Search

Abstract The global outbreak of mpox in 2022 and subsequent sporadic outbreaks in 2023 highlighted the importance of nonpharmaceutical interventions such as case isolation. Individual variations in viral shedding dynamics may lead to either premature ending of isolation for infectious individuals, or unnecessarily prolonged isolation for those who are no longer infectious. Here, we developed a modeling framework to characterize heterogeneous mpox infectiousness profiles – specifically, when infected individuals cease to be infectious – based on viral load data. We examined the potential effectiveness of three different isolation rules: a symptom-based rule (the current guideline in many countries) and rules permitting individuals to stop isolating after either a fixed duration or following tests that indicate that they are no longer likely to be infectious. Our analysis suggests that the duration of viral shedding ranges from 23 to 50 days between individuals. The risk of infected individuals ending isolation too early was estimated to be 8.8% (95% CI: 6.7–10.5) after symptom clearance and 5.4% (95% CI: 4.1–6.7) after 3 weeks of isolation. While these results suggest that the current standard practice for ending isolation is effective, we found that unnecessary isolation following the infectious period could be reduced by adopting a testing-based rule.

As we learned during the COVID-19 pandemic, vaccines are one of the most important tools in infectious disease control. To date, an unprecedentedly large volume of high-quality data on COVID-19 vaccinations have been accumulated. For preparedness in future pandemics beyond COVID-19, these valuable datasets should be analyzed to best shape an effective vaccination strategy. We are collecting longitudinal data from a community-based cohort in Fukushima, Japan, that consists of 2,407 individuals who underwent serum sampling two or three times after a two-dose vaccination with either BNT162b2 or mRNA-1273. Using the individually reconstructed time courses of the vaccine-elicited antibody response based on mathematical modeling, we first identified basic demographic and health information that contributed to the main features of the antibody dynamics, i.e., the peak, the duration, and the area under the curve. We showed that these three features of antibody dynamics were partially explained by underlying medical conditions, adverse reactions to vaccinations, and medications, consistent with the findings of previous studies. We then applied to these factors a recently proposed computational method to optimally fit an "antibody score", which resulted in an integer-based score that can be used as a basis for identifying individuals with higher or lower antibody titers from basic demographic and health information. The score can be easily calculated by individuals themselves or by medical practitioners. Although the sensitivity of this score is currently not very high, in the future, as more data become available, it has the potential to identify vulnerable populations and encourage them to get booster vaccinations. Our mathematical model can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics.

Chronic infection with hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. Thus, quantifying and understanding the dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, such study requires repeated liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because liver biopsy is potentially morbid and not common during hepatitis B treatment. We here aimed to develop a noninvasive method for quantifying cccDNA in the liver using surrogate markers in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations, integrates experimental data from in vitro and in vivo investigations. By applying this model, we roughly predicted the amount and dynamics of intrahepatic cccDNA within a certain range using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The noninvasive quantification of cccDNA using our proposed method holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

Abstract During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.

Summary: The pandemic HIV-1, HIV-1 group M, emerged from a single spillover event of its ancestral lentivirus from a chimpanzee. During human-to-human spread worldwide, HIV-1 diversified into multiple subtypes. Here, our interdisciplinary investigation mainly sheds light on the evolutionary scenario of the viral budding system of HIV-1 subtype C (HIV-1C), a most successfully spread subtype. Of the two amino acid motifs for HIV-1 budding, the P(T/S)AP and YPxL motifs, HIV-1C loses the YPxL motif. Our data imply that HIV-1C might lose this motif to evade immune pressure. Additionally, the P(T/S)AP motif is duplicated dependently of the level of HIV-1 spread in the human population, and >20% of HIV-1C harbored the duplicated P(T/S)AP motif. We further show that the duplication of the P(T/S)AP motif is caused by the expansion of the CTG triplet repeat. Altogether, our results suggest that HIV-1 has experienced a two-step evolution of the viral budding process during human-to-human spread worldwide.

Hematopoietic stem cells (HSCs) are somatic stem cells that continuously generate lifelong supply of blood cells through a balance of symmetric and asymmetric divisions. It is well established that the HSC pool increases with age. However, not much is known about the underlying cause for these observed changes. Here, using a novel method combining single-cell ex vivo HSC expansion with mathematical modeling, we quantify HSC division types (stem cell-stem cell (S-S) division, stem cell-progenitor cell (S-P) division, and progenitor cell-progenitor cell (P-P) division) as a function of the aging process. Our time-series experiments reveal how changes in these three modes of division can explain the increase in HSC numbers with age. Contrary to the popular notion that HSCs divide predominantly through S-P divisions, we show that S-S divisions are predominant throughout the lifespan of the animal, thereby expanding the HSC pool. We, therefore, provide a novel mathematical model-based experimental validation for reflecting HSC dynamics in vivo.

