The following dashboards provide data on contagious respiratory viruses, including acute respiratory diseases, COVID-19, influenza (flu), and respiratory syncytial virus (RSV) in Massachusetts. The data presented here can help track trends in respiratory disease and vaccination activity across Massachusetts.

The main entity of this document is a taxonomy with accession number 1865945

The table includes 1 products with the active ingredient Influenza A Virus A/massachusetts/18/2022 (h3n2) Recombinant Hemagglutinin Antigen.

BackgroundInfluenza virus is one of the most common pathogens that cause viral pneumonia. During pneumonia, host immune inflammation regulation involves microbiota in the intestine and glycolysis in the lung tissues. In the clinical guidelines for pneumonia treatment in China, Ma Xing Shi Gan Decoction (MXSG) is a commonly prescribed traditional Chinese medicine formulation with significant efficacy, however, it remains unclear whether its specific mechanism of action is related to the regulation of intestinal microbiota structure and lung tissue glycolysis.ObjectiveThis study aimed to investigate the mechanism of action of MXSG in an animal model of influenza virus-induced pneumonia. Specifically, we aimed to elucidate how MXSG modulates intestinal microbiota structure and lung tissue glycolysis to exert its therapeutic effects on pneumonia.MethodsWe established a mouse model of influenza virus-induced pneumoni, and treated with MXSG. We observed changes in inflammatory cytokine levels and conducted 16S rRNA gene sequencing to assess the intestinal microbiota structure and function. Additionally, targeted metabolomics was performed to analyze lung tissue glycolytic metabolites, and Western blot and enzyme-linked immunosorbent assays were performed to assess glycolysis-related enzymes, lipopolysaccharides (LPSs), HIF-1a, and macrophage surface markers. Correlation analysis was conducted between the LPS and omics results to elucidate the relationship between intestinal microbiota and lung tissue glycolysis in pneumonia animals under the intervention of Ma Xing Shi Gan Decoction.ResultsMXSG reduced the abundance of Gram-negative bacteria in the intestines, such as Proteobacteria and Helicobacter, leading to reduced LPS content in the serum and lungs. This intervention also suppressed HIF-1a activity and lung tissue glycolysis metabolism, decreased the number of M1-type macrophages, and increased the number of M2-type macrophages, effectively alleviating lung damage caused by influenza virus-induced pneumonia.ConclusionMXSG can alleviate glycolysis in lung tissue, suppress M1-type macrophage activation, promote M2-type macrophage activation, and mitigate inflammation in lung tissue. This therapeutic effect appears to be mediated by modulating gut microbiota and reducing endogenous LPS production in the intestines. This study demonstrates the therapeutic effects of MXSG on pneumonia and explores its potential mechanism, thus providing data support for the use of traditional Chinese medicine in the treatment of respiratory infectious diseases.

Over 12 million people in the United States died from all causes between the beginning of January 2020 and August 21, 2023. Over 1.1 million of those deaths were with confirmed or presumed COVID-19.

Vaccine rollout in the United States Finding a safe and effective COVID-19 vaccine was an urgent health priority since the very start of the pandemic. In the United States, the first two vaccines were authorized and recommended for use in December 2020. One has been developed by Massachusetts-based biotech company Moderna, and the number of Moderna COVID-19 vaccines administered in the U.S. was over 250 million. Moderna has also said that its vaccine is effective against the coronavirus variants first identified in the UK and South Africa.

