Global Seasonal Activities of Respiratory Syncytial Virus Before the Coronavirus Disease 2019 Pandemic: A Systematic Review.

Varied seasonal patterns of respiratory syncytial virus (RSV) have been reported worldwide. We conducted a systematic review on articles identified in PubMed reporting RSV seasonality based on data collected before 1 January 2020. RSV seasonal patterns were examined by geographic location, calendar month, analytic method, and meteorological factors including temperature and absolute humidity. Correlation and regression analyses were conducted to explore the relationship between RSV seasonality and study methods and characteristics of study locations. RSV seasons were reported in 209 articles published in 1973-2023 for 317 locations in 77 countries. Regular RSV seasons were similarly reported in countries in temperate regions, with highly variable seasons identified in subtropical and tropical countries. Longer durations of RSV seasons were associated with a higher daily average mean temperature and daily average mean absolute humidity. The global seasonal patterns of RSV provided important information for optimizing interventions against RSV infection.

A retrospective cohort study of Paxlovid efficacy depending on treatment time in hospitalized COVID-19 patients.

Paxlovid, a SARS-CoV-2 antiviral, not only prevents severe illness but also curtails viral shedding, lowering transmission risks from treated patients. By fitting a mathematical model of within-host Omicron viral dynamics to electronic health records data from 208 hospitalized patients in Hong Kong, we estimate that Paxlovid can inhibit over 90% of viral replication. However, its effectiveness critically depends on the timing of treatment. If treatment is initiated three days after symptoms first appear, we estimate a 17% chance of a post-treatment viral rebound and a 12% (95% CI: 0-16%) reduction in overall infectiousness for non-rebound cases. Earlier treatment significantly elevates the risk of rebound without further reducing infectiousness, whereas starting beyond five days reduces its efficacy in curbing peak viral shedding. Among the 104 patients who received Paxlovid, 62% began treatment within an optimal three-to-five-day day window after symptoms appeared. Our findings indicate that broader global access to Paxlovid, coupled with appropriately timed treatment, can mitigate the severity and transmission of SARS-Cov-2.

Public Health Impact of Paxlovid as Treatment for COVID-19, United States.

We evaluated the population-level benefits of expanding treatment with the antiviral drug Paxlovid (nirmatrelvir/ritonavir) in the United States for SARS-CoV-2 Omicron variant infections. Using a multiscale mathematical model, we found that treating 20% of symptomatic case-patients with Paxlovid over a period of 300 days beginning in January 2022 resulted in life and cost savings. In a low-transmission scenario (effective reproduction number of 1.2), this approach could avert 0.28 million (95% CI 0.03-0.59 million) hospitalizations and save US $56.95 billion (95% CI US $2.62-$122.63 billion). In a higher transmission scenario (effective reproduction number of 3), the benefits increase, potentially preventing 0.85 million (95% CI 0.36-1.38 million) hospitalizations and saving US $170.17 billion (95% CI US $60.49-$286.14 billion). Our findings suggest that timely and widespread use of Paxlovid could be an effective and economical approach to mitigate the effects of COVID-19.

Nowcasting and Forecasting Seasonal Influenza Epidemics - China, 2022-2023.

Background: Seasonal influenza resurged in China in February 2023, causing a large number of hospitalizations. While influenza epidemics occurred across China during the coronavirus disease 2019 (COVID-19) pandemic, the relaxation of COVID-19 containment measures in December 2022 may have contributed to the spread of acute respiratory infections in winter 2022/2023. Methods: Using a mathematical model incorporating influenza activity as measured by influenza-like illness (ILI) data for northern and southern regions of China, we reconstructed the seasonal influenza incidence from October 2015 to September 2019 before the COVID-19 pandemic. Using this trained model, we predicted influenza activities in northern and southern China from March to September 2023. Results: We estimated the effective reproduction number R e as 1.08 [95% confidence interval ( CI): 0.51, 1.65] in northern China and 1.10 (95% CI: 0.55, 1.67) in southern China at the start of the 2022-2023 influenza season. We estimated the infection attack rate of this influenza wave as 18.51% (95% CI: 0.00%, 37.78%) in northern China and 28.30% (95% CI: 14.77%, 41.82%) in southern China. Conclusions: The 2023 spring wave of seasonal influenza in China spread until July 2023 and infected a substantial number of people.

