Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1371/journal.pcbi.1011173

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1111/cas.15997

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1073/pnas.2305451120

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1128/spectrum.04311-22

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1371/journal.ppat.1011231

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DOI： 10.1016/j.jtbi.2022.111403

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1093/infdis/jiad058

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3389/fimmu.2022.1049458

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Language：English  

DOI： 10.1111/1348-0421.13034

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.isci.2022.105237

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Journal of Theoretical Biology  

Seasonal variations in environmental conditions lead to changing infectious disease epidemic risks at different times of year. The probability that early cases initiate a major epidemic depends on the season in which the pathogen enters the population. The instantaneous epidemic risk (IER) can be tracked. This quantity is straightforward to calculate, and corresponds to the probability of a major epidemic starting from a single case introduced at time t=t0, assuming that environmental conditions remain identical from that time onwards (i.e. for all t≥t0). However, the threat when a pathogen enters the population in fact depends on changes in environmental conditions occurring within the timescale of the initial phase of the outbreak. For that reason, we compare the IER with a different metric: the case epidemic risk (CER). The CER corresponds to the probability of a major epidemic starting from a single case entering the population at time t=t0, accounting for changes in environmental conditions after that time. We show how the IER and CER can be calculated using different epidemiological models (the stochastic Susceptible-Infectious-Removed model and a stochastic host-vector model that is parameterised using temperature data for Miami) in which transmission parameter values vary temporally. While the IER is always easy to calculate numerically, the adaptable method we provide for calculating the CER for the host-vector model can also be applied easily and solved using widely available software tools. In line with previous research, we demonstrate that, if a pathogen is likely to either invade the population or fade out on a fast timescale compared to changes in environmental conditions, the IER closely matches the CER. However, if this is not the case, the IER and the CER can be significantly different, and so the CER should be used. This demonstrates the need to consider future changes in environmental conditions carefully when assessing the risk posed by emerging pathogens.

DOI： 10.1016/j.jtbi.2022.111195

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Antiviral Research  

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron subvariant BA.2 has spread in many countries, replacing the earlier Omicron subvariant BA.1 and other variants. Here, using a cell culture infection assay, we quantified the intrinsic sensitivity of BA.2 and BA.1 compared with other variants of concern, Alpha, Gamma, and Delta, to five approved-neutralizing antibodies and antiviral drugs. Our assay revealed the diverse sensitivities of these variants to antibodies, including the loss of response of both BA.1 and BA.2 to casirivimab and of BA.1 to imdevimab. In contrast, EIDD-1931 and nirmatrelvir showed a more conserved activities to these variants. The viral response profile combined with mathematical analysis estimated differences in antiviral effects among variants in the clinical concentrations. These analyses provide essential evidence that gives insight into variant emergence's impact on choosing optimal drug treatment.

DOI： 10.1016/j.antiviral.2022.105372

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Nature Communications  

Appropriate isolation guidelines for COVID-19 patients are warranted. Currently, isolating for fixed time is adopted in most countries. However, given the variability in viral dynamics between patients, some patients may no longer be infectious by the end of isolation, whereas others may still be infectious. 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. In this study, we develop a data-driven computational framework to compute the population-level risk and the burden of different isolation guidelines with rapid antigen tests (i.e., lateral flow tests). Here, we show that when the detection limit is higher than the infectiousness threshold values, additional consecutive negative results are needed to ascertain infectiousness status. Further, rapid antigen tests should be designed to have lower detection limits than infectiousness threshold values to minimize the length of prolonged isolation.

DOI： 10.1038/s41467-022-32663-9

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：BMC Infectious Diseases  

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.

DOI： 10.1186/s12879-022-07646-2

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Journal of Theoretical Biology  

Human immunodeficiency virus type-1 (HIV-1) attaches to target cells and releases the capsid, an essential component of the viral core that contains viral RNA, into the cytoplasm. After invading target cells, the core structure gradually collapses. The timing of the disassembly of the HIV-1 capsid is essential for efficient viral cDNA synthesis and transport into the nucleus. HIV-1 uncoating is controlled by the host factor maternal embryonic leucine zipper kinase (MELK); however, the quantitative and dynamic relationship between the HIV-1 uncoating process and HIV-1 infection remains unresolved. In this study, we quantified the uncoating process on HIV-1 cDNA synthesis and transport into the nucleus by combining a mathematical model with in vitro data. In addition, detailed in silico simulations demonstrated host factors, including MELK, optimize transport efficiency. Our experimental-mathematical approach revealed quantitative dynamics of the HIV-1 uncoating process, indicating that increasing the speed of uncoating always reduces the amount of HIV-1 cDNA in the nucleus.

DOI： 10.1016/j.jtbi.2022.111152

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PLoS Computational Biology  

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.

DOI： 10.1371/journal.pcbi.1010053

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Complexity  

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.

DOI： 10.1155/2022/2158052

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Vaccines  

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.

DOI： 10.3390/vaccines10040515

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jtbi.2022.111029

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/s41598-022-07116-4

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Language：English  

DOI： 10.1101/2022.01.24.22269769

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1016/j.crmeth.2021.100122

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.26508/LSA.202101049

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

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.

DOI： 10.7554/eLife.69340

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Background Development 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 findings A 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. Conclusions In 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.

DOI： 10.1371/journal.pmed.1003660

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Language：English   Publishing type：Research paper (scientific journal)  

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.

DOI： 10.3389/fmicb.2021.651403

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1186/s13063-021-05282-w

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1016/j.isci.2021.102367

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1098/rsif.2020.0947

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.epidem.2021.100454

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1371/journal.pbio.3001128

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1097/QAI.0000000000002660

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jtbi.2020.110493

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Language：English  

DOI： 10.1016/j.jtbi.2020.110502

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3389/fmicb.2020.575255

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1186/s12976-020-00135-6

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1093/ve/veab003

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Catalyst Unit  

<p>To evaluate the effects of antiviral drugs against coronavirus disease (COVID-19), we calculated the sample size needed to detect significant differences in daily viral load in randomized controlled trials. While calculating sample sizes, simulated viral loads that mimicked longitudinal clinical data were used. The sample size computed from the viral load was the smallest 2 to 4 days after trial participation, while the smallest sample size computed from the positive rate was 4 to 7 days after clinical trial participation.</p>

DOI： 10.33611/trs.2021-025

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Language：English  

DOI： 10.1128/JVI.01204-20

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Language：English  

DOI： 10.1371/journal.pcbi.1007612

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jtbi.2020.110295

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1371/journal.pbio.3000562

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Language：English  

DOI： 10.1074/jbc.AC119.010366

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Language：Japanese  

Language：Japanese  

Event date： 2023.6 - 2023.7

Presentation type：Oral presentation (general)  

Event date： 2023.4

Presentation type：Oral presentation (invited, special)  

Event date： 2023.3

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2022.11

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Event date： 2022.11

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Event date： 2022.10

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Event date： 2022.9

Language：English   Presentation type：Oral presentation (invited, special)  

Event date： 2022.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.6 - 2022.7

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2022.6

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Event date： 2022.6

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Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2022.3

Language：Japanese   Presentation type：Oral presentation (invited, special)  

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