Epidemics最新文献

The generation time, representing the interval between infections in primary and secondary cases, is essential for understanding and predicting the transmission dynamics of seasonal influenza, including the real-time effective reproduction number (Rt). However, comprehensive generation time estimates for seasonal influenza, especially since the 2009 influenza pandemic, are lacking. We estimated the generation time utilizing data from a 7-site case-ascertained household study in the United States over two influenza seasons, 2021/2022 and 2022/2023. More than 200 individuals who tested positive for influenza and their household contacts were enrolled within 7 days of the first illness in the household. All participants were prospectively followed for 10 days, completing daily symptom diaries and collecting nasal swabs, which were then tested for influenza via RT-PCR. We analyzed these data by modifying a previously published Bayesian data augmentation approach that imputes infection times of cases to obtain both intrinsic (assuming no susceptible depletion) and realized (observed within household) generation times. We assessed the robustness of the generation time estimate by varying the incubation period, and generated estimates of the proportion of transmission occurring before symptomatic onset, the infectious period, and the latent period. We estimated a mean intrinsic generation time of 3.2 (95 % credible interval, CrI: 2.9-3.6) days, with a realized household generation time of 2.8 (95 % CrI: 2.7-3.0) days. The generation time exhibited limited sensitivity to incubation period variation. Estimates of the proportion of transmission that occurred before symptom onset, the infectious period, and the latent period were sensitive to variations in the incubation period. Our study contributes to the ongoing efforts to refine estimates of the generation time for influenza. Our estimates, derived from recent data following the COVID-19 pandemic, are consistent with previous pre-pandemic estimates, and will be incorporated into real-time Rt estimation efforts.

Accurate measurement of exposure to SARS-CoV-2 in the population is crucial for understanding the dynamics of disease transmission and evaluating the impacts of interventions. However, it was particularly challenging to achieve this in the early phase of a pandemic because of the sparsity of epidemiological data. We previously developed an early pandemic diagnostic tool that linked minimum datasets: seroprevalence, mortality and infection testing data to estimate the true exposure in different regions of England and found levels of SARS-CoV-2 population exposure to be considerably higher than suggested by seroprevalence surveys. Here, we re-examine and evaluate the model in the context of reconstructing the first COVID-19 epidemic wave in England from three perspectives: validation against the Office for National Statistics (ONS) Coronavirus Infection Survey, relationship among model performance and data abundance and time-varying case detection ratios. We find that our model can recover the first, unobserved, epidemic wave of COVID-19 in England from March 2020 to June 2020 if two or three serological measurements are given as additional model inputs, while the second wave during winter of 2020 is validated by estimates from the ONS Coronavirus Infection Survey. Moreover, the model estimates that by the end of October in 2020 the UK government's official COVID-9 online dashboard reported COVID-19 cases only accounted for 9.1 % of cumulative exposure, dramatically varying across the two epidemic waves in England in 2020, 4.3 % vs 43.7 %.

Background: Our study aims to enhance future pandemic preparedness by integrating lessons from historical pandemics, focusing on the multidimensional analysis of past outbreaks. It addresses the gap in existing modelling studies by combining various pandemic parameters in a comprehensive setting. Using Zurich as a case study, we seek a deeper understanding of pandemic dynamics to inform future scenarios.

Data and methods: We use newly digitized weekly aggregated epidemic/pandemic time series (incidence, hospitalisations, mortality and sickness absences from work) to retrospectively model the 1918-1920 pandemic in Zurich and investigate how different parameters correspond, how transmissibility changed during the different waves, and how public health interventions were associated with changes in these pandemic parameters.

Results: In general, the various time series show a good temporal correspondence, but differences in their expression can also be observed. The first wave in the summer of 1918 did lead to illness, absence from work and hospitalisations, but to a lesser extent to increased mortality. In contrast, the second, longest and strongest wave in the autumn/winter of 1918 also led to greatly increased (excess) mortality in addition to the burden of illness. The later wave in the first months of 1920 was again associated with an increase in all pandemic parameters. Furthermore, we can see that public health measures such as bans on gatherings and school closures were associated with a decrease in the course of the pandemic, while the lifting or non-compliance with these measures was associated with an increase of reported cases.

Discussion: Our study emphasizes the need to analyse a pandemic's disease burden comprehensively, beyond mortality. It highlights the importance of considering incidence, hospitalizations, and work absences as distinct but related aspects of disease impact. This approach reveals the nuanced dynamics of a pandemic, especially crucial during multi-wave outbreaks.

Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 2021/22 influenza season, the forecasting targets for this challenge have been based on hospital admissions reported in the CDC's National Healthcare Safety Network (NHSN) surveillance system. Reporting of influenza hospital admissions through NHSN began within the last few years, and as such only a limited amount of historical data are available for this target signal. To produce forecasts in the presence of limited data for the target surveillance system, we augmented these data with two signals that have a longer historical record: 1) ILI+, which estimates the proportion of outpatient doctor visits where the patient has influenza; and 2) rates of laboratory-confirmed influenza hospitalizations at a selected set of healthcare facilities. Our model, Flusion, is an ensemble model that combines two machine learning models using gradient boosting for quantile regression based on different feature sets with a Bayesian autoregressive model. The gradient boosting models were trained on all three data signals, while the autoregressive model was trained on only data for the target surveillance signal, NHSN admissions; all three models were trained jointly on data for multiple locations. In each week of the influenza season, these models produced quantiles of a predictive distribution of influenza hospital admissions in each state for the current week and the following three weeks; the ensemble prediction was computed by averaging these quantile predictions. Flusion emerged as the top-performing model in the CDC's influenza prediction challenge for the 2023/24 season. In this article we investigate the factors contributing to Flusion's success, and we find that its strong performance was primarily driven by the use of a gradient boosting model that was trained jointly on data from multiple surveillance signals and multiple locations. These results indicate the value of sharing information across multiple locations and surveillance signals, especially when doing so adds to the pool of available training data.

Over three years since the first identified SARS-CoV-2 case was discovered, the role of adolescents and children in spreading the virus remains unclear. Specifically, estimating the relative susceptibility of a child with respect to an adult is still an open question. In our work, we generalize a well-known household model for modeling infectious diseases, to include missing tests. Due to missingness, the likelihood of the generalized model cannot be maximized directly. Thus, we propose an estimation methodology, using a novel EM algorithm, for estimating the MLE in the presence of missing data. We implement the proposed mechanism using R software. Using a simulation study, we illustrate the performance of the proposed estimation methodology compared with the estimation procedure in the complete case. Finally, using the proposed estimation methodology we analyzed a dataset containing SARS-CoV-2 testing results, collected from the city of Bnei Brak, Israel, during the beginning of the pandemic. Using this dataset, we show that adolescents are less susceptible than adults, and children are less susceptible than adolescents.

Background: The prevention and control of infectious disease outbreaks in carceral settings face unique challenges. Transmission modeling is a powerful tool for understanding and addressing these challenges, but reviews of modeling work in this context pre-date the proliferation of outbreaks in jails and prisons during the SARS-CoV-2 pandemic. We conducted a systematic review of studies using transmission models of respiratory infections in carceral settings before and during the pandemic.

Methods: We searched PubMed, Embase, Scopus, CINAHL, and PsycInfo to identify studies published between 1970 and 2024 that modeled transmission of respiratory infectious diseases in carceral settings. We extracted information on the diseases, populations, and settings modeled; approaches used for parameterizing models and simulating transmission; outcomes of interest and techniques for model calibration, validation, and sensitivity analyses; and types, impacts, and ethical aspects of modeled interventions.

Results: Forty-six studies met eligibility criteria, with transmission dynamics of tuberculosis modeled in 24 (52 %), SARS-CoV-2 in 20 (43 %), influenza in one (2 %), and varicella-zoster virus in one (2 %). Carceral facilities in the United States were the most common focus (15, 33 %), followed by Brazil (8, 17 %). Most studies (36, 80 %) used compartmental models (vs. individual- or agent-based). Tuberculosis studies typically modeled transmission within a single facility, while most SARS-CoV-2 studies simulated transmission in multiple places, including between carceral and community settings. Half of studies fit models to epidemiological data; three validated model predictions. Models were used to estimate past or potential future intervention impacts in 32 (70 %) studies, forecast the status quo (without changing conditions) in six (13 %), and examine only theoretical aspects of transmission in eight (17 %). Interventions commonly involved testing and treatment, quarantine and isolation, and/or facility ventilation. Modeled interventions substantially reduced transmission, but some were not well-defined or did not consider ethical issues.

Conclusion: The pandemic prompted urgent attention to transmission dynamics in jails and prisons, but there has been little modeling of respiratory infections other than SARS-CoV-2 and tuberculosis. Increased attention to calibration, validation, and the practical and ethical aspects of intervention implementation could improve translation of model estimates into tangible benefits for the highly vulnerable populations in carceral settings.