Tabulated individual data points for data reported in the associated publication: Spackman E, Suarez DL, Lee CW, Pantin-Jackwood MJ, Lee SA, Youk S, Ibrahim S. Efficacy of inactivated and RNA particle vaccines against a North American Clade 2.3.4.4b H5 highly pathogenic avian influenza virus in chickens. Vaccine. 2023 Nov 30;41(49):7369-7376. doi: 10.1016/j.vaccine.2023.10.070. Epub 2023 Nov 4. PMID: 37932132.Description of methodsVirusesThe highly pathogenic avian influenza virus (HPAIV) isolate A/turkey/Indiana/22-003707-003/2022 H5N1 (TK/IN/22) and A/Gyrfalcon/Washington/41088/2014 H5N8 (GF/WA/14) isolate were each propagated and titrated in embryonating specific pathogen free (SPF) chicken eggs using standard procedures and titers were determined using the Reed-Muench method.VaccinesAn in-house vaccine was produced by de novo synthesizing the HA gene of TK/IN/22 that was modified to be low pathogenic (LP) and placing it in a PR8 backbone using rg methods as described . The vaccine (SEP-22-N9) contained 6 genes from PR8 and a de novo synthesized N9 NA from A/blue winged teal/Wyoming/AH0099021/2016 (H7N9). The rg virus was inactivated by treatment with 0.1% beta-propiolactone. Vaccines were produced with Montanide ISA 71 VG (Seppic Inc., Fairfield, NJ) adjuvant at ambient temperature in a L5M-A high shear mixer (Silverson Machines, Inc., East Longmeadow, MA) for 30sec at 1,000rpm, then for 3min at 4,000rpm using an emulsifying screen in accordance with the adjuvant manufacturer’s instructions.Sham vaccine was prepared in-house using sterile phosphate buffered saline as described above.Commercial vaccines were supplied by the manufacturers. The commercial inactivated vaccine (1057.R1 serial 590088) (rgH5N1) (Zoetis Inc., Parsippany, NJ) was produced with the GF/WA/14 (clade 2.3.4.4c HA gene) and the remaining 7 gene segments including the NA from PR8 (1). The Sequivity vaccine (serial V040122NCF) (RP) (Merck and Co. Inc., Rahway, NJ) is an updated version of their replication restricted alphavirus vector vaccine that expresses the TK/IN/22 H5 HA (modified to be low pathogenic LP).Challenge study designThree-week-old, mixed sex, SPF white leghorn chickens (Gallus gallus domesticus) were obtained from in-house flocks and were randomly assigned to vaccine groups.All vaccines were administered by the subcutaneous route at the nape of the neck. Commercial vaccines were given at the volumes instructed by the manufacturer (0.5ml each). In-house vaccine was given at a dose of 512 hemagglutination units per bird in 0.5ml. Three weeks post vaccination chickens were challenged with 6.7 log10 50% egg infectious doses (EID50) of TK/IN/22 in 0.1ml by the intrachoanal route.Oropharyngeal (OP) and cloacal (CL) swabs were collected from all birds at 2-, 4-, and 7-days post challenge (DPC). Swabs were also collected from dead and euthanized sham vaccinates at 1DPC.To evaluate antibody-based DIVA-VI tests, blood for serum was collected from the RP and SEP-22-N9 vaccinated groups at 7, 10 and 14DPC because the SEP-22-N9 vaccine does not elicit antibodies to N1 and the RP vaccine does not elicit antibodies to the N1 or NP proteins.Mortality and morbidity were recorded for 14DPC after which time the remaining birds were euthanized. If birds were severely lethargic or had neurological signs they were euthanized and were counted as mortality at the next observation time for mean death time calculations.Evaluation of antibody titers based on prime-boost order with the RP and inactivated vaccinesTo determine if there was a difference in antibody levels based on the order of vaccination with the RP vaccine and an inactivated vaccine, groups of 20 chickens (hatch-mates of the chickens in the challenge study) were given one dose of each vaccine three weeks apart (Supplementary Table 1). The first dose was administered at three weeks of age using the RP or SEP-22-N9 vaccine as described above. Then a second dose of either the same vaccine or the other vaccine was administered three weeks later (six weeks of age). All birds were bled for serum three weeks after the second vaccination (nine weeks of age). Antibody was quantified by hemagglutination inhibition (HI) assay as described below using the homologous antigen (TK/IN/22).Quantitative rRT-PCR (qRRT-PCR)RNA was extracted from OP and CL swabs using the MagMax (Thermo Fisher Scientific, Waltham, MA) magnetic bead extraction kit with the modifications described by Das et al., (2). Quantitative real-time RT-PCR was conducted as described previously (3) on a QuantStudio 5 (Thermo Fisher Scientific). A standard curve was generated from a titrated stock of TK/IN/22 and was used to calculate titer equivalents using the real time PCR instrument’s software.Hemagglutination inhibition assayHemagglutination inhibition assays were run in accordance with standard procedures. All pre-challenge sera were tested against the challenge virus. Sera from birds vaccinated with the rgH5N1 vaccine were also tested against the vaccine antigen, GF/WA/14. Titers of 8 or below were considered non-specific binding, therefore negative.Commercial ELISAPre-vaccination sera from 30 chickens were tested to confirm the absence of antibodies to AIV with a commercial AIV antibody ELISA (IDEXX laboratories, Westbrook, ME) in accordance with the manufacturer’s instructions. Pre- and post-challenge sera from the RP vaccine group (the only vaccine utilized here that does not induce antibodies to the NP) were also tested with this ELISA to characterize the detection of anti-NP antibodies post-challenge.Enzyme-linked lectin assay (ELLA) and neuraminidase inhibition (NI) to detect N1 antibody in serum from challenged chickensThe ELLA assay was performed in accordance with a previously published protocol with minor modifications (4). Absorbance data were fit to a non-linear regression curve with Prism 9.5 (GraphPad Software LLC, Boston, MA) to determine the effective concentration, and the 98% effective concentration (EC98) of the N1 source virus was subsequently used for NI assays.To detect N1 antibody with the optimized N1 NA concentrations, serum samples from the sham, SEP-22-N9, and RP vaccinated groups collected pre-challenge, 7, 10 and 14DPC, were heat inactivated at 56°C for one hour and diluted 1:20 and 1:40 using sample dilution buffer. Equal volumes of the N1 NA source virus at a concentration of 2X EC98 was added to each of the diluted serum samples. Then 100µl of the serum-virus mixture was added to the fetuin coated plates after the fetuin plates were washed as described above for the NA assay. Fetuin plates with the serum-virus mixture were then incubated overnight (approximately 17-19hr) at 37°C. The NA assay protocol described above was followed for the remaining NI assay steps.The percent NI activity of individual serum samples was determined by subtracting percent NA activity from 100. To calculate the percent NA activity, the average background absorbance value was subtracted from the sample absorbance value. The result was then divided by the average value of the NA source virus only (no serum) wells then multiplying by 100. A cut-off value for NI activity for positive detection of N1 antibody from chickens post-challenge was calculated by adding three standard deviations to the mean value obtained from pre-challenge sera of corresponding vaccine group for each dilution tested (1:20 and 1:40).References1. Kapczynski DR, Sylte MJ, Killian ML, Torchetti MK, Chrzastek K, Suarez DL. Protection of commercial turkeys following inactivated or recombinant H5 vaccine application against the 2015U.S. H5N2 clade 2.3.4.4 highly pathogenic avian influenza virus. Vet Immunol Immunopathol. 2017;191:74-9. Epub 2017/09/13. doi: 10.1016/j.vetimm.2017.08.001.2. Das A, Spackman E, Pantin-Jackwood MJ, Suarez DL. Removal of real-time reverse transcription polymerase chain reaction (RT-PCR) inhibitors associated with cloacal swab samples and tissues for improved diagnosis of Avian influenza virus by RT-PCR. Journal of Veterinary Diagnostic Investigation. 2009;21(6):771-8.3. Spackman E, Senne DA, Myers TJ, Bulaga LL, Garber LP, Perdue ML, et al. Development of a real-time reverse transcriptase PCR assay for type A influenza virus and the avian H5 and H7 hemagglutinin subtypes. Journal of Clinical Microbiology. 2002;40(9):3256-60.4. Bernard MC, Waldock J, Commandeur S, Strauss L, Trombetta CM, Marchi S, et al. Validation of a Harmonized Enzyme-Linked-Lectin-Assay (ELLA-NI) Based Neuraminidase Inhibition Assay Standard Operating Procedure (SOP) for Quantification of N1 Influenza Antibodies and the Use of a Calibrator to Improve the Reproducibility of the ELLA-NI With Reverse Genetics Viral and Recombinant Neuraminidase Antigens: A FLUCOP Collaborative Study. Front Immunol. 2022;13:909297. Epub 2022/07/06.

