Pandemic potential of a strain of influenza A (H1N1): early findings

SARS-CoV-2 epidemiology, kinetics, and evolution: A narrative review

Since winter 2019, SARS-CoV-2 has emerged, spread, and evolved all around the globe. We explore 4 y of evolutionary epidemiology of this virus, ranging from the applied public health challenges to the more conceptual evolutionary biology perspectives. Through this review, we first present the spread and lethality of the infections it causes, starting from its emergence in Wuhan (China) from the initial epidemics all around the world, compare the virus to other betacoronaviruses, focus on its airborne transmission, compare containment strategies ("zero-COVID" vs. "herd immunity"), explain its phylogeographical tracking, underline the importance of natural selection on the epidemics, mention its within-host population dynamics. Finally, we discuss how the pandemic has transformed (or should transform) the surveillance and prevention of viral respiratory infections and identify perspectives for the research on epidemiology of COVID-19.

Influenza RNA fluxes monitoring in wastewater as a complementary epidemiological surveillance indicator: A four-year nationwide study in Luxembourg

Wastewater surveillance has demonstrated success in monitoring SARS-CoV-2 in communities, indicating potential for extension to other respiratory viruses. This study investigates influenza A and B viruses (IAV; IBV) in raw urban wastewater over a 4-year period, introducing two key concepts: the use of viral RNA fluxes instead of concentration measurements and the determination of epidemiological parameters directly from wastewater data. The estimation of daily fluxes, representing the number of viral genome copies per day per 100,000 inhabitants, offers an integrative approach that combines microbiological and hydrological measurements to better assess viral particle dynamics in a water system. A total of 1013 wastewater samples collected between March 2020 and March 2024 from Luxembourg's four largest wastewater treatment plants (covering about 52 % of the population) were analysed using RT-qPCR and RT-droplet digital PCR (RT-ddPCR), following concentration of viral particles by ultrafiltration. Data on the presence of IAV and IBV were expressed as either detection rates or fluxes. Significant correlations were observed between the number of laboratory-confirmed influenza cases and both wastewater detection rates (RT-qPCR: Spearman ρ = 0.52; RT-ddPCR: ρ = 0.61, p-value <10-13) and viral RNA fluxes (RT-ddPCR: ρ = 0.64, p-value <10-15). More importantly, our results demonstrated that critical influenza seasonality parameters (start, peak and end weeks of the epidemic) can be effectively determined from wastewater data. These findings establish wastewater surveillance as a cost-effective, non-invasive approach to support and complement existing influenza surveillance programs, with potential applications for other respiratory pathogens.

Estimating the generation time for influenza transmission using household data in the United States

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.

Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning

Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management, yet existing methods are often context-specific, require a long preparation time, and non-outbreak prediction remains understudied. To address this gap, we propose a novel framework using a feature-based time series classification (TSC) method to forecast outbreaks and non-outbreaks. We tested our methods on synthetic data from a Susceptible-Infected-Recovered (SIR) model for slowly changing, noisy disease dynamics. Outbreak sequences give a transcritical bifurcation within a specified future time window, whereas non-outbreak (null bifurcation) sequences do not. We identified incipient differences, reflected in 22 statistical features and 5 early warning signal indicators, in time series of infectives leading to future outbreaks and non-outbreaks. Classifier performance, given by the area under the receiver-operating curve (AUC), ranged from 0 . 99 for large expanding windows of training data to 0 . 7 for small rolling windows. The framework is further evaluated on four empirical datasets: COVID-19 incidence data from Singapore, 18 other countries, and Edmonton, Canada, as well as SARS data from Hong Kong, with two classifiers exhibiting consistently high accuracy. Our results highlight detectable statistical features distinguishing outbreak and non-outbreak sequences well before potential occurrence, in both synthetic and real-world datasets presented in this study.

Targeted Elimination of Influenza Virus and Infected Cells with Near-Infrared Antiviral Photoimmunotherapy (NIR-AVPIT)

Background: Seasonal influenza causes significant morbidity and mortality each year. Since viruses can easily acquire drug-resistant mutations, it is necessary to develop new antiviral strategies with different targets. Near-infrared photoimmunotherapy (NIR-PIT) is a type of anti-cancer therapy that has recently attracted considerable attention, with favorable outcomes reported for several cancers. In this study, we investigated whether this approach could be used as a novel anti-influenza therapy to destroy influenza virus and infected cells. Methods: To evaluate the efficacy of near-infrared antiviral photoimmunotherapy (NIR-AVPIT), we prepared an anti-hemagglutinin (HA) monoclonal antibody without neutralizing activity against influenza A virus (FluV) labeled with IR-700 and reacted it with FluV and infected cells, as well as HA-expressing HEK293 cells. Results: NIR-AVPIT strongly inactivated FluV virions, suppressed cytopathic effects, and achieved more than a 4-log reduction in viral RNA amplification. Treatment of FluV-infected cells with the antibody-IR700 complex and NIR in the early stages of infection significantly inhibited viral propagation, and double treatment with time apart exerted a greater inhibitory effect. NIR-AVPIT rapidly induced morphological changes in HA-expressing HEK293 cells and inhibited the proliferation of these cells. Conclusions: These results suggest that NIR-AVPIT targeting HA antigens could inactivate FluV and eliminate infected cells in vitro. This strategy is a promising approach to treat various viral infections, including influenza.

A Narrative Review on the Pandemic Zoonotic RNA Virus Infections Occurred During the Last 25 Years

BACKGROUND

Pandemic zoonotic RNA virus infections have continued to threaten humans and animals worldwide. The objective of this review was to highlight the epidemiology and socioeconomic impacts of pandemic zoonotic RNA virus infections that occurred between 1997 and 2021.

METHODS

Literature search was done from Web of Science, PubMed, Google Scholar and Scopus databases, cumulative case fatalities of individual viral infection calculated, and geographic coverage of the pandemics were shown by maps.

