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

Immunometabolic Signature during Respiratory Viral Infection: A Potential Target for Host-Directed Therapies

RNA viruses are known to induce a wide variety of respiratory tract illnesses, from simple colds to the latest coronavirus pandemic, causing effects on public health and the economy worldwide. Influenza virus (IV), parainfluenza virus (PIV), metapneumovirus (MPV), respiratory syncytial virus (RSV), rhinovirus (RhV), and coronavirus (CoV) are some of the most notable RNA viruses. Despite efforts, due to the high mutation rate, there are still no effective and scalable treatments that accompany the rapid emergence of new diseases associated with respiratory RNA viruses. Host-directed therapies have been applied to combat RNA virus infections by interfering with host cell factors that enhance the ability of immune cells to respond against those pathogens. The reprogramming of immune cell metabolism has recently emerged as a central mechanism in orchestrated immunity against respiratory viruses. Therefore, understanding the metabolic signature of immune cells during virus infection may be a promising tool for developing host-directed therapies. In this review, we revisit recent findings on the immunometabolic modulation in response to infection and discuss how these metabolic pathways may be used as targets for new therapies to combat illnesses caused by respiratory RNA viruses.

An approximate Bayesian approach for estimation of the instantaneous reproduction number under misreported epidemic data

In epidemic models, the effective reproduction number is of central importance to assess the transmission dynamics of an infectious disease and to orient health intervention strategies. Publicly shared data during an outbreak often suffers from two sources of misreporting (underreporting and delay in reporting) that should not be overlooked when estimating epidemiological parameters. The main statistical challenge in models that intrinsically account for a misreporting process lies in the joint estimation of the time-varying reproduction number and the delay/underreporting parameters. Existing Bayesian approaches typically rely on Markov chain Monte Carlo algorithms that are extremely costly from a computational perspective. We propose a much faster alternative based on Laplacian-P-splines (LPS) that combines Bayesian penalized B-splines for flexible and smooth estimation of the instantaneous reproduction number and Laplace approximations to selected posterior distributions for fast computation. Assuming a known generation interval distribution, the incidence at a given calendar time is governed by the epidemic renewal equation and the delay structure is specified through a composite link framework. Laplace approximations to the conditional posterior of the spline vector are obtained from analytical versions of the gradient and Hessian of the log-likelihood, implying a drastic speed-up in the computation of posterior estimates. Furthermore, the proposed LPS approach can be used to obtain point estimates and approximate credible intervals for the delay and reporting probabilities. Simulation of epidemics with different combinations for the underreporting rate and delay structure (one-day, two-day, and weekend delays) show that the proposed LPS methodology delivers fast and accurate estimates outperforming existing methods that do not take into account underreporting and delay patterns. Finally, LPS is illustrated in two real case studies of epidemic outbreaks.

Breathing-Driven Self-Powered Pyroelectric ZnO Integrated Face Mask for Bioprotection

Rapid spread of infectious diseases is a global threat and has an adverse impact on human health, livelihood, and economic stability, as manifested in the ongoing coronavirus disease 2019 (COVID-19) pandemic. Even though people wear a face mask as protective equipment, direct disinfection of the pathogens is barely feasible, which thereby urges the development of biocidal agents. Meanwhile, repetitive respiration generates temperature variation wherein the heat is regrettably wasted. Herein, a biocidal ZnO nanorod-modified paper (ZNR-paper) composite that is 1) integrated on a face mask, 2) harvests waste breathing-driven thermal energy, 3) facilitates the pyrocatalytic production of reactive oxygen species (ROS), and ultimately 4) exhibits antibacterial and antiviral performance is proposed. Furthermore, in situ generated compressive/tensile strain of the composite by being attached to a curved mask is investigated for high pyroelectricity. The anisotropic ZNR distortion in the bent composite is verified with changes in ZnO bond lengths and OZnO bond angles in a ZnO₄ tetrahedron, resulting in an increased polarization state and possibly contributing to the following pyroelectricity. The enhanced pyroelectric behavior is demonstrated by efficient ROS production and notable bioprotection. This study exploring the pre-strain effect on the pyroelectricity of ZNR-paper might provide new insights into the piezo-/pyroelectric material-based applications.

Estimating the size of the monkeypox virus outbreak in Nigeria and implications for global control

BACKGROUND

A multi-country outbreak caused by monkeypox virus (MPXV) has been unfolding across endemic and non-endemic countries since May 2022. Throughout April and May 2022, Nigeria reported 31 MPXV cases, of which 11 were confirmed via testing. In May 2022, three internationally exported cases of MPXV, presumed to have originated in Nigeria, were reported, suggesting that a larger than reported outbreak might be occurring in the country.

METHODS

We used previously established methods to estimate the true size of the MPXV outbreak in Nigeria. We estimated the incidence rate of exported MPXV cases among all outbound international air travellers from Nigeria during the time period of April and May 2022, using forecasted air traveller volumes. We then applied this incidence rate to the entire population of Nigeria during April and May 2022 assuming that the rate of infection was the same in Nigeria for both travellers and the resident population. Information on the subset of population that were considered to be travellers was obtained from the United Nations World Tourism Organization (UNWTO).

RESULTS

We estimated that there were approximately 4000 (N = 4013; 95% CI: 828-11 728) active cases of MPXV in Nigeria in April and May 2022. This is approximately 360-fold greater than the confirmed number and approximately 130-fold greater than the reported number of cases in Nigeria.

CONCLUSION

Our findings suggest that a larger outbreak than is appreciated may be ongoing in Nigeria. The observed international spread of MPXV offers important insights into the scale of the epidemic at its origin, where clinical detection and disease surveillance may be limited. These findings highlight the need to expand and support clinical, laboratory, and public health capacity to enable earlier detection of epidemics of international significance.

Reconstructing Mayotte 2018-19 Rift Valley Fever outbreak in humans by combining serological and surveillance data

BACKGROUND

Rift Valley Fever (RVF) is a zoonosis that affects large parts of Africa and the Arabian Peninsula. RVF virus (RVFV) is transmitted to humans through contacts with infected animals, animal products, mosquito bites or aerosols. Its pathogenesis in humans ranges from asymptomatic forms to potentially deadly haemorrhagic fevers, and the true burden of human infections during outbreaks is generally unknown.

METHODS

We build a model fitted to both passive surveillance data and serological data collected throughout a RVF epidemic that occurred in Mayotte Island in 2018-2019.

RESULTS

We estimate that RVFV infected 10,797 (95% CrI 4,728-16,127) people aged ≥15 years old in Mayotte during the entire outbreak, among which only 1.2% (0.67%-2.2%) were reported to the syndromic surveillance system. RVFV IgG seroprevalence in people ≥15 years old was estimated to increase from 5.5% (3.6%-7.7%) before the outbreak to 12.9% (10.4%-16.3%) thereafter.

CONCLUSIONS

Our results suggest that a large part of RVFV infected people present subclinical forms of the disease and/or do not reach medical care that could lead to their detection by the surveillance system. This may threaten the implementation of exhaustive RVF surveillance and adequate control programs in affected countries.

