Time to symptom elimination is a statistically inefficient end point for respiratory virus trials that ignores the most clinically relevant time points. Alternatively, clearance slope and area under the curve may efficiently capture improvement early during treatment when symptoms are most severe.

Broadly neutralizing antibodies (bnAbs) are a promising intervention for HIV prevention, therapy, and cure. bnAb optimization requires precise quantification of in vivo functions, many of which cannot be directly measured in humans. We therefore performed a mathematical modeling meta-analysis which integrated four clinical trials and reproduced serial bnAb concentrations, viral loads, and bnAb sensitivities (IC50) in 43 viremic trial participants who received an infusion of VRC01, VRC01LS, VRC07-523LS, 3BNC117 or 10-1074. We compared >300 mathematical models for their ability to recapitulate multi-strain HIV dynamics following bnAb infusion. For each bnAb, our best model identified a scaling factor of 36-462 to project in vivo activity from in vitro IC50, quantified Fc-mediated infected cell killing in humans over time, and projected the timing of bnAb-resistant strain emergence. Using this holistic profile, VRC07-523-LS was generally optimal.

Antiviral therapies such as nirmatrelvir-ritonavir are widely used for COVID-19, yet their real-world effectiveness and sources of heterogeneity in treatment response remain incompletely understood. Here, we integrate longitudinal viral load data from a large cohort of SARS-CoV-2 BA.2-infected patients in Shanghai (n=48,243) with a mechanistic within-host viral dynamics model coupled to pharmacokinetic/pharmacodynamic principles to quantify in vivo antiviral efficacy. We estimate that nirmatrelvir-ritonavir reduces viral production by approximately 55% on average. Treatment response exhibits substantial heterogeneity, with higher efficacy observed in vaccinated individuals and reduced efficacy in older adults. Sensitivity analyses demonstrate that the vaccination effect is robust across model specifications, whereas age-related differences depend on assumptions about early viral kinetics, highlighting structural identifiability challenges when analyzing sparse real-world data. These findings provide a mechanistic interpretation of heterogeneous treatment effects and establish a generalizable framework for integrating real-world clinical data with within-host models to inform antiviral optimization and personalized treatment strategies. ### Competing Interest Statement HY has received research funding from Sanofi Pasteur, Shenzhen Sanofi Pasteur Biological Products Co., Ltd, Shanghai Roche Pharmaceutical Company, and SINOVAC Biotech Ltd. None of the research funding is related to this work. ### Funding Statement This study was supported in part by the Ministry of Education, Singapore, under the Academic Research Fund Tier 1 Seed Award (RLMOE100201900000001) (to KE), a Singapore Ministry of Education Startup Grant (LKCMedicine-SUG, #022487-00001) (to KE), and Japan Science and Technology Agency (JST) PRESTO (JPMJPR23R3) (to KE). The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript. The authors and their institutions did not receive payment or services from any third party for any aspect of the submitted work beyond the stated funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics Committee of Huashan Hospital, Fudan University gave ethical approval for this work. Written informed consent was obtained from each participant. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data that support the findings of this study are not publicly available due to data governance and privacy restrictions but are available from the corresponding authors upon reasonable request and with permission from the data providers.

