Epidemics最新文献

This short communication reflects upon the challenges and recommendations of multiple COVID-19 modelling and data analytic groups that provided quantitative evidence to support health policy discussions in Switzerland and Germany during the SARS-CoV-2 pandemic.

Capacity strengthening outside infectious disease emergencies will be required to enable an environment for a timely, efficient, and data-driven response to support decisions during any future infectious disease emergency.

This will require 1) a critical mass of trained experts who continuously advance state-of-the-art methodological tools, 2) the establishment of structural liaisons amongst scientists and decision-makers, and 3) the foundation and management of data-sharing frameworks.

During the COVID-19 pandemic, contact tracing was used to identify individuals who had been in contact with a confirmed case so that these contacted individuals could be tested and quarantined to prevent further spread of the SARS-CoV-2 virus. Many countries developed mobile apps to find these contacted individuals faster. We evaluate the epidemiological effectiveness of the Dutch app CoronaMelder, where we measure effectiveness as the reduction of the reproduction number R. To this end, we use a simulation model of SARS-CoV-2 spread and contact tracing, informed by data collected during the study period (December 2020 - March 2021) in the Netherlands. We show that the tracing app caused a clear but small reduction of the reproduction number, and the magnitude of the effect was found to be robust in sensitivity analyses. The app could have been more effective if more people had used it, and if notification of contacts could have been done directly by the user and thus reducing the time intervals between symptom onset and reporting of contacts. The model has two innovative aspects: i) it accounts for the clustered nature of social networks and ii) cases can alert their contacts informally without involvement of health authorities or the tracing app.

Although the most recent respiratory virus pandemic was triggered by a Coronavirus, sustained and elevated prevalence of highly pathogenic avian influenza viruses able to infect mammalian hosts highlight the continued threat of pandemics of influenza A virus (IAV) to global health. Retrospective analysis of pandemic outcomes, including comparative investigation of intervention efficacy in different regions, provide important contributions to the evidence base for future pandemic planning. The swine-origin IAV pandemic of 2009 exhibited regional variation in onset, infection dynamics and annual infection attack rates (IARs). For example, the UK experienced three severe peaks of infection over two influenza seasons, whilst Australia experienced a single severe wave. We adopt a seasonally forced 2-subtype model for the transmission of pH1N12009 and seasonal H3N2 to examine the role vaccination campaigns may play in explaining differences in pandemic trajectories in temperate regions. Our model differentiates between the nature of vaccine- and infection-acquired immunity. In particular, we assume that immunity triggered by infection elicits heterologous cross-protection against viral shedding in addition to long-lasting neutralising antibody, whereas vaccination induces imperfect reduction in susceptibility. We employ an Approximate Bayesian Computation (ABC) framework to calibrate the model using data for pH1N12009 seroprevalence, relative subtype dominance, and annual IARs for Australia and the UK. Heterologous cross-protection substantially suppressed the pandemic IAR over the posterior, with the strength of protection against onward transmission inversely correlated with the initial reproduction number. We show that IAV pandemic timing relative to the usual seasonal influenza cycle influenced the size of the initial waves of pH1N12009 in temperate regions and the impact of vaccination campaigns.

The serial interval of an infectious disease is an important variable in epidemiology. It is defined as the period of time between the symptom onset times of the infector and infectee in a direct transmission pair. Under partially sampled data, purported infector–infectee pairs may actually be separated by one or more unsampled cases in between. Misunderstanding such pairs as direct transmissions will result in overestimating the length of serial intervals. On the other hand, two cases that are infected by an unseen third case (known as coprimary transmission) may be classified as a direct transmission pair, leading to an underestimation of the serial interval. Here, we introduce a method to jointly estimate the distribution of serial intervals factoring in these two sources of error. We simultaneously estimate the distribution of the number of unsampled intermediate cases between purported infector–infectee pairs, as well as the fraction of such pairs that are coprimary. We also extend our method to situations where each infectee has multiple possible infectors, and show how to factor this additional source of uncertainty into our estimates. We assess our method’s performance on simulated data sets and find that our method provides consistent and robust estimates. We also apply our method to data from real-life outbreaks of four infectious diseases and compare our results with published results. With similar accuracy, our method of estimating serial interval distribution provides unique advantages, allowing its application in settings of low sampling rates and large population sizes, such as widespread community transmission tracked by routine public health surveillance.