Competitive evolution of H1N1 and H3N2 influenza viruses in the United States: A mathematical modeling study

Seasonal influenza causes vast public health and economic impact globally. The prevention and control of the annual epidemics remain a challenge due to the antigenic evolution of the viruses. Here, we presented a novel modeling framework based on changes in amino acid sequences and relevant epidemiological data to retrospectively investigate the competitive evolution and transmission of H1N1 and H3N2 influenza viruses in the United States during October 2002 and April 2019. To do so, we estimated the time-varying disease transmission rate from the reported influenza cases and the time-varying antigenic change rate of the viruses from the changes in amino acid sequences. By incorporating the time-varying antigenic change rate into the transmission models, we found that the models could capture the evolutionary transmission dynamics of influenza viruses in the United States. Our modeling results also showed that the antigenic change of the virus plays an essential role in seasonal influenza dynamics.

Sequential Transmission of Influenza Viruses in Ferrets Does Not Enhance Infectivity and Does Not Predict Transmissibility in Humans

Airborne transmission in ferrets is a key component of pandemic risk assessment. However, some emerging avian influenza viruses transmit between ferrets but do not spread in humans. Therefore, we evaluated sequential rounds of airborne transmission as an approach to enhance the predictive accuracy of the ferret model. We reasoned that infection of ferrets via the respiratory route and onward transmission would more closely model transmission in humans. We hypothesized that pandemic and seasonal viruses would transmit efficiently over two rounds of transmission, while emerging avian viruses would fail to transmit in a second round. The 2009 pandemic H1N1 (pdm09) and seasonal H3N2 viruses were compared to avian-origin H7N9 and H3N8 viruses. Depending on the virus strain, transmission efficiency varied from 50 to 100% during the first round of transmission; the efficiency for each virus did not change during the second round, and viral replication kinetics in both rounds of transmission were similar. Both the H1N1pdm09 and H7N9 viruses acquired specific mutations during sequential transmission, while the H3N2 and H3N8 viruses did not; however, a global analysis of host-adaptive mutations revealed that minimal changes were associated with transmission of H1N1 and H3N2 viruses, while a greater number of changes occurred in the avian H3N8 and H7N9 viruses. Thus, influenza viruses that transmit in ferrets maintain their transmission efficiency through serial rounds of transmission. This answers the question of whether ferrets can propagate viruses through more than one round of airborne transmission and emphasizes that transmission in ferrets is necessary but not sufficient to infer transmissibility in humans. IMPORTANCE Airborne transmission in ferrets is used to gauge the pandemic potential of emerging influenza viruses; however, some emerging influenza viruses that transmit between ferrets do not spread between humans. Therefore, we evaluated sequential rounds of airborne transmission in ferrets as a strategy to enhance the predictive accuracy of the ferret model. Human influenza viruses transmitted efficiently (>83%) over two rounds of airborne transmission, demonstrating that, like humans, ferrets infected by the respiratory route can propagate the infection onward through the air. However, emerging avian influenza viruses with associated host-adaptive mutations also transmitted through sequential transmission. Thus, airborne transmission in ferrets is necessary but not sufficient to infer transmissibility in humans, and sequential transmission did not enhance pandemic risk assessment.

The Legacy of the Pandemic Preparedness Regime: An Integrative Review

Objectives: The global response to COVID-19 inherited a long history of preparedness features pertaining to various threats, including bioterrorism, (re)-emerging infectious diseases, and pandemics. We describe the evolution of pandemic preparedness frameworks, before and after the COVID-19 pandemic. Methods: We conducted an integrative literature review of publicly available documents, including grey and scientific literature, on pandemic preparedness frameworks. We relied on social science literature as a main source and used search keywords: pandemic preparedness, H1N1, COVID-19, "whole-of-society"/"whole-of-community." Results: The H1N1 pandemic (2009-2010) tested pandemic preparedness frameworks. Lessons-learned reports concluded that the global H1N1 response were too strong and unnecessarily alarming. Such critiques, pandemic fatigue, and budgetary cuts post-2008 explain lack of preparedness for COVID-19. Critiques culminated in a shift towards a "whole-of-society" approach to health crises, although its uptake has not been ideal. Conclusion: Traditional preparedness regime limits arose again during the COVID-19 pandemic. The "whole-of-society" approach was not fully deployed in COVID-19 responses. A "whole-of-organizations" approach could be designed, ensuring that countries consider local organizations' potential to partake in containing infectious disease and counter undesirable side-effects of non-pharmaceutical measures.

The notable global heterogeneity in the distribution of COVID-19 cases and the association with pre-existing parasitic diseases

BACKGROUND

The coronavirus Disease 2019 (COVID-19) is a respiratory disease that has caused extensive ravages worldwide since being declared a pandemic by the World Health Organization (WHO). Unlike initially predicted by WHO, the incidence and severity of COVID-19 appeared milder in many Low-to-Middle-Income Countries (LMIC). To explain this noticeable disparity between countries, many hypotheses, including socio-demographic and geographic factors, have been put forward. This study aimed to estimate the possible association of parasitic diseases with COVID-19 as either protective agents or potential risk factors.

METHODS/PRINCIPAL FINDINGS

A country-level ecological study using publicly available data of countries was conducted. We conceptualized the true number of COVID-19 infections based on a function of test positivity rate (TPR) and employed linear regression analysis to assess the association between the outcome and parasitic diseases. We considered demographic, socioeconomic, and geographic confounders previously suggested. A notable heterogeneity was observed across WHO regions. The countries in Africa (AFRO) showed the lowest rates of COVID-19 incidence, and the countries in the Americas (AMRO) presented the highest. The multivariable model results were computed using 165 countries, excluding missing values. In the models analyzed, lower COVID-19 incidence rates were consistently observed in malaria-endemic countries, even accounting for potential confounding variables, Gross Domestic Product (GDP) per capita, the population aged 65 and above, and differences in the duration of COVID-19. However, the other parasitic diseases were not significantly associated with the spread of the pandemic.

CONCLUSIONS/SIGNIFICANCE

This study suggests that malaria prevalence is an essential factor that explains variability in the observed incidence of COVID-19 cases at the national level. Potential associations of COVID-19 with schistosomiasis and soil-transmitted helminthiases (STHs) are worthy of further investigation but appeared unlikely, based on this analysis, to be critical factors of the variability in COVID-19 epidemic trends. The quality of publicly accessible data and its ecological design constrained our research, with fundamental disparities in monitoring and testing capabilities between countries. Research at the subnational or individual level should be conducted to explore hypotheses further.