The effective reproduction number serves as a metric of population-wide, time-varying disease spread. During the early years of the COVID-19 pandemic, this metric was primarily derived from case data, which has varied in quality and representativeness due to changes in testing volume, test-seeking behavior, and resource constraints. Deriving nowcasting estimates from alternative data sources such as wastewater provides complementary information that could inform future public health responses. We estimated county-aggregated, sewershed-restricted wastewater-based SARS-CoV-2 effective reproduction numbers from May 1, 2022 to April 30, 2023 for five counties in California with heterogeneous population sizes, clinical testing rates, demographics, wastewater coverage, and sampling frequencies. We used two methods to produce sewershed-restricted effective reproduction numbers, both based on smoothed and deconvolved wastewater concentrations. We then population-weighted and aggregated these sewershed-level estimates to arrive at county-level effective reproduction numbers. Using mean absolute error (MAE), Spearman's rank correlation (ρ), confusion matrix classification, and cross-correlation analyses, we compared the timing and trajectory of our two wastewater-based models to: (1) a publicly available, county-level ensemble of case-based estimates, and (2) county-aggregated, sewershed-restricted case-based estimates. Both wastewater models demonstrated high concordance with the traditional case-based estimates, as indicated by low mean absolute errors (MAE ≤ 0.09), significant positive Spearman correlation (ρ ≥ 0.66), and high confusion matrix classification accuracy (≥ 0.81). The relative timings of wastewater- and case-based estimates were less clear, with cross-correlation analyses suggesting strong associations for a wide range of temporal lags that varied by county and wastewater model type. This methodology provides a generalizable, robust, and operationalizable framework for estimating county-level wastewater-based effective reproduction numbers. Our retrospective evaluation supports the potential usage of real-time wastewater-based nowcasting as a complementary epidemiological tool for surveillance by public health agencies at the state and local levels. Based on this research, we produced publicly available wastewater-based nowcasts for the California Communicable diseases Assessment Tool (calcat.cdph.ca.gov).

The introduction of non-pharmaceutical interventions (NPIs) against COVID-19 disrupted circulation of many respiratory pathogens and eventually caused large, delayed outbreaks, owing to the build up of the susceptible pool during the intervention period. In contrast to other common respiratory pathogens that re-emerged soon after the NPIs were lifted, longer delays (> 3 years) in the outbreaks of Mycoplasma pneumoniae (Mp), a bacterium commonly responsible for respiratory infections and pneumonia, have been reported in Europe and Asia. As Mp cases are continuing to increase in the US, predicting the size of an imminent outbreak is timely for public health agencies and decision makers. Here, we use simple mathematical models to provide robust predictions about a large Mp outbreak ongoing in the US. Our model further illustrates that NPIs and waning immunity are important factors in driving long delays in epidemic resurgence.

We present a review and primer of methods to understand epidemiological dynamics and identify past exposures from serological data, referred to as serodynamics. We discuss processing and interpreting serological data prior to fitting serodynamical models, and review approaches for estimating epidemiological trends and past exposures, ranging from serocatalytic models applied to binary serostatus data, to more complex models incorporating quantitative antibody measurements and immunological understanding. Although these methods are seemingly disparate, we demonstrate how they are derived within a common mathematical framework. Finally, we discuss key areas for methodological development to improve scientific discovery and public health insights in seroepidemiology.

The emergence of SARS-CoV-2 variants of concern (VOCs) punctuated the dynamics of the COVID-19 pandemic in multiple occasions. The stages subsequent to their identification have been particularly challenging due to the hurdles associated with a prompt assessment of transmissibility and immune evasion characteristics of the newly emerged VOC. Here, we retrospectively analyze the performance of a modeling strategy developed to evaluate, in real-time, the risks posed by the Alpha and Omicron VOC soon after their emergence. Our approach utilized multi-strain, stochastic, compartmental models enriched with demographic information, age-specific contact patterns, the influence of non-pharmaceutical interventions, and the trajectory of vaccine distribution. The models' preliminary assessment about Omicron's transmissibility and immune evasion closely match later findings. Additionally, analyses based on data collected since our initial assessments demonstrate the retrospective accuracy of our real-time projections in capturing the emergence and subsequent dominance of the Alpha VOC in seven European countries and the Omicron VOC in South Africa. This study shows the value of relatively simple epidemic models in assessing the impact of emerging VOCs in real time, the importance of timely and accurate data, and the need for regular evaluation of these methodologies as we prepare for future global health crises.