RESULTS

Seven major pandemic zoonotic RNA virus infections occurred from 1997 to 2021 and were presented in three groups: The first group consists of highly pathogenic avian influenza (HPAI-H5N1) and swine-origin influenza (H1N1) viruses with cumulative fatality rates of 53.5% and 0.5% in humans, respectively. Moreover, HPAI-H5N1 infection caused 90-100% death in poultry and economic losses of >$10 billion worldwide. Similarly, H1N1 caused a serious infection in swine and economic losses of 0.5-1.5% of the Gross Domestic Product (GDP) of the affected countries. The second group consists of severe acute respiratory syndrome-associated coronavirus infection (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV) and Coronavirus disease 2019 (COVID-19) with case fatalities of 9.6%, 34.3% and 2.0%, respectively in humans; but this group only caused mild infections in animals. The third group consists of Ebola and Zika virus infections with case fatalities of 39.5% and 0.02%, respectively in humans but causing only mild infections in animals.

CONCLUSION

Similar infections are expected in the near future, and hence strict implementation of conventional biosecurity-based measures and development of efficacious vaccines would help minimize the impacts of the next pandemic infection.

Policy impact of the Imperial College COVID-19 Response Team: global perspective and United Kingdom case study

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

Strong long ties facilitate epidemic containment on mobility networks

The analysis of connection strengths and distances in the mobility network is pivotal for delineating critical pathways, particularly in the context of epidemic propagation. Local connections that link proximate districts typically exhibit strong weights. However, ties that bridge distant regions with high levels of interaction intensity, termed strong long (SL) ties, warrant increased scrutiny due to their potential to foster satellite epidemic clusters and extend the duration of pandemics. In this study, SL ties are identified as outliers on the joint distribution of distance and flow in the mobility network of Shanghai constructed from 1 km × 1 km high-resolution mobility data. We propose a grid-joint isolation strategy alongside a reaction-diffusion transmission model to assess the impact of SL ties on epidemic propagation. The findings indicate that regions connected by SL ties exhibit a small spatial autocorrelation and display a temporal similarity pattern in disease transmission. Grid-joint isolation based on SL ties reduces cumulative infections by an average of 17.1% compared with other types of ties. This work highlights the necessity of identifying and targeting potentially infected remote areas for spatially focused interventions, thereby enriching our comprehension and management of epidemic dynamics.

The Peculiar Emergence of Mpox (Monkeypox): Directions for the Search for the Natural Reservoir and Vaccination Strategies

Background/Objectives: Mpox (monkeypox) is a zoonosis with origins in a currently unknown African reservoir. The first epidemiological accounts of mpox date back to the early 1980s, yet mpox only emerged as a pandemic threat in 2022-2023, more than 40 years later. This scenario is very different from those of other emerging diseases such as HIV and SARS, which immediately spread globally, in fully susceptible populations, starting from patients zero. Methods: We use mathematical modeling to illustrate the dynamics of mpox herd immunity in small communities in touch with the mpox natural reservoir. In particular, we employ an SEIR stochastic model. Results: The peculiar emergence of mpox can be explained by its relationship with smallpox, which was eradicated through universal mass vaccination in 1980. Mpox first emerged in small rural communities in touch with mpox's animal reservoir and then spread globally. The relative isolation of these communities and their herd-immunity dynamics against mpox worked to delay the introduction of mpox in large urban centers. Conclusions: Mathematical modeling suggests that the search for the mpox animal reservoir would be most fruitful in communities with high mpox seroprevalence and small outbreaks. These are communities is tight contact with the mpox natural reservoir. We propose vaccinating individuals in communities in these communities to severely reduce the importation of cases elsewhere.

Best practices for estimating and reporting epidemiological delay distributions of infectious diseases

Epidemiological delays are key quantities that inform public health policy and clinical practice. They are used as inputs for mathematical and statistical models, which in turn can guide control strategies. In recent work, we found that censoring, right truncation, and dynamical bias were rarely addressed correctly when estimating delays and that these biases were large enough to have knock-on impacts across a large number of use cases. Here, we formulate a checklist of best practices for estimating and reporting epidemiological delays. We also provide a flowchart to guide practitioners based on their data. Our examples are focused on the incubation period and serial interval due to their importance in outbreak response and modeling, but our recommendations are applicable to other delays. The recommendations, which are based on the literature and our experience estimating epidemiological delay distributions during outbreak responses, can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.

Inference of epidemic dynamics in the COVID-19 era and beyond

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

Estimating the generation time for influenza transmission using household data in the United States

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 post 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 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 before symptomatic onset, infectious period, and 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 variation in incubation periods. 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.

Epidemiological inference at the threshold of data availability: an influenza A(H1N2)v spillover event in the United Kingdom

Viruses that infect animals regularly spill over into the human population, but individual events may lead to anything from a single case to a novel pandemic. Rapidly gaining an understanding of a spillover event is critical to calibrating a public health response. We here propose a novel method, using likelihood-free rejection sampling, to evaluate the properties of an outbreak of swine-origin influenza A(H1N2)v in the United Kingdom, detected in November 2023. From the limited data available, we generate historical estimates of the probability that the outbreak had died out in the days following the detection of the first case. Our method suggests that the outbreak could have been said to be over with 95% certainty between 19 and 29 days after the first case was detected, depending upon the probability of a case being detected. We further estimate the number of undetected cases conditional upon the outbreak still being live, the epidemiological parameter R 0, and the date on which the spillover event itself occurred. Our method requires minimal data to be effective. While our calculations were performed after the event, the real-time application of our method has potential value for public health responses to cases of emerging viral infection.

Infectious disease surveillance needs for the United States: lessons from Covid-19

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.

Exploring Potential Intermediates in the Cross-Species Transmission of Influenza A Virus to Humans

The influenza A virus (IAV) has been a major cause of several pandemics, underscoring the importance of elucidating its transmission dynamics. This review investigates potential intermediate hosts in the cross-species transmission of IAV to humans, focusing on the factors that facilitate zoonotic events. We evaluate the roles of various animal hosts, including pigs, galliformes, companion animals, minks, marine mammals, and other animals, in the spread of IAV to humans.

