, and discuss lessons learned for future similar projects. [ABSTRACT FROM AUTHOR]

Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and transmission networks at individual and ZIP code scales. We find considerable heterogeneity in reported close contacts and secondary infections and evidence of extensive transmission across ZIP code areas. Our analysis reveals the spatial pattern of SARS-CoV-2 spread and communities that are tightly interconnected by exposure and transmission. We find that locations with higher vaccination coverage and lower numbers of visitors to points-of-interest had reduced within- and cross-ZIP code transmission events, highlighting potential measures for curtailing SARS-CoV-2 spread in urban settings. In this study, the authors analyse contact tracing records for ~650,000 suspected or confirmed COVID-19 cases in New York City during the second epidemic wave. They reconstruct transmission networks and find that vaccination and zone-based control policies likely contributed to control of the epidemic. [ABSTRACT FROM AUTHOR]

2 Density plots of maximum (orange) and mean (teal) daily temperature (A), heat index (B), and WBGT (C) on days with HSI case-definition encounters occurring at 24 US military installations from 1998 to 2019. Pairwise mean (A-C) and maximum (D-F) daily indices on all HSI encounter days throughout full temperature/index ranges (n = 31,642) Graph Fig S7. 4 Overall HSI odds ratios and 95% confidence intervals for mean (A-C) and maximum (D-F) daily indices (1998-2019). [Extracted from the article]

Heat stress illnesses represent a rising public health threat; however, associations between environmental heat and observed adverse health outcomes across populations and geographies remain insufficiently elucidated to evaluate risk and develop prevention strategies. In particular, military-relevant large-scale studies of daily heat stress morbidity responses among physically active, working-age adults to various indices of heat have been limited. We evaluated daily means, maximums, minimums, and early morning measures of temperature, heat index, and wet bulb globe temperature (WBGT) indices, assessing their association with 31,642 case-definition heat stroke and heat exhaustion encounters among active duty servicemembers diagnosed at 24 continental US installations from 1998 to 2019. We utilized anonymized encounter data consisting of hospitalizations, ambulatory (out-patient) visits, and reportable events to define heat stress illness cases and select the 24 installations with the highest case counts. We derived daily indices of heat from hourly-scale gridded climate data and applied a case-crossover study design incorporating distributed-lag, nonlinear models with 5 days of lag to estimate odds ratios at one-degree increments for each index of heat. All indices exhibited nonlinear odds ratios with short-term lag effects throughout observed temperature ranges. Responses were positive, monotonic, and exponential in nature, except for maximum daily WBGT, minimum daily temperature, temperature at 0600 h (local), and WBGT at 0600 h (local), which, while generally increasing, showed decreasing risk for the highest heat category days. The risk for a heat stress illness on a day with a maximum WBGT of 32.2 °C (90.0 °F) was 1.93 (95% CI, 1.82 – 2.05) times greater than on a day with a maximum WBGT of 28.6 °C (83.4 °F). The risk was 2.53 (2.36—2.71) times greater on days with a maximum heat index of 40.6 °C (105 °F) compared to 32.8 °C (91.0 °F). Our findings suggest that prevention efforts may benefit from including prior-day heat levels in risk assessments, from monitoring temperature and heat index in addition to WBGT, and by promoting control measures and awareness across all heat categories. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic disrupted health systems and economies throughout the world during 2020 and was particularly devastating for the United States, which experienced the highest numbers of reported cases and deaths during 20201–3. Many of the epidemiological features responsible for observed rates of morbidity and mortality have been reported4–8; however, the overall burden and characteristics of COVID-19 in the United States have not been comprehensively quantified. Here we use a data-driven model-inference approach to simulate the pandemic at county-scale in the United States during 2020 and estimate critical, time-varying epidemiological properties underpinning the dynamics of the virus. The pandemic in the United States during 2020 was characterized by national ascertainment rates that increased from 11.3% (95% credible interval (CI): 8.3–15.9%) in March to 24.5% (18.6–32.3%) during December. Population susceptibility at the end of the year was 69.0% (63.6–75.4%), indicating that about one third of the US population had been infected. Community infectious rates, the percentage of people harbouring a contagious infection, increased above 0.8% (0.6–1.0%) before the end of the year, and were as high as 2.4% in some major metropolitan areas. By contrast, the infection fatality rate fell to 0.3% by year’s end.Data-driven modelling including numbers of cases and population movements is used to simulate the COVID-19 pandemic in the United States in 2020, providing insights into the transmission of the disease. [ABSTRACT FROM AUTHOR]

