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.

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.

Coinciding with the global outbreak of clade IIb mpox virus (MPXV), the Democratic Republic of the Congo (DRC) recently experienced a rapid surge in mpox cases with clade I MPXV. On 14 August 2024, the World Health Organization declared the continued cross-border spread of this clade in Africa a public health emergency of international concern (PHEIC). Clade I MPXV is known to be more fatal than clade IIb, but its clinical characteristics and prognosis differ between patients. Here, we used mathematical modeling to quantify temporal changes in total lesion counts during clade I MPXV infections, using data from a large cohort of patients with mpox in the DRC from 2007 to 2011. We further analyzed individuals’ clinical data to explore predictive biomarkers of high lesion counts. Our analysis indicates that patients with clade I mpox can be stratified into two groups according to lesion severity and that viral load in peripheral blood at symptom onset may serve as a predictor for this classification [area under the curve (AUC) = 0.70]. Our estimates also suggest substantial individual heterogeneity in the time period during which patients have lesions, ranging from 20 to 65 days. Understanding the severity and duration of lesions in different patients, as characterized by our approach, may contribute to more tailored treatment strategies and control measures in ongoing clade I mpox outbreaks.

A key issue in the post–COVID-19 pandemic era is the ongoing administration of COVID-19 vaccines. Repeated vaccination is essential for preparing against currently circulating and newly emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. However, optimizing vaccination strategies is crucial to efficiently manage medical resources and establish an effective vaccination framework. Therefore, a strategy to identify poor responders with lower sustained antibody titers would be beneficial because these individuals should be considered high priority for revaccination. We investigated longitudinal antibody titer data in a cohort of 2526 people in Fukushima, Japan, collected between April 2021 and November 2022. Using mathematical modeling and machine learning, we stratified the time-course patterns of antibody titers after two primary doses and one booster dose of COVID-19 messenger RNA vaccines. We identified three populations, which we refer to as the durable, the vulnerable, and the rapid-decliner populations, and approximately half of the participants remained in the same population after the booster dose. The rapid-decliner population experienced earlier infections than the others. Furthermore, when comparing spike protein–specific immunoglobulin G (IgG) titers, spike protein–specific IgA titers, and SARS-CoV-2–specific T cell responses between participants who experienced subsequent infections after booster vaccination and those who did not, we found that spike protein–specific IgA titers were lower during the early stage after booster vaccination in participants who went on to become infected with SARS-CoV-2. This approach could be used to inform policy decisions on vaccine distribution to maximize population-level immunity both in future pandemics and in the post–COVID-19 pandemic era.

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 quantified individual-level viral dynamics and stratified them into three groups using a clustering approach. Notably, the three groups exhibited distinct differences in viral RNA detection durations: 11.5 days (95% CI: 10.6–12.4), 17.4 days (16.6–18.2), and 30.0 days (28.1–31.8), 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 RNA detection 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.

Viral load data provide critical insights into host‐pathogen interactions and guide clinical and public health decisions. Because frequent testing is often infeasible, viral dynamics models are used to reconstruct infection trajectories, but optimal sampling strategies remain unclear. We compared two approaches for collecting SARS‐CoV‐2 viral load data: cross‐sectional sampling (one measurement at symptom onset) and longitudinal sampling (every 3 days after onset) under constraints on the total number of tests and tests per individual. A viral dynamics model was first fitted to data from the National Basketball Association cohort, and the estimated parameters were treated as ground truth. Synthetic data were then generated under each sampling design, refitted, and evaluated for accuracy in estimating viral load over 30 days, peak viral load, peak time, and viral shedding duration. Longitudinal sampling consistently yielded lower root mean squared error and narrower one standard deviation interval than cross‐sectional sampling. Peak timing and viral shedding duration were unbiased under both designs, but cross‐sectional designs underestimated peak viral load and produced wider one standard deviation intervals. Coverage of viral load estimates was markedly higher for longitudinal designs (> 0.90) compared with cross‐sectional ones (~0.10). Accuracy and coverage exceeded 0.96 even with just two tests per individual, with little additional benefit from more tests. In conclusion, longitudinal sampling—despite limited data—substantially improves accuracy and precision of viral load estimation compared with cross‐sectional designs. These findings highlight efficient strategies for study design and resource allocation in infectious disease research.