Stratification of viral shedding patterns in saliva of COVID-19 patients (preprint)

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.

Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution (preprint)

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 found that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher infectious periods 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 showed that a decreased incubation period and an increased proportion of asymptomatic infection were also positively selected for as SARS-CoV-2 mutated to the extent that people did not isolate. We demonstrated that the Omicron variants evolved in these ways to adapt to human behavior. The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.

A personalized antibody score for predicting individual COVID-19 vaccine-elicited antibody levels from basic demographic and health information (preprint)

Antibody titers wane after two-dose COVID-19 vaccinations, but individual variation in vaccine-elicited antibody dynamics remains to be explored. Here, we created a personalized antibody score that enables individuals to infer their antibody status by use of a simple calculation. We recently developed a mathematical model of B cell differentiation to accurately interpolate the longitudinal data from a community-based cohort in Fukushima, Japan, which consists of 2,159 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 course of the vaccine- elicited antibody response, we first elucidated individual background factors that contributed to the main features of antibody dynamics, i.e., the peak, the duration, and the area under the curve. We found that increasing age was a negative factor and a longer interval between the two doses was a positive factor for individual antibody level. We also found that the presence of underlying disease and the use of medication affected antibody levels negatively, whereas the presence of adverse reactions upon vaccination affected antibody levels positively. We then applied to these factors a recently proposed computational method to optimally fit clinical scores, which resulted in an integer-based score that can be used to evaluate the antibody status of individuals from their basic demographic and health information. This score can be easily calculated by individuals themselves or by medical practitioners. There is a potential usefulness of this score for identifying vulnerable populations and encouraging them to get booster vaccinations.

Stratifying elicited antibody dynamics after two doses of SARS-CoV-2 vaccine in a community-based cohort in Fukushima, Japan (preprint)

Recent studies have provided insights into the effect of vaccine boosters on recall immunity. Given the limited global supply of COVID-19 vaccines, identifying vulnerable populations with lower sustained vaccine-elicited antibody titers is important for targeting individuals for booster vaccinations. Here we investigated longitudinal data in a cohort of 2,526 people in Fukushima, Japan, from April 2021 to December 2021. Antibody titers following two doses of a COVID-19 vaccine were repeatedly monitored and information on lifestyle habits, comorbidities, adverse reactions, and medication use was collected. Using mathematical modeling and machine learning, we stratified the time-course patterns of antibody titers and identified vulnerable populations with low sustained antibody titers. Moreover, we showed that only 5.7% of the participants in our cohort were part of the "durable" population with high sustained antibody titers, which suggests that this durable population might be overlooked in small cohorts. We also found large variation in antibody waning within our cohort. There is a potential usefulness of our approach for identifying the neglected vulnerable population.

Safely return to schools and offices: early and frequent screening with high sensitivity antigen tests effectively identifies COVID-19 patients (preprint)

Background In-person interaction at school and offices offers invaluable experience to students and benefits to companies. In the midst of the pandemic, ways to safely go back to schools and offices have been argued. Centers for Disease Control and Prevention (CDC) recommends taking all precautions such as vaccination and universal indoor masking. However, even if all the precautions are implemented and transmission is perfectly prevented in the facilities, they may be infected outside of the facilities, which would be a source of transmission in the facilities. Therefore, identifying those infected outside of the facility through screening is essential to safely go back to schools or offices. However, studies investigating the effectiveness of screening are limited. Further, it is not well clarified now which screening strategy (e.g., low or high sensitivity antigen tests, intervals and the number of tests) effectively identify infected and infectious individuals to avoid transmission in facilities Methods We assessed the effectiveness of various screening strategies in schools and offices through quantitative simulation. The effectiveness was assessed by the proportion of identified infected and infectious participants. Infection dynamics in the facility is governed by transmission dynamics of the population they belong to, and the screening is initiated at different epidemic phases: growth, peak, and declining phases. The viral load trajectory over time for each infected individual was modelled by the viral dynamics model, and the transmission process at the population level was modelled by a deterministic compartment model. The model parameters were estimated from clinical and epidemiological data. Screening strategies were varied by antigen tests with different sensitivity and schedules of screening over 10 days. Results Under the daily screening, we found high sensitivity antigen tests (the detection limit: 6.3 × 10 4 copies/mL) yielded 88% (95%CI 86-89) of effectiveness by the end of 10 days screening period, which is about 20% higher than that with low sensitivity antigen tests (2.0 × 10 6 copies/mL). Comparing screening scenarios with different schedules, we found early and frequent screening is the key to maximize the effectiveness. Sensitivity analysis revealed that less frequent tests might be an option when the number of antigen tests is limited especially when the screening is performed at the growth phase. Discussion High sensitivity antigen tests, high frequency screening, and immediate initiation of screening are the key to safely restart educational and economic activities allowing in-person interactions. Our computational framework is useful in assessment of screening strategies by incorporating additional factors for specific situations.