Influenza A virus (IAV) infection can cause the often-lethal acute respiratory distress syndrome (ARDS) of the lung. Concomitantly, acute kidney injury (AKI) is frequently noticed during IAV infection, correlating with an increased mortality. The aim of this study was to elucidate the interaction of IAV with human kidney cells and, thereby, to assess the mechanisms underlying IAV-mediated AKI. We demonstrate productive replication of low and highly pathogenic IAV strains on primary and immortalized nephron cells. Comparison of our transcriptome and proteome analysis of H1N1-type IAV-infected human primary distal tubular cells (DTC) with existing data from H1N1-type IAV-infected lung and primary trachea cells revealed enrichment of specific factors responsible for regulated cell death in primary DTC, which could be targeted by specific inhibitors Primary human DTC were grown in six-well plates and either left uninfected or infected with H1N1pdm09 at a MOI = 1. Two replicates of each set were analyzed for transcript expression changes by RNAseq, while the remaining replicates were used for verification of RNAseq results by qPCR. Briefly, total RNA was extracted 12 hours after infection by Qiagen RNeasy Maxi Kit (Qiagen, Germany), including on-column removal of DNA by digestion with rDNase for 15 min at room temperature. For library preparation, 500 ng of RNA (each showing a RIN score from > 9) was used. Second to depletion of ribosomal RNA (QIAseq FastSelect RNA Removal Kit, Qiagen, Germany), directional libraries were prepared with a NEBNext® UltraTM Directional RNA Library Prep Kit for Illumina® sequencing (No. E7420S, New England Biolabs, Ipswich, MA, USA). Sequencing was performed by an Illumina NextSeq500® using a NextSeq® 500/550 High Output Kit v2 (75 cycles, No. FC-404-2005, Illumina, San Diego, CA, USA).