Importance:Remote sampling technologies are invaluable for protecting both participants and researchers when studying highly infectious diseases. When leveraged for longitudinal studies, remote sampling with transcriptomic readouts is a powerful tool for studying the host immune response. Additionally, remote study flexibility circumvents common barriers to research participation including length of commute, transportation, and scheduling, thereby expanding access to clinical research. Objective:In this work, we investigate the effectiveness of a remote study model for reaching women from underrepresented, underserved, and underreported (U3) populations. We sought to recruit individuals who qualify as underrepresented in clinical research, who are located in rural areas, or who come from disadvantaged backgrounds per the NIH definition. Design:In this longitudinal study, U3 women positive for COVID-19 were enrolled and followed over the course of 6 months. In the first month of the infection, participants (n = 40) self-collected a set of 5 nasal swabs, 5 homeRNA-stabilized blood samples, and 2 additional unstabilized blood samples at first and last sampling. Sampling time points were spaced 5 days apart, so that the total of the 5 time points was completed within 25 days. homeRNA is a platform for remote self-collection of blood samples with subsequent RNA stabilization. A subset of participants likely to develop post-acute sequelae of COVID-19 (PASC) and their age-matched controls were selected to self-collect an additional set of 5 nasal swabs and 5 homeRNA-stabilized blood samples during month 3 of study participation, with the same sampling frequency. All participants were resurveyed at months 4, 5, and 6 about their symptoms. Participants also completed surveys at each sampling and a more comprehensive survey about study experience after each set of 5 time points. Setting:This was a fully remote study with all sampling supplies and instructions shipped to the participants. Participants self-collected blood and nasal swabs at home and shipped these back to our lab for further processing. Surveys were administered electronically using REDCap. Participants:For this study, we enrolled women who were 18 or older, met the NIH criteria for U3, and who had tested positive for SARS-CoV-2 within a week of enrollment. Further, we excluded protected populations including individuals who were pregnant and/or incarcerated. Of the 334 individuals who completed the screening process, 65 were invited into the study based on the eligibility criteria and balancing age, race/ethnicity, and state of residence to closely correspond to the demographics of the United States. Of the 65 invited individuals, 40 were fully enrolled in the study and 39 completed all study components. Main Outcomes and Measures:Prior to the study, we proposed that the increased flexibility of a remote study design would allow for participation of populations underrepresented in clinical research. The primary measurements planned for this study consisted of usability data and general experience in a longitudinal study. These data were collected by self report using electronically administered surveys. The Consolidated Framework for Implementation Research (CFIR), a well-established implementation science framework, was used to guide the development of questions about usability and study experience. Results:40 women were recruited from 19 states, with diverse racial backgrounds (62% White, 15% Black or African American, 10% Asian, 5% American Indian or Alaska Native, 5% Other, 3% More than one race), a mostly even age distribution (26% ages 20 - 29, 15% ages 30 - 39, 31% ages 40 - 49, 28% ages 50+), and most of whom (80%) are categorized as having a disadvantaged background per the NIH. Survey responses show high satisfaction with the study, where all participants who completed the study (100%, n = 39/39) indicating that they would be willing to participate in a similar study again, with most (n = 32/39) indicating a willingness to participate for up to 4 years with around 15 samples collected per year. We note that 4 years was the longest time period that participants were able to select in their surveys, suggesting that participants may be willing to participate for even longer periods. Most (>90%) either agreed or strongly agreed that all components of the kit were easy to use. Conclusions and Relevance:The high retention (98%, n = 39/40) and satisfaction of participants in this study indicates the utility of a remote study design for longitudinal research. We also find that study topic, flexibility of study, and positive interactions with the study team are important factors for participant recruitment and retention. This work suggests that the increased flexibility of a fully remote design enables engagement of individuals who may otherwise be excluded from clinical research.

Despite a decrease in disease severity since the emergence of the severe acute respiratory syndrome coronavirus 2 Omicron variant, coronavirus disease-2019 (COVID-19) continues to pose a significant threat to patients with haematological malignancies (HM). Although repeated booster vaccinations enhance protection against severe illnesses in immunocompromised individuals, they remain at heightened risk of adverse outcomes. This underscores the crucial need for effective pharmacologic strategies to prevent and treat infection. This review examines current strategies for preventing severe COVID-19 in patients with HM, focusing on pre-exposure prophylaxis and early treatment of COVID-19. New monoclonal antibodies have been developed, offering effective pre-exposure prophylaxis. Antiviral agents and monoclonal antibodies demonstrated efficacy in limiting severe COVID-19 outcomes in patients with HM, though some patients, particularly the elderly, remain at risk of critical illness and death. Prolonged infection over months is also common, particularly in patients with lymphoid malignancies. Sustained viral shedding and ongoing mutation may be associated with chronic symptoms and is the likely source of several novel variants of concern that prolonged the pandemic. While HM subtype and advanced age are risk factors for severe or persistent COVID-19, there are no accurate tools for predicting individual risk. Given this uncertainty, prompt medical consultation, timely prescription of antiviral agents, and close monitoring are essential to minimize the risk of adverse outcomes in this vulnerable population.