Analytical estimation of maximum fraction of infected individuals with one-shot non-pharmaceutical intervention in a hybrid epidemic model (preprint)

Facing a global epidemic of new infectious diseases such as COVID-19, non-pharmaceutical interventions (NPIs), which reduce transmission rates without medical actions, are being implemented around the world to mitigate spreads. One of the problems in assessing the effects of NPIs is that different NPIs have been implemented at different times based on the situation of each country; therefore, few assumptions can be shared about how the introduction of policies affects the patient population. Mathematical models can contribute to further understanding these phenomena by obtaining analytical solutions as well as numerical simulations. In this study, an NPI was introduced into the SIR model for a conceptual study of infectious diseases under the condition that the transmission rate was reduced to a fixed value only once within a finite time duration, and its effect was analyzed numerically and theoretically. It was analytically shown that the maximum fraction of infected individuals and the final size could be larger if the intervention starts too early. The analytical results also suggested that more individuals may be infected at the peak of the second wave with a stronger intervention. This study provides quantitative relationship between the strength of a one-shot intervention and the reduction in the number of patients with no approximation. This suggests the importance of the strength and time of NPIs, although detailed studies are necessary for the implementation of NPIs in complicated real-world environments as the model used in this study is based on various simplifications.

Identification of Anti-COVID-19 Agents, Cepharanthine and Nelfinavir, and Their Potential Usage for Combination Treatment (preprint)

Antiviral treatments targeting the coronavirus disease 2019 (COVID-19) 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: the anti-inflammatory drug Cepharanthine and HIV protease inhibitor Nelfinavir. Cepharanthine inhibited SARS-CoV-2 entry into cells, whilst Nelfinavir inhibited the catalytic activity of viral main protease to suppress viral replication. Consistent with their different modes of action, in vitro assays highlight a synergistic effect of this combined treatment to limit SARS-CoV-2 proliferation. Mathematical modeling in vitro antiviral activity coupled with the known pharmacokinetics for these drugs predicts that Nelfinavir will shorten the period until viral clearance by 5.5-days and the combining Cepharanthine/Nelfinavir enhanced their predicted efficacy to control viral proliferation. In summary, this study identifies a new multidrug combination treatment for COVID-19.Funding: This work was supported by The Agency for Medical Research and Development (AMED) emerging/re-emerging infectious diseases project (JP19fk0108111, JP19fk0108110, JP20fk0108104); the AMED Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS, JP19am0101114, JP19am0101069, JP19am0101111) program; The Japan Society for the Promotion of Science 260 KAKENHI (JP17H04085, JP20H03499, JP15H05707, 19H04839); The JST MIRAI program; and Wellcome Trust funded Investigator award (200838/Z/16/Z). Conflict of Interest: None.