Leveraging wastewater surveillance to detect viral diseases in livestock settings

Wastewater surveillance has evolved into a powerful tool for monitoring public health-relevant analytes. Recent applications in tracking severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection highlight its potential. Beyond humans, it can be extended to livestock settings where there is increasing demand for livestock products, posing risks of disease emergence. Wastewater surveillance may offer non-invasive, cost-effective means to detect potential outbreaks among animals. This approach aligns with the "One Health" paradigm, emphasizing the interconnectedness of animal, human, and ecosystem health. By monitoring viruses in livestock wastewater, early detection, prevention, and control strategies can be employed, safeguarding both animal and human health, economic stability, and international trade. This integrated "One Health" approach enhances collaboration and a comprehensive understanding of disease dynamics, supporting proactive measures in the Anthropocene era where animal and human diseases are on the rise.

Genomic epidemiology uncovers the timing and origin of the emergence of mpox in humans

Five years before the 2022-2023 global mpox outbreak Nigeria reported its first cases in nearly 40 years, with the ongoing epidemic since driven by sustained human-to-human transmission. However, limited genomic data has left questions about the timing and origin of the mpox virus' (MPXV) emergence. Here we generated 112 MPXV genomes from Nigeria from 2021-2023. We identify the closest zoonotic outgroup to the human epidemic in southern Nigeria, and estimate that the lineage transmitting from human-to-human emerged around July 2014, circulating cryptically until detected in September 2017. The epidemic originated in Southern Nigeria, particularly Rivers State, which also acted as a persistent and dominant source of viral dissemination to other states. We show that APOBEC3 activity increased MPXV's evolutionary rate twenty-fold during human-to-human transmission. We also show how Delphy, a tool for near-real-time Bayesian phylogenetics, can aid rapid outbreak analytics. Our study sheds light on MPXV's establishment in West Africa before the 2022-2023 global outbreak and highlights the need for improved pathogen surveillance and response.

Optimal vaccine allocation strategy: Theory and application to the early stage of COVID-19 in Japan

In this paper, we construct an age-structured epidemic model to analyze the optimal vaccine allocation strategy in an epidemic. We focus on two topics: the first one is the optimal vaccination interval between the first and second doses, and the second one is the optimal vaccine allocation ratio between young and elderly people. On the first topic, we show that the optimal interval tends to become longer as the relative efficacy of the first dose to the second dose (RE) increases. On the second topic, we show that the heterogeneity in the age-dependent susceptibility (HS) affects the optimal allocation ratio between young and elderly people, whereas the heterogeneity in the contact frequency among different age groups (HC) tends to affect the effectiveness of the vaccination campaign. A counterfactual simulation suggests that the epidemic wave in the summer of 2021 in Japan could have been greatly mitigated if the optimal vaccine allocation strategy had been taken.

Anti-idiotype isolation of a broad and potent influenza A virus-neutralizing human antibody

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.

Lessons should be learned: Why did we not learn from the Spanish flu?

COVID-19 has become a global pandemic that has affected millions of people worldwide. The disease is caused by the novel coronavirus that was first reported in Wuhan, China, in December 2019. The virus is highly contagious and can spread from person to person through respiratory droplets when an infected person coughs, sneezes, talks, or breathes. The symptoms of COVID-19 include fever, cough, and shortness of breath, and in severe cases, it can lead to respiratory failure, pneumonia, and death. The Spanish flu, caused by the H1N1 influenza virus, and the COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 are two of the most significant global health crises in history. While these two pandemics occurred almost a century apart and are caused by different types of viruses, there are notable similarities in their impact, transmission, and public health responses. Here are some key similarities between the Spanish flu and SARS-CoV-2. The Spanish flu pandemic of 1918-1919 stands as one of the deadliest pandemics in human history, claiming the lives of an estimated 50 million people worldwide. Its impact reverberated across continents, leaving behind a legacy of devastation and lessons that, unfortunately, seem to have been forgotten or ignored over time. Despite the advancements in science, medicine, and public health in the intervening century, humanity found itself facing a strikingly similar situation with the outbreak of the COVID-19 pandemic. Additionally, amidst the search for effective measures to combat COVID-19, novel approaches such as iodine complexes, such as Iodine-V has emerged as potential interventions, reflecting the ongoing quest for innovative solutions to mitigate the impact of pandemics. This raises the poignant question: why did we not learn from the Spanish flu?

Timing of Assessment of Humoral and Cell-Mediated Immunity after Influenza Vaccination

Assessment of the immune response to influenza vaccines should include an assessment of both humoral and cell-mediated immunity. However, there is a lack of consensus regarding the timing of immunological assessment of humoral and cell-mediated immunity after vaccination. Therefore, we investigated the timing of immunological assessments after vaccination using markers of humoral and cell-mediated immunity. In the 2018/2019 influenza season, blood was collected from 29 healthy adults before and after vaccination with a quadrivalent inactivated influenza vaccine, and we performed serial measurements of humoral immunity (hemagglutination inhibition [HAI] and neutralizing antibody [NT]) and cell-mediated immunity (interferon-gamma [IFN-γ]). The HAI and NT titers before and after vaccination were strongly correlated, but no correlation was observed between the markers of cell-mediated and humoral immunity. The geometric mean titer and geometric mean concentration of humoral and cellular immune markers increased within 2 weeks after vaccination and had already declined by 8 weeks. This study suggests that the optimal time to assess the immune response is 2 weeks after vaccination. Appropriately timed immunological assessments can help ensure that vaccination is effective.

Modular Biomimetic Strategy Enables Discovery and SAR Exploration of Oxime Macrocycles as Influenza A Virus (H1N1) Inhibitors

Although vaccination remains the prevalent prophylactic means for controlling Influenza A virus (IAV) infections, novel structural antivirus small-molecule drugs with new mechanisms of action for treating IAV are highly desirable. Herein, we describe a modular biomimetic strategy to expeditiously achieve a new class of macrocycles featuring oxime, which might target the hemagglutinin (HA)-mediated IAV entry into the host cells. SAR analysis revealed that the size and linker of the macrocycles play an important role in improving potency. Particularly, as a 14-membered macrocyclic oxime, 37 exhibited potent inhibitory activity against IAV H1N1 with an EC50 value of 23 nM and low cytotoxicity, which alleviated cytopathic effects and protected cell survival obviously after H1N1 infection. Furthermore, 37 showed significant synergistic activity with neuraminidase inhibitor oseltamivir in vitro.