ABSTRACT Nelfinavir, an orally administered inhibitor of human immunodeficiency virus protease, inhibits the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in vitro. We conducted a randomized controlled trial to evaluate the clinical efficacy and safety of nelfinavir in patients with SARS-CoV-2 infection. We included unvaccinated asymptomatic or mildly symptomatic adult patients who tested positive for SARS-CoV-2 infection within 3 days before enrollment. The patients were randomly assigned (1:1) to receive oral nelfinavir (750 mg; thrice daily for 14 days) combined with standard-of-care or standard-of-care alone. The primary endpoint was the time to viral clearance, confirmed using quantitative reverse-transcription PCR by assessors blinded to the assigned treatment. A total of 123 patients (63 in the nelfinavir group and 60 in the control group) were included. The median time to viral clearance was 8.0 (95% confidence interval [CI], 7.0 to 12.0) days in the nelfinavir group and 8.0 (95% CI, 7.0 to 10.0) days in the control group, with no significant difference between the treatment groups (hazard ratio, 0.815; 95% CI, 0.563 to 1.182; P = 0.1870). Adverse events were reported in 47 (74.6%) and 20 (33.3%) patients in the nelfinavir and control groups, respectively. The most common adverse event in the nelfinavir group was diarrhea (49.2%). Nelfinavir did not reduce the time to viral clearance in this setting. Our findings indicate that nelfinavir should not be recommended in asymptomatic or mildly symptomatic patients infected with SARS-CoV-2. The study is registered with the Japan Registry of Clinical Trials (jRCT2071200023). IMPORTANCE The anti-HIV drug nelfinavir suppresses the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in vitro. However, its efficacy in patients with COVID-19 has not been studied. We conducted a multicenter, randomized controlled trial to evaluate the efficacy and safety of orally administered nelfinavir in patients with asymptomatic or mildly symptomatic COVID-19. Compared to standard-of-care alone, nelfinavir (750 mg, thrice daily) did not reduce the time to viral clearance, viral load, or the time to resolution of symptoms. More patients had adverse events in the nelfinavir group than in the control group (74.6% [47/63 patients] versus 33.3% [20/60 patients]). Our clinical study provides evidence that nelfinavir, despite its antiviral effects on SARS-CoV-2 in vitro, should not be recommended for the treatment of patients with COVID-19 having no or mild symptoms.

Utilizing viral test results to determine isolation length would minimize both the risk of prematurely ending isolation of infectious patients and the unnecessary individual burden of redundant isolation of noninfectious patients. Here, the authors develop a framework to compute the population-level risk of different isolation guidelines with rapid antigen tests.

Abstract Background Multiple waves of the COVID-19 epidemic have hit most countries by the end of 2021. Most of those waves are caused by emergence and importation of new variants. To prevent importation of new variants, combination of border control and contact tracing is essential. However, the timing of infection inferred by interview is influenced by recall bias and hinders the contact tracing process. Methods We propose a novel approach to infer the timing of infection, by employing a within-host model to capture viral load dynamics after the onset of symptoms. We applied this approach to ascertain secondary transmission which can trigger outbreaks. As a demonstration, the 12 initial reported cases in Singapore, which were considered as imported because of their recent travel history to Wuhan, were analyzed to assess whether they are truly imported. Results Our approach suggested that 6 cases were infected prior to the arrival in Singapore, whereas other 6 cases might have been secondary local infection. Three among the 6 potential secondary transmission cases revealed that they had contact history to previously confirmed cases. Conclusions Contact trace combined with our approach using viral load data could be the key to mitigate the risk of importation of new variants by identifying cases as early as possible and inferring the timing of infection with high accuracy.

Mutations continue to accumulate within the SARS-CoV-2 genome, and the ongoing epidemic has shown no signs of ending. It is critical to predict problematic mutations that may arise in clinical environments and assess their properties in advance to quickly implement countermeasures against future variant infections. In this study, we identified mutations resistant to remdesivir, which is widely administered to SARS-CoV-2-infected patients, and discuss the cause of resistance. First, we simultaneously constructed eight recombinant viruses carrying the mutations detected in in vitro serial passages of SARS-CoV-2 in the presence of remdesivir. We confirmed that all the mutant viruses didn't gain the virus production efficiency without remdesivir treatment. Time course analyses of cellular virus infections showed significantly higher infectious titers and infection rates in mutant viruses than wild type virus under treatment with remdesivir. Next, we developed a mathematical model in consideration of the changing dynamic of cells infected with mutant viruses with distinct propagation properties and defined that mutations detected in in vitro passages canceled the antiviral activities of remdesivir without raising virus production capacity. Finally, molecular dynamics simulations of the NSP12 protein of SARS-CoV-2 revealed that the molecular vibration around the RNA-binding site was increased by the introduction of mutations on NSP12. Taken together, we identified multiple mutations that affected the flexibility of the RNA binding site and decreased the antiviral activity of remdesivir. Our new insights will contribute to developing further antiviral measures against SARS-CoV-2 infection.

