Optimizing Disease Outbreak Forecast Ensembles.

On the basis of historical influenza and COVID-19 forecasts, we found that more than 3 forecast models are needed to ensure robust ensemble accuracy. Additional models can improve ensemble performance, but with diminishing accuracy returns. This understanding will assist with the design of current and future collaborative infectious disease forecasting efforts.

Loss of the alpha subunit distal furin cleavage site blunts ENaC activation following Na+ restriction.

Epithelial Na+ channels (ENaCs) are activated by proteolysis of the α and γ subunits at specific sites flanking embedded inhibitory tracts. To examine the role of α subunit proteolysis in channel activation in vivo, we generated mice lacking the distal furin cleavage site in the α subunit (αF2M mice). On a normal Na+ control diet, no differences in ENaC protein abundance in kidney or distal colon were noted between wild-type (WT) and αF2M mice. Patch-clamp analyses revealed similar levels of ENaC activity in kidney tubules, while no physiologically relevant differences in blood chemistry or aldosterone levels were detected. Male αF2M mice did exhibit diminished ENaC activity in the distal colon, as measured by amiloride-sensitive short-circuit current (ISC). Following dietary Na+ restriction, WT and αF2M mice had similar natriuretic and colonic ISC responses to amiloride. However, single-channel activity was significantly lower in kidney tubules from Na+-restricted αF2M mice compared with WT littermates. ENaC α and γ subunit expression in kidney and distal colon were also enhanced in Na+-restricted αF2M vs. WT mice, in association with higher aldosterone levels. These data provide evidence that disrupting α subunit proteolysis impairs ENaC activity in vivo, requiring compensation in response to Na+ restriction. KEY POINTS: The epithelial Na+ channel (ENaC) is activated by proteolytic cleavage in vitro, but key questions regarding the role of ENaC proteolysis in terms of whole-animal physiology remain to be addressed. We studied the in vivo importance of this mechanism by generating a mouse model with a genetic disruption to a key cleavage site in the ENaC's α subunit (αF2M mice). We found that αF2M mice did not exhibit a physiologically relevant phenotype under normal dietary conditions, but have impaired ENaC activation (channel open probability) in the kidney during salt restriction. ENaC function at the organ level was preserved in salt-restricted αF2M mice, but this was associated with higher aldosterone levels and increased expression of ENaC subunits, suggesting compensation was required to maintain homeostasis. These results provide the first evidence that ENaC α subunit proteolysis is a key regulator of channel activity in vivo.

hubEnsembles: Ensembling Methods in R.

Combining predictions from multiple models into an ensemble is a widely used practice across many fields with demonstrated performance benefits. The R package hubEnsembles provides a flexible framework for ensembling various types of predictions, including point estimates and probabilistic predictions. A range of common methods for generating ensembles are supported, including weighted averages, quantile averages, and linear pools. The hubEnsembles package fits within a broader framework of open-source software and data tools called the "hubverse", which facilitates the development and management of collaborative modelling exercises.

Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Influence of proteolytic cleavage of ENaC's γ subunit upon Na+ and K+ handling.

The epithelial Na+ channel (ENaC) γ subunit is essential for homeostasis of Na+, K+, and body fluid. Dual γ subunit cleavage before and after a short inhibitory tract allows dissociation of this tract, increasing channel open probability (PO), in vitro. Cleavage proximal to the tract occurs at a furin recognition sequence (143RKRR146, in the mouse γ subunit). Loss of furin-mediated cleavage prevents in vitro activation of the channel by proteolysis at distal sites. We hypothesized that 143RKRR146 mutation to 143QQQQ146 (γQ4) in 129/Sv mice would reduce ENaC PO, impair flow-stimulated flux of Na+ (JNa) and K+ (JK) in perfused collecting ducts, reduce colonic amiloride-sensitive short-circuit current (ISC), and impair Na+, K+, and body fluid homeostasis. Immunoblot of γQ4/Q4 mouse kidney lysates confirmed loss of a band consistent in size with the furin-cleaved proteolytic fragment. However, γQ4/Q4 male mice on a low Na+ diet did not exhibit altered ENaC PO or flow-induced JNa, though flow-induced JK modestly decreased. Colonic amiloride-sensitive ISC in γQ4/Q4 mice was not altered. γQ4/Q4 males, but not females, exhibited mildly impaired fluid volume conservation when challenged with a low Na+ diet. Blood Na+ and K+ were unchanged on a regular, low Na+, or high K+ diet. These findings suggest that biochemical evidence of γ subunit cleavage should not be used in isolation to evaluate ENaC activity. Furthermore, factors independent of γ subunit cleavage modulate channel PO and the influence of ENaC on Na+, K+, and fluid volume homeostasis in 129/Sv mice, in vivo.NEW & NOTEWORTHY The epithelial Na+ channel (ENaC) is activated in vitro by post-translational proteolysis. In vivo, low Na+ or high K+ diets enhance ENaC proteolysis, and proteolysis is hypothesized to contribute to channel activation in these settings. Using a mouse expressing ENaC with disruption of a key proteolytic cleavage site, this study demonstrates that impaired proteolytic activation of ENaC's γ subunit has little impact upon channel open probability or the ability of mice to adapt to low Na+ or high K+ diets.