Reformulating the susceptible-infectious-removed model in terms of the number of detected cases: well-posedness of the observational model

Compartmental models are popular in the mathematics of epidemiology for their simplicity and wide range of applications. Although they are typically solved as initial value problems for a system of ordinary differential equations, the observed data are typically akin to a boundary value-type problem: we observe some of the dependent variables at given times, but we do not know the initial conditions. In this paper, we reformulate the classical susceptible-infectious-recovered system in terms of the number of detected positive infected cases at different times to yield what we term the observational model. We then prove the existence and uniqueness of a solution to the boundary value problem associated with the observational model and present a numerical algorithm to approximate the solution. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics

The estimation from available data of parameters governing epidemics is a major challenge. In addition to usual issues (data often incomplete and noisy), epidemics of the same nature may be observed in several places or over different periods. The resulting possible inter-epidemic variability is rarely explicitly considered. Here, we propose to tackle multiple epidemics through a unique model incorporating a stochastic representation for each epidemic and to jointly estimate its parameters from noisy and partial observations. By building on a previous work for prevalence data, a Gaussian state-space model is extended to a model with mixed effects on the parameters describing simultaneously several epidemics and their observation process. An appropriate inference method is developed, by coupling the SAEM algorithm with Kalman-type filtering. Moreover, we consider here incidence data, which requires to develop a new version of the filtering algorithm. Its performances are investigated on SIR simulated epidemics for prevalence and incidence data. Our method outperforms an inference method separately processing each dataset. An application to SEIR influenza outbreaks in France over several years using incidence data is also carried out. Parameter estimations highlight a non-negligible variability between influenza seasons, both in transmission and case reporting. The main contribution of our study is to rigorously and explicitly account for the inter-epidemic variability between multiple outbreaks, both from the viewpoint of modeling and inference with a parsimonious statistical model.

What We Can Learn from the Exported Cases in Detecting Disease Outbreaks - A Case Study of the COVID-19 Epidemic

Purpose

Early warning in the travel origins is crucial to prevent disease spreading. When travel origins have delays in reporting disease outbreaks, the exported cases could be used to estimate the epidemic.

Methods

We developed a Bayesian model to jointly estimate the epidemic prevalence and detection delay using the exported cases and their arrival and detection dates. We used simulation studies to discuss potential biases generated by the exported cases. We proposed a hypothesis testing framework to determine the epidemic severity.

Results

We applied the method to the early phase of the COVID-19 epidemic of Wuhan, United States, Italy, and Iran and found that the indicators estimated from the exported cases were consistent with the domestic data under certain scenarios. The exported cases could generate various biases if not modeled properly. We presented the required number of exported cases for determining different severity levels of the outbreak.

Conclusions

The exported case data is a good addition to the domestic data but also has its drawbacks. Utilizing the diagnosis resources from all countries, we advocate that countries work collaboratively to strengthen the global infectious disease surveillance system.

Histological characteristics of matrix metalloproteinase-9 and tissue inhibitor of metalloproteinases-1 in asthmatic murine model during A(H1N1)pdm09 infection

Pandemic influenza virus A(H1N1)pdm09 infection occurred in healthy children and young adults, but asthmatic patients presented more rapid progression of respiratory distress and plastic bronchitis. To investigate the pathogenesis of worsening respiratory symptoms after A(H1N1)pdm09 infection, we focused on matrix metalloproteinase-9 (MMP-9) and tissue inhibitor of metalloproteinases-1 (TIMP-1). MMP-9 and TIMP-1 levels in bronchoalveolar lavage fluid and serum from mice with and without asthma were evaluated after A(H1N1)pdm09 or seasonal A(H1N1) infection. MMP-9 levels were more elevated in Asthma/A(H1N1)pdm09-infected mice than in non-Asthma/A(H1N1)pdm09-infected mice on both 3 and 7 days post-infection. Immunohistochemical findings in this pneumonia model showed that MMP-9 and TIMP-1 positive cells were observed in blood vessels and bronchus of lung tissue in severe pathological findings of pneumonia with asthma. Microscopically, shedding cells and secretions were conspicuous in the trachea on days 3 and 7 post-infection, in the A(H1N1)pdm09-infected mice with asthma. Our results suggest that MMP-9 and TIMP-1 expressions are related to severe pneumonia in the A(H1N1)pdm09 infection with asthma, leading to cause epithelial cell shedding.

Quantifying the information in noisy epidemic curves

Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to the time series. Here, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections, as well as a metric for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring the instantaneous reproduction number: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.

Whole-genome sequencing of SARS-CoV-2 reveals diverse mutations in circulating Alpha and Delta variants during the first, second, and third waves of COVID-19 in South Kivu, east of the Democratic Republic of the Congo

OBJECTIVES

We used whole-genome sequencing of SARS-CoV-2 to identify variants circulating in the Democratic Republic of the Congo and obtain molecular information useful for diagnosis, improving treatment, and general pandemic control strategies.

METHODS

A total of 74 SARS-CoV-2 isolates were sequenced using Oxford Nanopore platforms. Generated reads were processed to obtain consensus genome sequences. Sequences with more than 80% genome coverage were used for variant calling, phylogenetic analysis, and classification using Pangolin lineage annotation nomenclature.

RESULTS

Phylogenetic analysis based on Pangolin classification clustered South Kivu sequences into seven lineages (A.23.1, B.1.1.6, B.1.214, B.1.617.2, B.1.351, C.16, and P.1). The Delta (B.1.617.2) variant was the most dominant and responsible for outbreaks during the third wave. Based on the Wuhan reference genome, 289 distinct mutations were detected, including 141 missenses, 123 synonymous, and 25 insertions/deletions when our isolates were mapped to the Wuhan reference strain. Most of these point mutations were located within the coding sequences of the SARS-CoV-2 genome that includes spike, ORF1ab, ORF3, and nucleocapsid protein genes. The most common mutation was D614G (1841A>G) observed in 61 sequences, followed by L4715L (14143 C>T) found in 60 sequences.

CONCLUSION

Our findings highlight multiple introductions of SARS-CoV-2 into South Kivu through different sources and subsequent circulation of variants in the province. These results emphasize the importance of timely monitoring of genetic variation and its effect on disease severity. This work set a foundation for the use of genomic surveillance as a tool for future global pandemic management and control.

An HLD Model for Tomato Bacterial Canker Focusing on Epidemics of the Pathogen Due to Cutting by Infected Scissors

A healthy, latently infected, diseased (HLD) plant model for botanical epidemics was defined for tomato bacterial canker (TBC) caused by the pathogenic plant bacteria, Clavibacter michiganensis subsp. michiganensis (Cmm). To estimate the infection probability parameter, inoculation experiments were conducted in which it was assumed that infection is transferred to healthy plants through contaminated scissors used to cut symptomless infected plants. The approximate concentration of Cmm in symptomless infected plants was 1 × 10⁶ cells/mL, and the probability of infection of healthy tomato plants was approximately 0.75 due to cutting with scissors soaked in a cell suspension of Cmm at 1 × 10⁶ cells/mL. Three different HLD models were developed by changing some parameters, and the D curve calculated by the developed HLD model A was quite similar to the curve of the proportion of diseased plants observed in fields that had a severe disease incidence. Under a simulation of disease incidence using this model, the basic reproduction number (R₀) was 2.6. However, if the infected scissors were disinfected using ethanol, R₀ was estimated as 0.3. The HLD model for TBC can be used to simulate the increasing number of diseased plants and the term of disease incidence.