Although studies have demonstrated that infections with various viruses, bacteria, and parasites can modulate the immune system, no study has investigated changes in antibodies against microbial antigens after the COVID-19 mRNA vaccination. IgG antibodies against microbial antigens in the blood of vaccinees were comprehensively analyzed using microbial protein microarrays that carried approximately 5000 microbe-derived proteins. Changes in antibodies against microbial antigens were scrutinized in healthy participants enrolled in the Fukushima Vaccination Community Survey conducted in Fukushima Prefecture, Japan, after their second and third COVID-19 mRNA vaccinations. Antibody profiling of six groups stratified by antibody titer and the remaining neutralizing antibodies was also performed to study the dynamics of neutralizing antibodies against SARS-CoV-2 and the changes in antibodies against microbial antigens. The results showed that changes in antibodies against microbial antigens other than SARS-CoV-2 antigens were extremely limited after COVID-19 vaccination. In addition, antibodies against a staphylococcal complement inhibitor have been identified as microbial antigens that are associated with increased levels of neutralizing antibodies against SARS-CoV-2. These antibodies may be a predictor of the maintenance of neutralizing antibodies following the administration of a COVID-19 mRNA vaccine.

Abstract Men who have sex with men (MSM) have been disproportionally affected by the HIV epidemic in many countries, including Japan. Although pre-exposure prophylaxis (PrEP) is a strong prevention tool, it is not yet approved in Japan. A Markov model was developed to describe HIV infection and disease progression in an MSM cohort (N = 1000) in Japan receiving a PrEP program. The model was used to evaluate the cost-effectiveness of a PrEP program. HIV/AIDS treatment, screening, hospitalization due to AIDS, and PrEP were considered as costs and quality-adjusted life-years (QALYs) gained as utilities. Cost-effectiveness was assessed by comparing the incremental cost-effectiveness ratio (ICER) over a 30-year period against the willingness to pay (WTP) threshold. One-way sensitivity and probabilistic sensitivity analyses were performed. With 50% PrEP coverage, the PrEP program became dominant against the program without PrEP, using a threshold of 5.0 million JPY/QALY (45,455 USD). The probabilistic sensitivity analysis revealed that the PrEP program was dominant or at least cost-effective in most cases of 10,000 simulations. Therefore, preparing cheaper PrEP pills, which results in PrEP being dominant or ICER being lower than the WTP threshold, is important to make the program cost-effective. Introduction of PrEP to an MSM cohort in Japan would be cost-effective over a 30-year time horizon.

IntroductionA key feature of the COVID-19 pandemic has been the emergence of SARS-CoV-2 variants with different transmission characteristics. However, when a novel variant arrives in a host population, it will not necessarily lead to many cases. Instead, it may fade out, due to stochastic effects and the level of immunity in the population. Immunity against novel SARS-CoV-2 variants may be influenced by prior exposures to related viruses, such as other SARS-CoV-2 variants and seasonal coronaviruses, and the level of cross-reactive immunity conferred by those exposures.MethodsHere, we investigate the impact of cross-reactive immunity on the emergence of SARS-CoV-2 variants in a simplified scenario in which a novel SARS-CoV-2 variant is introduced after an antigenically related virus has spread in the population. We use mathematical modelling to explore the risk that the novel variant invades the population and causes a large number of cases, as opposed to fading out with few cases.ResultsWe find that, if cross-reactive immunity is complete (i.e. someone infected by the previously circulating virus is not susceptible to the novel variant), the novel variant must be more transmissible than the previous virus to invade the population. However, in a more realistic scenario in which cross-reactive immunity is partial, we show that it is possible for novel variants to invade, even if they are less transmissible than previously circulating viruses. This is because partial cross-reactive immunity effectively increases the pool of susceptible hosts that are available to the novel variant compared to complete cross-reactive immunity. Furthermore, if previous infection with the antigenically related virus assists the establishment of infection with the novel variant, as has been proposed following some experimental studies, then even variants with very limited transmissibility are able to invade the host population.DiscussionOur results highlight that fast assessment of the level of cross-reactive immunity conferred by related viruses against novel SARS-CoV-2 variants is an essential component of novel variant risk assessments.

Summary: Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-negative tensor factorization. We recruited healthcare workers who received two doses of the BNT162b2 mRNA COVID-19 vaccine at Chiba University Hospital and collected information on adverse reactions using a smartphone/web-based platform. We analyzed the adverse-reaction data after each dose obtained for 1,516 participants who received two doses of vaccine. The non-negative tensor factorization revealed four time-evolving components that represent typical temporal patterns of adverse reactions for both doses. These components were differently associated with background factors and post-vaccine antibody titers. These results demonstrate that complex adverse reactions against vaccines can be explained by a limited number of time-evolving components identified by tensor factorization.

