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

Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset

IMPORTANCE

Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation.

OBJECTIVE

To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus.

DESIGN, SETTING, AND PARTICIPANTS

The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated.

EXPOSURES

Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay.

MAIN OUTCOMES AND MEASURES

The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC).

CONCLUSIONS AND RELEVANCE

This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

Longitudinal Projection of Herd Prevalence of Influenza A(H1N1)pdm09 Virus Infection in the Norwegian Pig Population by Discrete-Time Markov Chain Modelling

In order to quantify projections of disease burden and to prioritise disease control strategies in the animal population, good mathematical modelling of infectious disease dynamics is required. This article investigates the suitability of discrete-time Markov chain (DTMC) as one such model for forecasting disease burden in the Norwegian pig population after the incursion of influenza A(H1N1)pdm09 virus (H1N1pdm09) in Norwegian pigs in 2009. By the year-end, Norway's active surveillance further detected 20 positive herds from 54 random pig herds, giving an estimated initial population prevalence of 37% (95% CI 25-52). Since then, Norway's yearly surveillance of pig herd prevalence has given this study 11 years of data from 2009 to 2020 to work with. Longitudinally, the pig herd prevalence for H1N1pdm09 rose sharply to >40% in three years and then fluctuated narrowly between 48% and 49% for 6 years before declining. This initial longitudinal pattern in herd prevalence from 2009 to 2016 inspired this study to of test the steady-state discrete-time Markov chain model in forecasting disease prevalence. With the pig herd as the unit of analysis, the parameters for DTMC came from the initial two years of surveillance data after the outbreak, namely vector prevalence, first herd incidence and recovery rates. The latter two probabilities formed the fixed probability transition matrix for use in a discrete-time Markov chain (DTMC) that is quite similar to another compartmental model, the susceptible-infected-susceptible (SIS) model. These DTMC of predicted prevalence (DTMCP) showed good congruence (Pearson correlation = 0.88) with the subsequently observed herd prevalence for seven years from 2010 to 2016. While the DTMCP converged to the stationary (endemic) state of 48% in 2012, after three time steps, the observed prevalence declined instead from 48% after 2016 to 25% in 2018 before rising to 29% in 2020. A sudden plunge in H1N1pdm09 prevalence amongst Norwegians during the 2016/2017 human flu season may have had a knock-on effect in reducing the force of infection in pig herds in Norway. This paper endeavours to present the discrete-time Markov chain (DTMC) as a feasible but limited tool in forecasting the sequence of a predicted infectious disease's prevalence after it's incursion as an exotic disease.

Superposed Natural Hazards and Pandemics: Breaking Dams, Floods, and COVID-19

Within the engineering domain, safety issues are often related to engineering design and typically exclude factors such as epidemics, famine, and disease. This article provides a perspective on the reciprocal relationship and interaction between a natural hazard and a simultaneous pandemic outbreak and discusses how a catastrophic dam break, combined with the ongoing COVID-19 pandemic, poses a risk to human life. The paper uses grey- and peer-reviewed literature to support the discussion and reviews fundamentals of dam safety management, potential loss of life due to a dam break, and the recent evolution in dam risk analysis to account for the COVID-19 outbreak. Conventional risk reduction recommendations, such as quick evacuation and sheltering in communal centers, are revisited in the presence of a pandemic when social distancing is recommended. This perspective manuscript aims to provide insight into the multi-hazard risk problem resulting from a concurring natural hazard and global pandemic.

Evaluating Epidemiological Risk by Using Open Contact Tracing Data: Correlational Study

Background

During the 2020s, there has been extensive debate about the possibility of using contact tracing (CT) to contain the SARS-CoV-2 pandemic, and concerns have been raised about data security and privacy. Little has been said about the effectiveness of CT. In this paper, we present a real data analysis of a CT experiment that was conducted in Italy for 8 months and involved more than 100,000 CT app users.

Objective

We aimed to discuss the technical and health aspects of using a centralized approach. We also aimed to show the correlation between the acquired contact data and the number of SARS-CoV-2–positive cases. Finally, we aimed to analyze CT data to define population behaviors and show the potential applications of real CT data.

Methods

We collected, analyzed, and evaluated CT data on the duration, persistence, and frequency of contacts over several months of observation. A statistical test was conducted to determine whether there was a correlation between indices of behavior that were calculated from the data and the number of new SARS-CoV-2 infections in the population (new SARS-CoV-2–positive cases).