Estimation of the incubation period of COVID-19 using viral load data (preprint)

The incubation period, or the time from infection to symptom onset of COVID-19 has been usually estimated using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in recalling effort 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 the reported viral load data from multiple countries (Singapore, China, Germany, France, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.23 days (95% CI: 5.17, 5.25), 3.29 days (3.25, 3.37), and 8.22 days (8.02, 8.46), 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 impractical to directly observe the infection event.

Rethinking antiviral effects for COVID-19 in clinical studies: early initiation is key to successful treatment (preprint)

Development of an effective antiviral drug for 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 for effective drugs from clinical studies is limited. The lack of evidence could be in part due to heterogeneity of virus dynamics among patients and late initiation of treatment. We first quantified the heterogeneity of viral dynamics which could be a confounder in compassionate use programs. Second, we demonstrated that an antiviral drug is unlikely to be effective if initiated after a short period following symptom onset. For accurate evaluation of the efficacy of an antiviral drug for COVID-19, antiviral treatment should be initiated before or soon after symptom onset in randomized clinical trials.

Multidrug treatment with nelfinavir and cepharanthine against COVID-19 (preprint)

Antiviral treatments targeting the emerging coronavirus disease 2019 (COVID-19) 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 antiviral agents: the HIV protease inhibitor Nelfinavir and the anti-inflammatory drug Cepharanthine. In silico modeling shows Nelfinavir binds the SARS-CoV-2 main protease consistent with its inhibition of viral replication, whilst Cepharanthine inhibits viral attachment and entry into cells. Consistent with their different modes of action, in vitro assays highlight a synergistic effect of this combined treatment to limit SARS-CoV-2 proliferation. Mathematical modeling in vitro antiviral activity coupled with the known pharmacokinetics for these drugs predicts that Nelfinavir will facilitate viral clearance. Combining Nelfinavir/Cepharanthine enhanced their predicted efficacy to control viral proliferation, to ameliorate both the progression of disease and risk of transmission. In summary, this study identifies a new multidrug combination treatment for COVID-19.

Inferring Timing of Infection Using Within-host SARS-CoV-2 Infection Dynamics Model: Are "Imported Cases" Truly Imported? (preprint)

Importance: Although the COVID-19 epidemic in some countries such as China are in the last phase by large effort for containment of the disease, another outbreaks can occur because huge susceptible population remains. Further, there remain countries in the early phase of outbreak with zero or limited number of cases in southern hemisphere countries. In those countries at risk of future outbreak, ascertaining whether cases are imported or the result of local secondary transmission is important for government to shape appropriate public health strategies. Objective: To develop a method to estimate timing of infection establishment, which helps differentiate imported and autochthonous cases. Design, Setting and Participants: Of the first 18 cases reported in Singapore, 12 were used in our study (1 case with insufficient data and 5 on anti-viral treatment were excluded from the analysis). The viral load data from these initial cases considered imported due to their travel history to Wuhan were analyzed. Another viral load data from 3 cases reported from Zhuhai, China, for whom exposed day were known, were also analyzed to determine the viral load threshold for infection establishment. Exposures: SARS-CoV-2 infection confirmed by the polymerase-chain-reaction (PCR) test. Main Outcomes and Measures: The timing of infection establishment of each case was assessed by analysing viral load data after symptom onset using a within-host viral dynamics model for SARS-CoV-2. Estimated timing of infection will indicate whether cases are imported or autochthonous transmission within Singapore. Results: Six among the 12 cases were clearly imported cases, whereas we could not rule out the possibility of secondary transmission for the rest of 6 cases, which collectively evidenced ongoing transmission in Singapore. For the 6 cases who could be the results of secondary transmission, further investigation to identify the source of infection within Singapore should be warranted (i.e., contact tracing). Conclusions and Relevance: In an early phase of outbreak due to entrance or re-entrance of the virus to countries/communities, collecting viral load data over time from cases from symptom onset is highly recommended, because viral load data are valuable to infer the timing of infection and distinguish between imported cases and ongoing local transmission.