Abstract Objectives The aim of this trial is to evaluate the antiviral efficacy, clinical efficacy, and safety of nelfinavir in patients with asymptomatic and mild COVID-19. Trial design The study is designed as a multicenter, open-label, blinded outcome assessment, parallel group, investigator-initiated, exploratory, randomized (1:1 ratio) controlled clinical trial. Participants Asymptomatic and mild COVID-19 patients will be enrolled in 10 university and teaching hospitals in Japan. The inclusion and exclusion criteria are as follows: Inclusion criteria: (1) Japanese male or female patients aged ≥ 20 years (2) SARS-CoV-2 detected from a respiratory tract specimen (e.g., nasopharyngeal swab or saliva) using PCR, LAMP, or an antigen test within 3 days before obtaining the informed consent (3) Provide informed consent Exclusion criteria: (1) Symptoms developed ≥ 8 days prior to enrolment (2) SpO2 < 96 % (room air) (3) Any of the following screening criteria: a) ALT or AST ≥ 5 × upper limit of the reference range b) Child-Pugh class B or C c) Serum creatinine ≥ 2 × upper limit of the reference range and creatinine clearance < 30 mL/min (4) Poorly controlled diabetes (random blood glucose ≥ 200 mg/dL or HbA1c ≥ 7.0%, despite treatment) (5) Unsuitable serious complications based on the assessment of either the principal investigator or the sub-investigator (6) Hemophiliac or patients with a marked hemorrhagic tendency (7) Severe diarrhea (8) Hypersensitivity to the investigational drug (9) Breastfeeding or pregnancy (10) With childbearing potential and rejecting contraceptive methods during the study period from the initial administration of the investigational drug (11) Receiving rifampicin within the previous 2 weeks (12) Participated in other clinical trials and received drugs within the previous 12 weeks (13) Undergoing treatment for HIV infection (14) History of SARS-CoV-2 vaccination or wishes to be vaccinated against SARS-CoV-2 (15) Deemed inappropriate (for miscellaneous reasons) based on the assessment of either the principal investigator or the sub-investigator Intervention and comparator Patients who meet the inclusion criteria and do not meet any of the exclusion criteria will be randomized to either the nelfinavir group or the symptomatic treatment group. The nelfinavir group will be administered 750 mg of nelfinavir orally, three times daily for 14 days (treatment period). However, if a participant tests negative on two consecutive PCR tests of saliva samples, administration of the investigational drug for that participant can be discontinued at the discretion of the investigators. The symptomatic treatment group will not be administered the investigational drug, but all other study procedures and conditions will be the same for both groups for the duration of the treatment period. After the treatment period of 14 days, each group will be followed up for 14 days (observational period). Main outcomes The primary endpoint is the time to negative conversion of SARS-CoV-2. During the study period from Day 1 to Day 28, two consecutive negative PCR results of saliva samples will be considered as the negative conversion of the virus. The secondary efficacy endpoints are as follows: For patients with both asymptomatic and mild disease: area under the curve of viral load, half decay period of viral load, body temperature at each time point, all-cause mortality, incidence rate of pneumonia, percentage of patients with newly developed pneumonia, rate of oxygen administration, and the percentage of patients who require oxygen administration. For asymptomatic patients: incidence of symptomatic COVID-19, incidence of fever (≥ 37.0 °C for two consecutive days), incidence of cough For patients with mild disease: incidence of defervescence (< 37.0 °C), incidence of recovery from clinical symptoms, incidence of improvement of each symptom The secondary safety endpoints are adverse events and clinical examinations. Randomization Patients will be randomized to either the nelfinavir group or the symptomatic treatment group using the electric data capture system (1:1 ratio, dynamic allocation based on severity [asymptomatic], and age [< 60 years]). Blinding (masking) Only the assessors of the primary outcome will be blinded (blinded outcome assessment). Numbers to be randomized (sample size) The sample size was determined based on our power analysis to reject the null hypothesis, S (t | z =1) = S (t | z = 0) where S is a survival function, t is time to negative conversion, and z denotes randomization group, by the log-rank test with a two-sided p value of 0.05. We estimated viral dynamic parameters by fitting a nonlinear mixed-effects model to reported viral load data, and simulated our primary endpoint from viral-load time-courses that were realized from sets of viral dynamics parameters sampled from the estimated probability distribution of the parameters (sample size: 2000; 1000 each for randomization group). From this estimation of the hazard ratio between the randomization groups for the event of negative conversion using this simulation dataset, the required number of events for rejecting our null hypothesis with a power of 0.80 felled 97.345 by plugging the estimated hazard ratio, 1.79, in Freedman’s equation. Therefore, we decided the required number of randomizations to be 120 after consideration of the frequency of censoring and the anticipated rate of withdrawal caused by factors such as withdrawal of consent. Trial Status Protocol version 6.0 of February 12, 2021. Recruitment started on July 22, 2020 and is anticipated to be completed by March 31, 2022. Trial registration This trial was registered in Japan Registry of Clinical Trials (jRCT) ( jRCT2071200023 ) on 21 July 21, 2020. Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines (Additional file 2).