To support COVID-19 pandemic planning, we develop a model-inference system to estimate epidemiological properties of new SARS-CoV-2 variants of concern using case and mortality data while accounting for under-ascertainment, disease seasonality, non-pharmaceutical interventions, and mass-vaccination. Applying this system to study three variants of concern, we estimate that B.1.1.7 has a 46.6% (95% CI: 32.3–54.6%) transmissibility increase but nominal immune escape from protection induced by prior wild-type infection; B.1.351 has a 32.4% (95% CI: 14.6–48.0%) transmissibility increase and 61.3% (95% CI: 42.6–85.8%) immune escape; and P.1 has a 43.3% (95% CI: 30.3–65.3%) transmissibility increase and 52.5% (95% CI: 0–75.8%) immune escape. Model simulations indicate that B.1.351 and P.1 could outcompete B.1.1.7 and lead to increased infections. Our findings highlight the importance of preventing the spread of variants of concern, via continued preventive measures, prompt mass-vaccination, continued vaccine efficacy monitoring, and possible updating of vaccine formulations to ensure high efficacy. Quantification of the transmissibility and immune escape properties of SARS-CoV-2 variants is necessary to support pandemic planning. Here, the authors develop a model inference system to estimate these properties using incidence and mortality data for three variants of concern. [ABSTRACT FROM AUTHOR]

Improved understanding of the effects of meteorological conditions on the transmission of SARS-CoV-2, the causative agent for COVID-19 disease, is needed. Here, we estimate the relationship between air temperature, specific humidity, and ultraviolet radiation and SARS-CoV-2 transmission in 2669 U.S. counties with abundant reported cases from March 15 to December 31, 2020. Specifically, we quantify the associations of daily mean temperature, specific humidity, and ultraviolet radiation with daily estimates of the SARS-CoV-2 reproduction number (Rt) and calculate the fraction of Rt attributable to these meteorological conditions. Lower air temperature (within the 20–40 °C range), lower specific humidity, and lower ultraviolet radiation were significantly associated with increased Rt. The fraction of Rt attributable to temperature, specific humidity, and ultraviolet radiation were 3.73% (95% empirical confidence interval [eCI]: 3.66–3.76%), 9.35% (95% eCI: 9.27–9.39%), and 4.44% (95% eCI: 4.38–4.47%), respectively. In total, 17.5% of Rt was attributable to meteorological factors. The fractions attributable to meteorological factors generally were higher in northern counties than in southern counties. Our findings indicate that cold and dry weather and low levels of ultraviolet radiation are moderately associated with increased SARS-CoV-2 transmissibility, with humidity playing the largest role. The role of meteorological factors in SARS-COV-2 transmission is not well understood. Here, the authors use county-level data from the United States to the end of 2020 and find evidence of a moderate association between increased transmissibility and cold, dry weather and low ultraviolet radiation. [ABSTRACT FROM AUTHOR]

Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens – human metapneumovirus and seasonal coronavirus – from 35 US states and can be used to guide systemic allocation of surveillance efforts. Lack of a widespread surveillance network hampers accurate infectious disease forecasting. Here the authors provide a framework to optimize the selection of surveillance site locations and show that accurate forecasting of respiratory diseases for locations without surveillance is feasible. [ABSTRACT FROM AUTHOR]

Childhood diarrheal disease causes significant morbidity and mortality in low and middle-income countries, yet our ability to accurately predict diarrhea incidence remains limited. El Niño-Southern Oscillation (ENSO) has been shown to affect diarrhea dynamics in South America and Asia. However, understanding of its effects in sub-Saharan Africa, where the burden of under-5 diarrhea is high, remains inadequate. Here we investigate the connections between ENSO, local environmental conditions, and childhood diarrheal disease in Chobe District, Botswana. Our results demonstrate that La Niña conditions are associated with cooler temperatures, increased rainfall, and higher flooding in the Chobe region during the rainy season. In turn, La Niña conditions lagged 0–5 months are associated with higher than average incidence of under-5 diarrhea in the early rainy season. These findings demonstrate the potential use of ENSO as a long-lead prediction tool for childhood diarrhea in southern Africa. Here, Heaney et al. show that La Niña conditions are associated with higher than average incidence of childhood diarrheal disease in Botswana in the early rainy season. This finding could help to predict childhood diarrhea outbreaks in southern Africa. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to describe and characterize the spatial and temporal clustering patterns of suicide in the ten states with the greatest suicide burden in the United States from 1999 to 2016.Methods: All suicide deaths from January 1, 1999 to December 31, 2016 in the United States were identified using data from the Wide-ranging Online Data for Epidemiologic Research (WONDER) dataset. The ten states with the highest age-adjusted suicide rates were Montana, Alaska, Wyoming, New Mexico, Nevada, Utah, Idaho, Colorado, Arizona, and Oklahoma. A spatiotemporal scan statistic using a discrete Poisson model was employed to retrospectively detect spatiotemporal suicide clusters.Results: From 1999 to 2016, a total of 649,843 suicides were recorded in the United States. Nineteen statistically significant spatiotemporal suicide mortality clusters were identified in the states with the greatest suicide rates, and 13.53% of the suicide cases within these states clustered spatiotemporally. The risk ratio of the clusters ranged from 1.45 to 3.64 (p < 0.001). All states had at least one cluster, with three clusters spanning multiple states, and four clusters were found in Arizona. While there was no clear secular trend in the average size of suicide clusters, the number of clusters increased from 1999 to 2016.Conclusions: Hot spots for suicidal behavior in the United States warrant public health intervention and continued surveillance. As suicide rates in the US continue to increase annually, public health efforts could be maximized by focusing on regions with substantial clustering. [ABSTRACT FROM AUTHOR]