Magnesium in hypertension: mechanisms and clinical implications.

Hypertension is associated with increased risk of cardiovascular disease and death. Evidence suggests that Mg2+ depletion contributes to hypertension. It is estimated that 25% or more of the United States population experiences chronic, latent Mg2+ depletion. This review explores mechanisms by which Mg2+ influences blood pressure, modifying risk of hypertension and complicating its treatment. Mechanisms addressed include effects upon i) sympathetic tone, via the modulation of N-methyl-D-aspartate (NMDA) receptor and N-type Ca2+ channel activity, influencing catecholamine release from sympathetic nerve endings; ii) vascular tone, via alteration of L-type Ca2+ and endothelial nitric oxide synthase (eNOS) activity and prostacyclin release; iii) renal K+ handling, influencing systemic K+ balance and potentially indirectly influencing blood pressure; iv) aldosterone secretion from the adrenal cortex; and v) modulation of pro-hypertensive inflammatory processes in dendritic cells and macrophages, including activation of the NLR family pyrin domain containing 3 (NLRP3) inflammasome and stimulation of isolevuglandin (IsoLG) production. Discovery of these mechanisms has furthered our understanding of the pathogenesis of hypertension, with implications for treatment and has highlighted the role of Mg2+ balance in hypertension and cardiovascular disease.

Proteolytic Cleavage of the ENaC γ Subunit - Impact Upon Na + and K + Handling.

The ENaC gamma subunit is essential for homeostasis of Na + , K + , and body fluid. Dual subunit cleavage before and after a short inhibitory tract allows dissociation of this tract, increasing channel open probability (P O ), in vitro . Cleavage proximal to the tract occurs at a furin recognition sequence ( 143 RKRR 146 in mouse). Loss of furin-mediated cleavage prevents in vitro activation of the channel by proteolysis at distal sites. We hypothesized that 143 RKRR 146 mutation to 143 QQQQ 146 ( Q4 ) in 129/Sv mice would reduce ENaC P O , impair flow-stimulated flux of Na + (J Na ) and K + (J K ) in perfused collecting ducts, reduce colonic amiloride-sensitive short circuit current (I SC ), and impair Na + , K + , and body fluid homeostasis. Immunoblot of Q4/Q4 mouse kidney lysates confirmed loss of a band consistent in size with the furin-cleaved proteolytic fragment. However, Q4/Q4 male mice on a low Na + diet did not exhibit altered ENaC P O or flow-induced J Na , though flow-induced J K modestly decreased. Colonic amiloride-sensitive I SC in Q4/Q4 mice was not altered. Q4/Q4 males, but not females, exhibited mildly impaired fluid volume conservation when challenged with a low Na + diet. Blood Na + and K + were unchanged on a regular, low Na + , or high K + diet. These findings suggest that biochemical evidence of gamma subunit cleavage should not be used in isolation to evaluate ENaC activity. Further, factors independent of gamma subunit cleavage modulate channel P O and the influence of ENaC on Na + , K + , and fluid volume homeostasis in 129/Sv mice, in vivo .

Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States.

Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.

Mice lacking γENaC palmitoylation sites maintain benzamil-sensitive Na+ transport despite reduced channel activity.