Mitigation of respiratory syncytial virus epidemics by RSVpreF vaccines after the COVID-19 pandemic in the UK: a modelling study.

BACKGROUND: The RSVpreF vaccines have breakthrough progress. The respiratory syncytial virus (RSV) vaccine for older adults from GlaxoSmithKline was the first RSV vaccine approved by the US Food and Drug Administration (FDA) in early May 2023, followed by the subsequent FDA approval of Pfizer's RSV vaccines for older adults and pregnant women. We aimed to estimate the public health impact of the potential population-level administrations of the RSVpreF vaccine in the UK. METHODS: In this modelling study, we used national census and contact survey data to construct an individual-based mathematical model, with interpersonal connections characterising household structure, social settings, and age-specific contact patterns. We considered both within-host viral-load dynamics and between-host RSV transmission. We modelled the coverages of RSV vaccines for older people (age ≥60 years) and pregnant women, using influenza vaccination data from the 2018-19 season. We explored a range of possible transmissibility and estimated the health burden averted by RSVpreF vaccine over a 300-day period as compared with the control scenario without vaccines. FINDINGS: In a low-transmission scenario (Re=1·2), RSVpreF would avert a total population of 2·35 (95% credible interval [CrI] 1·24-3·77) million infections, 12.80 (95% CrI 8·60-17·06) thousand hospital admissions, and 0·93 (95% CrI 0·69-1·25) thousand deaths, with 1·82 (1·41-2·33) million infections, 12·44 (8·50-16·38) thousand hospital admissions, and 0·93 (0·67-1·23) thousand deaths averted for people aged 60 years and older. In a high-transmission scenario (Re=2·0), RSVpreF would avert 2·01 (1·37-2·68) million infections, 14·67 (10·05-18·33) thousand hospital admissions, and 1·12 (0·80-1·35) thousand deaths. The majority averted would still be among older adults. INTERPRETATION: Our mathematical models will help improve the vaccine schedules of RSVpreF. Future work will address several limitations when data become available, including the incorporation of population immunity, potential vaccine hesitancy, and other factors affecting vaccine uptake and effectiveness. FUNDING: Government of the Hong Kong Special Administrative Region, the European Research Council, and Ministry of Science and Technology of the People's Republic of China.

Identifying Phage Sequences From Metagenomic Data Using Deep Neural Network With Word Embedding and Attention Mechanism.

Phages are the functional viruses that infect bacteria and they play important roles in microbial communities and ecosystems. Phage research has attracted great attention due to the wide applications of phage therapy in treating bacterial infection in recent years. Metagenomics sequencing technique can sequence microbial communities directly from an environmental sample. Identifying phage sequences from metagenomic data is a vital step in the downstream of phage analysis. However, the existing methods for phage identification suffer from some limitations in the utilization of the phage feature for prediction, and therefore their prediction performance still need to be improved further. In this article, we propose a novel deep neural network (called MetaPhaPred) for identifying phages from metagenomic data. In MetaPhaPred, we first use a word embedding technique to encode the metagenomic sequences into word vectors, extracting the latent feature vectors of DNA words. Then, we design a deep neural network with a convolutional neural network (CNN) to capture the feature maps in sequences, and with a bi-directional long short-term memory network (Bi-LSTM) to capture the long-term dependencies between features from both forward and backward directions. The feature map consists of a set of feature patterns, each of which is the weighted feature extracted by a convolution filter with convolution kernels in the CNN slide along the input feature vectors. Next, an attention mechanism is used to enhance contributions of important features. Experimental results on both simulated and real metagenomic data with different lengths demonstrate the superiority of the proposed MetaPhaPred over the state-of-the-art methods in identifying phage sequences.

The public health impact of Paxlovid COVID-19 treatment in the United States.

