In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contacts of infected individuals. In this study, we propose a multi-layer temporal contact network to model transmission dynamics and assess the impact of different TTI implementations, using SARS-CoV-2 as a case study. The model was used to evaluate TTI effectiveness both in containing an outbreak and mitigating the impact of an epidemic. We estimated that a TTI strategy based on home isolation and testing of both primary and secondary contacts can contain outbreaks only when the reproduction number is up to 1.3, at which the epidemic prevention potential is 88.2% (95% CI: 87.9%-88.5%). On the other hand, for higher value of the reproduction number, TTI is estimated to noticeably mitigate disease burden but at high social costs (e.g., over a month in isolation/quarantine per person for reproduction numbers of 1.7 or higher). We estimated that strategies considering quarantine of contacts have a larger epidemic prevention potential than strategies that either avoid tracing contacts or require contacts to be tested before isolation. Combining TTI with other social distancing measures can improve the likelihood of successfully containing an outbreak but the estimated epidemic prevention potential remains lower than 50% for reproduction numbers higher than 2.1. In conclusion, our model-based evaluation highlights the challenges of relying on TTIs to contain an outbreak of a novel pathogen with characteristics similar to SARS-CoV-2, and that the estimated effectiveness of TTI depends on the way contact patterns are modeled, supporting the relevance of obtaining comprehensive data on human social interactions to improve preparedness.

Our analysis seeks best social distancing strategies optimally balancing the direct costs of a threatening outbreak with its societal-level costs by investigating the effects of different levels of restrictions' intensity and of the continued importation of infective travellers, while controlling for the key dimensions of the response, such as early action, adherence and the relative weight of societal costs. We identify two primary degrees of freedom in epidemic control, namely the maximum intensity of control measures and their duration. In the absence of travellers, a lower (higher) maximum intensity requires a longer (shorter) duration to achieve similar control outcomes. However, uncontrollable external factors, like the importation of undetected infectives, significantly constrain these degrees of freedom so that the optimal strategy results to be one with low/moderate intensity but prolonged in time. These findings underscore the necessity for resilient health systems and coordinated global responses in preparedness plans.

Global countries often apply containment policies (CPs) to combat infectious disease surges. Whether countries with longer cumulative duration of CPs are associated with slower long-term epidemic progression necessitates a thorough evaluation.

We collected CP and COVID-19 data of 185 territories during 2020-2022, with a total of 23 CPs. Using the target trial emulation and cloning-censoring-weighting approaches, we assessed the effectiveness of CPs with different cumulative durations in delaying countries from reaching the 1% and 10% cumulative infection incidence end points (i.e. 10,000 and 100,000 COVID-19 cases per million population, respectively) over a 3-year observation period.

For reaching the 1% cumulative infection incidence, recommending closing workplaces and limiting gatherings to 10 people, each presented that a longer cumulative duration of those CPs is associated with a lower proportion of countries achieving this end point throughout 2020-2022. For reaching the 10% cumulative infection incidence, mandatory bans on public events and domestic movements, closing public transports, and screening and quarantining inbound tourists, each showed similar associations. Notably, long-lasting border bans upon high-risk regions are associated with a higher proportion of countries reaching the 10% cumulative infection incidence.

From the long-term perspective, we highlight CPs that warrant extending the duration to achieve slower epidemic progression. By contrast, our findings demonstrate the limited effectiveness of the ban on regions in slowing the long-term epidemic progression.

Baloxavir marboxil (baloxavir) rapidly reduces influenza virus shedding, which suggests that it may reduce transmission. Studies of treatment with neuraminidase inhibitors have not shown sufficient evidence that they prevent transmission to contacts.

We conducted a multicountry, phase 3b trial to assess the efficacy of single-dose baloxavir treatment to reduce influenza transmission from index patients to household contacts. Influenza-positive index patients 5 to 64 years of age were randomly assigned in a 1:1 ratio to receive baloxavir or placebo within 48 hours after symptom onset. The primary end point was transmission of influenza virus from an index patient to a household contact by day 5. The first secondary end point was transmission of influenza virus by day 5 that resulted in symptoms.

Overall, 1457 index patients and 2681 household contacts were enrolled across the 2019-2024 influenza seasons; 726 index patients were assigned to the baloxavir group, and 731 to the placebo group. By day 5, transmission of laboratory-confirmed influenza was significantly lower with baloxavir than with placebo (adjusted incidence, 9.5% vs. 13.4%; adjusted odds ratio, 0.68; 95.38% confidence interval [CI], 0.50 to 0.93; P = 0.01), with an adjusted relative risk reduction of 29% (95.38% CI, 12 to 45). The adjusted incidence of transmission of influenza virus by day 5 that resulted in symptoms was 5.8% with baloxavir and 7.6% with placebo; however, the difference was not significant (adjusted odds ratio, 0.75; 95.38% CI, 0.50 to 1.12; P = 0.16). Emergence of drug-resistant viruses during the follow-up period occurred in 7.2% (95% CI, 4.1 to 11.6) of the index patients in the baloxavir group; no resistant viruses were detected in household contacts. No new safety signals were identified.

Treatment with a single oral dose of baloxavir led to a lower incidence of transmission of influenza virus to close contacts than placebo. (Funded by F. Hoffmann-La Roche and others; CENTERSTONE ClinicalTrials.gov number, NCT03969212.).