Living with COVID-19 requires continued vigilance against the spread and emergence of variants of concern (VOCs). Rapid and accurate saliva diagnostic testing, alongside basic public health responses, is a viable option contributing to effective transmission control. Nevertheless, our knowledge regarding the dynamics of SARS-CoV-2 infection in saliva is not as advanced as our understanding of the respiratory tract. Here we analyzed longitudinal viral load data of SARS-CoV-2 in saliva samples from 144 patients with mild COVID-19 (a combination of our collected data and published data). Using a mathematical model, we successfully stratified infection dynamics into three distinct groups with clear patterns of viral shedding: viral shedding durations in the three groups were 11.5 days (95% CI: 10.6 to 12.4), 17.4 days (16.6 to 18.2), and 30.0 days (28.1 to 31.8), respectively. Surprisingly, this stratified grouping remained unexplained despite our analysis of 47 types of clinical data, including basic demographic information, clinical symptoms, results of blood tests, and vital signs. Additionally, we quantified the expression levels of 92 micro-RNAs in a subset of saliva samples, but these also failed to explain the observed stratification, although the mir-1846 level may have been weakly correlated with peak viral load. Our study provides insights into SARS-CoV-2 infection dynamics in saliva, highlighting the challenges in predicting the duration of viral shedding without indicators that directly reflect an individual's immune response, such as antibody induction. Given the significant individual heterogeneity in the kinetics of saliva viral shedding, identifying biomarker(s) for viral shedding patterns will be crucial for improving public health interventions in the era of living with COVID-19.

Early during the COVID-19 pandemic, non-human primate (NHP) infection models emerged as highly useful tools for preclinical screening of antiviral drugs. However, it is uncertain whether NHP models can be used to precisely inform optimal dosing in humans. We previously established and validated mathematical models which were fit to SARS-CoV-2 viral loads from human clinical trials. These models identified that plasma drug concentrations required to inhibit viral replication by 50% in humans ( in vivo EC50) differ substantially from in vitro EC50 estimates in cell culture systems. Here we apply models to sequential viral load data from SARS-CoV-2 infected rhesus macaques (RM) that were untreated or treated with nirmatrelvir/ritonavir, molnupiravir, or both drugs. We identify that equivalent plasma drug concentrations correspond to greater antiviral potency in lungs compared to nasal passages for nirmatrelvir and molnupiravir. Average nirmatrelvir antiviral efficacy in RM (30% in nasal passages and 46% in lungs) was estimated to be less than in humans (82%) due to shorter plasma drug half-life. Molnupiravir efficacy in RM (95% in nasal and 99% in lungs) is estimated to be similar to efficacy in humans against omicron variants. Our model estimates that 10-fold higher plasma nirmatelvir concentrations are needed in humans versus RM to achieve 50% reduction in viral replication, whereas 20-fold lower plasma molnupiravir concentrations are needed. Our results suggest that dose optimization in humans based on modeling of NHP viral loads is limited by drug-specific differences in pharmacokinetic, pharmacodynamic and virologic profiles, and that data from human phase 1 and 2 trials is better suited for this task.

ABSTRACT Antiviral clinical trial simulation (CTS) is a type of mathematical modeling that couples viral- immune dynamics (VID) unique to each human viral pathogen, with mechanistic, pharmacokinetic (PK), and pharmacodynamic (PD) drug characteristics. Validation is achieved by matching model output to detailed viral load trajectories from trials. Antiviral CTS can be applied at all stages of drug development to viruses with distinct shedding patterns. Models can capture the activity of small molecules, neutralizing antibodies, and cellular therapies, as well as combination strategies to enhance potency and avoid drug resistance. Several principles are observed across antiviral CTS models. First, PK and PD models that recapitulate drug levels and concentration-dependent antiviral activity are often necessary, but never sufficient to predict trial results. VID equations are also required to guide optimal treatment timing because expanding immune responses synergistically eliminate infection but are deleterious if too sustained or intense. Therefore, equivalent antiviral doses may have different efficacy if given during different infection stages. Second, antiviral CTS models identify effective plasma drug concentrations in humans, which are often poorly predicted by in vitro assays. Finally, models that do not consider drug mechanisms lead to incorrect efficacy estimates. Data-validated CTS is increasingly used to inform drug dose and dosing interval, treatment timing and duration, virologic endpoint selection, and sample size, particularly when applied to detailed phase 1 and 2 trial data. Given the high expense of antiviral licensure trials, CTS models are vital to optimize trial efficacy and de-risk the drug development process.

