Simulation of four respiratory viruses and inference of epidemiological parameters

Identifying the panorama of potential pandemic pathogens and their key characteristics: a systematic scoping review

The globe has recently seen several terrifying pandemics and outbreaks, underlining the ongoing danger presented by infectious microorganisms. This literature review aims to explore the wide range of infections that have the potential to lead to pandemics in the present and the future and pave the way to the conception of epidemic early warning systems. A systematic review was carried out to identify and compile data on infectious agents known to cause pandemics and those that pose future concerns. One hundred and fifteen articles were included in the review. They provided insights on 25 pathogens that could start or contribute to creating pandemic situations. Diagnostic procedures, clinical symptoms, and infection transmission routes were analyzed for each of these pathogens. Each infectious agent's potential is discussed, shedding light on the crucial aspects that render them potential threats to the future. This literature review provides insights for policymakers, healthcare professionals, and researchers in their quest to identify potential pandemic pathogens, and in their efforts to enhance pandemic preparedness through building early warning systems for continuous epidemiological monitoring.

Knowledge, attitudes and behaviours of a sample of Italian paediatricians towards RSV and its preventive strategies: a cross-sectional study

BACKGROUND

Respiratory Syncytial Virus (RSV) infection mainly affects newborns, infants and young children aged < 2 years. Since an RSV vaccine is in the European Medicines Agency's waitlist validation, nowadays the prevention only includes passive immunization with monoclonal antibodies (mAb). In the present study we aimed at investigating Italian paediatricians' knowledge, attitudes and behaviours towards RSV and its prevention.

METHODS

From February to May 2023, an anonymous online questionnaire, with answers based on the Likert scale, was administered to a sample of Italian paediatricians' members of the Italian Society of Paediatrics. Descriptive and inferential statistical analyses were performed using STATA 17.

RESULTS

The paediatricians who answered the questionnaire were 507, mostly women (70.6%), aged 30-45 (33.1%), employed in hospitals in 66.6% of cases. The 10.8% of respondents reported that RSV is transmitted only among children younger than 2 years of age and 80.33% of participants that school-age children are not at risk of developing severe forms of RSV disease. The 25% of participants thought that active immunization is currently available to prevent RSV infection and 35.7% that does not exist passive immunization to prevent RSV for infants and newborns aged < 2 years. The 97.5% of physicians managed bronchiolitis cases and 65.6% of participants did not prescribe the administration of mAb. Higher age, seniority and RSV knowledge score were found to be associated with having a higher mAb knowledge score (p < 0.001) and having a higher RSV knowledge was associated with a higher mAb knowledge score (p < 0.001). The logistic regression model found that the odds of a positive attitude towards mAB knowledge score increased by over 3 times (OR 3.23, 95% CI [1.41, 7.40], p = 0.006) for being female and the odds of a positive attitude towards mAB knowledge score increased by almost 10 times (OR 9.73, 95% CI [3.06, 30.89], p < 0.001) for a one-unit increase in RSV knowledge score.

CONCLUSIONS

Paediatricians' limited knowledge or awareness could represent a barrier to the implementation of preventive strategies against RSV infection. Strategies to improve paediatricians' education on RSV prevention are, therefore, crucial.

Human respiratory syncytial virus subgroups A and B outbreak in a kindergarten in Zhejiang Province, China, 2023

Background

In May-June 2023, an unprecedented outbreak of human respiratory syncytial virus (HRSV) infections occurred in a kindergarten, Zhejiang Province, China. National, provincial, and local public health officials investigated the cause of the outbreak and instituted actions to control its spread.

Methods

We interviewed patients with the respiratory symptoms by questionnaire. Respiratory samples were screened for six respiratory pathogens by real-time quantitative polymerase chain reaction (RT-PCR). The confirmed cases were further sequenced of G gene to confirm the HRSV genotype. A phylogenetic tree was reconstructed by maximum likelihood method.

Results

Of the 103 children in the kindergarten, 45 were classified as suspected cases, and 25 cases were confirmed by RT-PCR. All confirmed cases were identified from half of classes. 36% (9/25) were admitted to hospital, none died. The attack rate was 53.19%. The median ages of suspected and confirmed cases were 32.7 months and 35.8 months, respectively. Nine of 27 confirmed cases lived in one community. Only two-family clusters among 88 household contacts were HRSV positive. A total of 18 of the G gene were obtained from the confirmed cases. Phylogenetic analyses revealed that 16 of the sequences belonged to the HRSV B/BA9 genotype, and the other 2 sequences belonged to the HRSV A/ON1 genotype. The school were closed on June 9 and the outbreak ended on June 15.

Conclusion

These findings suggest the need for an increased awareness of HRSV coinfections outbreak in the kindergarten, when HRSV resurges in the community after COVID-19 pandemic.