Given that mass gathering events involve heterogeneous and time-varying contact between residents and visitors, we sought to identify possible measures to prevent the potential acceleration of the outbreak of an emerging infectious disease induced by such events. An individual-based simulator was built based on a description of the reproduction rate among people infected with the infectious disease in a hypothetical city. Three different scenarios were assessed using our simulator, in which controls aimed at reduced contact were assumed to be carried out only in the main event venue or at subsequent additional events, or in which behavior restrictions were carried out among the visitors to the main event. The simulation results indicated that the increase in the number of patients with COVID-19 could possibly be suppressed to a level equivalent to that if the event were not being held so long as the prevalence among visitors was only slightly higher than that among domestic residents and strict requirements were applied to the activities of visitors.

Global emphasis has been focused on tracking the trends of the COVID-19 pandemic. Numerous techniques have been developed or utilized for this purpose. In this study, we seek to present and evaluate a model that, in our opinion, has not received adequate attention, using Algeria as a case study. We developed two distinct Poisson autoregressive (PAR) models using the Monte Carlo Markov Chain (MCMC) simulation method and the Bayesian method: one based solely on short-term dependence and the other incorporating both short- and long-term dependence. The study aimed to apply these models to enhance the prediction of new infections and determine whether the disease is spreading or declining. This information can guide decisions on implementing or relaxing containment measures. Our findings suggest that Algeria's epidemiological state was relatively stable at the end of the study period, with the combined long-term and short-term dependence factors being less than 1 (α + β = 0.994 ) . This indicates that while the epidemic is in decline, the infection rates are not expected to drop significantly in the near future. Furthermore, the short-term dependence parameter α = 0.987 constitutes a significant portion (99%) of the total dependence. This high value of α suggest that the COVID-19 epidemic in Algeria is experiencing a strong decline, though the rate of new infections is expected to persist at a lower level for the foreseeable future. Given these findings, it is recommended that authorities remain vigilant and continue public health measures, including educational campaigns and awareness efforts, to promote COVID-19 vaccination and adherence to health guidelines.

When an infectious disease outbreak is of a relatively small size, describing the ancestry of a sample of infected individuals is difficult because most ancestral models assume large population sizes. Given a set of infected individuals, we show that it is possible to express exactly the probability that they have the same infector, either inclusively (so that other individuals may have the same infector too) or exclusively (so that they may not). To compute these probabilities requires knowledge of the offspring distribution, which determines how many infections each infected individual causes. We consider transmission both without and with superspreading, in the form of a Poisson and a Negative-Binomial offspring distribution, respectively. We show how our results can be incorporated into a new Lambda-coalescent model which allows multiple lineages to coalesce together. We call this new model the Omega-coalescent, we compare it with previously proposed alternatives, and advocate its use in future studies of infectious disease outbreaks.

Hawkes point process models have been shown to forecast the number of daily new cases of epidemic diseases, including SARS-CoV-2 (Covid-19), with high accuracy. Here, we explore how accurately Hawkes models forecast surges of Covid-19 in the United States. We use Hawkes models to estimate the effective reproduction rate Rt and transmission density parameters for Covid-19 case counts in each of the 50 United States, then forecast Rt in future weeks with simple exponential smoothing. A classifier based on Rt>x is applied to predict upcoming surges in cases each week from August 2020 to December 2021, using only data available up to that week. At false alarm rates below 5%, the forecasts based on Rt are correct more often than forecasts based on smoothing the raw case count data, achieving a maximum accuracy of 90% with Rt>1.39. The optimal decision boundary uses a combination of Rt and observed data.

Understanding the dynamical behavior of infectious disease propagation within enclosed spaces is crucial for effectively establishing control measures. In this article, we present a modeling approach to analyze the dynamics of individuals in enclosed spaces, where such spaces are comprised of different chambers. Our focus is on capturing the movement of individuals and their infection status using an open Markov chain framework. Unlike ordinary Markov chains, an open Markov chain accounts for individuals entering and leaving the system. We categorize individuals within the system into three different groups: susceptible, carrier, and infected. A discrete-time process is employed to model the behavior of individuals throughout the system. To quantify the risk of infection, we derive a probability function that takes into account the total number of individuals inside the system and the distribution among the different groups. Furthermore, we calculate mathematical expressions for the average number of susceptible, carrier, and infected individuals at each time step. Additionally, we determine mathematical expressions for the mean number and stationary mean populations of these groups. To validate our modeling approach, we compare the theoretical and numerical models proposed in this work.

Faced with a dangerous epidemic humans will spontaneously social distance to reduce their risk of infection at a socioeconomic cost. Compartmentalized epidemic models have been extended to include this endogenous decision making: Individuals choose their behavior to optimize a utility function, self-consistently giving rise to population behavior. Here, we study the properties of the resulting Nash equilibria, in which no member of the population can gain an advantage by unilaterally adopting different behavior. We leverage an analytic solution that yields fully time-dependent rational population behavior to obtain, 1) a simple relationship between rational social distancing behavior and the current number of infections; 2) scaling results for how the infection peak and number of total cases depend on the cost of contracting the disease; 3) characteristic infection costs that divide regimes of strong and weak behavioral response; 4) a closed form expression for the value of the utility. We discuss how these analytic results provide a deep and intuitive understanding of the disease dynamics, useful for both individuals and policymakers. In particular, the relationship between social distancing and infections represents a heuristic that could be communicated to the population to encourage, or "bootstrap," rational behavior.

