Fuminari Miura

Background: Differential contributions to transmission across age groups have been reported for many respiratory infections, including SARS-CoV-2. They are crucial for estimating the impact of age-specific interventions. Disentangling these age-dependent contributions remains challenging, as they may reflect differences in contact rates, biological susceptibility, or infectiousness. Aim: We aim to jointly estimate age-specific per-contact infectiousness and susceptibility and their effect on the impact of age-specific interventions. Methods: The age-specific infectiousness and susceptibility were jointly estimated in a Bayesian framework by combining contact data with transmission pair data (who-infected-whom). We applied this approach to 197,840 self-reported household transmission pairs collected in the Netherlands during the COVID-19 pandemic. Using these estimates, we projected the expected impact of school closure and work-from-home measures during the early stages of an epidemic in the absence of other interventions. Results: Both infectiousness and susceptibility to SARS-CoV-2 infection were lowest in children aged 0-9 years and highest in adults over 30 years old, with 2-to 4.5-fold differences between these groups. Projected impacts of age-specific interventions indicated that school closures would reduce the reproduction number by 8% or 29% when age-specific susceptibility and infectiousness were or were not considered, respectively. Conversely, working-from-home policies would lead to reductions of 41% with and 20% without age-specific infectiousness and susceptibility. Conclusion: Our method enables robust estimation of age-specific infectiousness and susceptibility. Accounting for these age heterogeneities is essential for projecting the impact of age-targeted interventions. Our approach is adaptable to other respiratory infections and can guide more tailored public health responses.

Background: Human challenge trials provide a unique opportunity to quantify pathogen infectivity in terms of the probability of infection given an inoculated dose. However, between-pathogen comparisons are often distorted by individual heterogeneity in host susceptibility and by differences in background immunity across trial populations. We examined how dose–dependent infection risks differ across common respiratory viruses when such heterogeneity is explicitly incorporated. Methods: We conducted a systematic review of human challenge trials for four respiratory viruses: respiratory syncytial virus (RSV), influenza virus, rhinovirus, and adenovirus. Using the extracted data, we fitted dose–response models under different distributional assumptions, allowing both continuous susceptibility variation and discrete immune fractions. We compared alternative heterogeneity models and evaluated pathogen-specific dose–response patterns using original and scaled dose metrics. Results: All four viruses showed substantial heterogeneity in host susceptibility, and models assuming homogeneous susceptibility were unsupported. RSV and influenza were best described by models with a distinct immune or effectively non-susceptible subgroup, and the estimated immune proportions were approximately 40% and 25%, respectively. In contrast, rhinovirus and adenovirus were better explained by continuously distributed susceptibility, with little evidence of a fully immune subgroup. On a scaled dose axis, rhinovirus and adenovirus showed steeper increases in infection risk with dose than RSV and influenza. Conclusions: The structure of susceptibility heterogeneity differs across common respiratory viruses, which in turn shapes dose–dependent infection risks. Incorporating this heterogeneity is essential for valid cross-pathogen comparison and for interpreting human challenge data in epidemiologic and public health contexts. Summary: We systematically reviewed human challenge trials and analyzed the extracted data using dose–response models for four respiratory viruses. The analyses revealed between-pathogen differences in dose–dependent infection risks and highlighted substantial heterogeneity in host susceptibility, even under controlled exposure conditions.

The intensifying outbreaks of the novel monkeypox virus clade Ib in the Democratic Republic of the Congo have raised global concern about the potential for wider epidemic spread. Some clade Ib mpox outbreaks have shown a distinct transmission pattern in which transmission associated with both sexual and nonsexual contacts coexist. Here, we characterize these outbreaks in a network epidemic model, which incorporates sexual and nonsexual contacts, and project age- and route-specific transmission potentials under a wide range of scenarios. Our analyses suggest that the dominant route of transmission may shift over time from sexual to nonsexual contacts, which leads to larger epidemics. The age groups contributing most to overall infections and mortality also change over time, suggesting that target groups for intervention should be adjusted accordingly. For countries at risk of travel-associated mpox outbreaks, these findings highlight the importance of monitoring evolving monkeypox virus transmission patterns and interacting transmission routes to support timely and effective control measures.