Herpes simplex virus 1 (HSV-1) infection of epithelial cells is lytic, while infection of neurons typically results in long-term latency. However, the rates at which HSV-1 replicates and spreads in epithelial cells versus neurons under low and high multiplicity of infection (MOI) conditions remain undefined. Identifying these rates requires the application of mathematical models to carefully designed viral kinetic experiments. It is also critical to differentiate the dynamics of infectious viral particles versus viral DNA, as both quantities are routinely measured in in vitro experiments and human studies using plaque assays and polymerase chain reactions, respectively. Here, we developed mechanistic mathematical models to describe HSV-1 dynamics after infection of epithelial Vero cells and neuronal N2A cells, at high (3) and low (0.01) MOI. Our model recapitulates the dynamics of cell-free and cell-associated viral DNA and plaque-forming units (PFU). In epithelial cells, the model describes a pre-productive eclipse phase with a mean duration of 10.9 and 12.8 hours prior to HSV DNA replication and PFU production, respectively. Cells exited the eclipse phase as early and late as 2.5 and 32 hours, respectively. Infected cells produced a single PFU for every 224 HSV DNA genomes. PFU egressed at a constant rate, whereas the HSV DNA egress rate increased over time, before saturating at a 15 times higher rate. Under low relative to high MOI conditions, Vero cells spent 7 hours longer in the eclipse phase, had a 12-hour delay prior to egress, and had a longer mean duration of productive infection (14 versus 3.5-hour half-life). Secondary epithelial cell infection in low MOI experiments was overwhelmingly due to cell-to-cell viral spread and originated from a small number of early-producer cells. Neuronal cells produced viruses at a 5-fold lower rate and had a longer (mean: 42 hours) and more variable eclipse phase, with some neurons remaining in eclipse for more than a week. Our results highlighted large differences in HSV egress rates, as well as infected cell eclipse phase duration and death rates, in epithelial cells versus neurons during low and high MOI infection. The observed viral dynamics in neurons reflect a balance between active replication and latency.

Molnupiravir is an antiviral medicine that induces lethal copying errors during SARS-CoV-2 RNA replication. Molnupiravir reduced hospitalization in one pivotal trial by 50% and had variable effects on reducing viral RNA levels in three separate trials. We used mathematical models to simulate these trials and closely recapitulated their virologic outcomes. Model simulations suggested lower antiviral potency against pre-Omicron SARS-CoV-2 variants than against Omicron. We estimated that in vitro assays underestimated in vivo potency by 6- to 7-fold against Omicron variants. Our model suggested that because polymerase chain reaction detects molnupiravir mutated variants, the true reduction in non-mutated viral RNA was underestimated by approximately 0.4 log10 in the two trials conducted while Omicron variants dominated. Viral area under the curve estimates differed significantly between non-mutated and mutated viral RNA. Our results reinforce past work suggesting that in vitro assays are unreliable for estimating in vivo antiviral drug potency and suggest that virologic endpoints for respiratory virus clinical trials should be catered to the drug mechanism of action.