Understanding factors affecting the infectiousness of influenza cases is crucial for disease prevention and control. Viral shedding is expected to correlate with infectiousness of cases, but it is strongly associated with age and the presence of symptoms.

To elucidate this complex interplay, we analyze with an individual-based household transmission model a detailed household transmission study of influenza with 442 households and 1710 individuals from 2008 to 2017 in Hong Kong, to characterize the household transmission dynamics and identify factors affecting transmissions.

We estimate that age, fever symptoms, and viral load were all associated with higher infectiousness. However, by model comparison, the best model included age and fever as factors affecting individual infectiousness, and estimates that preschool and school-aged children were 317% (95% credible interval [CrI], 103%, 1042%) and 161% (95% CrI, 33%, 601%) more infectious than adults, respectively, and patients having fever had 146% (95% CrI, 37%, 420%) higher infectiousness. Adding heterogeneity on individual infectiousness of cases does not improve the model fit, suggesting these factors could explain the difference in individual infectiousness.

Our study clarifies the contribution of age, symptoms, and viral shedding to individual infectiousness of influenza cases in households.

School closures are a safe and important strategy for preventing infectious diseases in schools. However, the effects of school closures have not been fully demonstrated, and prolonged school closures have a negative impact on students and communities. This study evaluated class-specific school closure strategies to prevent the spread of seasonal influenza and determine the optimal timing and duration. We constructed a new model to describe the incidence of influenza in each class based on a stochastic susceptible-exposed-infected-removed model. We collected data on the number of infected absentees and class-specific school closures due to influenza from four high schools and the number of infected cases from the community in a Japanese city over three seasons (2016-2017, 2017-2018, and 2018-2019). The parameters included in the model were estimated using epidemic data. We evaluated the effects of class-specific school closures by measuring the reduced cumulative incidence of class closures per day. The greatest reduction in the cumulative absences per day was observed in the four-day class closure. When class-specific school closures lasted for four days, the reduction in the cumulative number of infections per class closure day was greater when the closure was timed earlier. The highest reduction in the number of class closures per person-day occurred when the threshold was around 5.0%. Large variations in the reduction of cumulative incidence were noted owing to stochastic factors. Reactive, class-specific school closures for seasonal influenza were most efficient when the percentage of newly infected students exceeded around 5.0%, with a closure duration of four days. The optimal strategy of class-specific school closure provides good long-term performance but may be affected by random variations.

Respiratory viral co-infections present significant challenges in clinical settings due to their impact on disease severity and patient outcomes. Current diagnostic methods often miss these co-infections, complicating the epidemiology and management of these cases. Research, primarily conducted in vitro and in vivo, suggests that co-infections can lead to more severe illnesses, increased hospitalization rates, and greater healthcare utilization, especially in high-risk groups such as children, the elderly, and immunocompromised individuals. Common co-infection patterns, risk factors, and their impact on disease dynamics highlight the need for advanced diagnostic techniques and tailored therapeutic strategies. Understanding the virological interactions and immune response modulation during co-infections is crucial for developing effective public health interventions and improving patient outcomes. Future research should focus on the molecular mechanisms of co-infection and the development of specific therapies to mitigate the adverse effects of these complex infections.

Traditional epidemiological models tend to oversimplify the transmission dynamics of Mycobacterium tuberculosis (M.tb) to replicate observed tuberculosis (TB) epidemic patterns. This has led to growing interest in advanced methodologies like agent-based modelling (ABM), which can more accurately represent the complex heterogeneity of TB transmission.

To better understand the use of agent-based models (ABMs) in this context, we conducted a systematic review with two main objectives: (1) to examine how ABMs have been employed to model the intricate heterogeneity of M.tb transmission, and (2) to identify the challenges and opportunities associated with implementing ABMs for M.tb.

We conducted a systematic search following PRISMA guidelines across four databases (MEDLINE, EMBASE, Global Health, and Scopus), limiting our review to peer-reviewed articles published in English up to December 2022. Data were extracted by two investigators using a standardized extraction tool. Prospero registration: CRD42022380580.

Our review included peer-reviewed articles in English that implement agent-based, individual-based, or microsimulation models of M.tb transmission. Models focusing solely on in-vitro or within-host dynamics were excluded. Data extraction targeted the methodological, epidemiological, and computational characteristics of ABMs used for TB transmission. A risk of bias assessment was not conducted as the review synthesized modelling studies without pooling epidemiological data.

Our search initially identified 5,077 studies, from which we ultimately included 26 in our final review after exclusions. These studies varied in population settings, time horizons, and model complexity. While many incorporated population heterogeneity and household structures, some lacked essential features like spatial structures or economic evaluations. Only eight studies provided publicly accessible code, highlighting the need for improved transparency.

ABMs are a versatile approach for representing complex disease dynamics, particularly in cases like TB, where they address heterogeneous mixing and household transmission often overlooked by traditional models. However, their advanced capabilities come with challenges, including those arising from their stochastic nature, such as parameter tuning and high computational expense. To improve transparency and reproducibility, open-source code sharing, and standardised reporting are recommended to enhance ABM reliability in studying epidemiologically complex diseases like TB.