In 2023 the European Centre for Disease Prevention and Control (ECDC) launched RespiCast, the first European Respiratory Diseases Forecasting Hub, to provide probabilistic forecasts for influenza-like illness (ILI) and acute respiratory infection (ARI) incidence across 26 European countries. During the 2023/24 and 2024/25 winter seasons, RespiCast collected one- to four-week-ahead forecasts from multiple models contributed by different international teams and combined them into an ensemble. Our analysis shows that, when evaluated using the weighted interval score (WIS) and the absolute error (AE), the ensemble consistently outperformed the baseline model (defined as a persistence model that projects the last observed value forward) as well as individual models across most countries and forecasting rounds for both ILI and ARI incidence in the two seasons. Analysis of ensemble coverage (defined as the proportion of times observed values fall within the specified prediction intervals) indicated that forecast prediction intervals were reliable, although a general overconfidence trend (i.e., prediction intervals that are too narrow) was observed, particularly in specific countries. The relative performance of the ensemble declined in certain weeks, likely due to reduced participation from modelling teams, epidemic dynamics, higher data noise, and reporting delays. Forecast scores varied across countries, with some exhibiting consistently higher relative errors than others. Overall, the findings highlight the strengths of ensemble approaches in improving the accuracy and reliability of epidemiological forecasts while identifying areas for improvement, such as managing overconfidence and addressing variability in performance across countries and over time.

During the COVID-19 pandemic non-pharmaceutical interventions (NPIs) were taken to mitigate virus spread. Assessing their effectiveness is essential in policy support but often challenging, due to interactions between measures, the increase of immunity, variant emergence and seasonal effects. These factors make results difficult to interpret over a long period of time. Using a mechanistic approach, we estimate the overall effectiveness of sets of NPIs in reducing transmission over time. Our approach quantifies the effectiveness by comparing the observed effective reproduction number, which is the number of secondary infections caused by a typical infected person, to a counterfactual reproduction number if no NPIs were taken. The counterfactual reproduction number accounts for seasonal variations in transmissibility, for emergence of more transmissible variants, and for changes in immunity in the population. The immune fraction is reconstructed from age-specific data of longitudinal serological surveys and vaccination coverage, taking immunity loss due to waning into account. We estimate the effectiveness of NPIs in the Netherlands from the start of the pandemic in March 2020 until the emergence of the Omicron variant in November 2021. We find that the effectiveness of NPIs was high in March and April 2020 during the first pandemic wave and in January and February 2021, coinciding with the two periods with the most stringent measures. For both periods the effectiveness was estimated at approximately 50%, i.e., without any measures the reproduction number would have been twice as high as observed. The proposed approach synthesises available epidemiological data from different sources to reconstruct the population-level immunity. With sufficient data, it can be applied not only to COVID-19 but also to other directly transmitted diseases, such as influenza. This method provides a near real-time assessment of the effectiveness of control measures when the required data are available.

The intensifying outbreaks of the novel monkeypox virus clade Ib in the Democratic Republic of the Congo have raised global concern about the potential for wider epidemic spread. Some clade Ib mpox outbreaks have shown a distinct transmission pattern in which transmission associated with both sexual and non-sexual contacts co-exist. Here, we characterize these outbreaks in a network epidemic model, which incorporates sexual and non-sexual contacts, and project age- and route-specific transmission potentials under a wide range of scenarios. Our analyses suggest that the dominant route of transmission may shift over time from sexual to non-sexual contacts, which leads to larger epidemics. The age groups contributing most to overall infections and mortality also change over time, suggesting that target groups for intervention should be adjusted accordingly. For countries at risk of travel-associated mpox outbreaks, these findings highlight the importance of monitoring evolving monkeypox virus transmission patterns and interacting transmission routes to support timely and effective control measures. Significance statement: The initial outbreak of clade Ib monkeypox virus in the Democratic Republic of Congo was characterized by differences in how the virus spreads, with transmission through both sexual and non-sexual contacts playing a crucial role. This study addresses two key public health challenges: how does an outbreak evolve when multiple transmission routes coexist, and how to translate that understanding into effective interventions. We develop a mathematical framework to address these challenges and show that the dominant transmission route can shift over time, from sexual to non-sexual. We show that even small increases in transmission through non-sexual contacts can lead to substantially larger epidemics, underscoring the need for continuous monitoring and adaptive response strategies. One sentence summary: During an mpox outbreak, the dominant route of transmission and optimal target groups for intervention may shift over time

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.