For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models. [ABSTRACT FROM AUTHOR]

Little monitoring has been conducted of temperature and humidity inside homes despite the fact that these conditions may be relevant to health outcomes. Previous studies have observed associations between self-reported perceptions of the indoor environment and health. Here, we investigate associations between measured temperature and humidity, perceptions of indoor environmental conditions, and health symptoms in a sample of New York City apartments. We measured temperature and humidity in 40 New York City apartments during summer and winter seasons and collected survey data from the households' residents. Health outcomes of interest were (1) sleep quality, (2) symptoms of heat illness (summer season), and (3) symptoms of respiratory viral infection (winter season). Using mixed-effects logistic regression models, we investigated associations between the perceptions, symptoms, and measured conditions in each season. Perceptions of indoor temperature were significantly associated with measured temperature in both the summer and the winter, with a stronger association in the summer season. Sleep quality was inversely related to measured and perceived indoor temperature in the summer season only. Heat illness symptoms were associated with perceived, but not measured, temperature in the summer season. We did not find an association between any measured or perceived condition and cases of respiratory infection in the winter season. Although limited in size, the results of this study reveal that indoor temperature may impact sleep quality, and that thermal perceptions of the indoor environment may indicate vulnerability to heat illness. These are both important avenues for further investigation. [ABSTRACT FROM AUTHOR]

Influenza transmission occurs throughout the planet across wide-ranging environmental conditions. However, our understanding of the environmental factors mediating transmission is evaluated using outdoor environmental measurements, which may not be representative of the indoor conditions where influenza is transmitted. In this study, we examined the relationship between indoor environment and influenza transmission in a low-resource tropical population. We used a case-based ascertainment design to enroll 34 households with a suspected influenza case and then monitored households for influenza, while recording indoor temperature and humidity data in each household. We show that the indoor environment is not commensurate with outdoor conditions and that the relationship between indoor and outdoor conditions varies significantly across homes. We also show evidence of influenza transmission in extreme indoor environments. Specifically, our data suggests that indoor environments averaged 29 °C, 18 g/kg specific humidity, and 68 % relative humidity across 15 transmission events observed. These indoor settings also exhibited significant temporal variability with temperatures as high as 39 °C and specific and relative humidity increasing to 22 g/kg and 85 %, respectively, during some transmission events. However, we were unable to detect differences in the transmission efficiency by indoor temperature or humidity conditions. Overall, these results indicate that laboratory studies investigating influenza transmission and virus survival should increase the range of environmental conditions that they assess and that observational studies investigating the relationship between environment and influenza activity should use caution using outdoor environmental measurements since they can be imprecise estimates of the conditions that mediate transmission indoors. [ABSTRACT FROM AUTHOR]

Most SARS-CoV-2 infections are undocumented. French health-care records and modelling were used to assess the rate of documentation of COVID-19 cases. The findings highlight the need for improved identification of infections.Disease documentation assessed using medical records and modelling. [ABSTRACT FROM AUTHOR]

A number of recent studies have used model projections to investigate how the North Atlantic environment in which tropical storms develop, as well as hurricane activity itself, might change in a warming world. However, accurate projection of the North Atlantic environment in the future requires, at a minimum, accurate representation of its mean state and variability in the current climate. Here we examine one metric of Atlantic basin tropical cyclone variability-its well-documented association with the El Niño-Southern Oscillation (ENSO)-in reanalyses and Intergovernmental Panel of Climate Change (IPCC) 4th Assessment Report (AR4) twentieth century and Atmospheric Model Intercomparison Project simulations. We find that no individual model provides consistently good representation of ENSO-related variability in the North Atlantic for variables relevant to hurricane activity (e.g. vertical wind shear, genesis potential). Model representation of the ENSO influence is biased due to both inaccurate representation of ENSO itself and inaccurate representation of the response to ENSO within the North Atlantic. Among variables examined, ENSO impacts on vertical wind shear and potential intensity were most poorly simulated. The multi-model ensemble mean representation of North Atlantic environmental response to ENSO is better matched with reanalysis than most individual AR4 models; however, this mean response still possesses some considerable bias. A few models do provide comparable or slightly better simulation of these ENSO-North Atlantic teleconnections than the multi-model ensemble average; however, for both the multi-model mean and the well performing models, good simulation of the ENSO-related variability of genesis potential within portions of the North Atlantic does not stem from accurate representation of the ENSO-related variability of the individual environmental variables that comprise genesis potential (e.g. vertical wind shear, potential intensity). [ABSTRACT FROM AUTHOR]