Streptococcus pneumoniae is an opportunistic pathogen that, while usually carried asymptomatically, can cause severe invasive diseases like meningitis and bacteremic pneumonia. A central goal in S. pneumoniae public health management is to identify which serotypes (immunologically distinct strains) pose the most risk of invasive disease. The most common invasiveness metrics use cross-sectional data (i.e., invasive odds ratios (IOR)), or longitudinal data (i.e., attack rates (AR)). To assess the reliability of these metrics we developed an epidemiological model of carriage and invasive disease. Our mathematical analyses illustrate qualitative failures with the IOR metric (e.g., IOR can decline with increasing invasiveness parameters). Fitting the model to both longitudinal and cross-sectional data, our analysis supports previous work indicating that invasion risk is maximal at or near time of colonization. This pattern of early invasive disease risk leads to substantial (up to 5-fold) biases when estimating underlying differences in invasiveness from IOR metrics, due to the impact of carriage duration on IOR. Together, these results raise serious concerns with the IOR metric as a basis for public health decision-making and lend support for multiple alternate metrics including AR.

We proposed the SIkJalpha model at the beginning of the COVID-19 pandemic (early 2020). Since then, as the pandemic evolved, more complexities were added to capture crucial factors and variables that can assist with projecting desired future scenarios. Throughout the pandemic, multi-model collaborative efforts have been organized to predict short-term outcomes (cases, deaths, and hospitalizations) of COVID-19 and long-term scenario projections. We have been participating in five such efforts. This paper presents the evolution of the SIkJalpha model and its many versions that have been used to submit to these collaborative efforts since the beginning of the pandemic. Specifically, we show that the SIkJalpha model is an approximation of a class of epidemiological models. We demonstrate how the model can be used to incorporate various complexities, including under-reporting, multiple variants, waning of immunity, and contact rates, and to generate probabilistic outputs.

Non-pharmaceutical interventions minimize social contacts, hence the spread of respiratory pathogens such as influenza and SARS-CoV-2. Globally, there is a paucity of social contact data from the workforce. In this study, we quantified two-day contact patterns among USA employees. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic self-kept diaries. Data were collected over 4 rounds from 2020 to 2021 during the COVID-19 pandemic. Mean (standard deviation) contacts reported by 1456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April–June 2020), 2 (November 2020–January 2021), 3 (June–August 2021), and 4 (November–December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2–4. We then modeled SARS-CoV-2 transmission at home, work, and community settings. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained exceptionally low in work settings. To accurately parameterize models of infection transmission and control, we need empirical social contact data that capture human mixing behavior across time.

Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.

Monitoring time-varying vaccine effectiveness (e.g., due to waning of immunity and the emergence of novel variants) provides crucial information for outbreak control. Existing studies of time-varying vaccine effectiveness have used individual-level data, most importantly dates of vaccination and variant classification, which are often not available in a timely manner or from a wide range of population groups. We present a novel Bayesian framework for estimating the waning of variant-specific vaccine effectiveness in the presence of multi-variant circulation from population-level surveillance data. Applications to simulated outbreaks and the COVID-19 epidemic in Japan are also presented. Our results show that variant-specific waning vaccine effectiveness estimated from population-level surveillance data could approximately reproduce the estimates from previous test-negative design studies, allowing for rapid, if crude, assessment of the epidemic situation before fine-scale studies are made available.

Peste des petits ruminants (PPR) is an acute infectious disease of small ruminants targeted for global eradication by 2030. The Global Strategy for Control and Eradication (GSCE) recommends mass vaccination targeting 70% coverage of small ruminant populations in PPR-endemic regions. These small ruminant populations are diverse with heterogeneous mixing patterns that may influence PPR virus (PPRV) transmission dynamics. This paper evaluates the impact of heterogeneous mixing on (i) PPRV transmission and (ii) the likelihood of different vaccination strategies achieving PPRV elimination, including the GSCE recommended strategy. We develop models simulating heterogeneous transmission between hosts, including a metapopulation model of PPRV transmission between villages in lowland Ethiopia fitted to serological data. Our results demonstrate that although heterogeneous mixing of small ruminant populations increases the instability of PPRV transmission—increasing the chance of fadeout in the absence of intervention—a vaccination coverage of 70% may be insufficient to achieve elimination if high-risk populations are not targeted. Transmission may persist despite very high vaccination coverage (>90% small ruminants) if vaccination is biased towards more accessible but lower-risk populations such as sedentary small ruminant flocks. These results highlight the importance of characterizing small ruminant mobility patterns and identifying high-risk populations for vaccination and support a move towards targeted, risk-based vaccination programmes in the next phase of the PPRV eradication programme. Our modelling approach also illustrates a general framework for incorporating heterogeneous mixing patterns into models of directly transmitted infectious diseases where detailed contact data are limited. This study improves understanding of PPRV transmission and elimination in heterogeneous small ruminant populations and should be used to inform and optimize the design of PPRV vaccination programmes.