Travel surveillance uncovers dengue virus dynamics and introductions in the Caribbean

Dengue is the most prevalent mosquito-borne viral disease in humans, and cases are continuing to rise globally. In particular, islands in the Caribbean have experienced more frequent outbreaks, and all four dengue virus (DENV) serotypes have been reported in the region, leading to hyperendemicity and increased rates of severe disease. However, there is significant variability regarding virus surveillance and reporting between islands, making it difficult to obtain an accurate understanding of the epidemiological patterns in the Caribbean. To investigate this, we used travel surveillance and genomic epidemiology to reconstruct outbreak dynamics, DENV serotype turnover, and patterns of spread within the region from 2009-2022. We uncovered two recent DENV-3 introductions from Asia, one of which resulted in a large outbreak in Cuba, which was previously under-reported. We also show that while outbreaks can be synchronized between islands, they are often caused by different serotypes. Our study highlights the importance of surveillance of infected travelers to provide a snapshot of local introductions and transmission in areas with limited local surveillance and suggests that the recent DENV-3 introductions may pose a major public health threat in the region.

Scenario-based assessment of emergency management of urban infectious disease outbreaks

Infectious diseases pose a severe threat to human health and are accompanied by significant economic losses. Studies of urban outbreaks of infectious diseases are diverse. However, previous studies have neglected the identification of critical events and the evaluation of scenario-based modeling of urban infectious disease outbreak emergency management mechanisms. In this paper, we aim to conduct an empirical analysis and scenario extrapolation using a questionnaire survey of 18 experts, based on the CIA-ISM method and scenario theory, to identify the key factors influencing urban infectious disease outbreaks. Subsequently, we evaluate the effectiveness of urban infectious disease outbreak emergency management mechanisms. Finally, we compare and verify the actual situation of COVID-19 in China, drawing the following conclusions and recommendations. (1) The scenario-based urban infectious disease emergency management model can effectively replicate the development of urban infectious diseases. (2) The establishment of an emergency command center and the isolation and observation of individuals exposed to infectious diseases are crucial factors in the emergency management of urban outbreaks of infectious disease.

Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences

Quantifying transmission intensity and heterogeneity is crucial to ascertain the threat posed by infectious diseases and inform the design of interventions. Methods that jointly estimate the reproduction number R and the dispersion parameter k have however mainly remained limited to the analysis of epidemiological clusters or contact tracing data, whose collection often proves difficult. Here, we show that clusters of identical sequences are imprinted by the pathogen offspring distribution, and we derive an analytical formula for the distribution of the size of these clusters. We develop and evaluate an inference framework to jointly estimate the reproduction number and the dispersion parameter from the size distribution of clusters of identical sequences. We then illustrate its application across a range of epidemiological situations. Finally, we develop a hypothesis testing framework relying on clusters of identical sequences to determine whether a given pathogen genetic subpopulation is associated with increased or reduced transmissibility. Our work provides tools to estimate the reproduction number and transmission heterogeneity from pathogen sequences without building a phylogenetic tree, thus making it easily scalable to large pathogen genome datasets.

Nationwide survey of ticks on domesticated animals in Taiwan: Revealing the hidden threat to animal and public health

Ticks are not only bloodsucking ectoparasites but also important vectors of tick-borne diseases (TBDs), posing significant threats to public and animal health. Domesticated animals serve as critical hosts for numerous ticks, highlighting the importance of understanding tick infestations in Taiwan. To address this knowledge gap, we conducted a nationwide survey to identify ticks on domesticated animals and associated environments in 2018 and 2019. A total of 6,205 ticks were collected from 1,337 host animals, revealing the presence of seven tick species, with Rhipicephalus microplus, and Rhipicephalus sanguineus being the dominant species. High infestation rates and widespread distribution of ticks were observed on domesticated animals, especially on dogs and cattle (yellow cattle and angus cattle), and the neighbouring grassland of yellow cattle. While this study has certain limitations, it provides valuable insights into the distribution and prevalence of ticks on domesticated animals in Taiwan and their implications for controlling TBDs. Further research is needed to comprehensively understand the complex interactions among ticks, hosts and pathogens.

Multi-feature SEIR model for epidemic analysis and vaccine prioritization

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

Clinical predictors of severe forms of influenza A(H1N1)pdm09 in adults and children during the 2009 epidemic in Brazil

The World Health Organization (WHO) raised the global alert level for the A(H1N1) influenza pandemic in June 2009. However, since the beginning of the epidemic, the fight against the epidemic lacked foundations for managing cases to reduce the disease lethality. It was urgent to carry out studies that would indicate a model for predicting severe forms of influenza. This study aimed to identify risk factors for severe forms during the 2009 influenza epidemic and develop a prediction model based on clinical epidemiological data. A case-control of cases notified to the health secretariats of the states of Rio de Janeiro, São Paulo, Minas Gerais, Paraná, and Rio Grande do Sul was conducted. Cases had fever, respiratory symptoms, positive confirmatory test for the presence of the virus associated with one of the three conditions: (i) presenting respiratory complications such as pneumonia, ventilatory failure, severe acute respiratory distress syndrome, sepsis, acute cardiovascular complications or death; or respiratory failure requiring invasive or non-invasive ventilatory support, (ii) having been hospitalized or (iii) having been admitted to an Intensive Care Unit. Controls were individuals diagnosed with the disease on the same date (or same week) as the cases. A total of 1653 individuals were included in the study, (858 cases/795 controls). These participants had a mean age of 26 years, a low level of education, and were mostly female. The most important predictors identified were systolic blood pressure in mmHg, respiratory rate in bpm, dehydration, obesity, pregnancy (in women), and vomiting (in children). Three clinical prediction models of severity were developed, for adults, adult women, and for children. The performance evaluation of these models indicated good predictive capacity. The area values under the ROC curve of these models were 0.89; 0.98 and 0.91 respectively for the model of adults, adult women, and children respectively.