We present a method for the ensemble seasonal prediction of human St. Louis encephalitis (SLE) incidence and SLE virus transmission in Florida. We combine empirical relationships between modeled land surface wetness and the incidence of human clinical cases of SLE and modeled land surface wetness and the occurrence of SLE virus transmission throughout south Florida with a previously developed method for generating ensemble, seasonal hydrologic forecasts. Retrospective seasonal forecasts of human SLE incidence are made for Indian River County, Florida, and forecast skill is demonstrated for 2-4 months. A sample seasonal forecast of human SLE incidence is presented. This study establishes the skill of a potential component of an operational SLE forecast system in south Florida, one that provides information well in advance of transmission and may enable early interventions that reduce transmission. Future development of this method and operational application of these forecasts are discussed. The methodology also will be applied to West Nile virus monitoring and forecasting. [ABSTRACT FROM AUTHOR]

During the 20th century the northeastern U.S.A. has undergone an annual temperature increase of 1 °C, the combined effect of winter warming and an increase in daily summer minimum temperatures. A significant cooling of spring through autumn in maximum air temperatures is also evident since 1950. Therefore, the primary objective of this study is to document these climate trends and variability over the last century. A secondary objective is to provide a preliminary analysis of how these changes may have impacted hydrologic and ecosystem processes. Specifically, with respect to ecosystem processes, we examine how the cooling of daytime maximum temperatures may have impacted plant respiration and biomass accumulation. The study site is the Black Rock Forest, an experimental forest located in Hudson Highlands of New York that has been maintained as a conservation area over the last 100 years. For the region centered about the forest, there exists a climate/weather record and an extensively maintained biomass record that extends continuously from the early part of the 20th century through present. With such an extensive physical and biological record to draw from, this forest provides a microcosm for studying how changes in 20th century local and regional climate may have impacted ecosystem processes such as species adaptation, biomass growth, and 20th century carbon sequestration. In a subsequent paper we will more extensively explore the relationship between this record of changing climate and eco-hydrological processes. [ABSTRACT FROM AUTHOR]

States that HLA-DM as a peptide-binding molecule is a critical regulatory molecule in class II-restricted antigen presentation. Suggestion on the functions at an intracellular site to promote class II molecule-peptide association; Modulation by the level of expression of HLA-DM.

The HLA-DM locus encodes class II-like A and B chains and apparently regulates the antigen presentation function of conventional major histocompatibility complex (MHC) class II molecules. Here we describe the HLA-DMB mutations in three presentation defective B lymphoblastoid cells lines (B-LCL), 7.19.6, 10.6.6, and 10.78.6, which express DMB transcripts of abnormal length. Mutant 7.19.6 has a C → T point mutation that introduces a 5′ splice site into exon 3 of DMB. The independently derived mutants, 10.6.6 and 10.78.6, each harbor a G → A mutation in exon 3 and also lack identical downstream segment of RNA. Mapping of DMB intron/exon borders, using a genomic clone, revealed that the segment missing in mutants 10.6.6 and 10.78.6 represents the fourth exon of DMB; no mutations were found within exon 4 in either 10.6.6 or 10.78.6, however. In addition, the DMB gene was found to have a six exon genomic structure, typical of MHC class II B genes. [ABSTRACT FROM AUTHOR]

Forecasting is beginning to be integrated into decision-making processes for infectious disease outbreak response. We discuss how technologies could accelerate the adoption of forecasting among public health practitioners, improve epidemic management, save lives, and reduce the economic impact of outbreaks. [ABSTRACT FROM AUTHOR]

In many social and biological networks, the collective dynamics of the entire system can be shaped by a small set of influential units through a global cascading process, manifested by an abrupt first-order transition in dynamical behaviors. Despite its importance in applications, efficient identification of multiple influential spreaders in cascading processes still remains a challenging task for large-scale networks. Here we address this issue by exploring the collective influence in general threshold models of cascading process. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a scalable algorithm for massively large-scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given the same number of seeds compared with other scalable heuristic approaches. [ABSTRACT FROM AUTHOR]