Epithelial Na+ channels (ENaCs) control extracellular fluid volume by facilitating Na+ absorption across transporting epithelia. In vitro studies showed that Cys-palmitoylation of the γENaC subunit is a major regulator of channel activity. We tested whether γ subunit palmitoylation sites are necessary for channel function in vivo by generating mice lacking the palmitoylated cysteines (γC33A,C41A) using CRISPR/Cas9 technology. ENaCs in dissected kidney tubules from γC33A,C41A mice had reduced open probability compared with wild-type (WT) littermates maintained on either standard or Na+-deficient diets. Male mutant mice also had higher aldosterone levels than WT littermates following Na+ restriction. However, γC33A,C41A mice did not have reduced amiloride-sensitive Na+ currents in the distal colon or benzamil-induced natriuresis compared to WT mice. We identified a second, larger conductance cation channel in the distal nephron with biophysical properties distinct from ENaC. The activity of this channel was higher in Na+-restricted γC33A,C41A versus WT mice and was blocked by benzamil, providing a possible compensatory mechanism for reduced prototypic ENaC function. We conclude that γ subunit palmitoylation sites are required for prototypic ENaC activity in vivo but are not necessary for amiloride/benzamil-sensitive Na+ transport in the distal nephron or colon.

Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S.

The characteristics of influenza seasons vary substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. Uniting theoretical results from the forecasting literature with domain-specific forecasts from influenza outbreaks, we applied parametric forecast combination methods that simultaneously optimize model weights and calibrate the ensemble via a beta transformation and made adjustments to the methods to reduce their complexity. We used the beta-transformed linear pool, the finite beta mixture model, and their equal weight adaptations to produce ensemble forecasts retrospectively for the 2016/2017, 2017/2018, and 2018/2019 influenza seasons in the U.S. We compared their performance to methods that were used in the FluSight challenge to produce the FluSight Network ensemble, namely the equally weighted linear pool and the linear pool. Ensemble forecasts produced from methods with a beta transformation were shown to outperform those from the equally weighted linear pool and the linear pool for all week-ahead targets across in the test seasons based on average log scores. We observed improvements in overall accuracy despite the beta-transformed linear pool or beta mixture methods' modest under-prediction across all targets and seasons. Combination techniques that explicitly adjust for known calibration issues in linear pooling should be considered to improve probabilistic scores in outbreak settings.

The role of dietary magnesium deficiency in inflammatory hypertension.

Nearly 30% of adults consume less than the estimated average daily requirement of magnesium (Mg2+), and commonly used medications, such as diuretics, promote Mg2+ deficiency. Higher serum Mg2+ levels, increased dietary Mg2+ in-take, and Mg2+ supplementation are each associated with lower blood pressure, suggesting that Mg2+-deficiency contributes to the pathogenesis of hypertension. Antigen-presenting cells, such as monocytes and dendritic cells, are well-known to be involved in the pathogenesis of hypertension. In these cells, processes implicated as necessary for increased blood pressure include activation of the NLRP3 inflammasome, IL-1ß production, and oxidative modification of fatty acids such as arachidonic acid, forming isolevuglandins (IsoLGs). We hypothesized that increased blood pressure in response to dietary Mg2+-depletion leads to increased NLRP3, IL-1ß, and IsoLG production in antigen presenting cells. We found that a Mg2+-depleted diet (0.01% Mg2+ diet) increased blood pressure in mice compared to mice fed a 0.08% Mg2+ diet. Mg2+-depleted mice did not exhibit an increase in total body fluid, as measured by quantitative magnetic resonance. Plasma IL-1ß concentrations were increased (0.13 ± 0.02 pg/mL vs. 0.04 ± 0.02 pg/mL). Using flow cytometry, we observed increased NLRP3 and IL-1ß expression in antigen-presenting cells from spleen, kidney, and aorta. We also observed increased IsoLG production in antigen-presenting cells from these organs. Primary culture of CD11c+ dendritic cells confirmed that low extracellular Mg2+ exerts a direct effect on these cells, stimulating IL-1ß and IL-18 production. The present findings show that NLRP3 inflammasome activation and IsoLG-adduct formation are stimulated when dietary Mg2+ is depleted. Interventions and increased dietary Mg2+ consumption may prove beneficial in decreasing the prevalence of hypertension and cardiovascular disease.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

Physiology of a Forgotten Electrolyte-Magnesium Disorders.