BharatSim is an open-source agent-based modelling framework for the Indian population. It can simulate populations at multiple scales, from small communities to states. BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. This synthetic population defines individual agents with multiple attributes, among them age, gender, home and work locations, pre-existing health conditions, and socio-economic and employment status. BharatSim's domain-specific language provides a framework for the simulation of diverse models. Its computational core, coded in Scala, supports simulations of a large number of individual agents, up to 50 million. Here, we describe the design and implementation of BharatSim, using it to address three questions motivated by the COVID-19 pandemic in India: (i) When can schools be safely reopened given specified levels of hybrid immunity?, (ii) How do new variants alter disease dynamics in the background of prior infections and vaccinations? and (iii) How can the effects of varied non-pharmaceutical interventions (NPIs) be quantified for a model Indian city? Through its India-specific synthetic population, BharatSim allows disease modellers to address questions unique to this country. It should also find use in the computational social sciences, potentially providing new insights into emergent patterns in social behaviour.

Mathematical models and advanced analytics play an important role in policy decision making and mobilizing action. The Imperial College Coronavirus Disease 2019 (COVID-19) Response Team (ICCRT) provided continuous, timely and robust epidemiological analyses to inform the policy responses of governments and public health agencies around the world. This study aims to quantify the policy impact of ICCRT outputs, and understand which evidence was considered policy-relevant during the COVID-19 pandemic.

We collated all outputs published by the ICCRT between 01-01-2020 and 24-02-2022 and conducted inductive thematic analysis. A systematic search of the Overton database identified policy document references, as an indicator of policy impact.

We identified 620 outputs including preprints (16%), reports (29%), journal articles (37%) and news items (18%). More than half (56%) of all reports and preprints were subsequently peer-reviewed and published as a journal article after 202 days on average. Reports and preprints were crucial during the COVID-19 pandemic to the timely distribution of important research findings. One-fifth of ICCRT outputs (21%) were available to or considered by United Kingdom government meetings. Policy documents from 41 countries in 26 different languages referenced 43% of ICCRT outputs, with a mean time between publication and reference in the policy document of 256 days. We analysed a total of 1746 policy document references. Two-thirds (61%) of journal articles, 39% of preprints, 31% of reports and 16% of news items were referenced in one or more policy documents (these 217 outputs had a mean of 8 policy document references per output). The most frequent themes of the evidence produced by the ICCRT reflected the evidence-need for policy decision making, and evolved accordingly from the pre-vaccination phase [severity, healthcare demand and capacity, and non-pharmaceutical interventions (NPIs)] to the vaccination phase of the epidemic (variants and genomics).

The work produced by the ICCRT affected global and domestic policy during the COVID-19 pandemic. The focus of evidence produced by the ICCRT corresponded with changing policy needs over time. The policy impact from ICCRT news items highlights the effectiveness of this unique communication strategy in addition to traditional research outputs, ensuring research informs policy decisions more effectively.

Influenza vaccine effectiveness (VE) is commonly assessed against prevention of illness that requires medical attention. Few studies have evaluated VE against secondary influenza infections.

To determine the estimated effectiveness of influenza vaccines in preventing secondary infections after influenza was introduced into households.

During 3 consecutive influenza seasons (2017-2020), primary cases (the first household members with laboratory-confirmed influenza) and their household contacts in Tennessee and Wisconsin were enrolled into a prospective case-ascertained household transmission cohort study. Participants collected daily symptom diaries and nasal swabs for up to 7 days. Data were analyzed from September 2022 to February 2024.

Vaccination history, self-reported and verified through review of medical and registry records.

Specimens were tested using reverse transcription-polymerase chain reaction to determine influenza infection. Longitudinal chain binomial models were used to estimate secondary infection risk and the effectiveness of influenza vaccines in preventing infection among household contacts overall and by virus type and subtype and/or lineage.

The analysis included 699 primary cases and 1581 household contacts. The median (IQR) age of the primary cases was 13 (7-38) years, 381 (54.5%) were female, 60 (8.6%) were Hispanic, 46 (6.6%) were non-Hispanic Black, 553 (79.1%) were Non-Hispanic White, and 343 (49.1%) were vaccinated. Among household contacts, the median age was 31 (10-41) years, 833 (52.7%) were female, 116 (7.3%) were Hispanic, 78 (4.9%) were non-Hispanic Black, 1283 (81.2%) were non-Hispanic White, 792 (50.1%) were vaccinated, and 356 (22.5%) had laboratory-confirmed influenza during follow-up. The overall secondary infection risk of influenza among household contacts was 18.8% (95% CI, 15.9% to 22.0%). The risk was highest among children and was 20.3% (95% CI, 16.4% to 24.9%) for influenza A and 15.9% (95% CI, 11.8% to 21.0%) for influenza B. The overall estimated VE for preventing secondary infections among unvaccinated household contacts was 21.0% (95% CI, 1.4% to 36.7%) and varied by type; estimated VE against influenza A was 5.0% (95% CI, -22.3% to 26.3%) and 56.4% (95% CI, 30.1% to 72.8%) against influenza B.

After influenza was introduced into households, the risk of secondary influenza among unvaccinated household contacts was approximately 15% to 20%, and highest among children. Estimated VE varied by influenza type, with demonstrated protection against influenza B virus infection.