In HIV-1-infected individuals, transmitted/founder (TF) virus contributes to establish new infection and expands during the acute phase of infection, while chronic control (CC) virus emerges during the chronic phase of infection. TF viruses are more resistant to interferon-alpha (IFN-α)-mediated antiviral effects than CC virus, however, its virological relevance in infected individuals remains unclear. Here we perform an experimental-mathematical investigation and reveal that IFN-α strongly inhibits cell-to-cell infection by CC virus but only weakly affects that by TF virus. Surprisingly, IFN-α enhances cell-free infection of HIV-1, particularly that of CC virus, in a virus-cell density-dependent manner. We further demonstrate that LY6E, an IFN-stimulated gene, can contribute to the density-dependent enhancement of cell-free HIV-1 infection. Altogether, our findings suggest that the major difference between TF and CC viruses can be explained by their resistance to IFN-α-mediated inhibition of cell-to-cell infection and their sensitivity to IFN-α-mediated enhancement of cell-free infection. Author summary HIV-1 experiences a strong bottleneck during transmission, and only the virus(es) with higher resistance to the host’s innate immunity, interferon (IFN), can be successfully transmitted. Because the IFN resistance tends to be disappeared during infection in infected individuals, this phenotype would be crucial for human-to-human transmission. By combining mathematical modeling with well-designed time-series viral infection experiments, we investigated the difference on the IFN resistance of two types of HIV-1, which were respectively isolated at the acute and chronic phases of infection, and classified it into two virus transmission modes, cell-free and cell-to-cell infections. We found that IFN suppresses HIV-1 cell-to-cell infection, but surprisingly, promotes cell-free infection. Moreover, the virus isolated during chronic infection is more sensitive to the IFN-mediated promoting effect than that isolated during acute infection. Our results suggest that HIV-1 selects different strategies to adapt to different host environments. We further provide an insight how viruses evolve to counteract or hijack the host immunity.

Cancer chemotherapy has been the most common cancer treatment. However, it has side effects that kill both tumor cells and immune cells, which can ravage the patient’s immune system. Chemotherapy should be administered depending on the patient’s immunity as well as the level of cancer cells. Thus, we need to design an efficient treatment protocol. In this work, we study a feedback control problem of tumor-immune system to design an optimal chemotherapy strategy. For this, we first propose a mathematical model of tumor-immune interactions and conduct stability analysis of two equilibria. Next, the feedback control is found by solving the Hamilton–Jacobi–Bellman (HJB) equation. Here, we use an upwind finite-difference method for a numerical approximate solution of the HJB equation. Numerical simulations show that the feedback control can help determine the treatment protocol of chemotherapy for tumor and immune cells depending on the side effects.

BackgroundDevelopment of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials.Methods and findingsA modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d-1 (95% CI: 1.06 to 1.27 d-1), 0.777 d-1 (0.716 to 0.838 d-1), and 0.450 d-1 (0.378 to 0.522 d-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation.ConclusionsIn this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

Since the start of the COVID-19 pandemic, two mainstream guidelines for defining when to end the isolation of SARS-CoV-2-infected individuals have been in use: the one-size-fits-all approach (i.e. patients are isolated for a fixed number of days) and the personalized approach (i.e. based on repeated testing of isolated patients). We use a mathematical framework to model within-host viral dynamics and test different criteria for ending isolation. By considering a fixed time of 10 days since symptom onset as the criterion for ending isolation, we estimated that the risk of releasing an individual who is still infectious is low (0–6.6%). However, this policy entails lengthy unnecessary isolations (4.8–8.3 days). In contrast, by using a personalized strategy, similar low risks can be reached with shorter prolonged isolations. The obtained findings provide a scientific rationale for policies on ending the isolation of SARS-CoV-2-infected individuals.

The incubation period, or the time from infection to symptom onset, of COVID-19 has usually been estimated by using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in the cases’ recall of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used reported data on viral load for 30 hospitalized patients from multiple countries (Singapore, China, Germany, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.85 days (95 % CI: 5.05, 6.77), 2.65 days (2.04, 3.41), and 12.99 days (9.98, 16.79), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach, especially when it is impractical to directly observe the infection event.

Summary: Antiviral treatments targeting the coronavirus disease 2019 are urgently required. We screened a panel of already approved drugs in a cell culture model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and identified two new agents having higher antiviral potentials than the drug candidates such as remdesivir and chroloquine in VeroE6/TMPRSS2 cells: the anti-inflammatory drug cepharanthine and human immunodeficiency virus protease inhibitor nelfinavir. Cepharanthine inhibited SARS-CoV-2 entry through the blocking of viral binding to target cells, while nelfinavir suppressed viral replication partly by protease inhibition. Consistent with their different modes of action, synergistic effect of this combined treatment to limit SARS-CoV-2 proliferation was highlighted. Mathematical modeling in vitro antiviral activity coupled with the calculated total drug concentrations in the lung predicts that nelfinavir will shorten the period until viral clearance by 4.9 days and the combining cepharanthine/nelfinavir enhanced their predicted efficacy. These results warrant further evaluation of the potential anti-SARS-CoV-2 activity of cepharanthine and nelfinavir.