Differences in clearance of lung viral particles in vaccinated mice 4 days post-challenge with live ma-pH1N1.

This dataset represents preliminary weekly estimates of cumulative U.S. RSV-associated hospitalizations for the 2024-2025 season. Estimates are preliminary, and use reported weekly hospitalizations among laboratory-confirmed respiratory syncytial virus (RSV) infections. The data are updated week-by-week as new RSV-associated hospitalizations are reported to CDC from the RSV-NET surveillance system and include both new admissions that occurred during the reporting week, as well as those admitted in previous weeks that may not have been included in earlier reporting. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated hospitalizations that have occurred since October 1, 2024. For details, please refer to the publication [7].

Note: Data are preliminary and subject to change as more data become available. Rates for recent RSV-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

Note: Preliminary burden estimates are not inclusive of data from all RSV-NET sites. Due to model limitations, sites with small sample sizes can impact estimates in unpredictable ways and are excluded for the benefit of model stability. CDC is working to address model limitations and include data from all sites in final burden estimates.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

Note: After November 1, 2024, this dataset will no longer be updated due to a transition in NHSN Hospital Respiratory Data reporting that occurred on Friday, November 1, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Due to a recent update in voluntary NHSN Hospital Respiratory Data reporting that occurred on Wednesday, October 9, 2024, reporting levels and other data displayed on this page may fluctuate week-over-week beginning Friday, October 18, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. 

This dataset represents weekly respiratory virus-related hospitalization data and metrics aggregated to national and state/territory levels reported during two periods: 1) data for collection dates from August 1, 2020 to April 30, 2024, represent data reported by hospitals during a mandated reporting period as specified by the HHS Secretary; and 2) data for collection dates beginning May 1, 2024, represent data reported voluntarily by hospitals to CDC’s National Healthcare Safety Network (NHSN). NHSN monitors national and local trends in healthcare system stress and capacity for up to approximately 6,000 hospitals in the United States. Data reported represent aggregated counts and include metrics capturing information specific to COVID-19- and influenza-related hospitalizations, hospital occupancy, and hospital capacity. Find more information about reporting to NHSN at: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Source: COVID-19 hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN).