Antiretroviral therapy (ART) suppresses HIV replication in people living with HIV (PWH), but a persistent population of reservoir cells prevents cure. Reservoir cells are mostly anatomically dispersed, latently infected CD4+ T cells harboring one copy of chromosomally integrated, replication-competent HIV proviral DNA. Despite their low frequency (0.01%-0.1%) among CD4+ T cells and the quiescence of most genetically intact proviruses, viremia usually recurs within weeks after ART cessation. When PWH are not on ART, the reservoir is sustained through viral infection and infected cell proliferation. During suppressive ART, HIV reservoir cells persist via mechanisms sustaining uninfected CD4+ T cells including antigen-responsive and homeostatic clonal proliferation, programmed cell death, and T cell subset differentiation. Rates of latently infected cell proliferation and death must exist in quasi-equilibrium to explain limited change in reservoir volume over decades of ART, and the rarity of cancers or lymphoproliferative disorders emerging from infected cells. Some reservoir cells are under additional selection forces during ART, illustrated by slightly higher clearance rates of genetically intact versus replication-defective HIV proviral DNA and by a gradual transition to a less transcriptionally active and more clonal reservoir. While a small but meaningful percentage of latently infected cells are negatively selected due to lytic viral replication or elimination by adaptive immune responses, most reservoir cell death occurs independently of harboring intact HIV DNA. Given that HIV is often a passenger in reservoir cells, CD4+ T cell proliferation, targeted death, and subset differentiation may be viable therapeutic targets for curative interventions.

Alphaviruses, including chikungunya virus (CHIKV), pose a significant global health threat, yet specific antiviral therapies remain unavailable. We evaluated combinations of three oral directly acting antiviral drugs (sofosbuvir (SOF), molnupiravir (MPV), and favipiravir (FAV)), which are approved for other indications, against CHIKV, Semliki Forest virus (SFV), Sindbis virus (SINV), and Venezuelan Equine Encephalitis virus (VEEV) in vitro and in vivo. We assessed antiviral efficacy in human skin fibroblasts and liver cells, as well as in a mouse model of CHIKV-induced arthritis. In human skin fibroblasts, synergistic antiviral effects were observed for combinations of MPV + SOF and FAV + SOF against CHIKV, and for FAV + SOF against SFV. In human liver cells, FAV + MPV conferred additive to synergistic activity against VEEV and SINV, while SOF synergized with FAV against SINV. In mice, MPV improved CHIKV-induced foot swelling and reduced systemic infectious virus titres. Combination treatment with MPV and SOF significantly reduced swelling and infectious titres compared to monotherapies of each drug. Sequencing of CHIKV RNA from joint tissue revealed that MPV caused dose-dependent increases in mutations in the CHIKV genome. Upon combination therapy of MPV with SOF, the number of mutations was significantly lower compared to monotherapy with several higher doses of MPV. Combining these approved oral nucleoside analogues confers potent suppression of multiple alphaviruses in vitro and in vivo with enhanced control of viral genetic evolution in face of antiviral pressure. These drug combinations may ultimately lead to the development of potent combinations of pan-family alphavirus inhibitors.

To inform cure in children living with HIV (CWH), we elucidated the dynamics and mechanisms underlying HIV persistence during antiretroviral therapy (ART). In 120 Kenyan CWH who initiated ART between 1-12 months of age, 55 had durable viral load suppression, and 65 experienced ART interruptions. We measured plasma HIV RNA levels, CD4+ T cell count, and levels of intact and defective HIV DNA proviruses via the cross-subtype intact proviral DNA assay (CS-IPDA). By modeling data from the durably suppressed subset, we found that during early ART (year 0-1 on ART), plasma RNA levels decayed rapidly and biphasically and intact and defective HIV DNA decayed with mean 3 and 9 month half-lives, respectively. After viral suppression was achieved (years 1-8 on ART), intact HIV DNA decay slowed to a mean 22 month half-life, whilst defective HIV DNA no longer decayed. In five CWH, we found individual CD4+ TCRβ clones wax and wane, but average kinetics resembled those of defective DNA and CD4 count, suggesting that differential decay of intact HIV DNA arises from selective pressures overlaying normal CD4+ T cell kinetics. Finally, by modeling HIV RNA and DNA in CWH with treatment interruptions, we linked temporary viremia to transient rises in HIV DNA, but long-term intact reservoirs were not strongly influenced, suggesting brief treatment interruptions may not significantly increase HIV reservoirs in children.