Coronavirus disease 2019 (COVID-19) has caused serious public health, social, and economic damage worldwide and effective drugs that prevent or cure COVID-19 are urgently needed. Approved drugs including Hydroxychloroquine, Remdesivir or Interferon were reported to inhibit the infection or propagation of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2), however, their clinical efficacies have not yet been well demonstrated. To identify drugs with higher antiviral potency, we screened approved anti-parasitic/anti-protozoal drugs and identified an anti-malarial drug, Mefloquine, which showed the highest anti-SARS-CoV-2 activity among the tested compounds. Mefloquine showed higher anti-SARS-CoV-2 activity than Hydroxychloroquine in VeroE6/TMPRSS2 and Calu-3 cells, with IC50 = 1.28 μM, IC90 = 2.31 μM, and IC99 = 4.39 μM in VeroE6/TMPRSS2 cells. Mefloquine inhibited viral entry after viral attachment to the target cell. Combined treatment with Mefloquine and Nelfinavir, a replication inhibitor, showed synergistic antiviral activity. Our mathematical modeling based on the drug concentration in the lung predicted that Mefloquine administration at a standard treatment dosage could decline viral dynamics in patients, reduce cumulative viral load to 7% and shorten the time until virus elimination by 6.1 days. These data cumulatively underscore Mefloquine as an anti-SARS-CoV-2 entry inhibitor.

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2-3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.

Viral infection induces dynamic changes in transcriptional profiles. Virus-induced and antiviral responses are intertwined during the infection. Epstein-Barr virus (EBV) is a human gammaherpesvirus that provides a model of herpesvirus latency. To measure the transcriptome changes during the establishment of EBV latency, we infected EBV-negative Akata cells with EBV-EGFP and performed transcriptome sequencing (RNA-seq) at 0, 2, 4, 7, 10, and 14 days after infection. We found transient downregulation of mitotic division-related genes, reflecting reprogramming of cell growth by EBV, and a burst of viral lytic gene expression in the early phase of infection. Experimental and mathematical investigations demonstrate that infectious virions were not produced in the pre-latent phase, suggesting the presence of an abortive lytic infection. Fate mapping using recombinant EBV provided direct evidence that the abortive lytic infection in the pre-latent phase converges to latent infection during EBV infection of B-cells, shedding light on novel roles of viral lytic gene(s) in establishing latency. Furthermore, we find that the BZLF1 protein, which is a key regulator of reactivation, was dispensable for abortive lytic infection in the pre-latent phase, suggesting the divergent regulation of viral gene expressions from a productive lytic infection.

This was a retrospective cohort study, which aimed to investigate the factors associated with hesitancy to receive a third dose of a coronavirus disease 2019 (COVID-19) vaccine. A paper-based questionnaire survey was administered to all participants. This study included participants who provided answers in the questionnaire about whether they had an intent to receive a third dose of a vaccine. Data on sex, age, area of residence, adverse reactions after the second vaccination, whether the third vaccination was desired, and reasons to accept or hesitate over the booster vaccination were retrieved. Among the 2439 participants, with a mean (±SD) age of 52.6 ± 18.9 years, and a median IgG-S antibody titer of 324.9 (AU/mL), 97.9% of participants indicated their intent to accept a third vaccination dose. The logistic regression revealed that participants of a younger age (OR = 0.98; 95% CI: 0.96–1.00) and with a higher antibody level (OR = 2.52; 95% CI: 1.27–4.99) were positively associated with hesitancy over the third vaccine. The efficacy of the COVID-19 vaccine and concerns about adverse reactions had a significant impact on behavior regarding the third vaccination. A rapid increase in the booster dose rate is needed to control the pandemic, and specific approaches should be taken with these groups that are likely to hesitate over the third vaccine, subsequently increasing booster contact rate.

Interaction between filovirus glycoprotein (GP) and the Niemann-Pick C1 (NPC1) protein is essential for membrane fusion during virus entry. Some single-nucleotide polymorphism (SNPs) in two surface-exposed loops of NPC1 are known to reduce viral infectivity. However, the dependence of differences in entry efficiency on SNPs remains unclear. Using vesicular stomatitis virus pseudotyped with Ebola and Marburg virus GPs, we investigated the cell-to-cell spread of viruses in cultured cells expressing NPC1 or SNP derivatives. Eclipse and virus-producing phases were assessed by in vitro infection experiments, and we developed a mathematical model describing spatial-temporal virus spread. This mathematical model fit the plaque radius data well from day 2 to day 6. Based on the estimated parameters, we found that SNPs causing the P424A and D508N substitutions in NPC1 most effectively reduced the entry efficiency of Ebola and Marburg viruses, respectively. Our novel approach could be broadly applied to other virus plaque assays.

Virus proliferation involves gene replication inside infected cells and transmission to new target cells. Once positive-strand RNA virus has infected a cell, the viral genome serves as a template for copying ("stay-strategy") or is packaged into a progeny virion that will be released extracellularly ("leave-strategy"). The balance between genome replication and virion release determines virus production and transmission efficacy. The ensuing trade-off has not yet been well characterized. In this study, we use hepatitis C virus (HCV) as a model system to study the balance of the two strategies. Combining viral infection cell culture assays with mathematical modeling, we characterize the dynamics of two different HCV strains (JFH-1, a clinical isolate, and Jc1-n, a laboratory strain), which have different viral release characteristics. We found that 0.63% and 1.70% of JFH-1 and Jc1-n intracellular viral RNAs, respectively, are used for producing and releasing progeny virions. Analysis of the Malthusian parameter of the HCV genome (i.e., initial proliferation rate) and the number of de novo infections (i.e., initial transmissibility) suggests that the leave-strategy provides a higher level of initial transmission for Jc1-n, whereas, in contrast, the stay-strategy provides a higher initial proliferation rate for JFH-1. Thus, theoretical-experimental analysis of viral dynamics enables us to better understand the proliferation strategies of viruses, which contributes to the efficient control of virus transmission. Ours is the first study to analyze the stay-leave trade-off during the viral life cycle and the significance of the replication-release switching mechanism for viral proliferation.