The antiviral drug Paxlovid has been shown to rapidly reduce viral load. Coupled with vaccination, timely administration of safe and effective antivirals could provide a path towards managing COVID-19 without restrictive non-pharmaceutical measures. Here, we estimate the population-level impacts of expanding treatment with Paxlovid in the US using a multi-scale mathematical model of SARS-CoV-2 transmission that incorporates the within-host viral load dynamics of the Omicron variant. We find that, under a low transmission scenario Re∼1.2 treating 20% of symptomatic cases would be life and cost saving, leading to an estimated 0.26 (95% CrI: 0.03, 0.59) million hospitalizations averted, 30.61 (95% CrI: 1.69, 71.15) thousand deaths averted, and US$52.16 (95% CrI: 2.62, 122.63) billion reduction in health- and treatment-related costs. Rapid and broad use of the antiviral Paxlovid could substantially reduce COVID-19 morbidity and mortality, while averting socioeconomic hardship.

Estimate of COVID-19 Deaths, China, December 2022-February 2023.

China announced a slight easing of its zero-COVID rules on November 11, 2022, and then a major relaxation on December 7, 2022. We estimate that the ensuing wave of SARS-CoV-2 infections caused 1.41 million deaths in China during December 2022-February 2023, substantially higher than that reported through official channels.

Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics.

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

Protocol for the automatic extraction of epidemiological information via a pre-trained language model.

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model.1 We describe steps for preparing supervised training data and executing python scripts for named entity recognition and text category classification. We then detail the use of machine evaluation and manual validation to illustrate the effectiveness of CCIE. For complete details on the use and execution of this protocol, please refer to Wang et al.2.

Medical-Knowledge-Based Graph Neural Network for Medication Combination Prediction.

Medication combination prediction (MCP) can provide assistance for experts in the more thorough comprehension of complex mechanisms behind health and disease. Many recent studies focus on the patient representation from the historical medical records, but neglect the value of the medical knowledge, such as the prior knowledge and the medication knowledge. This article develops a medical-knowledge-based graph neural network (MK-GNN) model which incorporates the representation of patients and the medical knowledge into the neural network. More specifically, the features of patients are extracted from their medical records in different feature subspaces. Then these features are concatenated to obtain the feature representation of patients. The prior knowledge, which is calculated according to the mapping relationship between medications and diagnoses, provides heuristic medication features according to the diagnosis results. Such medication features can help the MK-GNN model learn optimal parameters. Moreover, the medication relationship in prescriptions is formulated as a drug network to integrate the medication knowledge into medication representation vectors. The results reveal the superior performance of the MK-GNN model compared with the state-of-the-art baselines on different evaluation metrics. The case study manifests the application potential of the MK-GNN model.

Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020.

What is already known about this topic?: People are likely to engage in collective behaviors online during extreme events, such as the coronavirus disease 2019 (COVID-19) crisis, to express awareness, take action, and work through concerns. What is added by this report?: This study offers a framework for evaluating interactions among individuals' emotions, perceptions, and online behaviors in Hong Kong Special Administrative Region (SAR) during the first two waves of COVID-19 (February to June 2020). Its results indicate a strong correlation between online behaviors, such as Google searches, and the real-time reproduction numbers. To validate the model's output of risk perception, this investigation conducted 10 rounds of cross-sectional telephone surveys on 8,593 local adult residents from February 1 through June 20 in 2020 to quantify risk perception levels over time. What are the implications for public health practice?: Compared to the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people's risk perception (individuals who are worried about being infected) during the studied period. We may need to reinvigorate the public by involving people as part of the solution that reduced the risk to their lives.

Impact of Human Mobility on COVID-19 Transmission According to Mobility Distance, Location, and Demographic Factors in the Greater Bay Area of China: Population-Based Study.