Transcriptome dataset of six human pathogen RNA viruses generated by nanopore sequencing

Long-read sequencing (LRS) approaches shed new light on the complexity of viral (Kakuk et al., 2021 [1]; Boldogkői et al., 2019 [2]; Depledge et a., 2019 [3]), bacterial (Yan et al., 2018 [4]) and eukaryotic (Tilgner et al., 2014 [5]) transcriptomes. Emerging RNA viruses are zoonotic (Woolhouse et al., 2016 [6]) and create public health problems, e.g. influenza pandemic caused by H1N1 virus in (Fraser et al., 2009 [7]), as well as the current SARS-CoV-2 pandemic (Kim et al., 2020 [8]). In this study, we carried out nanopore sequencing for generating transcriptomic data valuable for structural and kinetic profiling of six important human pathogen RNA viruses, the H1N1 subtype of Influenza A virus (IVA), the Zika virus (ZIKV), the West Nile virus (WNV), the Crimean-Congo hemorrhagic fever virus (CCHFV), the Coxsackievirus [group B serotype 5 (CVB5)] and the Vesicular stomatitis Indiana virus (VSIV), and the response of host cells upon viral infection. The raw sequencing data were filtered during basecalling and only high quality reads (Qscore ≥ 7) were mapped to the appropriate viral and host genomes. Length distribution of sequencing reads were assessed and statistics of data were plotted by the ReadStat.4 python script. The datasets can be used to profile the transcriptomic landscape of RNA viruses, provide information for novel gene annotations, can serve as resource for studying the virus-host interactions, and for the analysis of RNA base modifications. These datasets can be used to compare the different sequencing techniques, library preparation approaches, bioinformatics pipelines, and to analyze the RNA profiles of viruses with small RNA genomes.

Enhancing Covid-19 virus spread modeling using an activity travel model

Coronavirus 2019 (COVID-19) and its variants are still spreading rapidly with deadly consequences and profound impacts on the global health and world economy. Without a suitable vaccine, mobility restriction has been the most effective method so far to prevent its spreading and avoid overwhelming the heath system of the affected country. The compartmental model SIR (or Susceptible, Infected, and Recovered) is the most popular mathematical model used to predict the course of the COVID-19 pandemic in order to plan the control actions and mobility restrictions against its spreading. A major limitation of this model in relation to modeling the spreading of COVID-19, and the mobility limitation strategy, is that the SIR model does not include mobility or take into account changes in mobility within its structure. This paper develops and tests a new hybrid SIR model; SIR-M which is integrated with an urban activity travel model to explore how it might improve the prediction of pandemic course and the testing of mobility limitation strategies in managing virus spread. The paper describes the enhanced methodology and tests a range of mobility limitation strategies on virus spread outcomes. Implications for policy and research futures are suggested.

Mathematical Analysis of a COVID-19 Epidemic Model by Using Data Driven Epidemiological Parameters of Diseases Spread in India

This paper attempts to describe the outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) via an epidemic model. This virus has dissimilar effects in different countries. The number of new active coronavirus cases is increasing gradually across the globe. India is now in the second stage of COVID-19 spreading, it will be an epidemic very quickly if proper protection is not undertaken based on the database of the transmission of the disease. This paper is using the current data of COVID-19 for the mathematical modeling and its dynamical analysis. We bring in a new representation to appraise and manage the outbreak of infectious disease COVID-19 through SEQIR pandemic model, which is based on the supposition that the infected but undetected by testing individuals are send to quarantine during the incubation period. During the incubation period if any individual be infected by COVID-19, then that confirmed infected individuals are isolated and the necessary treatments are arranged so that they cannot taint the other residents in the community. Dynamics of the SEQIR model is presented by basic reproduction number R ₀ and the comprehensive stability analysis. Numerical results are depicted through apt graphical appearances using the data of five states and India.

Mathematical Analysis of a COVID-19 Epidemic Model by Using Data Driven Epidemiological Parameters of Diseases Spread in India

This paper attempts to describe the outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) via an epidemic model. This virus has dissimilar effects in different countries. The number of new active coronavirus cases is increasing gradually across the globe. India is now in the second stage of COVID-19 spreading, it will be an epidemic very quickly if proper protection is not undertaken based on the database of the transmission of the disease. This paper is using the current data of COVID-19 for the mathematical modeling and its dynamical analysis. We bring in a new representation to appraise and manage the outbreak of infectious disease COVID-19 through SEQIR pandemic model, which is based on the supposition that the infected but undetected by testing individuals are send to quarantine during the incubation period. During the incubation period if any individual be infected by COVID-19, then that confirmed infected individuals are isolated and the necessary treatments are arranged so that they cannot taint the other residents in the community. Dynamics of the SEQIR model is presented by basic reproduction number R 0 and the comprehensive stability analysis. Numerical results are depicted through apt graphical appearances using the data of five states and India.

Estimating the basic reproduction number at the beginning of an outbreak

We compare several popular methods of estimating the basic reproduction number, R₀, focusing on the early stages of an epidemic, and assuming weekly reports of new infecteds. We study the situation when data is generated by one of three standard epidemiological compartmental models: SIR, SEIR, and SEAIR; and examine the sensitivity of the estimators to the model structure. As some methods are developed assuming specific epidemiological models, our work adds a study of their performance in both a well-specified (data generating model and method model are the same) and miss-specified (data generating model and method model differ) settings. We also study R₀ estimation using Canadian COVID-19 case report data. In this study we focus on examples of influenza and COVID-19, though the general approach is easily extendable to other scenarios. Our simulation study reveals that some estimation methods tend to work better than others, however, no singular best method was clearly detected. In the discussion, we provide recommendations for practitioners based on our results.

Stationary distribution and extinction of a stochastic influenza virus model with disease resistance

Influenza is a respiratory infection caused influenza virus. To evaluate the effect of environment noise on the transmission of influenza, our study focuses on a stochastic influenza virus model with disease resistance. We first prove the existence and uniqueness of the global solution to the model. Then we obtain the existence of a stationary distribution to the positive solutions by stochastic Lyapunov function method. Moreover, certain sufficient conditions are provided for the extinction of the influenza virus flu. Finally, several numerical simulations are revealed to illustrate our theoretical results. Conclusively, according to the results of numerical models, increasing disease resistance is favorable to disease control. Furthermore, a simple example demonstrates that white noise is favorable to the disease's extinction.

Patterns of human social contact and mask wearing in high-risk groups in China

Background

The pandemic of coronavirus disease 2019 (COVID-19) has changed human behavior in areas such as contact patterns and mask-wearing frequency. Exploring human–human contact patterns and mask-wearing habits in high-risk groups is an essential step in fully understanding the transmission of respiratory infection-based diseases. This study had aims to quantify local human–human (H–H) contacts in high-risk groups in representative provinces of China and to explore the occupation-specific assortativity and heterogeneity of social contacts.

Methods

Delivery workers, medical workers, preschoolers, and students from Qinghai, Shanghai, and Zhejiang were recruited to complete an online questionnaire that queried general information, logged contacts, and assessed the willingness to wear a mask in different settings. The “group contact” was defined as contact with a group at least 20 individuals. The numbers of contacts across different characteristics were assessed and age-specific contact matrices were established. A generalized additive mixed model was used to analyze the associations between the number of individual contacts and several characteristics. The factors influencing the frequency of mask wearing were evaluated with a logistic regression model.

Results

A total of 611,287 contacts were reported by 15,635 participants. The frequency of daily individual contacts averaged 3.14 (95% confidence interval: 3.13–3.15) people per day, while that of group contacts was 37.90 (95% CI: 37.20–38.70). Skin-to-skin contact and long-duration contact were more likely to occur at home or among family members. Contact matrices of students were the most assortative (all contacts q-index = 0.899, 95% CI: 0.894–0.904). Participants with larger household sizes reported having more contacts. Higher household income per capita was significantly associated with a greater number of contacts among preschoolers (P_{50,000–99,999} = 0.033) and students (P_{10,000–29,999} = 0.017). In each of the public places, the frequency of mask wearing was highest for delivery workers. For preschoolers and students with more contacts, the proportion of those who reported always wearing masks was lower (P < 0.05) in schools/workplaces and public transportation than preschoolers and students with fewer contacts.