Zoonosis and zooanthroponosis of emerging respiratory viruses

Lung infections in Influenza-Like Illness (ILI) are triggered by a variety of respiratory viruses. All human pandemics have been caused by the members of two major virus families, namely Orthomyxoviridae (influenza A viruses (IAVs); subtypes H1N1, H2N2, and H3N2) and Coronaviridae (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). These viruses acquired some adaptive changes in a known intermediate host including domestic birds (IAVs) or unknown intermediate host (SARS-CoV-2) following transmission from their natural reservoirs (e.g. migratory birds or bats, respectively). Verily, these acquired adaptive substitutions facilitated crossing species barriers by these viruses to infect humans in a phenomenon that is known as zoonosis. Besides, these adaptive substitutions aided the variant strain to transmit horizontally to other contact non-human animal species including pets and wild animals (zooanthroponosis). Herein we discuss the main zoonotic and reverse-zoonosis events that occurred during the last two pandemics of influenza A/H1N1 and SARS-CoV-2. We also highlight the impact of interspecies transmission of these pandemic viruses on virus evolution and possible prophylactic and therapeutic interventions. Based on information available and presented in this review article, it is important to close monitoring viral zoonosis and viral reverse zoonosis of pandemic strains within a One-Health and One-World approach to mitigate their unforeseen risks, such as virus evolution and resistance to limited prophylactic and therapeutic interventions.

A Bayesian hierarchical approach to account for evidence and uncertainty in the modeling of infectious diseases: An application to COVID-19

Infectious disease models can serve as critical tools to predict the development of cases and associated healthcare demand and to determine the set of nonpharmaceutical interventions (NPIs) that is most effective in slowing the spread of an infectious agent. Current approaches to estimate NPI effects typically focus on relatively short time periods and either on the number of reported cases, deaths, intensive care occupancy, or hospital occupancy as a single indicator of disease transmission. In this work, we propose a Bayesian hierarchical model that integrates multiple outcomes and complementary sources of information in the estimation of the true and unknown number of infections while accounting for time-varying underreporting and weekday-specific delays in reported cases and deaths, allowing us to estimate the number of infections on a daily basis rather than having to smooth the data. To address dynamic changes occurring over long periods of time, we account for the spread of new variants, seasonality, and time-varying differences in host susceptibility. We implement a Markov chain Monte Carlo algorithm to conduct Bayesian inference and illustrate the proposed approach with data on COVID-19 from 20 European countries. The approach shows good performance on simulated data and produces posterior predictions that show a good fit to reported cases, deaths, hospital, and intensive care occupancy.

Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data

Genomic data hold increasing potential in the elucidation of transmission dynamics and geographical sources of infectious disease outbreaks. Phylogeographic methods that use epidemiological and genomic data obtained from surveillance enable us to infer the history of spatial transmission that is naturally embedded in the topology of phylogenetic trees as a record of the dispersal of infectious agents between geographical locations. In this Review, we provide an overview of phylogeographic approaches widely used for reconstructing the geographical sources of outbreaks of interest. These approaches can be classified into ancestral trait or state reconstruction and structured population models, with structured population models including popular structured coalescent and birth-death models. We also describe the major challenges associated with sequencing technologies, surveillance strategies, data sharing, and analysis frameworks that became apparent during the generation of large-scale genomic data in recent years, extending beyond inference approaches. Finally, we highlight the role of genomic data in geographical source inference and clarify how this enhances understanding and molecular investigations of outbreak sources.

Inferring country-specific import risk of diseases from the world air transportation network

Disease propagation between countries strongly depends on their effective distance, a measure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country-essentially, the likelihood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the 'import risk' model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak's origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model's precision increases for countries with stronger connections within the WAN, and it reveals a geographic distance dependence that implies a pull- rather than a push-dynamic in the distribution process.

Epidemic graph diagrams as analytics for epidemic control in the data-rich era

COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007-2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.

Travel surveillance uncovers dengue virus dynamics and introductions in the Caribbean

Dengue is the most prevalent mosquito-borne viral disease in humans, and cases are continuing to rise globally. In particular, islands in the Caribbean have experienced more frequent outbreaks, and all four dengue virus (DENV) serotypes have been reported in the region, leading to hyperendemicity and increased rates of severe disease. However, there is significant variability regarding virus surveillance and reporting between islands, making it difficult to obtain an accurate understanding of the epidemiological patterns in the Caribbean. To investigate this, we used travel surveillance and genomic epidemiology to reconstruct outbreak dynamics, DENV serotype turnover, and patterns of spread within the region from 2009-2022. We uncovered two recent DENV-3 introductions from Asia, one of which resulted in a large outbreak in Cuba, which was previously under-reported. We also show that while outbreaks can be synchronized between islands, they are often caused by different serotypes. Our study highlights the importance of surveillance of infected travelers to provide a snapshot of local introductions and transmission in areas with limited local surveillance and suggests that the recent DENV-3 introductions may pose a major public health threat in the region.

Using a Bayesian hierarchical approach to study the association between non-pharmaceutical interventions and the spread of Covid-19 in Germany

Non-Pharmaceutical Interventions (NPIs) are community mitigation strategies, aimed at reducing the spread of illnesses like the coronavirus pandemic, without relying on pharmaceutical drug treatments. This study aims to evaluate the effectiveness of different NPIs across sixteen states of Germany, for a time period of 21 months of the pandemic. We used a Bayesian hierarchical approach that combines different sub-models and merges information from complementary sources, to estimate the true and unknown number of infections. In this framework, we used data on reported cases, hospitalizations, intensive care unit occupancy, and deaths to estimate the effect of NPIs. The list of NPIs includes: "contact restriction (up to 5 people)", "strict contact restriction", "curfew", "events permitted up to 100 people", "mask requirement in shopping malls", "restaurant closure", "restaurants permitted only with test", "school closure" and "general behavioral changes". We found a considerable reduction in the instantaneous reproduction number by "general behavioral changes", "strict contact restriction", "restaurants permitted only with test", "contact restriction (up to 5 people)", "restaurant closure" and "curfew". No association with school closures could be found. This study suggests that some public health measures, including general behavioral changes, strict contact restrictions, and restaurants permitted only with tests are associated with containing the Covid-19 pandemic. Future research is needed to better understand the effectiveness of NPIs in the context of Covid-19 vaccination.