Evaluation of antibody-based preventive alternatives for respiratory syncytial virus: a novel multi-criteria decision analysis framework and assessment of nirsevimab in Spain

BACKGROUND

Respiratory syncytial virus (RSV) is a highly infectious disease that poses a significant clinical and medical burden, as well as social disruption and economic costs, recognized by the World Health Organization as a public health issue. After several failed attempts to find preventive candidates (compounds, products, including vaccines), new alternatives might be available, one being nirsevimab, the first and only option approved for RSV prevention in neonates and infants during their first RSV season. The objective of this study was to develop a novel multi-criteria decision analysis (MCDA) framework for RSV antibody-based preventive alternatives and to use it to assess the value of nirsevimab vs. placebo as a systematic immunization approach to prevent RSV in neonates and infants during their first RSV season in Spain.

METHODS

Based on a pre-established model called Vaccinex, an ad-hoc MCDA framework was created to reflect relevant attributes for the assessment of current and future antibody-based preventive measures for RSV. The estimated value of nirsevimab was obtained by means of an additive linear model combining weights and scores assigned by a multidisciplinary committee of 9 experts. A retest and three sensitivity analyses were conducted.

RESULTS

Nirsevimab was evaluated through a novel framework with 26 criteria by the committee as a measure that adds value (positive final estimated value: 0.56 ± 0.11) to the current RSV scenario in Spain, by providing a high efficacy for prevention of neonates and infants. In addition, its implementation might generate cost savings in hospitalizations and to the healthcare system and increase the level of public health awareness among the general population, while reducing health inequities.

CONCLUSIONS

Under a methodology with increasing use in the health field, nirsevimab has been evaluated as a measure which adds value for RSV prevention in neonates and infants during their first RSV season in Spain.

Impact of the COVID-19 pandemic on the epidemiology of other communicable diseases in Japan

OBJECTIVES

To elucidate the impact of the COVID-19 pandemic on the epidemiology of other infectious diseases.

DESIGN

We investigated the epidemiology of 36 communicable diseases during 2015-2021 in Japan and compared the number of cases in each disease between the prepandemic (2015-2019) and intrapandemic (2020-2021) periods. Relationships between the incidence of the infectious diseases and the COVID-19 pandemic were also investigated.

RESULTS

Of 36 communicable diseases, the number of cases in the 27 diseases (75%) mainly caused by pathogens transmitted by droplet or contact was lower intrapandemic than prepandemic, and the cases of 21 diseases (58%) continued to decrease intrapandemic. The number of cases of six diseases (17%) was higher intrapandemic than prepandemic, and the cases of two diseases (5.6%), Japanese spotted fever and syphilis, continued to increase intrapandemic. Time trend analyses revealed a positive correlation between case numbers of communicable diseases and the COVID-19 pandemic, whereas the case numbers of hand-foot-and-mouth disease and respiratory syncytial virus infection rebounded in 2021 after decreasing in 2020.

CONCLUSION

The COVID-19 pandemic has greatly impacted the epidemiology of communicable diseases, suggesting that countermeasures against COVID-19 and lifestyle changes might be involved in these epidemiological changes.

Lifting non-pharmaceutical interventions following the COVID-19 pandemic - the quiet before the storm?

INTRODUCTION

In the first months of the novel coronavirus (COVID-19) pandemic that begun in 2020, non-pharmaceutical interventions (NPIs) have been adopted worldwide. However, the effects of NPI implementation go beyond slowing the spread of COVID-19. Here, we review the non-intended effects that may have arisen from prolonged application of NPIs.

AREAS COVERED

NPIs also affected the epidemiology of other infectious diseases, with unprecedentedly low circulation of several respiratory and gastrointestinal viruses being observed worldwide in 2020. While this was a welcome effect for already strained healthcare systems, prolonged low exposure to pathogens may result in an increased pool of individuals susceptible to certain diseases. Out-of-season or unusually intense outbreaks of non-vaccine preventable diseases have already been documented as NPIs were gradually eased. In the context of widespread and important disruptions in national vaccination programs during the early phase of the pandemic, the risk of vaccine-preventable disease resurgence after NPIs are lifted cannot be excluded either.

EXPERT OPINION

Awareness must be raised of the risk of vaccine-preventable disease resurgence, and efforts need to be made to mitigate this risk, where possible, by increasing vaccination coverage. Research and regulatory opportunities brought on by the COVID-19 pandemic should be seized.

Distinguishing viruses responsible for influenza-like illness

The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.

Use of mathematical modelling to assess respiratory syncytial virus epidemiology and interventions: a literature review

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection worldwide, resulting in approximately sixty thousand annual hospitalizations of< 5-year-olds in the United States alone and three million annual hospitalizations globally. The development of over 40 vaccines and immunoprophylactic interventions targeting RSV has the potential to significantly reduce the disease burden from RSV infection in the near future. In the context of RSV, a highly contagious pathogen, dynamic transmission models (DTMs) are valuable tools in the evaluation and comparison of the effectiveness of different interventions. This review, the first of its kind for RSV DTMs, provides a valuable foundation for future modelling efforts and highlights important gaps in our understanding of RSV epidemics. Specifically, we have searched the literature using Web of Science, Scopus, Embase, and PubMed to identify all published manuscripts reporting the development of DTMs focused on the population transmission of RSV. We reviewed the resulting studies and summarized the structure, parameterization, and results of the models developed therein. We anticipate that future RSV DTMs, combined with cost-effectiveness evaluations, will play a significant role in shaping decision making in the development and implementation of intervention programs.