Degenerative cervical myelopathy (DCM) occurs when spondylotic changes compress the spinal cord and cause neurologic dysfunction. Because of a lack of comparative data on nonoperative care versus surgery for DCM, it has been difficult to support patients through the shared decision-making process regarding treatment options. Our objective was to synthesize the best available data in a manner that helps clinicians and patients to weigh the differences between nonoperative care and surgery at different ages and disease severity. The 2 patient-centered questions we sought to answer were (1) "am I more likely to experience worsening myelopathy with nonoperative care, or need more surgery if I have my myelopathy treated operatively?" and (2) "how much better will my quality of life be with nonoperative care versus surgery?"

We used a health economic technique, microsimulation, to model head-to-head comparisons of nonoperative care versus surgery for DCM. We incorporated the best available data, modeled patients over a lifetime horizon, used direct comparators, and incorporated uncertainty in both natural history and treatment effect.

Patients with mild DCM at baseline who were ≥75 years of age were less likely to neurologically decline under nonoperative care than to undergo a second surgery if the index surgery was an anterior cervical discectomy and fusion (ACDF), cervical disc arthroplasty (ADR), or posterior cervical decompression and instrumented fusion (PDIF). Using quality-adjusted life-years (QALYs), our results suggest that surgery for DCM may be superior to nonoperative care. However, for all patients except those with severe DCM who are of middle age or younger (depending on the procedure, ≤50 to ≤60 years of age), the lower bound of the 95% confidence interval for the estimated difference in QALYs was <0.

In most patient groups, neurologic progression with nonoperative management is more likely than the need for additional cervical surgery following operative management, with the exception of patients 75 to 80 years of age and older with mild DCM. Furthermore, on average, surgery for DCM tends to improve quality of life. However, patients with DCM who are older than middle age should be aware that the estimates of the quality-of-life benefit are highly uncertain, with a lower bound of <0.

Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Globally, there has been a commitment to produce and distribute a vaccine within 100 days of the next pandemic. This 100-day target will place pressure on countries to make swift decisions on how to optimise vaccine delivery. We used data from the COVID-19 pandemic to inform mathematical modelling of future pandemics in Indonesia for a wide range of pandemic characteristics. We explored the benefits of vaccination programs with different start dates, rollout capacity, and age-specific prioritisation within a year of the detection of a novel pathogen. Early vaccine availability, public uptake of vaccines, and capacity for consistent vaccine delivery were the key factors influencing vaccine benefit. Monitoring age-specific severity will be essential for optimising vaccine benefit. Our study complements existing pathogen-specific pandemic preparedness plans and contributes a tool for the rapid assessment of future threats in Indonesia and similar middle-income countries.

By collecting various control policies taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 2nd, 2020, we evaluate their impacts on the epidemic dynamics quantitatively through a combination of the multiple linear regression, neural-network-based nonlinear regression and sensitivity analysis. Remarkable differences in the public health policies are observed across these countries, which affect the spreading rate and infected population size to a great extent. Several key dynamical features, like the normalized cumulative numbers of confirmed/cured/death cases on the 100th day and the half time, show statistically significant linear correlations with the control measures, which thereby confirms their dramatic impacts. Most importantly, we perform the mediation analysis on the SEIR-QD model, a representative of general compartment models, by using the structure equation modeling for multiple mediators operating in parallel. This, to the best of our knowledge, is the first of its kind in the field of epidemiology. The infection rate and the protection rate of the SEIR-QD model are confirmed to exhibit a statistically significant mediation effect between the control measures and dynamical features of epidemics. The mediation effect along the pathway from control measures in Category 2 to four dynamical features through the infection rate, highlights the crucial role of nucleic acid testing and suspected cases tracing in containing the spread of the epidemic. Our data-driven analysis offers a deeper insight into the inherent correlations between the effectiveness of public health policies and the dynamic features of COVID-19 epidemics.

In this study, we propose a novel approach that integrates regime-shift detection with a mechanistic model to forecast the peak times of seasonal influenza. The key benefit of this approach is its ability to detect regime shifts from non-epidemic to epidemic states, which is particularly beneficial with the year-round presence of non-zero Influenza-Like Illness (ILI) data. This integration allows for the incorporation of external factors that trigger the onset of the influenza season-factors that mechanistic models alone might not adequately capture. Applied to ILI data collected in Korea from 2005 to 2020, our method demonstrated stable peak time predictions for seasonal influenza outbreaks, particularly in years characterized by unusual onset times or epidemic magnitudes.

The VH6-1 class of antibodies includes some of the broadest and most potent antibodies that neutralize influenza A virus. Here, we elicit and isolate anti-idiotype antibodies against germline versions of VH6-1 antibodies, use these to sort human leukocytes, and isolate a new VH6-1-class member, antibody L5A7, which potently neutralized diverse group 1 and group 2 influenza A strains. While its heavy chain derived from the canonical IGHV6-1 heavy chain gene used by the class, L5A7 utilized a light chain gene, IGKV1-9, which had not been previously observed in other VH6-1-class antibodies. The cryo-EM structure of L5A7 in complex with Indonesia 2005 hemagglutinin revealed a nearly identical binding mode to other VH6-1-class members. The structure of L5A7 bound to the isolating anti-idiotype antibody, 28H6E11, revealed a shared surface for binding anti-idiotype and hemagglutinin that included two critical L5A7 regions: an FG motif in the third heavy chain-complementary determining region (CDR H3) and the CDR L1 loop. Surprisingly, the chemistries of L5A7 interactions with hemagglutinin and with anti-idiotype were substantially different. Overall, we demonstrate anti-idiotype-based isolation of a broad and potent influenza A virus-neutralizing antibody, revealing that anti-idiotypic selection of antibodies can involve features other than chemical mimicry of the target antigen.