A Bayesian inference method to estimate transmission trees with multiple introductions; applied to SARS-CoV-2 in Dutch mink farms

Knowledge of who infected whom during an outbreak of an infectious disease is important to determine risk factors for transmission and to design effective control measures. Both whole-genome sequencing of pathogens and epidemiological data provide useful information about the transmission events and underlying processes. Existing models to infer transmission trees usually assume that the pathogen is introduced only once from outside into the population of interest. However, this is not always true. For instance, SARS-CoV-2 is suggested to be introduced multiple times in mink farms in the Netherlands from the SARS-CoV-2 pandemic among humans. Here, we developed a Bayesian inference method combining whole-genome sequencing data and epidemiological data, allowing for multiple introductions of the pathogen in the population. Our method does not a priori split the outbreak into multiple phylogenetic clusters, nor does it break the dependency between the processes of mutation, within-host dynamics, transmission, and observation. We implemented our method as an additional feature in the R-package phybreak. On simulated data, our method correctly identifies the number of introductions, with an accuracy depending on the proportion of all observed cases that are introductions. Moreover, when a single introduction was simulated, our method produced similar estimates of parameters and transmission trees as the existing package. When applied to data from a SARS-CoV-2 outbreak in Dutch mink farms, the method provides strong evidence for independent introductions of the pathogen at 13 farms, infecting a total of 63 farms. Using the new feature of the phybreak package, transmission routes of a more complex class of infectious disease outbreaks can be inferred which will aid infection control in future outbreaks.

Analysis of the current status of TB transmission in China based on an age heterogeneity model

Tuberculosis (TB) is an infectious disease transmitted through the respiratory system. China is one of the countries with a high burden of TB. Since 2004, an average of more than 800,000 cases of active TB has been reported each year in China. Analyzing the case data from 2004 to 2018, we found significant differences in TB incidence by age group. A model of TB is put forward to explore the effect of age heterogeneity on TB transmission. The nonlinear least squares method is used to obtain the key parameters in the model, and the basic reproduction number Rv = 0.8017 is calculated and the sensitivity analysis of Rv to the parameters is given. The simulation results show that reducing the number of new infections in the elderly population and increasing the recovery rate of elderly patients with the disease could significantly reduce the transmission of TB. Furthermore, the feasibility of achieving the goals of the World Health Organization (WHO) End TB Strategy in China is assessed, and we obtained that with existing TB control measures it will take another 30 years for China to reach the WHO goal to reduce 90% of the number of new cases by the year 2049. However, in theory it is feasible to reach the WHO strategic goal of ending TB by 2035 if the group contact rate in the elderly population can be reduced, though it is difficult to reduce the contact rate.

Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics

Researchers and policymakers have proposed systems to detect novel pathogens earlier than existing surveillance systems by monitoring samples from hospital patients, wastewater, and air travel, in order to mitigate future pandemics. How much benefit would such systems offer? We developed, empirically validated, and mathematically characterized a quantitative model that simulates disease spread and detection time for any given disease and detection system. We find that hospital monitoring could have detected COVID-19 in Wuhan 0.4 weeks earlier than it was actually discovered, at 2,300 cases (standard error: 76 cases) compared to 3,400 (standard error: 161 cases). Wastewater monitoring would not have accelerated COVID-19 detection in Wuhan, but provides benefit in smaller catchments and for asymptomatic or long-incubation diseases like polio or HIV/AIDS. Monitoring of air travel provides little benefit in most scenarios we evaluated. In sum, early detection systems can substantially mitigate some future pandemics, but would not have changed the course of COVID-19.

Genomic epidemiology of SARS-CoV-2 from Uttar Pradesh, India

The various strains and mutations of SARS-CoV-2 have been tracked using several forms of genomic classification systems. The present study reports high-throughput sequencing and analysis of 99 SARS-CoV-2 specimens from Western Uttar Pradesh using sequences obtained from the GISAID database, followed by phylogeny and clade classification. Phylogenetic analysis revealed that Omicron lineages BA-2-like (55.55%) followed by Delta lineage-B.1.617.2 (45.5%) were predominantly circulating in this area Signature substitution at positions S: N501Y, S: D614G, S: T478K, S: K417N, S: E484A, S: P681H, and S: S477N were commonly detected in the Omicron variant-BA-2-like, however S: D614G, S: L452R, S: P681R and S: D950N were confined to Delta variant-B.1.617.2. We have also identified three escape variants in the S gene at codon position 19 (T19I/R), 484 (E484A/Q), and 681 (P681R/H) during the fourth and fifth waves in India. Based on the phylogenetic diversification studies and similar changes in other lineages, our analysis revealed indications of convergent evolution as the virus adjusts to the shifting immunological profile of its human host. To the best of our knowledge, this study is an approach to comprehensively map the circulating SARS-CoV-2 strains from Western Uttar Pradesh using an integrated approach of whole genome sequencing and phylogenetic analysis. These findings will be extremely valuable in developing a structured approach toward pandemic preparedness and evidence-based intervention plans in the future.

The genomic landscape of swine influenza A viruses in Southeast Asia

Swine are a primary source for the emergence of pandemic influenza A viruses. The intensification of swine production, along with global trade, has amplified the transmission and zoonotic risk of swine influenza A virus (swIAV). Effective surveillance is essential to uncover emerging virus strains; however gaps remain in our understanding of the swIAV genomic landscape in Southeast Asia. More than 4,000 nasal swabs were collected from pigs in Cambodia, yielding 72 IAV-positive samples by RT-qPCR and 45 genomic sequences. We unmasked the cocirculation of multiple lineages of genetically diverse swIAV of pandemic concern. Genomic analyses revealed a novel European avian-like H1N2 swIAV reassortant variant with North American triple reassortant internal genes, that emerged approximately seven years before its first detection in pigs in 2021. Using phylogeographic reconstruction, we identified south central China as the dominant source of swine viruses disseminated to other regions in China and Southeast Asia. We also identified nine distinct swIAV lineages in Cambodia, which diverged from their closest ancestors between two and 15 B.P., indicating significant undetected diversity in the region, including reverse zoonoses of human H1N1/2009 pandemic and H3N2 viruses. A similar period of cryptic circulation of swIAVs occurred in the decades before the H1N1/2009 pandemic. The hidden diversity of swIAV observed here further emphasizes the complex underlying evolutionary processes present in this region, reinforcing the importance of genomic surveillance at the human-swine interface for early warning of disease emergence to avoid future pandemics.