• Data source description (updated October 18, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or ‘COVID-19 hospital data’) are reported to HHS through CDC’s National Healthcare Safety Network based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data are voluntarily reported to NHSN as of May 1, 2024 until November 1, 2024, at which time CMS will require acute care and critical access hospitals to electronically report information via NHSN about COVID-19, Influenza, and RSV, hospital bed census and capacity, and limited patient demographic information, including age. Data for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary. Data for collection dates May 1, 2024, and onwards represent data reported voluntarily to NHSN; as such, data included represents reporting hospitals only for a given week and might not be complete or representative of all hospitals. NHSN monitors national and local trends in healthcare system stress and capacity for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. 
• Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data since December 1, 2020, have had error correction methodology applied; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailable.
• Metrics and inclusion criteria: Many hospital subtypes, including acute care and critical access hospitals, are included in the metric calculations included in this dataset. Psychiatric, rehabilitation, and religious non-medical hospital types, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are excluded from calculations. For a given metric calculation, hospitals that reported those data at least one day during a given week are included.
• Find full details on NHSN Hospital Respiratory Data (HRD) reporting guidance, including additional information on bed type definitions at https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Notes: May 10, 2024: Due to missing hospital data for the April 28, 2024 through May 4, 2024 reporting period, data for Commonwealth of the Northern Mariana Islands (CNMI) are not available for this period in the Weekly NHSN Hospitalization Metrics report released on May 10, 2024.

May 17, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Minnesota (MN), and Guam (GU) for the May 5,2024 through May 11, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 1, 2024.

May 24, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), and Minnesota (MN) for the May 12, 2024 through May 18, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 24, 2024.

May 31, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), and Minnesota (MN) for the May 19, 2024 through May 25, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 31, 2024.

June 7, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), Guam (GU), and Minnesota (MN) for the May 26, 2024 through June 1, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 7, 2024.

June 14, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), and Minnesota (MN) for the June 2, 2024 through June 8, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 14, 2024.

June 21, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Guam (GU), Virgin Islands (VI), and Minnesota (MN) for the June 9, 2024 through June 15, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 21, 2024.

June 28, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 16, 2024 through June 22, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 28, 2024.

July 5, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 23, 2024 through June 29, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 5, 2024.

July 12, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 30, 2024 through July 6, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 12, 2024.

July 19, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 7, 2024 through July 13, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 19, 2024.

July 26, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 13, 2024 through July 20, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 26, 2024.

August 2, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), West Virginia (WV), and Minnesota (MN) for the July 21, 2024 through July 27, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 2, 2024.

August 9, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Guam (GU), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 28, 2024 through August 3, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 9, 2024.

August 16, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 4, 2024 through August 10, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 16, 2024.

August 23, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 11, 2024 through August 17, 2024 reporting period are not available for the Weekly

This dataset represents preliminary estimates of cumulative U.S. RSV –associated disease burden estimates for the 2024-2025 season, including outpatient visits, hospitalizations, and deaths. Real-time estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed respiratory syncytial virus (RSV) infections. The data come from the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET), a surveillance platform that captures data from hospitals that serve about 8% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated disease burden estimates that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent RSV-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

Note: Preliminary burden estimates are not inclusive of data from all RSV-NET sites. Due to model limitations, sites with small sample sizes can impact estimates in unpredictable ways and are excluded for the benefit of model stability. CDC is working to address model limitations and include data from all sites in final burden estimates.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