Conducting real-world public health experiments is often costly, time-consuming, and ethically challenging, so mathematical models have a long-standing history of being used to inform policy. Applications include estimating disease burden, performing economic evaluation of interventions, and responding to health emergencies such as pandemics. Models played a pivotal role during the COVID-19 pandemic, providing early detection of SARS-CoV-2's pandemic potential and informing subsequent public health measures. While models offer valuable policy insights, they often carry limitations, especially when they depend on assumptions and incomplete data. Striking a balance between accuracy and timely decision-making in rapidly evolving situations such as disease outbreaks is challenging. Modelers need to explore the extent to which their models deviate from representing the real world. The uncertainties inherent in models must be effectively communicated to policy makers and the public. As the field becomes increasingly influential, it needs to develop reporting standards that enable rigorous external scrutiny.

Coronavirus disease 2019 (COVID-19) is a severe global public health emergency that has caused a major crisis in the safety of human life, health, global economy, and social order. Moreover, COVID-19 poses significant challenges to healthcare systems worldwide. The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics. However, because of the complexity of epidemics, predicting infectious diseases on a global scale faces significant challenges. In this study, we developed the second version of Global Prediction System for Epidemiological Pandemic (GPEP-2), which combines statistical methods with a modified epidemiological model. The GPEP-2 introduces various parameterization schemes for both impacts of natural factors (seasonal variations in weather and environmental impacts) and human social behaviors (government control and isolation, personnel gathered, indoor propagation, virus mutation, and vaccination). The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%. It also provided prediction and decision-making bases for several regional-scale COVID-19 pandemic outbreaks in China, with an average accuracy rate of 89.3%. Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread. The predicted results could serve as a reference for public health planning and policymaking.

The COVID-19 pandemic has necessitated various unavoidable social restrictions, leading to questions about the effectiveness of public emergency interventions and their impact economic growth. Block et al. (2020) conducted a notably study using an agent-based model to evaluate policies for reducing contact and demonstrated how choices in contact behavior can influence the rate and spread of the virus. However, their approach did not consider the economic consequences of these social restrictions. In response, we propose a set of strategies for governments to plan and evaluate policies during emergencies, aiming to contain infections while minimizing negative economic consequences. Our results indicate that there is no trade-off between containment strategies and economic output loss, making containment measures necessary policy instruments. However, potential trade-offs do emerge when selecting the most effective strategy. In this context, we propose and evaluate various policy alternatives to extreme "social distancing" measures, which can partially restore essential social interactions while preventing economic disasters induced by productivity losses.

Compartmental models that describe infectious disease transmission across subpopulations are central for assessing the impact of non-pharmaceutical interventions, behavioral changes and seasonal effects on the spread of respiratory infections. We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laboratory-confirmed cases. For the third feature, we implemented three methods: Brownian motion, B-splines, and approximate Gaussian processes (aGP). We compared their performance in terms of the number of effective samples per second, and the error and sharpness in estimating the time-varying transmission rate over a selection of ordinary differential equation solvers and tuning parameters, using simulated seroprevalence and laboratory-confirmed case data. Even though all methods could recover the time-varying dynamics in the transmission rate accurately, we found that B-splines perform up to four and ten times faster than Brownian motion and aGPs, respectively. We validated the B-spline model with simulated age-stratified data. We applied this model to 2020 laboratory-confirmed SARS-CoV-2 cases and two seroprevalence studies from the canton of Geneva. This resulted in detailed estimates of the transmission rate over time and the case ascertainment. Our results illustrate the potential of the presented workflow including stratified transmission to estimate age-specific epidemiological parameters. The workflow is freely available in the R package HETTMO, and can be easily adapted and applied to other infectious diseases.

Influenza is a common, seasonal infectious disease with broad medical, economic, and social consequences. Real-world evidence on the effect of influenza treatment on household transmission and healthcare resource utilization is limited in outpatient settings in the USA. This study examined the real-world effectiveness of baloxavir vs oseltamivir in reducing influenza household transmission and healthcare resource utilization.

This prospective electronic survey on patient-reported outcomes was conducted between October 2022 and May 2023 via CVS Pharmacy in the USA. Adult participants (≥ 18 years old) were eligible if they filled a prescription for baloxavir or oseltamivir at a CVS Pharmacy within 2 days of influenza symptom onset. Participant demographics, household transmission, and all-cause healthcare resource utilization were collected. Transmission and utilization outcomes were assessed using χ2 and Fisher exact tests.

Of 87,871 unique patients contacted, 1346 (1.5%) consented. Of 374 eligible patients, 286 (90 baloxavir- and 196 oseltamivir-treated patients) completed the survey and were included in the analysis. Mean age of participants was 45.4 years, 65.6% were female, and 86.7% were White. Lower household transmission was observed with baloxavir compared with oseltamivir therapy (17.8% vs 26.5%; relative risk = 0.67; 95% CI 0.41-1.11). Healthcare resource utilization, particularly emergency department visits (0.0% vs 4.6%), was also numerically lower in the baloxavir-treated group; no hospitalizations were reported in either cohort.

The findings from this real-world study suggest that antiviral treatment of influenza with baloxavir may decrease household transmission and reduce healthcare resource utilization compared with oseltamivir.