Conclusions

Contact screening efforts should be concentrated in the home, school, and workplace after an outbreak of an epidemic, as more than 75% of all contacts, on average, will be found in such places. Efforts should be made to improve the mask-wearing rate and age-specific health promotion measures aimed at reducing transmission for the younger demographic. Age-stratified and occupation-specific social contact research in high-risk groups could help inform policy-making decisions during the post-relaxation period of the COVID-19 pandemic.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00988-8.

Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

The time-varying reproduction number (Rt) is an important measure of transmissibility during outbreaks. Estimating whether and how rapidly an outbreak is growing (Rt > 1) or declining (Rt < 1) can inform the design, monitoring and adjustment of control measures in real-time. We use a popular R package for Rt estimation, EpiEstim, as a case study to evaluate the contexts in which Rt estimation methods have been used and identify unmet needs which would enable broader applicability of these methods in real-time. A scoping review, complemented by a small EpiEstim user survey, highlight issues with the current approaches, including the quality of input incidence data, the inability to account for geographical factors, and other methodological issues. We summarise the methods and software developed to tackle the problems identified, but conclude that significant gaps remain which should be addressed to enable easier, more robust and applicable estimation of Rt during epidemics.

Frequency and distribution of H1N1 influenza A viruses with oseltamivir-resistant mutations worldwide before and after the 2009 pandemic

H1N1 influenza has brought serious threats to people's health and a high socio-economic burden to society. Oseltamivir, a kind of neuraminidase (NA) inhibitor, is the second-generation specific drug that is broadly used currently. However, H1N1 influenza viruses have exhibited oseltamivir resistance in the past decades, which might be a hidden danger. To understand the frequency and distribution laws of oseltamivir-resistant viruses, we conducted a thorough and deep analysis of the available NA protein sequences of H1N1 influenza viruses worldwide from 1918 to 2020. The differences and similarities before and after 2009 were also considered since the dominant viruses changed in this period. Results showed that 3.76% of H1N1 viruses harbored oseltamivir resistance currently. Among various significative mutations, H274Y had the highest frequency of 3.30%, while the frequencies of the other mutations were far below this whether before or after 2009. The oseltamivir resistance was mainly found in three hosts, human, swine, and avian. Different mutation sites could exhibit different distributions in each host. Our results showed that the resistance level reached a peak during the 2007-2008 influenza season and then quickly decreased in 2009. The resistance also displayed a global distribution. The densely populated countries usually had a high resistance level. However, frequent significative mutations were also found in some small countries. Our findings indicated the necessity of monitoring oseltamivir resistance around the world. The study could provide a unique perspective towards the cognition of viruses and facilitate the future study of both pandemic and drug development. This article is protected by copyright. All rights reserved.

Tracing the connections between international business and communicable diseases

We posit that international business and the emergence and spread of communicable diseases are intrinsically connected. To support our arguments, we first start with a historical timeline that traces the connections between international business and communicable diseases back to the sixth century. Second, following the epidemiology of communicable diseases, we identify two crucial transitions related to international business: the emergence of epidemics within a host country and the shift from epidemics to global pandemics. Third, we highlight international business contextual factors (host country regulatory quality, urbanization, trade barriers, global migration) and multinationals' activities (foreign direct investment, corporate political activity, global supply chain management, international travel) that could accelerate each transition. Finally, building on public health insights, we suggest research implications for business scholars on how to integrate human health challenges into their studies and practical implications for global managers on how to help prevent the emergence and spread of communicable diseases.

An Easy-to-Use Public Health-Driven Method (the Generalized Logistic Differential Equation Model) Accurately Simulated COVID-19 Epidemic in Wuhan and Correctly Determined the Early Warning Time

Introduction

Modeling on infectious diseases is significant to facilitate public health policymaking. There are two main mathematical methods that can be used for the simulation of the epidemic and prediction of optimal early warning timing: the logistic differential equation (LDE) model and the more complex generalized logistic differential equation (GLDE) model. This study aimed to compare and analyze these two models.

Methods

We collected data on (coronavirus disease 2019) COVID-19 and four other infectious diseases and classified the data into four categories: different transmission routes, different epidemic intensities, different time scales, and different regions, using R2 to compare and analyze the goodness-of-fit of LDE and GLDE models.

Results

Both models fitted the epidemic curves well, and all results were statistically significant. The R2 test value of COVID-19 was 0.924 (p &lt; 0.001) fitted by the GLDE model and 0.916 (p &lt; 0.001) fitted by the LDE model. The R2 test value varied between 0.793 and 0.966 fitted by the GLDE model and varied between 0.594 and 0.922 fitted by the LDE model for diseases with different transmission routes. The R2 test values varied between 0.853 and 0.939 fitted by the GLDE model and varied from 0.687 to 0.769 fitted by the LDE model for diseases with different prevalence intensities. The R2 test value varied between 0.706 and 0.917 fitted by the GLDE model and varied between 0.410 and 0.898 fitted by the LDE model for diseases with different time scales. The GLDE model also performed better with nation-level data with the R2 test values between 0.897 and 0.970 vs. 0.731 and 0.953 that fitted by the LDE model. Both models could characterize the patterns of the epidemics well and calculate the acceleration weeks.

Conclusion

The GLDE model provides more accurate goodness-of-fit to the data than the LDE model. The GLDE model is able to handle asymmetric data by introducing shape parameters that allow it to fit data with various distributions. The LDE model provides an earlier epidemic acceleration week than the GLDE model. We conclude that the GLDE model is more advantageous in asymmetric infectious disease data simulation.

Optimal control and cost-effective analysis of an age-structured emerging infectious disease model

Emerging infectious diseases are one of the global public health problems which may lead to widespread epidemics and potentially life-threatening infection. Integrated vaccination and physical distancing interventions are two elementary methods for preventing infectious diseases transmission. In this paper, we construct a continuous age-structured model for investigating the transmission dynamics of an emerging infection disease during a short period. We derive the basic regeneration number R0, the spectral radius of the next generation operator K, which determines the disease outbreak or not. Furthermore, we propose an optimal control problem to take account for the cost-effectiveness of social distancing intervention and vaccination. We rigorously obtain sufficient conditions for a L¹ control problem. Numerical simulations show that coupling integrated vaccination and physical distancing intervention could effectively eliminate the infection, and such control strategy is more sensitive for people aged 10–39 and over 60.

Staphylococcus epidermidis Controls Opportunistic Pathogens in the Nose, Could It Help to Regulate SARS-CoV-2 (COVID-19) Infection?