This paper presents a scenario-based model predictive control (MPC) scheme designed to control an evolving pandemic via non-pharmaceutical intervention (NPIs). The proposed approach combines predictions of possible pandemic evolution to decide on a level of severity of NPIs to be implemented over multiple weeks to maintain hospital pressure below a prescribed threshold, while minimizing their impact on society. Specifically, we first introduce a compartmental model which divides the population into Susceptible, Infected, Detected, Threatened, Healed, and Expired (SIDTHE) subpopulations and describe its positive invariant set. This model is expressive enough to explicitly capture the fraction of hospitalized individuals while preserving parameter identifiability w.r.t. publicly available datasets. Second, we devise a scenario-based MPC scheme with recourse actions that captures potential uncertainty of the model parameters. e.g., due to population behavior or seasonality. Our results show that the scenario-based nature of the proposed controller manages to adequately respond to all scenarios, keeping the hospital pressure at bay also in very challenging situations when conventional MPC methods fail.

During the COVID-19 pandemic non-pharmaceutical interventions (NPIs) were taken to mitigate virus spread. Assessing their effectiveness is essential in policy support but often challenging, due to interactions between measures, the increase of immunity, variant emergence and seasonal effects. These factors make results difficult to interpret over a long period of time. Using a mechanistic approach, we estimate the overall effectiveness of sets of NPIs in reducing transmission over time. Our approach quantifies the effectiveness by comparing the observed effective reproduction number, which is the number of secondary infections caused by a typical infected person, to a counterfactual reproduction number if no NPIs were taken. The counterfactual reproduction number accounts for seasonal variations in transmissibility, for emergence of more transmissible variants, and for changes in immunity in the population. The immune fraction is reconstructed from age-specific data of longitudinal serological surveys and vaccination coverage, taking immunity loss due to waning into account. We estimate the effectiveness of NPIs in the Netherlands from the start of the pandemic in March 2020 until the emergence of the Omicron variant in November 2021. We find that the effectiveness of NPIs was high in March and April 2020 during the first pandemic wave and in January and February 2021, coinciding with the two periods with the most stringent measures. For both periods the effectiveness was estimated at approximately 50%, i.e., without any measures the reproduction number would have been twice as high as observed. The proposed approach synthesises available epidemiological data from different sources to reconstruct the population-level immunity. With sufficient data, it can be applied not only to COVID-19 but also to other directly transmitted diseases, such as influenza. This method provides a near real-time assessment of the effectiveness of control measures when the required data are available. Author summary: During the COVID-19 pandemic, many interventions were taken aimed at reducing contacts between people, such as school closures and working-from-home recommendations. At the same time, the reproduction number - denoting the number of infections one infected person can cause - became a well-known term. We asked what the value of the reproduction number would have been in absence of interventions. This value depends on factors such as the number of persons that is infected or vaccinated over time, immunity waning, seasonal effects, and the emergence of more transmissible variants. By combining multiple data sources from the Netherlands, we accounted for these factors and calculated the reproduction number without any interventions. We found that during the two periods with the most stringent measures in the Netherlands the reproduction number without interventions would have been twice as high as the observed effective reproduction number with interventions, indicating an effectiveness of about 50% of the measures at that time. A similar approach to estimating the effectiveness of intervention measures can be applied to COVID-19 data of other countries or to other directly transmitted diseases.

Disinfection is key to controlling the infection risk caused by viral contaminations. Recent disinfection guidelines often refer to a single virus resistant to the disinfectant of interest, despite a large variation in sensitivity to disinfectants between viruses or even strains within the same species. Here, we show a statistical framework to integrate multiple experimental datasets and measure the variation in sensitivity to disinfectants across different virus species by a parametric distribution, termed the disinfectant sensitivity distribution. To illustrate this framework, we collected 37, 9, and 28 species-dependent inactivation rate constants for ultraviolet (UV), ozone, and free chlorine from systematic reviews, and constructed the sensitivity distributions for those disinfectants. Our approach incorporated the uncertainty in individual inactivation rate constants using a Bayesian framework. The estimated sensitivity distributions suggested that 93.0% (95% CrI: 84.4–97.6%), 99.4% (95% CrI: 86.7–100%), and 90.3% (95% CrI: 78.2–96.6%) of the examined virus species for UV, ozone, and free chlorine are expected to be inactivated by 4-log if the disinfectant dose complies with the values recommended by USEPA Guidelines for 4-log inactivation, respectively. The proposed approach provides a reasonable extrapolation of observed inactivation kinetics to untested viruses and tools for more transparent risk assessments. One sentence summary We propose a statistical framework to quantify the variation in sensitivity to common disinfectants across virus species synthesizing disinfection data.