Data are the individual group values for oral and cloacal virus shedding and antibody titers for reach treatment group from: Mo et al., The pathogenicity and transmission of live bird market H2N2 avian influenza viruses in chickens, Pekin ducks, and guinea fowl. Vet Mic 260:109180, 2021. https://doi.org/10.1016/j.vetmic.2021.109180 Methods: Six H2N2 low pathogenic avian influenza viruses from US LBMs were selected based on recency and to represent the different genotypes present in the live birds markets during the time period (i.e., the presence or absence of a NA stalk deletion): A/duck/PA/14-030488-5/2014 (Dk/PA/14), A/chicken/NY/16-032621-2/2016 (Ck/NY/16), A/chicken/CT/17-008911-4/2017 (Ck/CT/17), A/chicken/NY/18-002471-4/2018 (CK/NY/02471/18), A/chicken/NY/18-042097-3/2018 (Ck/NY/042097/18) and A/chicken/NY/19-012787-1/2019 (Ck/NY/19). Isolates were evaluated in White Leghorn chickens (Gallus gallus), guinea fowl (Numida meleagris) and Pekin ducks (Anas platyrhynchos). Chickens and guinea fowl were challenged at 4 weeks of age and Pekin ducks were challenged at 2 weeks of age with 6log10 of virus by the intra-choanal route. “Contact” birds, which were hatch-mates of the inoculated birds, were co-housed with the inoculated birds 24hrs post inoculation to evaluate transmission. Viral loads in OP and CL swabs collected at 2, 4, 7, 10, and 14 days post inoculation were determined by quantitative real-time reverse-transcriptase polymerase chain reaction (qRT-PCR). RNA was extracted from swabs using the MagMAX96 Viral RNA Isolation Kit (Thermo Fisher Scientific, Waltham, MA) and the KingFisher Flex Magnetic Particle Processing System (Thermo Fisher Scientific), with an additional wash step to remove inhibitors (Das et al., 2009). The qRT‐PCR for AIV detection was conducted based on the standard USDA M gene AIV qRT‐PCR procedure (Spackman et al., 2002) using an Applied Biosystems® 7500 Fast Real‐Time PCR system (Thermo Fisher Scientific). Cycle threshold (Ct) values were determined by the 7500 Fast Software v2.3. For relative quantification, Ct values were converted to titer equivalents based on the standard curve method (Larionov et al., 2005). Values were established from ten-fold dilutions of the same titrated stock of the virus used to challenge the birds. The limit of detection was determined to be 0.8Log10 per reaction. Serological testing for antibodies to the virus utilized the hemagglutination inhibition (HI) assays using homologous antigens were performed to quantify antibody responses with serum collected from chickens, guinea fowl and Pekin ducks at 14 dpi based on the standard protocol (OIE, 2019). HI titers were reported as reciprocal log2 titers, and titers greater than 3 log2 (1:8) were considered positive. Resources in this dataset:Resource Title: H2N2 influenza pathobiology data for avian species. File Name: H2N2 data for Archive.xlsxResource Description: Data by day post exposure for birds exposed to low pathogenic H2N2 avian influenza virus.

Influenza epidemics represent a significant threat to global public health, primarily caused by the influenza viruses A and B. Although antiviral drugs targeting the influenza virus, such as zanamivir and oseltamivir, are clinically available, the emergence of virus evolution and drug resistance necessitates the development of host-directed therapies. Protein kinases are essential components of host signaling pathways, including the orchestration of virus–host interactions. By screening a library of kinase inhibitors, we identified that OTS167, a pharmacological inhibitor of maternal embryonic leucine zipper kinase (MELK), strongly inhibits the infections caused by multiple influenza virus subtypes in cell culture. This antiviral activity was further confirmed by treatment with another MELK pharmacological inhibitor, MELK-8a, and siRNA-mediated MELK gene silencing. In mice challenged with the influenza A virus, treatment with OTS167 inhibited both viral replication and lung inflammation. Mechanistically, inhibition of MELK by OTS167 downregulates the downstream effector CDK1, thereby inhibiting influenza virus M1 mRNA splicing to reduce viral replication and virus particle assembly. Finally, we demonstrated that combining OTS167 with zanamivir or oseltamivir resulted in additive antiviral activity. In conclusion, we identified MELK as a crucial host kinase that supports the influenza virus infection. OTS167, a pharmacological inhibitor of MELK currently undergoing phase II clinical trials for treating cancer, potently inhibits influenza virus infections in vitro and in mice, representing a promising lead for developing novel influenza antivirals.