Staphylococcus epidermidis is more abundant in the anterior nares than internal parts of the nose, but its relative abundance changes along with age; it is more abundant in adolescents than in children and adults. Various studies have shown that S. epidermidis is the guardian of the nasal cavity because it prevents the colonization and infection of respiratory pathogens (bacteria and viruses) through the secretion of antimicrobial molecules and inhibitors of biofilm formation, occupying the space of the membrane mucosa and through the stimulation of the host's innate and adaptive immunity. There is a strong relationship between the low number of S. epidermidis in the nasal cavity and the increased risk of serious respiratory infections. The direct application of S. epidermidis into the nasal cavity could be an effective therapeutic strategy to prevent respiratory infections and to restore nasal cavity homeostasis. This review shows the mechanisms that S. epidermidis uses to eliminate respiratory pathogens from the nasal cavity, also S. epidermidis is proposed to be used as a probiotic to prevent the development of COVID-19 because S. epidermidis induces the production of interferon type I and III and decreases the expression of the entry receptors of SARS-CoV-2 (ACE2 and TMPRSS2) in the nasal epithelial cells.

Purifying Selection Determines the Short-Term Time Dependency of Evolutionary Rates in SARS-CoV-2 and pH1N1 Influenza

High-throughput sequencing enables rapid genome sequencing during infectious disease outbreaks and provides an opportunity to quantify the evolutionary dynamics of pathogens in near real-time. One difficulty of undertaking evolutionary analyses over short timescales is the dependency of the inferred evolutionary parameters on the timespan of observation. Crucially, there are an increasing number of molecular clock analyses using external evolutionary rate priors to infer evolutionary parameters. However, it is not clear which rate prior is appropriate for a given time window of observation due to the time-dependent nature of evolutionary rate estimates. Here, we characterize the molecular evolutionary dynamics of SARS-CoV-2 and 2009 pandemic H1N1 (pH1N1) influenza during the first 12 months of their respective pandemics. We use Bayesian phylogenetic methods to estimate the dates of emergence, evolutionary rates, and growth rates of SARS-CoV-2 and pH1N1 over time and investigate how varying sampling window and data set sizes affect the accuracy of parameter estimation. We further use a generalized McDonald-Kreitman test to estimate the number of segregating nonneutral sites over time. We find that the inferred evolutionary parameters for both pandemics are time dependent, and that the inferred rates of SARS-CoV-2 and pH1N1 decline by ∼50% and ∼100%, respectively, over the course of 1 year. After at least 4 months since the start of sequence sampling, inferred growth rates and emergence dates remain relatively stable and can be inferred reliably using a logistic growth coalescent model. We show that the time dependency of the mean substitution rate is due to elevated substitution rates at terminal branches which are 2-4 times higher than those of internal branches for both viruses. The elevated rate at terminal branches is strongly correlated with an increasing number of segregating nonneutral sites, demonstrating the role of purifying selection in generating the time dependency of evolutionary parameters during pandemics.

Entre los discursos de odio y el miedo: tirar el mal al otro lado de la frontera

Resumen En este texto se busca problematizar una representación dominante sobre las epidemias, pandemias y grandes catástrofes, que describe su origen como externo, exótico, extranjero y foráneo. En general, tanto desde el cine catástrofe hollywoodense, hasta los discursos médico-científicos, como desde la filosofía hasta las teorías conspirativas y los discursos de odio, se coloca cualquier amenaza o mal afuera de la propia sociedad, siempre existe un “otro”, quien posee una falla moral que justifica la necesidad de combatirlos, aislarlos o eliminarlos. Proponemos analizar ciertas argumentaciones que han circulado en torno a la actual pandemia de coronavirus, especialmente aquellas que colocan la posibilidad de salvación en el aislamiento y el miedo, para problematizar ciertas ideas naturalizadas en los discursos que luego se traducen en prácticas o acciones políticas.

Characterizing the transmission patterns of seasonal influenza in Italy: lessons from the last decade

BACKGROUND

Despite thousands of influenza cases annually recorded by surveillance systems around the globe, estimating the transmission patterns of seasonal influenza is challenging.

METHODS

We develop an age-structured mathematical model to influenza transmission to analyze ten consecutive seasons (from 2010 to 2011 to 2019-2020) of influenza epidemiological and virological data reported to the Italian surveillance system.

RESULTS

We estimate that 18.4-29.3% of influenza infections are detected by the surveillance system. Influenza infection attack rate varied between 12.7 and 30.5% and is generally larger for seasons characterized by the circulation of A/H3N2 and/or B types/subtypes. Individuals aged 14 years or less are the most affected age-segment of the population, with A viruses especially affecting children aged 0-4 years. For all influenza types/subtypes, the mean effective reproduction number is estimated to be generally in the range 1.09-1.33 (9 out of 10 seasons) and never exceeding 1.41. The age-specific susceptibility to infection appears to be a type/subtype-specific feature.

CONCLUSIONS

The results presented in this study provide insights on type/subtype-specific transmission patterns of seasonal influenza that could be instrumental to fine-tune immunization strategies and non-pharmaceutical interventions aimed at limiting seasonal influenza spread and burden.

Basic reproduction numbers of three strains of mouse hepatitis viruses in mice

Mouse hepatitis virus (MHV) is a murine coronavirus and one of the most important pathogens in laboratory mice. Although various strains of MHV have been isolated, they are generally excreted in the feces and transmitted oronasally via aerosols and contaminated bedding. In this study, we attempted to determine the basic reproduction numbers (R₀) of three strains of MHV to improve our understanding of MHV infections in mice. Five‐week‐old female C57BL/6J mice were inoculated intranasally with either the Y, NuU, or JHM variant strain of MHV and housed with two naïve mice. After 4 weeks, the presence or absence of anti‐MHV antibody in the mice was determined by ELISA. We also examined the distribution of MHV in the organs of Y, NuU, or JHM variant‐infected mice. Our data suggest that the transmissibility of MHV is correlated with viral growth in the gastrointestinal tract of infected mice. To the best of our knowledge, this is the first report to address the basic reproduction numbers among pathogens in laboratory animals.

Mitigating bias in estimating epidemic severity due to heterogeneity of epidemic onset and data aggregation

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, R0, defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) R0 represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer R0. Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of R0 from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data.

A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2

The outbreak of COVID-19 caused by SARS-coronavirus (CoV)-2 has made millions of deaths since 2019. Although a variety of computational methods have been proposed to repurpose drugs for treating SARS-CoV-2 infections, it is still a challenging task for new viruses, as there are no verified virus-drug associations (VDAs) between them and existing drugs. To efficiently solve the cold-start problem posed by new viruses, a novel constrained multi-view nonnegative matrix factorization (CMNMF) model is designed by jointly utilizing multiple sources of biological information. With the CMNMF model, the similarities of drugs and viruses can be preserved from their own perspectives when they are projected onto a unified latent feature space. Based on the CMNMF model, we propose a deep learning method, namely VDA-DLCMNMF, for repurposing drugs against new viruses. VDA-DLCMNMF first initializes the node representations of drugs and viruses with their corresponding latent feature vectors to avoid a random initialization and then applies graph convolutional network to optimize their representations. Given an arbitrary drug, its probability of being associated with a new virus is computed according to their representations. To evaluate the performance of VDA-DLCMNMF, we have conducted a series of experiments on three VDA datasets created for SARS-CoV-2. Experimental results demonstrate that the promising prediction accuracy of VDA-DLCMNMF. Moreover, incorporating the CMNMF model into deep learning gains new insight into the drug repurposing for SARS-CoV-2, as the results of molecular docking experiments reveal that four antiviral drugs identified by VDA-DLCMNMF have the potential ability to treat SARS-CoV-2 infections.