Human herpesvirus-8 (HHV-8) is a gamma herpesvirus linked to the development of Kaposi sarcoma (KS). KS is more common in persons living with HIV (PLWH), but endemic KS in HIV-negative individuals is also common in sub-Saharan Africa. HHV-8 shedding occurs in the oral mucosa and is likely responsible for transmission. The mechanistic drivers of different HHV-8 shedding patterns in infected individuals are unknown. We applied stochastic mathematical models to a longitudinal study of HHV-8 oral shedding in 295 individuals in Uganda who were monitored daily with oral swabs. Participants were divided into four groups based on whether they were HIV-negative or -positive, as well as KS-negative or -positive. In all groups, we observed a wide variance of shedding patterns, including no shedding, brief episodic low viral load shedding, prolonged episodic medium viral load shedding, and persistent high viral load shedding. Our model closely replicates patterns in individual data and attributes higher shedding rates to increased rates of viral reactivation and lower median viral load values to more rapid and effective engagement of cytolytic immune responses. Our model provides a framework for understanding different shedding patterns observed in individuals with HHV-8 infection.

Mathematical models have been used for about 30 years to improve our understanding of virus-host interaction, in particular during chronic infections. During the COVID-19 pandemic, these models have been used to provide insights into the natural history of acute SARS-CoV-2 infection, optimize antiviral treatment strategies, understand factors associated with transmission, and optimize surveillance systems. The impact of modeling has been accelerated by the availability of unprecedented multidimensional immune data from animal and human systems, which enhanced partnerships between experimentalists and theorists and led to exciting new modeling and statistical developments. In this mini review, we examine the lessons learned from the COVID-19 pandemic and discuss the main insights provided by mathematical models of viral dynamics at the different stages of the outbreak. Although we focus on respiratory infection, we also consider the new areas for development in anticipation of future acute infections from new or reemerging pathogens.

Fitting mathematical models of viral dynamics to serial, quantitative viral load data provides inferences on the mechanisms in virus infection. This process can reveal the speed and magnitude of viral replication, cell proliferation and death, immune responses, and/or treatment efficacy. Viral dynamics modeling involves developing conceptual models, translating them into equations, and applying the appropriate statistical tools to determine the optimal parameters such that the model recapitulates observations from human and animal infections. In this review, we outline the theoretical foundations needed to understand model fitting, parameter estimation, and what it means to achieve a good fit. We provide examples and explain the strengths and limitations of three commonly used model fitting approaches: individual fitting, population mixed effects fitting, and feature fitting. We briefly review fitting algorithms and highlight powerful available computer software packages that can be used for fitting and parameter estimation. We discuss different model types, parameter identifiability, and how future modeling efforts can leverage advances in multi-dimensional data. Finally, we conclude with simple guidelines for choosing the best approach based on available data and scientific questions.

Background:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccine showed high clinical efficacy against the ancestral variant, but immunological waning, emergence of new variants, and the durability of infection-induced immunity complicate the estimation of population-level effectiveness. We used mathematical modeling to calculate the proportion of hospitalizations averted by vaccination in Washington and Oregon. Methods:We used an age- and region-structured compartmental model of vaccine-induced and infection-induced immunity from January 2020 until December 2022. We parameterized the strength and durability of immunity via a meta-regression of vaccine efficacy. We calibrated a time-varying contact matrix to weekly hospitalizations reported by the Washington Department of Health and Oregon Health Authority. We validated our model with Centers for Disease Control and Prevention serosurveillance data. To estimate vaccine effectiveness, we created counterfactual scenarios with no vaccination either in the entire population or in select age groups. Results:We found that total hospitalizations were reduced 74% (95% credible interval [CrI], 69%-78%) and 15% (95% CrI, 9%-19%) by primary vaccination and boosters, respectively. Vaccination effectiveness was highest during the Alpha wave, averting 90% (95% CrI, 88%-93%) of hospitalizations and in people aged 65+, averting 78% (95% CrI, 73%-81%). Relative to only vaccinating individuals aged 50+, vaccination of individuals aged 18-49 averted 52% (95% CrI, 44%-58%) of hospitalizations overall and 42% (95% CrI, 35%-48%) of hospitalizations among individuals 65+. Conclusions:The SARS-CoV-2 vaccination program in Washington and Oregon averted the majority of hospitalizations. Vaccinating individuals aged 18-49 significantly reduced hospitalization among individuals aged 65+.