From pandemic to endemic: Spatial-temporal patterns of influenza-like illness incidence in a Swiss canton, 1918-1924

In pandemics, past and present, there is no textbook definition of when a pandemic is over, and how and when exactly a respiratory virus transitions from pandemic to endemic spread. In this paper we have compared the 1918/19 influenza pandemic and the subsequent spread of seasonal flu until 1924. We analysed 14,125 reports of newly stated 32,198 influenza-like illnesses from the Swiss canton of Bern. We analysed the temporal and spatial spread at the level of 497 municipalities, 9 regions, and the entire canton. We calculated incidence rates per 1000 inhabitants of newly registered cases per calendar week. Further, we illustrated the incidences of each municipality for each wave (first wave in summer 1918, second wave in fall/winter 1918/19, the strong later wave in early 1920, as well as the two seasonal waves in 1922 and 1924) on a choropleth map. We performed a spatial hotspot analysis to identify spatial clusters in each wave, using the Gi* statistic. Furthermore, we applied a robust negative binomial regression to estimate the association between selected explanatory variables and incidence on the ecological level. We show that the pandemic transitioned to endemic spread in several waves (including another strong wave in February 1920) with lower incidence and rather local spread until 1924 at least. At the municipality and regional levels, there were different patterns of spread both between pandemic and seasonal waves. In the first pandemic wave in summer 1918 the probability of higher incidence was increased in municipalities with a higher proportion of factories (OR 2.60, 95%CI 1.42-4.96), as well as in municipalities that had access to a railway station (OR 1.50, 95%CI 1.16-1.96). In contrast, the strong fall/winter wave 1918 was very widespread throughout the canton. In general, municipalities at higher altitude showed lower incidence. Our study adds to the sparse literature on incidence in the 1918/19 pandemic and subsequent years. Before Covid-19, the last pandemic that occurred in several waves and then became endemic was the 1918-19 pandemic. Such scenarios from the past can inform pandemic planning and preparedness in future outbreaks.

Risk averse reproduction numbers improve resurgence detection

The effective reproduction number R is a prominent statistic for inferring the transmissibility of infectious diseases and effectiveness of interventions. R purportedly provides an easy-to-interpret threshold for deducing whether an epidemic will grow (R>1) or decline (R<1). We posit that this interpretation can be misleading and statistically overconfident when applied to infections accumulated from groups featuring heterogeneous dynamics. These groups may be delineated by geography, infectiousness or sociodemographic factors. In these settings, R implicitly weights the dynamics of the groups by their number of circulating infections. We find that this weighting can cause delayed detection of outbreak resurgence and premature signalling of epidemic control because it underrepresents the risks from highly transmissible groups. Applying E-optimal experimental design theory, we develop a weighting algorithm to minimise these issues, yielding the risk averse reproduction number E. Using simulations, analytic approaches and real-world COVID-19 data stratified at the city and district level, we show that E meaningfully summarises transmission dynamics across groups, balancing bias from the averaging underlying R with variance from directly using local group estimates. An E>1generates timely resurgence signals (upweighting risky groups), while an E<1ensures local outbreaks are under control. We propose E as an alternative to R for informing policy and assessing transmissibility at large scales (e.g., state-wide or nationally), where R is commonly computed but well-mixed or homogeneity assumptions break down.

Spatial genetic structure of 2009 H1N1 pandemic influenza established as a result of interaction with human populations in mainland China

Identifying the spatial patterns of genetic structure of influenza A viruses is a key factor for understanding their spread and evolutionary dynamics. In this study, we used phylogenetic and Bayesian clustering analyses of genetic sequences of the A/H1N1pdm09 virus with district-level locations in mainland China to investigate the spatial genetic structure of the A/H1N1pdm09 virus across human population landscapes. Positive correlation between geographic and genetic distances indicates high degrees of genetic similarity among viruses within small geographic regions but broad-scale genetic differentiation, implying that local viral circulation was a more important driver in the formation of the spatial genetic structure of the A/H1N1pdm09 virus than even, countrywide viral mixing and gene flow. Geographic heterogeneity in the distribution of genetic subpopulations of A/H1N1pdm09 virus in mainland China indicates both local to local transmission as well as broad-range viral migration. This combination of both local and global structure suggests that both small-scale and large-scale population circulation in China is responsible for viral genetic structure. Our study provides implications for understanding the evolution and spread of A/H1N1pdm09 virus across the population landscape of mainland China, which can inform disease control strategies for future pandemics.

Accessibility of the three-year comprehensive prevention and control of brucellosis in Ningxia: a mathematical modeling study

BACKGROUND

Brucellosis is a chronic zoonotic disease, and Ningxia is one of the high prevalence regions in China. To mitigate the spread of brucellosis, the government of Ningxia has implemented a comprehensive prevention and control plan (2022-2024). It is meaningful to quantitatively evaluate the accessibility of this strategy.

METHODS

Based on the transmission characteristics of brucellosis in Ningxia, we propose a dynamical model of sheep-human-environment, which coupling with the stage structure of sheep and indirect environmental transmission. We first calculate the basic reproduction number [Formula: see text] and use the model to fit the data of human brucellosis. Then, three widely applied control strategies of brucellosis in Ningxia, that is, slaughtering of sicked sheep, health education to high risk practitioners, and immunization of adult sheep, are evaluated.

RESULTS

The basic reproduction number is calculated as [Formula: see text], indicating that human brucellosis will persist. The model has a good alignment with the human brucellosis data. The quantitative accessibility evaluation results show that current brucellosis control strategy may not reach the goal on time. "Ningxia Brucellosis Prevention and Control Special Three-Year Action Implementation Plan (2022-2024)" will be achieved in 2024 when increasing slaughtering rate [Formula: see text] by 30[Formula: see text], increasing health education to reduce [Formula: see text] to 50[Formula: see text], and an increase of immunization rate of adult sheep [Formula: see text] by 40[Formula: see text].

CONCLUSION

The results demonstrate that the comprehensive control measures are the most effective for brucellosis control, and it is necessary to further strengthen the multi-sectoral joint mechanism and adopt integrated measures to prevention and control brucellosis. These results can provide a reliable quantitative basis for further optimizing the prevention and control strategy of brucellosis in Ningxia.