The recent SARS-CoV-2 pandemic has exhibited diverse patterns of spread across countries and communities, emphasizing the need to consider the underlying population dynamics in modeling its progression and the importance of evaluating the effectiveness of non-pharmaceutical intervention strategies in combating viral transmission within human communities. Such an understanding requires accurate modeling of the interplay between the community dynamics and the disease propagation dynamics within the community.

We build on an interaction-driven model of an airborne disease over contact networks that we have defined. Using the model, we evaluate the effectiveness of temporal, spatial, and spatiotemporal social distancing policies. Temporal social distancing involves a pure dilation of the timeline while preserving individual activity potential and thus prolonging the period of interaction; spatial distancing corresponds to social distancing pods; and spatiotemporal distancing pertains to the situation in which fixed subgroups of the overall group meet at alternate times. We evaluate these social distancing policies over real-world interactions' data and over history-preserving synthetic temporal random networks. Furthermore, we evaluate the policies for the disease's with different number of initial patients, corresponding to either the phase in the progression of the infection through a community or the number of patients infected together at the initial infection event. We expand our model to consider the exposure to viral load, which we correlate with the meetings' duration.

Our results demonstrate the superiority of decreasing social interactions (i.e., time dilation) within the community over partial isolation strategies, such as the spatial distancing pods and the spatiotemporal distancing strategy. In addition, we found that slow-spreading pathogens (i.e., pathogens that require a longer exposure to infect) spread roughly at the same rate as fast-spreading ones in highly active communities. This result is surprising since the pathogens may follow different paths. However, we demonstrate that the dilation of the timeline considerably slows the spread of the slower pathogens.

Our results demonstrate that the temporal dynamics of a community have a more significant effect on the spread of the disease than the characteristics of the spreading processes.

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.

New Zealand's (NZ) complete absence of community transmission of influenza and respiratory syncytial virus (RSV) after May 2020, likely due to COVID-19 elimination measures, provided a rare opportunity to assess the impact of border restrictions on common respiratory viral infections over the ensuing 2 years.

We collected the data from multiple surveillance systems, including hospital-based severe acute respiratory infection surveillance, SHIVERS-II, -III and -IV community cohorts for acute respiratory infection (ARI) surveillance, HealthStat sentinel general practice (GP) based influenza-like illness surveillance and SHIVERS-V sentinel GP-based ARI surveillance, SHIVERS-V traveller ARI surveillance and laboratory-based surveillance. We described the data on influenza, RSV and other respiratory viral infections in NZ before, during and after various stages of the COVID related border restrictions.

We observed that border closure to most people, and mandatory government-managed isolation and quarantine on arrival for those allowed to enter, appeared to be effective in keeping influenza and RSV infections out of the NZ community. Border restrictions did not affect community transmission of other respiratory viruses such as rhinovirus and parainfluenza virus type-1. Partial border relaxations through quarantine-free travel with Australia and other countries were quickly followed by importation of RSV in 2021 and influenza in 2022.

Our findings inform future pandemic preparedness and strategies to model and manage the impact of influenza and other respiratory viral threats.

Extensive real-data indicate that human motion exhibits novel patterns and has a significant impact on the epidemic spreading process. The research on the influence of human motion patterns on epidemic spreading dynamics still lacks a systematic study in network science. Based on an agent-based model, this paper simulates the spread of the disease in the gathered population by combining the susceptible-infected-susceptible epidemic process with human motion patterns, described by moving speed and gathering preference. Our simulation results show that the emergence of a hysteresis loop is observed in the system when the moving speed is slow, particularly when humans prefer to gather; that is, the epidemic prevalence of the systems depends on the fraction of initial seeds. Regardless of the gathering preference, the hysteresis loop disappears when the population moves fast. In addition, our study demonstrates that there is an optimal moving speed for the gathered population, at which the epidemic prevalence reaches its maximum value.

Objective. This is a single-center retrospective cohort study that aimed to quantitatively assess the association between prolonged school closure (>2 weeks) and pediatric respiratory hospitalization during the COVID-19 pandemic. Methods. Subjects included 1243 patients presenting to Children's Hospital of Michigan during the winters of 2019, 2020, and 2021. The primary outcome measures were total respiratory hospitalizations and respiratory diagnoses. Results. Data was analyzed using a 2-sample z-test for proportions. We found that pediatric patients in the setting of prolonged school closure had significantly fewer hospitalizations in 2020 compared to 2019 (9% vs 47%; P < .001) and 2021 (9% vs 45%; P < .001). There were decreases in bronchiolitis, asthma/reactive airway disease (RAD), and pneumonia hospitalizations compared to 2019 and 2021. Conclusions. Our study showed that during prolonged school closure, there was a significant decrease in pediatric respiratory hospitalization. As such, it should be considered when creating a pandemic response strategy.

Workplaces are high-risk environments for epidemic transmission, and the COVID-19 pandemic has highlighted the significant impacts that health emergencies can have on both the healthcare system and the economy. This study presents executive and hierarchical models for participatory response to health emergencies in the workplace, with a focus on COVID-19.

The study was conducted in three phases. Content analysis of interviews with 101 stakeholders and national documents was used to identify key themes and dimensions for an executive model. A focus group discussion and review of international documents were then used to refine and expand the executive and hierarchical models. The alignment and trustworthiness of the final models, as well as feedback, were gathered from 117 informants working in various workplaces.