The basic reproductive number and particle-to-plaque ratio: comparison of these two parameters of viral infectivity

The COVID-19 pandemic has brought more widespread attention to the basic reproductive number (R_o), an epidemiologic measurement. A lesser-known measure of virologic infectivity is the particle-to-plaque ratio (P:PFU). We suggest that comparison between the two parameters may assist in better understanding viral transmission dynamics.

Effects of weather-related social distancing on city-scale transmission of respiratory viruses: a retrospective cohort study

Background

Unusually high snowfall in western Washington State in February 2019 led to widespread school and workplace closures. We assessed the impact of social distancing caused by this extreme weather event on the transmission of respiratory viruses.

Methods

Residual specimens from patients evaluated for acute respiratory illness at hospitals in the Seattle metropolitan area were screened for a panel of respiratory viruses. Transmission models were fit to each virus to estimate the magnitude reduction in transmission due to weather-related disruptions. Changes in contact rates and care-seeking were informed by data on local traffic volumes and hospital visits.

Results

Disruption in contact patterns reduced effective contact rates during the intervention period by 16 to 95%, and cumulative disease incidence through the remainder of the season by 3 to 9%. Incidence reductions were greatest for viruses that were peaking when the disruption occurred and least for viruses in an early epidemic phase.

Conclusion

High-intensity, short-duration social distancing measures may substantially reduce total incidence in a respiratory virus epidemic if implemented near the epidemic peak. For SARS-CoV-2, this suggests that, even when SARS-CoV-2 spread is out of control, implementing short-term disruptions can prevent COVID-19 deaths.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06028-4.

Suicide and the agent-host-environment triad: leveraging surveillance sources to inform prevention

Suicide in the US has increased in the last decade, across virtually every age and demographic group. Parallel increases have occurred in non-fatal self-harm as well. Research on suicide across the world has consistently demonstrated that suicide shares many properties with a communicable disease, including person-to-person transmission and point-source outbreaks. This essay illustrates the communicable nature of suicide through analogy to basic infectious disease principles, including evidence for transmission and vulnerability through the agent-host-environment triad. We describe how mathematical modeling, a suite of epidemiological methods, which the COVID-19 pandemic has brought into renewed focus, can and should be applied to suicide in order to understand the dynamics of transmission and to forecast emerging risk areas. We describe how new and innovative sources of data, including social media and search engine data, can be used to augment traditional suicide surveillance, as well as the opportunities and challenges for modeling suicide as a communicable disease process in an effort to guide clinical and public health suicide prevention efforts.

Transmissibility and transmission of respiratory viruses

Human respiratory virus infections lead to a spectrum of respiratory symptoms and disease severity, contributing to substantial morbidity, mortality and economic losses worldwide, as seen in the COVID-19 pandemic. Belonging to diverse families, respiratory viruses differ in how easy they spread (transmissibility) and the mechanism (modes) of transmission. Transmissibility as estimated by the basic reproduction number (R₀) or secondary attack rate is heterogeneous for the same virus. Respiratory viruses can be transmitted via four major modes of transmission: direct (physical) contact, indirect contact (fomite), (large) droplets and (fine) aerosols. We know little about the relative contribution of each mode to the transmission of a particular virus in different settings, and how its variation affects transmissibility and transmission dynamics. Discussion on the particle size threshold between droplets and aerosols and the importance of aerosol transmission for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus is ongoing. Mechanistic evidence supports the efficacies of non-pharmaceutical interventions with regard to virus reduction; however, more data are needed on their effectiveness in reducing transmission. Understanding the relative contribution of different modes to transmission is crucial to inform the effectiveness of non-pharmaceutical interventions in the population. Intervening against multiple modes of transmission should be more effective than acting on a single mode.

Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness

Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings.

Superensemble forecast of respiratory syncytial virus outbreaks at national, regional, and state levels in the United States

Respiratory syncytial virus (RSV) infections peak during the winter months in the United States, yet the timing, intensity, and onset of these outbreaks vary each year. An RSV vaccine is on the cusp of being released; precise models and accurate forecasts of RSV epidemics may prove vital for planning where and when the vaccine should be deployed. Accurate forecasts with sufficient spatial and temporal resolution could also be used to support the prevention or treatment of RSV infections. Previously, we developed and validated an RSV forecast system at the regional scale in the United States. This model-inference system had considerable forecast skill, relative to the historical expectance, for outbreak peak intensity, total outbreak size, and onset, but only marginal skill for predicting the timing of the outbreak peak. Here, we use a superensemble approach to combine three forecasting methods for RSV prediction in the US at three different spatial resolutions: national, regional, and state. At the regional and state levels, we find a substantial improvement of forecast skill, relative to historical expectance, for peak intensity, timing, and onset outbreak up to two months in advance of the predicted outbreak peak. Moreover, due to the greater variability of RSV outbreaks at finer spatial scales, we find that improvement of forecast skill at the state level exceeds that at the regional and national levels. Such finer scale superensemble forecasts may be more relevant for effecting local-scale interventions, particularly in communities with a high burden of RSV infection.