Abstract HIV-1 accumulates changes in its genome through both recombination and mutation during the course of infection. For recombination to occur, a single cell must be infected by two HIV strains. These coinfection events were experimentally demonstrated to occur more frequently than would be expected for independent infection events and do not follow a random distribution. Previous mathematical modeling approaches demonstrated that differences in target cell susceptibility can explain the non-randomness, both in the context of direct cell-to-cell transmission, and in the context of free virus transmission (Q. Dang et al., Proc. Natl. Acad. Sci. USA 101:632-7, 2004: K. M. Law et al., Cell reports 15:2711-83, 2016). Here, we build on these notions and provide a more detailed and extensive quantitative framework. We developed a novel mathematical model explicitly considering the heterogeneity of target cells and analysed datasets of cell-free HIV-1 single and double infection experiments in cell culture. Particularly, in contrast to the previous studies, we took into account the different susceptibility of the target cells as a continuous distribution. Interestingly, we showed that the number of infection events per cell during cell-free HIV-1 infection follows a negative-binomial distribution, and our model reproduces these datasets.

[This corrects the article DOI: 10.1371/journal.ppat.1006348.].

APOBEC3 (A3) family proteins are DNA cytosine deaminases recognized for contributing to HIV-1 restriction and mutation. Prior studies have demonstrated that A3D, A3F, and A3G enzymes elicit a robust anti-HIV-1 effect in cell cultures and in humanized mouse models. Human A3H is polymorphic and can be categorized into three phenotypes: stable, intermediate, and unstable. However, the anti-viral effect of endogenous A3H in vivo has yet to be examined. Here we utilize a hematopoietic stem cell-transplanted humanized mouse model and demonstrate that stable A3H robustly affects HIV-1 fitness in vivo. In contrast, the selection pressure mediated by intermediate A3H is relaxed. Intriguingly, viral genomic RNA sequencing reveled that HIV-1 frequently adapts to better counteract stable A3H during replication in humanized mice. Molecular phylogenetic analyses and mathematical modeling suggest that stable A3H may be a critical factor in human-to-human viral transmission. Taken together, this study provides evidence that stable variants of A3H impose selective pressure on HIV-1.

Cell-to-cell viral infection, in which viruses spread through contact of infected cell with surrounding uninfected cells, has been considered as a critical mode of virus infection. However, since it is technically difficult to experimentally discriminate the two modes of viral infection, namely cell-free infection and cell-to-cell infection, the quantitative information that underlies cell-to-cell infection has yet to be elucidated, and its impact on virus spread remains unclear. To address this fundamental question in virology, we quantitatively analyzed the dynamics of cell-to-cell and cell-free human immunodeficiency virus type 1 (HIV-1) infections through experimental-mathematical investigation. Our analyses demonstrated that the cell-to-cell infection mode accounts for approximately 60% of viral infection, and this infection mode shortens the generation time of viruses by 0.9 times and increases the viral fitness by 3.9 times. Our results suggest that even a complete block of the cell-free infection would provide only a limited impact on HIV-1 spread.

Several APOBEC3 proteins, particularly APOBEC3D, APOBEC3F, and APOBEC3G, induce G-to-A hypermutations in HIV-1 genome, and abrogate viral replication in experimental systems, but their relative contributions to controlling viral replication and viral genetic variation in vivo have not been elucidated. On the other hand, an HIV-1-encoded protein, Vif, can degrade these APOBEC3 proteins via a ubiquitin/proteasome pathway. Although APOBEC3 proteins have been widely considered as potent restriction factors against HIV-1, it remains unclear which endogenous APOBEC3 protein(s) affect HIV-1 propagation in vivo. Here we use a humanized mouse model and HIV-1 with mutations in Vif motifs that are responsible for specific APOBEC3 interactions, DRMR/AAAA (4A) or YRHHY/AAAAA (5A), and demonstrate that endogenous APOBEC3D/F and APOBEC3G exert strong anti-HIV-1 activity in vivo. We also show that the growth kinetics of 4A HIV-1 negatively correlated with the expression level of APOBEC3F. Moreover, single genome sequencing analyses of viral RNA in plasma of infected mice reveal that 4A HIV-1 is specifically and significantly diversified. Furthermore, a mutated virus that is capable of using both CCR5 and CXCR4 as entry coreceptor is specifically detected in 4A HIV-1-infected mice. Taken together, our results demonstrate that APOBEC3D/F and APOBEC3G fundamentally work as restriction factors against HIV-1 in vivo, but at the same time, that APOBEC3D and APOBEC3F are capable of promoting viral diversification and evolution in vivo.