Epidemic local final size in a metapopulation network as indicator of geographical priority for control strategies in SIR type diseases

The main limitation on designing epidemic control strategies lies in their economic and social costs. Thus, a practical and efficient approach takes into consideration these factors. Most epidemics evolve in a structured population, being the geographical structure the most evident. In this situation, having a criteria for identifying the most effective locations where control measures can optimize available resources is desirable. In this paper, a regional index based on the final epidemic size predicted by a metapopulation model is proposed. An efficient algorithm to calculate explicit index values was developed, and different control strategies that used the recommended index were compared with others that do not take the index information into account. We found that the proposed index represents an easy and fast criterion to guide simple control strategies. This type of index offers a new powerful approach where the information encoded in a deterministic mathematical model can be summarized to guide realistic and practical control strategies for disease spreading and epidemics.

Determining travel fluxes in epidemic areas

Infectious diseases attack humans from time to time and threaten the lives and survival of people all around the world. An important strategy to prevent the spatial spread of infectious diseases is to restrict population travel. With the reduction of the epidemic situation, when and where travel restrictions can be lifted, and how to organize orderly movement patterns become critical and fall within the scope of this study. We define a novel diffusion distance derived from the estimated mobility network, based on which we provide a general model to describe the spatiotemporal spread of infectious diseases with a random diffusion process and a deterministic drift process of the population. We consequently develop a multi-source data fusion method to determine the population flow in epidemic areas. In this method, we first select available subregions in epidemic areas, and then provide solutions to initiate new travel flux among these subregions. To verify our model and method, we analyze the multi-source data from mainland China and obtain a new travel flux triggering scheme in the selected 29 cities with the most active population movements in mainland China. The testable predictions in these selected cities show that reopening the borders in accordance with our proposed travel flux will not cause a second outbreak of COVID-19 in these cities. The finding provides a methodology of re-triggering travel flux during the weakening spread stage of the epidemic.

Human infectious diseases spread from their origins to other places with population movements. In order to curb the spatial spread of infectious diseases, many countries and regions may introduce some travel restrictions when the epidemic is severe, and reopen the borders as the epidemic eases. This process involves some important issues such as the start and end time of travel restrictions, the geographical scope of the implementation of the exit strategy, and the allowable passenger flow on traffic lines. Here, we integrate multi-source data with a mathematical model, and consequently develop a new method to determine the travel flux in epidemic areas. As an application, we use this method to calculate when and where the travel restrictions targeting COVID-19 in China in early 2020 could be lifted, and how to optimize passenger flow along the traffic lines among the reopened cities. The testable predictions indicate that the population flow in accordance with our proposed movement pattern will not cause a resurgent outbreak of COVID-19 in the cities studied.

Optimal control for COVID-19 pandemic with quarantine and antiviral therapy

In the absence of a proper cure for the disease, the recent pandemic caused by COVID-19 has been focused on isolation strategies and government measures to control the disease, such as lockdown, media coverage, and improve public hygiene. Mathematical models can help when these intervention mechanisms find some optimal strategies for controlling the spread of such diseases. We propose a set of nonlinear dynamic systems with optimal strategy including practical measures to limit the spread of the virus and to diagnose and isolate infected people while maintaining consciousness for citizens. We have used Pontryagin's maximum principle and solved our system by the finite difference method. In the end, several numerical simulations have been executed to verify the proposed model using Matlab. Also, we pursued the resilience of the parameters of control of the nonlinear dynamic systems, so that we can easily handle the pandemic situation.

Catch Me if You Can: Superspreading of COVID-19

While significant insights have been gained concerning COVID-19, superspreading of coronaviruses remains a mystery. The vast majority of cases have been linked to a relatively small portion of infected individuals. Yet, the genetic sequence of the virus, severity of disease, and underlying host parameters, such as age, sex, and health conditions, are not clearly driving the superspreading phenomenon. In this commentary we discuss what is known and what is not known about coronavirus superspreader transmission and explore whether characteristics of the virion, the donor, or the environment contribute to this phenomenon.

A model for the spread of infectious diseases compatible with case data

For epidemics such as COVID-19, with a significant population having asymptomatic, untested infection, model predictions are often not compatible with data reported only for the cases confirmed by laboratory tests. Additionally, most compartmental models have instantaneous recovery from infection, contrary to observation. Tuning such models with observed data to obtain the unknown infection rate is an ill-posed problem. Here, we derive from the first principle an epidemiological model with delay between the newly infected (N) and recovered (R) populations. To overcome the challenge of incompatibility between model and case data, we solve for the ratios of the observed quantities and show that log(N(t)/R(t)) should follow a straight line. This simple prediction tool is accurate in hindcasts verified using data for China and Italy. In traditional epidemiology, an epidemic wanes when much of the population is infected so that 'herd immunity' is achieved. For a highly contagious and deadly disease, herd immunity is not a feasible goal without human intervention or vaccines. Even before the availability of vaccines, the epidemic was suppressed with social measures in China and South Korea with much less than 5% of the population infected. Effects of social behaviour should be and are incorporated in our model.

COVID-19, the first pandemic in the post-genomic era

The scale of the international efforts to sequence SARS-CoV-2 genomes is unprecedented. Early availability of genomes allowed rapid characterisation of the virus, thus kickstarting many highly successful vaccine development programmes. Worldwide genomic resources have provided a good understanding of the pandemic, supported close monitoring of the emergence of viral genomic diversity and pinpointed those sites to prioritise for functional characterisation. Continued genomic surveillance of global viral populations will be crucial to inform the timing of vaccine updates so as to pre-empt the spread of immune escape lineages. While genome sequencing has provided us with an exceptionally powerful tool to monitor the evolution of SARS-CoV-2, there is room for further improvements in particular in the form of less heterogeneous global surveillance and tools to rapidly identify concerning viral lineages.

Anti-influenza A virus activity and structure-activity relationship of a series of nitrobenzoxadiazole derivatives

Influenza viruses represent a major threat to human health and are responsible for seasonal epidemics, along with pandemics. Currently, few therapeutic options are available, with most drugs being at risk of the insurgence of resistant strains. Hence, novel approaches targeting less explored pathways are urgently needed. In this work, we assayed a library of nitrobenzoxadiazole derivatives against the influenza virus A/Puerto Rico/8/34 H1N1 (PR8) strain. We identified three promising 4-thioether substituted nitrobenzoxadiazoles (12, 17, and 25) that were able to inhibit viral replication at low micromolar concentrations in two different infected cell lines using a haemagglutination assay. We further assessed these molecules using an In-Cell Western assay, which confirmed their potency in the low micromolar range. Among the three molecules, 12 and 25 displayed the most favourable profile of activity and selectivity and were selected as hit compounds for future optimisation studies.