Public opinion changing patterns under the double-hazard scenario of natural disaster and public health event

In the context of the COVID-19 epidemic, a "double-hazard scenario" consisting of a natural disaster and a public health event occurring simultaneously is likely to arise. Focusing on this double-hazard scenario, this study developed a new opinion dynamics model that verifies the effect of opinion dynamic in practical applications and extends the realistic meaning of the logic matrix. The new model can be used to quickly identify changing trends in public opinion about two co-occurring public safety events in China, helping the government to better anticipate and respond to these real double-hazard scenarios. The new model was tested with three real double-hazard scenarios involving natural disasters and public health events in China and the simulation results were analyzed. Using visualization and Pearson correlation coefficients to analyze more than a million items of network-wide public opinion data, the new model was found to show a good fit with reality. The study finally found that in China, public attention to both natural hazards and public health events was greater when these public safety events co-occurred (double-hazard scenario) than when they occurred separately (single-hazard scenarios). These results verify the coupling phenomenon of different disasters in a multi-hazard scenario at the information level for the first time, which is greatly meaningful for multi-hazard research.

Quantifying optimal resource allocation strategies for controlling epidemics

Frequent emergence of communicable diseases is a major concern worldwide. Lack of sufficient resources to mitigate the disease burden makes the situation even more challenging for lower-income countries. Hence, strategy development for disease eradication and optimal management of the social and economic burden has garnered a lot of attention in recent years. In this context, we quantify the optimal fraction of resources that can be allocated to two major intervention measures, namely reduction of disease transmission and improvement of healthcare infrastructure. Our results demonstrate that the effectiveness of each of the interventions has a significant impact on the optimal resource allocation in both long-term disease dynamics and outbreak scenarios. The optimal allocation strategy for long-term dynamics exhibits non-monotonic behaviour with respect to the effectiveness of interventions, which differs from the more intuitive strategy recommended in the case of outbreaks. Further, our results indicate that the relationship between investment in interventions and the corresponding increase in patient recovery rate or decrease in disease transmission rate plays a decisive role in determining optimal strategies. Intervention programmes with decreasing returns promote the necessity for resource sharing. Our study provides fundamental insights into determining the best response strategy when controlling epidemics in resource-constrained situations.

A Comparison of Etiology, Pathogenesis, Vaccinal and Antiviral Drug Development between Influenza and COVID-19

Influenza virus and coronavirus, two kinds of pathogens that exist widely in nature, are common emerging pathogens that cause respiratory tract infections in humans. In December 2019, a novel coronavirus SARS-CoV-2 emerged, causing a severe respiratory infection named COVID-19 in humans, and raising a global pandemic which has persisted in the world for almost three years. Influenza virus, a seasonally circulating respiratory pathogen, has caused four global pandemics in humans since 1918 by the emergence of novel variants. Studies have shown that there are certain similarities in transmission mode and pathogenesis between influenza and COVID-19, and vaccination and antiviral drugs are considered to have positive roles as well as several limitations in the prevention and control of both diseases. Comparative understandings would be helpful to the prevention and control of these diseases. Here, we review the study progress in the etiology, pathogenesis, vaccine and antiviral drug development for the two diseases.

Evaluating the Impact of the Pandemic Crisis on the Aviation Industry

This paper investigates the intellectual structure of the literature addressing "epidemic/pandemic" and "aviation industry" through a bibliometric approach to the literature from 1991 to 2021. The final count of 856 publications was collected from Web of Science and analyzed by CiteSpace (version 5.8.R1) and VOS Viewer. Visualization tools are used to perform the co-citation, co-occurrence, and thematic-based cluster analysis. The results highlight the most prominent nodes (articles, authors, journals, countries, and institutions) within the literature on "epidemic/pandemic" and "aviation industry." Furthermore, this study conceptualizes and compares the growth of literature before theCOVID-19 pandemic and during the COVID-19 ("hotspot") era. The conclusion is that the aviation industry is an engine for global economics on the road to recovery from COVID-19, in which soft (human) resources can play an integral part.

Immunomodulatory and antiviral effects of Lycium barbarum glycopeptide on influenza a virus infection

Influenza is caused by a respiratory virus and has a major global impact on human health. Influenza A viruses in particular are highly pathogenic to humans and have caused multiple pandemics. An important consequence of infection is viral pneumonia, and with serious complications of excessive inflammation and tissue damage. Therefore, simultaneously reducing direct damage caused by virus infection and relieving indirect damage caused by excessive inflammation would be an effective treatment strategy. Lycium barbarum glycopeptide (LbGp) is a mixture of five highly branched polysaccharide-protein conjuncts (LbGp1-5) isolated from Lycium barbarum fruit. LbGp has pro-immune activity that is 1-2 orders of magnitude stronger than that of other plant polysaccharides. However, there are few reports on the immunomodulatory and antiviral activities of LbGp. In this study, we evaluated the antiviral and immunomodulatory effects of LbGp in vivo and in vitro and investigated its therapeutic effect on H1N1-induced viral pneumonia and mechanisms of action. In vitro, cytokine secretion, NF-κB p65 nuclear translocation, and CD86 mRNA expression in LPS-stimulated RAW264.7 cells were constrained by LbGp treatment. In A549 cells, LbGp can inhibit H1N1 infection by blocking virus attachment and entry action. In vivo experiments confirmed that administration of LbGp can effectively increase the survival rate, body weight and decrease the lung index of mice infected with H1N1. Compared to the model group, pulmonary histopathologic symptoms in lung sections of mice treated with LbGp were obviously alleviated. Further investigation revealed that the mechanism of LbGp in the treatment of H1N1-induced viral pneumonia includes reducing the viral load in lung, regulating the phenotype of pulmonary macrophages, and inhibiting excessive inflammation. In conclusion, LbGp exhibits potential curative effects against H1N1-induced viral pneumonia in mice, and these effects are associated with its good immuno-regulatory and antiviral activities.

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R ₀ ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R ₀ values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R ₀ estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R ₀ estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R ₀ estimation and surveillance-based R ₀ estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.