The executive model highlighted that context understanding, management commitment, and participation play critical roles in developing tailored prevention and response plans, and adequate support is necessary for successful plan implementation. Monitoring and review processes should be established to ensure proper functioning. The hierarchical model emphasizes the need for collaborative efforts from various stakeholders to effectively implement pandemic prevention and participatory response plans.

Overall, the executive and hierarchical participatory models presented in this study provide a framework for effectively controlling pandemics and other health emergencies in the workplace, enhancing both health resilience and the sustainability of economic activities.

Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html .

The Covid-19 pandemic, which was declared a public health emergency on 30 January 2020, has made it crucial for humans to learn how to behave to control the pandemic's spread. Policymakers must assess human behaviour and their responses to pandemic breakouts to develop a strategy for limiting pandemics and their harm to society at large. The present study applying exploratory factor analysis assessed five aspects of human behaviour regarding Covid-19, namely compliance behaviour, avoidance behaviour, protective behaviour, informed behaviour, and risk perception. The study applying hierarchical regression discovered that by combining informed, protective, and avoidance behaviour, people can be convinced to embrace the compliance behaviour required by public authorities. Furthermore, higher risk perception also positively moderates the relationship between information and compliance behaviour.

Simulation models are an important tool used in health care and other disciplines to support operational research and decision-making. In the child protection literature, simulation models are an under-utilized source of research evidence.

In this paper, we describe the rationale for and the development of an agent-based simulation of a child protection system in the US. Using the investigation, prevention service, and placement histories of 600,000 children served in an urban child welfare system, we walk the reader through the development of a prototype known as OSPEDALE.

The governing equations built into OSPEDALE probabilistically simulate the onset of investigations. Then, drawing from empirical survival distributions, the governing equations trace the probability of subsequent interactions with the system (recurrence of maltreatment, service referrals, and placement) conditional on the characteristics of children, their assessed risk level, and prior child protection system involvement.

As an initial test of OSPEDALE's utility, we compare empirical admission counts with counts generated from OSPEDALE. Though the verification step is admittedly simple, the comparison shows that OSPEDALE replicates the empirical count of new admissions closely enough to justify further investment in OSPEDALE.

Management of public child protection systems is increasingly research evidence-dependent. The emphasis on research evidence as a decision-support tool has elevated evidence acquired through randomized clinical trials. Though important, the evidence from clinical trials represents only one type of research evidence. Properly specified, simulation models are another source of evidence with real-world relevance.

Vaccination has played a pivotal role in reducing the burden of COVID-19. Despite numerous studies highlighting its benefits in reducing the risk of severe disease and death, we still lack a quantitative understanding of how varying vaccination roll-out rates influence COVID-19 mortality.

We developed a framework for estimating the number of avertable COVID-19 deaths (ACDs) by vaccination in Iran. To achieve this, we compared Iran's vaccination roll-out rates with those of eight model countries that predominantly used inactivated virus vaccines. We calculated net differences in the number of fully vaccinated individuals under counterfactual scenarios where Iran's per-capita roll-out rate was replaced with that of the model countries. This, in turn, enabled us to determine age specific ACDs for the Iranian population under counterfactual scenarios where number of COVID-19 deaths are estimated using all-cause mortality data. These estimates covered the period from the start of 2020 to 20 April 2022.

We found that while Iran would have had an approximately similar number of fully vaccinated individuals under counterfactual roll-out rates based on Bangladesh, Nepal, Sri Lanka, and Turkey (~ 65-70%), adopting Turkey's roll-out rates could have averted 50,000 (95% confidence interval: 38,100-53,500) additional deaths, while following Bangladesh's rates may have resulted in 52,800 (17,400-189,500) more fatalities in Iran. Surprisingly, mimicking Argentina's slower roll-out led to only 12,600 (10,400-13,300) fewer deaths, despite a higher counterfactual percentage of fully vaccinated individuals (~ 79%). Emulating Montenegro or Bolivia, with faster per capita roll-out rates and approximately 50% counterfactual full vaccination, could have prevented more deaths in older age groups, especially during the early waves. Finally, replicating Bahrain's model as an upper-bound benchmark, Iran could have averted 75,300 (56,000-83,000) deaths, primarily in the > 50 age groups.

Our analysis revealed that faster roll-outs were consistently associated with higher numbers of averted deaths, even in scenarios with lower overall coverage. This study offers valuable insights into future decision-making regarding infectious disease epidemic management through vaccination strategies. It accomplishes this by comparing various countries' relative performance in terms of timing, pace, and vaccination coverage, ultimately contributing to the prevention of COVID-19-related deaths.

The COVID-19 pandemic has swept the globe, and countries have responded with various intervention policies to prevent its spread. In this study, we aim to analyze the effectiveness of intervention policies implemented in South Korea. We use a stochastic individual-based model (IBM) with a synthetic population to simulate the spread of COVID-19. Using statistical data, we make the synthetic population and assign sociodemographic attributes to each individual. Individuals go about their daily lives based on their assigned characteristics, and encountering infectors in their daily lives stochastically determines whether they are infected. We reproduce the transmission of COVID-19 using the IBM simulation from November 2020 to February 2021 when three phases of increasingly stringent intervention policies were implemented, and then assess their effectiveness. Additionally, we predict how the spread of infection would have been different if these policies had been implemented in January 2022. This study offers valuable insights into the effectiveness of intervention policies in South Korea, which can assist policymakers and public health officials in their decision-making process.