Biology and Pathogenesis of SARS-CoV-2: Understandings for Therapeutic Developments against COVID-19

Coronaviruses are positive sense, single-stranded, enveloped, and non-segmented RNA viruses that belong to the Coronaviridae family within the order Nidovirales and suborder Coronavirinae. Two Alphacoronavirus strains: HCoV-229E and HCoV-NL63 and five Betacoronaviruses: HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2 have so far been recognized as Human Coronaviruses (HCoVs). Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is currently the greatest concern for humanity. Despite the overflow of research on SARS-CoV-2 and other HCoVs published every week, existing knowledge in this area is insufficient for the complete understanding of the viruses and the diseases caused by them. This review is based on the analysis of 210 published works, and it attempts to cover the basic biology of coronaviruses, including the genetic characteristics, life cycle, and host-pathogen interaction, pathogenesis, the antiviral drugs, and vaccines against HCoVs, especially focusing on SARS-CoV-2. Furthermore, we will briefly discuss the potential link between extracellular vesicles (EVs) and SARS-CoV-2/COVID-19 pathophysiology.

Estimation of Human Mobility Patterns for Forecasting the Early Spread of Disease

Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm model based on the network science approach, which estimates the travel flow data in mainland China by transforming location big data and airline operation data into network structure information. In addition, we established a simplified deterministic SEIR (Susceptible-Exposed-Infectious-Recovered)-metapopulation model to verify the effectiveness of the estimated travel flow data in the study of predicting epidemic spread. The results show that individual travel distance in mainland China is mainly within 100 km. There is far more travel between prefectures within the same province than across provinces. The epidemic spatial spread model incorporating estimated travel data accurately predicts the spread of COVID-19 in mainland China. The results suggest that there are far more travelers than usual during the Spring Festival in mainland China, and the number of travelers from Wuhan mainly determines the number of confirmed cases of COVID-19 in each prefecture.

Essential Oil-Rich Chinese Formula Luofushan-Baicao Oil Inhibits the Infection of Influenza A Virus through the Regulation of NF-κB P65 and IRF3 Activation

Background

Luofushan-Baicao Oil (LBO) is an essential oil-rich traditional Chinese medicine (TCM) formula that is commonly used to treat cold, cough, headache, sore throat, swelling, and pain. However, the anti-influenza activities of LBO and the underlying mechanism remain to be investigated.

Methods

The in vitro anti-influenza activity of LBO was tested with methyl thiazolyl tetrazolium (MTT) and plaque assays. The effects of LBO on the expressions of viral nucleoprotein and cytokines were evaluated. In the polyinosinic-polycytidylic acid- (Poly I: C-) induced inflammation model, the influences of LBO on the expression of cytokines and the activation of NF-κB P65 (P65) and interferon regulatory factor 3 (IRF3) were tested. After influenza A virus (IVA) infection, mice were administered with LBO for 5 days. The lung index, histopathologic change, the expression of viral protein, P65, and IRF3 in the lung tissue were measured. The levels of proinflammatory cytokines in serum were examined.

Results

In vitro, LBO could significantly inhibit the infection of IVA, decrease the formation of plaques, and reduce the expression of viral nucleoprotein and cytokines. LBO could also effectively downregulate the expression of interleukin-1β (IL-1β), interleukin-6 (IL-6), and interferon-β and the activation of P65 and IRF3 in Poly I:C-treated cells. In the IVA-infected mice model, inhalation of LBO with atomizer could decrease the lung index, alleviate the pathological injury in the lung tissue, and reduce the serum levels of IL-1β and IL-6. LBO could significantly downregulate the expression of viral protein (nucleoprotein, PB2, and matrix 2 ion channel) and the phosphorylation of P65 and IRF3 in the lungs of mice.

Conclusion

The therapeutic effects of LBO on treating influenza might result from the regulation of the immune response of IVA infection. LBO can be developed as an alternative therapeutic agent for influenza prevention.

The effect of population size for pathogen transmission on prediction of COVID-19 spread

Extreme public health interventions play a critical role in mitigating the local and global prevalence and pandemic potential. Here, we use population size for pathogen transmission to measure the intensity of public health interventions, which is a key characteristic variable for nowcasting and forecasting of COVID-19. By formulating a hidden Markov dynamic system and using nonlinear filtering theory, we have developed a stochastic epidemic dynamic model under public health interventions. The model parameters and states are estimated in time from internationally available public data by combining an unscented filter and an interacting multiple model filter. Moreover, we consider the computability of the population size and provide its selection criterion. With applications to COVID-19, we estimate the mean of the effective reproductive number of China and the rest of the globe except China (GEC) to be 2.4626 (95% CI: 2.4142-2.5111) and 3.0979 (95% CI: 3.0968-3.0990), respectively. The prediction results show the effectiveness of the stochastic epidemic dynamic model with nonlinear filtering. The hidden Markov dynamic system with nonlinear filtering can be used to make analysis, nowcasting and forecasting for other contagious diseases in the future since it helps to understand the mechanism of disease transmission and to estimate the population size for pathogen transmission and the number of hidden infections, which is a valid tool for decision-making by policy makers for epidemic control.

COVID-19 Pandemic Outbreak in the Subcontinent: A Data Driven Analysis

Human civilization is experiencing a critical situation that presents itself for a new coronavirus disease 2019 (COVID-19). This virus emerged in late December 2019 in Wuhan city, Hubei, China. The grim fact of COVID-19 is, it is highly contagious in nature, therefore, spreads rapidly all over the world and causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Responding to the severity of COVID-19 research community directs the attention to the analysis of COVID-19, to diminish its antagonistic impact towards society. Numerous studies claim that the subcontinent, i.e., Bangladesh, India, and Pakistan, could remain in the worst affected region by the COVID-19. In order to prevent the spread of COVID-19, it is important to predict the trend of COVID-19 beforehand the planning of effective control strategies. Fundamentally, the idea is to dependably estimate the reproduction number to judge the spread rate of COVID-19 in a particular region. Consequently, this paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers. More specifically, we use various models (for example, susceptible infection recovery (SIR), exponential growth (EG), sequential Bayesian (SB), maximum likelihood (ML) and time dependent (TD)) to estimate the reproduction numbers and observe the model fitness in the corresponding data set. Experimental results show that the reproduction numbers produced by these models are greater than 1.2 (approximately) indicates that COVID-19 is gradually spreading in the subcontinent.

Short-term predictions and prevention strategies for COVID-19: A model-based study

An outbreak of respiratory disease caused by a novel coronavirus is ongoing from December 2019. As of December 14, 2020, it has caused an epidemic outbreak with more than 73 million confirmed infections and above 1.5 million reported deaths worldwide. During this period of an epidemic when human-to-human transmission is established and reported cases of coronavirus disease 2019 (COVID-19) are rising worldwide, investigation of control strategies and forecasting are necessary for health care planning. In this study, we propose and analyze a compartmental epidemic model of COVID-19 to predict and control the outbreak. The basic reproduction number and the control reproduction number are calculated analytically. A detailed stability analysis of the model is performed to observe the dynamics of the system. We calibrated the proposed model to fit daily data from the United Kingdom (UK) where the situation is still alarming. Our findings suggest that independent self-sustaining human-to-human spread ( R 0 > 1 , R c > 1 ) is already present. Short-term predictions show that the decreasing trend of new COVID-19 cases is well captured by the model. Further, we found that effective management of quarantined individuals is more effective than management of isolated individuals to reduce the disease burden. Thus, if limited resources are available, then investing on the quarantined individuals will be more fruitful in terms of reduction of cases.