This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

This dataset represents preliminary weekly estimates of cumulative U.S. COVID-19-associated hospitalizations for the 2024-2025 period. The weekly cumulatve COVID-19 –associated hospitalization estimates are preliminary, and use reported weekly hospitalizations among laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data are updated week-by-week as new COVID-19 hospitalizations are reported to CDC from the COVID-NET system and include both new admissions that occurred during the reporting week, as well as those admitted in previous weeks that may not have been included in earlier reporting. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated hospitalizations that have occurred since October 1, 2024. For details, please refer to the publication [7].

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

We performed single-cell RNA sequencing (scRNA-Seq) on nasal wash cells freshly collected from adults with COVID-19, influenza A, or no disease (healthy). Major cell types and subtypes were defined using cluster analysis and classic transcriptional markers. Seq-Well single-cell RNA-Seq analysis of cells taken from nasal wash samples from healthy donors and patients diagnosed with either COVID-19 or influenza A

This dataset contains all the data to replicate the analysis performed in the manuscript entitled: Joint COVID-19 and Influenza Forecasts in the United States using Internet Search Information

Influenza Home Testing Market Outlook

The global Influenza Home Testing market size was valued at approximately $1.2 billion in 2023 and is projected to reach around $2.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 9.7%. This robust growth can be attributed to the increasing demand for convenient and rapid diagnostic solutions, coupled with heightened consumer awareness regarding the timely detection and management of influenza.

One of the primary growth factors driving the Influenza Home Testing market is the rising incidence of influenza worldwide. Seasonal influenza affects millions of individuals each year, leading to significant morbidity and mortality, especially among vulnerable populations such as the elderly, young children, and individuals with pre-existing health conditions. The growing awareness regarding the benefits of early diagnosis and prompt treatment in mitigating the severity of the illness has significantly spurred the demand for home testing kits. Additionally, the COVID-19 pandemic has emphasized the importance of self-testing, thereby accelerating the adoption of home diagnostic solutions for various infectious diseases, including influenza.

Technological advancements in the field of diagnostic testing have also played a crucial role in driving market growth. The development of rapid influenza diagnostic tests (RIDTs) and molecular influenza tests has revolutionized the landscape of home testing. These tests offer high accuracy, ease of use, and quick results, thus enabling individuals to make informed decisions about their health without needing to visit healthcare facilities. The continuous innovation and improvement in test sensitivity and specificity are expected to further enhance the adoption of influenza home testing kits in the coming years.

Moreover, the increasing focus on preventive healthcare and the shift towards personalized medicine are encouraging consumers to take proactive measures in managing their health. Home testing kits for influenza provide the convenience of self-administration and reduce the need for frequent doctor visits, thereby saving time and reducing healthcare costs. The growing trend of at-home healthcare, combined with the rising investment in healthcare infrastructure and diagnostic technologies, is likely to propel the market growth during the forecast period.

On a regional level, North America currently dominates the Influenza Home Testing market, owing to the high prevalence of influenza, advanced healthcare infrastructure, and the presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the increasing healthcare expenditure, growing awareness about early diagnosis, and the large population base. The rapid urbanization and improving access to healthcare services in emerging economies like China and India are also contributing to the regional market expansion.

The Influenza A B Fast Test Kit is a notable innovation in the realm of influenza diagnostics, offering a rapid and reliable solution for detecting both types A and B influenza viruses. These test kits are designed to provide quick results, typically within minutes, allowing individuals to make timely decisions regarding their health and treatment options. The convenience of the Influenza A B Fast Test Kit makes it an ideal choice for home use, especially during peak flu seasons when the risk of infection is high. By enabling early detection, these kits play a crucial role in preventing the spread of the virus and reducing the overall impact of influenza outbreaks. The integration of advanced technologies in these test kits ensures high accuracy and ease of use, further driving their adoption among consumers seeking efficient diagnostic solutions.

