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.
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.
Abstract The global outbreak of mpox (formerly monkeypox) in 2022 raised public awareness about the disease. The ensuing sporadic outbreaks in 2023 highlighted the importance of sustaining nonpharmaceutical interventions, such as case isolation and contact tracing. Using viral load data, we developed a modelling framework to characterize the various infectiousness profiles of infected individuals. We used this model to examine the potential effectiveness of two different possible isolation rules: specifically, rules permitting infected individuals to stop isolating after either a fixed-duration or following negative tests for infection. Our analysis showed large individual variations in the duration of viral shedding, ranging from about 23 to 50 days. The risk of infected individuals ending isolation too early (i.e., while they remained an infection risk) was estimated to be about 5% after 3 weeks of isolation. Unnecessary isolation after the end of the infectious period could be reduced by use of a testing-based rule. These findings support the choice of a 3-week isolation period following symptom onset if a fixed-duration rule is used, but also demonstrate how testing can mitigate unnecessarily prolonged isolation for those who have shorter infectious periods.
Abstract 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. Clade I MPXV is known to be more fatal, but its clinical characteristics and prognosis differ between patients. Here, we used mathematical modelling to quantify disease progression in a large cohort of mpox patients in the DRC from 2007-2011, particularly focusing on lesion transition dynamics. We further analyzed individuals’ clinical data to find predictive biomarkers of severity of symptoms. Our analysis shows that mpox patients can be stratified into three groups according to symptom severity, and that viral load at symptom onset may serve as a predictor to distinguish groups with the most severe or mild symptoms after progression. Understanding the severity and duration of symptoms in different patients, as characterized by our approach, allows treatment strategies to be improved and individual-specific control measures (e.g isolation strategies based on disease progression) to be developed.
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 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.
Mpox virus (MPXV) is a zoonotic orthopoxvirus and caused an outbreak in 2022. Although tecovirimat and brincidofovir are approved as anti-smallpox drugs, their effects in mpox patients have not been well documented. In this study, by a drug repurposing approach, we identified potential drug candidates for treating mpox and predicted their clinical impacts by mathematical modeling.
Appropriate isolation guidelines for COVID-19 patients are warranted. Currently, isolating for fixed time is adapted in most countries. However, given the variability in viral dynamics between patients, some patients may no longer be infectious by the end of isolation (thus they are redundantly isolated), whereas others may still be infectious. Utilizing viral test results to determine ending isolation would minimize both the risk of ending isolation of infectious patients and the burden due to redundant isolation of noninfectious patients. In our previous study, we proposed a computational framework using SARS-CoV-2 viral dynamics models to compute the risk and the burden of different isolation guidelines with PCR tests. In this study, we extend the computational framework to design isolation guidelines for COVID-19 patients utilizing rapid antigen tests. Time interval of tests and number of consecutive negative tests to minimize the risk and the burden of isolation were explored. Furthermore, the approach was extended for asymptomatic cases. We found the guideline should be designed considering various factors: the infectiousness threshold values, the detection limit of antigen tests, symptom presence, and an acceptable level of releasing infectious patients. Especially, when detection limit is higher than the infectiousness threshold values, more consecutive negative results are needed to ascertain loss of infectiousness. To control the risk of releasing of infectious individuals under certain levels, rapid antigen tests should be designed to have lower detection limits than infectiousness threshold values to minimize the length of prolonged isolation, and the length of prolonged isolation increases when the detection limit is higher than the infectiousness threshold values, even though the guidelines are optimized for given conditions.
Abstract In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multi-scale 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 multi-scale models to design pre-emptive interventions against SARS-CoV-2 and other viruses.
Abstract 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.
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