The precise role of viral protein R (Vpr), an HIV-1-encoded protein, during HIV-1 infection and its contribution to the development of AIDS remain unclear. Previous reports have shown that Vpr has the ability to cause G2 cell cycle arrest and apoptosis in HIV-1-infected cells in vitro. In addition, vpr is highly conserved in transmitted/founder HIV-1s and in all primate lentiviruses, which are evolutionarily related to HIV-1. Although these findings suggest an important role of Vpr in HIV-1 pathogenesis, its direct evidence in vivo has not been shown. Here, by using a human hematopoietic stem cell-transplanted humanized mouse model, we demonstrated that Vpr causes G2 cell cycle arrest and apoptosis predominantly in proliferating CCR5(+) CD4(+) T cells, which mainly consist of regulatory CD4(+) T cells (Tregs), resulting in Treg depletion and enhanced virus production during acute infection. The Vpr-dependent enhancement of virus replication and Treg depletion is observed in CCR5-tropic but not CXCR4-tropic HIV-1-infected mice, suggesting that these effects are dependent on the coreceptor usage by HIV-1. Immune activation was observed in CCR5-tropic wild-type but not in vpr-deficient HIV-1-infected humanized mice. When humanized mice were treated with denileukin diftitox (DD), to deplete Tregs, DD-treated humanized mice showed massive activation/proliferation of memory T cells compared to the untreated group. This activation/proliferation enhanced CCR5 expression in memory CD4(+) T cells and rendered them more susceptible to CCR5-tropic wild-type HIV-1 infection than to vpr-deficient virus. Taken together, these results suggest that Vpr takes advantage of proliferating CCR5(+) CD4(+) T cells for enhancing viremia of CCR5-tropic HIV-1. Because Tregs exist in a higher cycling state than other T cell subsets, Tregs appear to be more vulnerable to exploitation by Vpr during acute HIV-1 infection.

The human immune system functions to provide continuous body-wide surveillance to detect and eliminate foreign agents such as bacteria and viruses as well as the body's own cells that undergo malignant transformation. To counteract this surveillance, tumor cells evolve mechanisms to evade elimination by the immune system; this tumor immunoescape leads to continuous tumor expansion, albeit potentially with a different composition of the tumor cell population ("immunoediting"). Tumor immunoescape and immunoediting are products of an evolutionary process and are hence driven by mutation and selection. Higher mutation rates allow cells to more rapidly acquire new phenotypes that help evade the immune system, but also harbor the risk of an inability to maintain essential genome structure and functions, thereby leading to an error catastrophe. In this paper, we designed a novel mathematical framework, based upon the quasispecies model, to study the effects of tumor immunoediting and the evolution of (epi)genetic instability on the abundance of tumor and immune system cells. We found that there exists an optimum number of tumor variants and an optimum magnitude of mutation rates that maximize tumor progression despite an active immune response. Our findings provide insights into the dynamics of tumorigenesis during immune system attacks and help guide the choice of treatment strategies that best inhibit diverse tumor cell populations.

Although vaccination can be a useful tool for control of avian influenza epidemics, it might engender emergence of a vaccine-resistant strain. Field and experimental studies show that some avian influenza strains acquire resistance ability against vaccination. We investigated, in the context of the emergence of a vaccine-resistant strain, whether a vaccination program can prevent the spread of infectious disease. We also investigated how losses from immunization by vaccination imposed by the resistant strain affect the spread of the disease.We designed and analyzed a deterministic compartment model illustrating transmission of vaccine-sensitive and vaccine-resistant strains during a vaccination program. We investigated how the loss of protection effectiveness impacts the program. Results show that a vaccination to prevent the spread of disease can instead spread the disease when the resistant strain is less virulent than the sensitive strain. If the loss is high, the program does not prevent the spread of the resistant strain despite a large prevalence rate of the program. The epidemic's final size can be larger than that before the vaccination program. We propose how to use poor vaccines, which have a large loss, to maximize program effects and describe various program risks, which can be estimated using available epidemiological data.We presented clear and simple concepts to elucidate vaccination program guidelines to avoid negative program effects. Using our theory, monitoring the virulence of the resistant strain and investigating the loss caused by the resistant strain better development of vaccination strategies is possible.

In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multiscale modelling framework for estimating the local outbreak risk for a viral disease (the probability that a major outbreak results from a single case introduced into the population), accounting for within-host viral dynamics. Compared to population-level models previously used to estimate outbreak risks, our approach enables more detailed analysis of how the risk can be mitigated through pre-emptive interventions such as antigen testing. Considering SARS-CoV-2 as a case study, we quantify the within-host dynamics using data from individuals with omicron variant infections. We demonstrate that regular antigen testing reduces, but may not eliminate, the outbreak risk, depending on characteristics of local transmission. In our baseline analysis, daily antigen testing reduces the outbreak risk by 45% compared to a scenario without antigen testing. Additionally, we show that accounting for heterogeneity in within-host dynamics between individuals affects outbreak risk estimates and assessments of the impact of antigen testing. Our results therefore highlight important factors to consider when using multiscale models to design pre-emptive interventions against SARS-CoV-2 and other viruses. [ABSTRACT FROM AUTHOR]