Magnesium (Mg2+) is the second most common intracellular cation and the fourth most abundant element on earth. However, Mg2+ is a frequently overlooked electrolyte and often not measured in patients. While hypomagnesemia is common in 15% of the general population, hypermagnesemia is typically only found in preeclamptic women after Mg2+ therapy and in patients with ESRD. Mild to moderate hypomagnesemia has been associated with hypertension, metabolic syndrome, type 2 diabetes mellitus, CKD, and cancer. Nutritional Mg2+ intake and enteral Mg2+ absorption are important for Mg2+ homeostasis, but the kidneys are the key regulators of Mg2+ homeostasis by limiting urinary excretion to less than 4% while the gastrointestinal tract loses over 50% of the Mg2+ intake in the feces. Here, we review the physiological relevance of Mg2+, the current knowledge of Mg2+ absorption in the kidneys and the gut, the different causes of hypomagnesemia, and a diagnostic approach on how to assess Mg2+ status. We highlight the latest discoveries of monogenetic conditions causing hypomagnesemia, which have enhanced our understanding of tubular Mg2+ absorption. We will also discuss external and iatrogenic causes of hypomagnesemia and advances in the treatment of hypomagnesemia.

Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States.

Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.

Home Subcutaneous Magnesium Infusion in Refractory Hypomagnesemia: A Case Report.

We describe a patient with renal magnesium wasting and prolonged, symptomatic hypomagnesemia that was refractory to oral therapies and intermittent intravenous infusion who achieved near-normal serum magnesium levels with subcutaneous magnesium infusions. A woman in her 40s was seen in nephrology clinic for evaluation and management of severe, chronic hypomagnesemia because of renal magnesium wasting in combination with frequent diarrhea. Clinical manifestations associated with hypomagnesemia included muscle weakness, cognitive impairment, and frequent seizures. Her hypomagnesemia had persisted for more than 20 years despite maximal oral magnesium supplementation and frequent intravenous magnesium infusions. To provide slower delivery of parenteral magnesium, she was prescribed 2 g/d of magnesium sulfate, delivered subcutaneously. This was well tolerated, rapidly normalized her serum magnesium levels, and improved her symptoms. We briefly discuss modalities used for treatment of hypomagnesemia, including shortcomings of intravenous therapy and limited literature discussing efficacy and tolerability of subcutaneous infusions. This case report demonstrates the efficacy and safety of subcutaneous magnesium infusions in a patient with refractory hypomagnesemia and suggests that subcutaneous infusion may be safe and effective for treatment of refractory hypomagnesemia in other patients with urinary magnesium wasting.

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction.

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).

Influence of glycoprotein MUC1 on trafficking of the Ca2+-selective ion channels, TRPV5 and TRPV6, and on in vivo calcium homeostasis.

Polymorphism of the gene encoding mucin 1 (MUC1) is associated with skeletal and dental phenotypes in human genomic studies. Animals lacking MUC1 exhibit mild reduction in bone density. These phenotypes could be a consequence of modulation of bodily Ca homeostasis by MUC1, as suggested by the previous observation that MUC1 enhances cell surface expression of the Ca2+-selective channel, TRPV5, in cultured unpolarized cells. Using biotinylation of cell surface proteins, we asked whether MUC1 influences endocytosis of TRPV5 and another Ca2+-selective TRP channel, TRPV6, in cultured polarized epithelial cells. Our results indicate that MUC1 reduces endocytosis of both channels, enhancing cell surface expression. Further, we found that mice lacking MUC1 lose apical localization of TRPV5 and TRPV6 in the renal tubular and duodenal epithelium. Females, but not males, lacking MUC1 exhibit reduced blood Ca2+. However, mice lacking MUC1 exhibited no differences in basal urinary Ca excretion or Ca retention in response to PTH receptor signaling, suggesting compensation by transport mechanisms independent of TRPV5 and TRPV6. Finally, humans with autosomal dominant tubulointerstitial kidney disease due to frame-shift mutation of MUC1 (ADTKD-MUC1) exhibit reduced plasma Ca concentrations compared to control individuals with mutations in the gene encoding uromodulin (ADTKD-UMOD), consistent with MUC1 haploinsufficiency causing reduced bodily Ca2+. In summary, our results provide further insight into the role of MUC1 in Ca2+-selective TRP channel endocytosis and the overall effects on Ca concentrations.

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policymakers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision-makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.

Correction: Evaluating epidemic forecasts in an interval format.

[This corrects the article DOI: 10.1371/journal.pcbi.1008618.].

The United States COVID-19 Forecast Hub dataset.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.