BACKGROUND: Mobility restriction was one of the primary measures used to restrain the spread of COVID-19 globally. Governments implemented and relaxed various mobility restriction measures in the absence of evidence for almost 3 years, which caused severe adverse outcomes in terms of health, society, and economy. OBJECTIVE: This study aimed to quantify the impact of mobility reduction on COVID-19 transmission according to mobility distance, location, and demographic factors in order to identify hotspots of transmission and guide public health policies. METHODS: Large volumes of anonymized aggregated mobile phone position data between January 1 and February 24, 2020, were collected for 9 megacities in the Greater Bay Area, China. A generalized linear model (GLM) was established to test the association between mobility volume (number of trips) and COVID-19 transmission. Subgroup analysis was also performed for sex, age, travel location, and travel distance. Statistical interaction terms were included in a variety of models that express different relations between involved variables. RESULTS: The GLM analysis demonstrated a significant association between the COVID-19 growth rate ratio (GR) and mobility volume. A stratification analysis revealed a higher effect of mobility volume on the COVID-19 GR among people aged 50-59 years (GR decrease of 13.17% per 10% reduction in mobility volume; P<.001) than among other age groups (GR decreases of 7.80%, 10.43%, 7.48%, 8.01%, and 10.43% for those aged ≤18, 19-29, 30-39, 40-49, and ≥60 years, respectively; P=.02 for the interaction). The impact of mobility reduction on COVID-19 transmission was higher for transit stations and shopping areas (instantaneous reproduction number [Rt] decreases of 0.67 and 0.53 per 10% reduction in mobility volume, respectively) than for workplaces, schools, recreation areas, and other locations (Rt decreases of 0.30, 0.37, 0.44, and 0.32, respectively; P=.02 for the interaction). The association between mobility volume reduction and COVID-19 transmission was lower with decreasing mobility distance as there was a significant interaction between mobility volume and mobility distance with regard to Rt (P<.001 for the interaction). Specifically, the percentage decreases in Rt per 10% reduction in mobility volume were 11.97% when mobility distance increased by 10% (Spring Festival), 6.74% when mobility distance remained unchanged, and 1.52% when mobility distance declined by 10%. CONCLUSIONS: The association between mobility reduction and COVID-19 transmission significantly varied according to mobility distance, location, and age. The substantially higher impact of mobility volume on COVID-19 transmission for longer travel distance, certain age groups, and specific travel locations highlights the potential to optimize the effectiveness of mobility restriction strategies. The results from our study demonstrate the power of having a mobility network using mobile phone data for surveillance that can monitor movement at a detailed level to measure the potential impacts of future pandemics.

Cost effectiveness of fractional doses of COVID-19 vaccine boosters in India.

BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to be a major global public health crisis that exacts significant human and economic costs. Booster vaccination of individuals can improve waning immunity and reduce the impact of community epidemics. METHODS: Using an epidemiological model that incorporates population-level severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and waning of vaccine-derived immunity, we identify the hypothetical potential of mass vaccination with fractionated vaccine doses specific to ChAdOx1 nCoV-19 (AZD1222 [Covishield]; AstraZeneca) as an optimal and cost-effective strategy in India's Omicron outbreak. FINDINGS: We find that the optimal strategy is 1/8 fractional dosing under mild (Re ∼ 1.2) and rapid (Re ∼ 5) transmission scenarios, leading to an estimated $6 (95% confidence interval [CI]: -13, 26) billion and $2 (95% CI: -26, 30) billion in health-related net monetary benefit over 200 days, respectively. Rapid and broad use of fractional dosing for boosters, together with delivery costs divided by fractionation, could substantially gain more net monetary benefit by $11 (95% CI: -10, 33) and $2 (95% CI: -23, 28) billion, respectively, under the mild and rapid transmission scenarios. CONCLUSIONS: Mass vaccination with fractional doses of COVID-19 vaccines to boost immunity in a vaccinated population could be a cost-effective strategy for mitigating the public health costs of resurgences caused by vaccine-evasive variants, and fractional dosing deserves further clinical and regulatory evaluation. FUNDING: Financial support was provided by the AIR@InnoHK Program from Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region.

Editorial: Infectious Disease Epidemiology and Transmission Dynamics.

Infectious diseases, such as COVID-19 [...].

The risk of SARS-CoV-2 Omicron variant emergence in low and middle-income countries (LMICs).

We estimated the probability of undetected emergence of the SARS-CoV-2 Omicron variant in 25 low and middle-income countries (LMICs) prior to December 5, 2021. In nine countries, the risk exceeds 50 %; in Turkey, Pakistan and the Philippines, it exceeds 99 %. Risks are generally lower in the Americas than Europe or Asia.

Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19.

The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.