Product Type Analysis

The Influenza Home Testing market is segmented based on product type into Rapid Influenza Diagnostic Tests (RIDTs) and Molecular Influenza Tests. Rapid Influenza Diagnostic Tests are widely used due to their ability to provide results within 15-30 minutes. These tests are designed to detect the presence of influenza antigens in a patient's sample, usually collected via nasal or throat swabs. The ease of use and quick turnaround time make RIDTs a preferred choice for home testing, especially in cases where immediate decision-making is crucial. The affordability and widespread availability of RIDTs further contribute to their substantial ma

Little is known about the pathobiology of SARS-CoV-2 infection in sub-Saharan Africa, where severe COVID-19 fatality rates are among the highest in the world and the immunological landscape is unique. In a prospective cohort study of 306 adults encompassing the entire clinical spectrum of SARS-CoV-2 infection in Uganda, we integrated profiling of the peripheral blood proteome and transcriptome to dissect the immunopathology of COVID-19 across multiple phases of the pandemic. Beyond the prognostic importance of myeloid cell-driven immune activation and lymphopenia, we show that multifaceted impairment of host protein synthesis and extensive redox imbalance define core biological signatures of severe COVID-19, with central roles for IL-7, IL-15, and lymphotoxin-α in COVID-19 respiratory failure. While prognostic signatures were generally consistent in SARS-CoV-2/HIV-coinfection, type I interferon responses uniquely scaled with COVID-19 severity in persons living with HIV. Throughout the p..., In cryopreserved serum samples from adults with SARS-CoV-2 infection and influenza and non-influenza severe acute respiratory infection in Uganda, concentrations of 48 soluble immune mediators were quantified using the Human Cytokine/Chemokine 48-Plex Discovery Assay Array (Eve Technologies, Calgary, Alberta, Canada; MilliporeSigma, Burlington, MA, USA). Samples from different pathogen and clinical severity groups were randomized across sample plates and analyzed by technicians blinded to pathogen and severity status. Soluble mediators were quantitated in duplicate with the mean concentration used for analysis presented. Values below the lower limit of assay quantification were replaced with the lowest value that could be reliably quantified for that particular mediator. Values above the upper limit of quantification were replaced with the highest standard curve value for each particular mediator. ,

Although Ma Huang Tang (MHT) has long been considered as a classical formula for respiratory infections like influenza, bronchitis and asthma, its chemical ingredients that really exert the main efficacy are still obscure. In this study we aimed to screen its antiviral components and investigate the potential mechanisms. The MDCK cellular research results showed that, among nine predominant ingredients of MHT, L-methylephedrin (LMEP), L-ephedrine (LEP) and D-pseudo- ephedrine (DPEP) significantly inhibited the proliferation of influenza A virus in vitro, and the inhibitory effect at 24 h after the treatment was more obvious than that at 48 h. They also significantly inhibited the mRNA expression levels of related genes in the TLR3, TLR4 and TLR7 signaling pathways, which were accompanied with the down-regulation of TNF-α level and the up-regulation of IFN-β level in the cell supernatant. Therefore, three Ephedra alkaloids exert an antiviral effect in vitro which may be closely related to the inhibition of viral replication and the modulation of inflammatory response. Animal research further indicated, at the 3rd and 7th days after infection, LEP and DPEP significantly attenuated lung injury, decreased lung index, virus load in the lung and the level of IL-1β in serum, inhibited the mRNA expression levels of TNF-α, TLR3, TLR4, TLR7, MyD88, NF-κB p65 and RIG-1 as well as the protein expression levels of TLR4, TLR7, MyD88 and NF-κB p65 and markedly increased thymus index, the level of IL-10 in serum and the mRNA expression level of IFN-γ. LEP and DPEP have certain protective effects on the influenza virus-infected mice, which may be associated with their abilities of effectively alleviating lung injury, improving the immunologic function of infected mice and adjusting the host’s TLRs and RIG-1 pathways. The overall findings demonstrate that, as effective and inexpensive natural substances, Ephedra alkaloids and MHT may have potential utility in clinical management.