Results

We found evidence of a correlation between a weighted measure of contacts and the number of new SARS-CoV-2–positive cases (Pearson coefficient=0.86), thereby paving the road to better and more accurate data analyses and spread predictions.

Conclusions

Our data have been used to determine the most relevant epidemiological parameters and can be used to develop an agent-based system for simulating the effects of restrictions and vaccinations. Further, we demonstrated our system's ability to identify the physical locations where the probability of infection is the highest. All the data we collected are available to the scientific community for further analysis.

Analysis of the comprehensive non-pharmaceutical interventions and measures in containing the COVID-19 epidemic in Shenzhen: a retrospective study

OBJECTIVE

To analyse the epidemiological characteristics of family clusters of COVID-19 and the three stages of the comprehensive non-pharmaceutical interventions and measures implemented in Shenzhen.

METHODS

The epidemic curve of COVID-19 was drawn and the impact of the comprehensive non-pharmaceutical interventions and measures was analysed by the different periods of the epidemic.

RESULTS

A total of 427 cases (417 confirmed cases and 10 asymptomatic infectious cases) were reported in Shenzhen, of which 259 (60.7%) were clustered cases. 97 cluster events were drawn and most cluster events (97.3%) occurred in families. There were three stages of the COVID-19 epidemic in Shenzhen. The epidemic increased rapidly, but the peak lasted for a short time, while the decline in incidence was rapid and large.

CONCLUSIONS

Family clusters were the main feature of the COVID-19 outbreak in Shenzhen in 2020, and the Shenzhen government rolled out a quick response to the epidemic. Non-pharmaceutical interventions and measures were proven to have effectively contained community transmission, limit the transmission to aggregation and reduce the scale of transmission within a household.

Assessing the Risk in Urban Public Transport for Epidemiologic Factors

Pandemics have presented new challenges for public transport organisers and operators. New diseases (e.g., influenza H1N1, severe acute respiratory syndrome—SARS, as well as, more recently, SARS-CoV-2) increase the need for new protection measures to prevent epidemic outbreaks in public transport infrastructure. The authors’ goal is to present a set of actions in the area of public transport that are adjusted to different levels of epidemic development. The goal goes back to the following question: how can the highest possible level of passenger safety be ensured and the losses suffered by urban public transport companies kept as low as possible? The sets of pro-active measures for selected epidemic scenarios presented in the article may offer support to local authorities and public transport operators. In the next steps, it is important to develop and implement tools for public transport management to ensure safety and tackle epidemic hazards.

Cure and death play a role in understanding dynamics for COVID-19: Data-driven competing risk compartmental models, with and without vaccination

Several factors have played a strong role in influencing the dynamics of COVID-19 in the U.S. One being the economy, where a tug of war has existed between lockdown measures to control disease versus loosening of restrictions to address economic hardship. A more recent effect has been availability of vaccines and the mass vaccination efforts of 2021. In order to address the challenges in analyzing this complex process, we developed a competing risk compartmental model framework with and without vaccination compartment. This framework separates instantaneous risk of removal for an infectious case into competing risks of cure and death, and when vaccinations are present, the vaccinated individual can also achieve immunity before infection. Computations are performed using a simple discrete time algorithm that utilizes a data driven contact rate. Using population level pre-vaccination data, we are able to identify and characterize three wave patterns in the U.S. Estimated mortality rates for second and third waves are 1.7%, which is a notable decrease from 8.5% of a first wave observed at onset of disease. This analysis reveals the importance cure time has on infectious duration and disease transmission. Using vaccination data from 2021, we find a fourth wave, however the effect of this wave is suppressed due to vaccine effectiveness. Parameters playing a crucial role in this modeling were a lower cure time and a signficantly lower mortality rate for the vaccinated.

Estimation of the reproduction number of influenza A(H1N1)pdm09 in South Korea using heterogeneous models

Background

The reproduction number is one of the most crucial parameters in determining disease dynamics, providing a summary measure of the transmission potential. However, estimating this value is particularly challenging owing to the characteristics of epidemic data, including non-reproducibility and incompleteness.

Methods

In this study, we propose mathematical models with different population structures; each of these models can produce data on the number of cases of the influenza A(H1N1)pdm09 epidemic in South Korea. These structured models incorporating the heterogeneity of age and region are used to estimate the reproduction numbers at various terminal times. Subsequently, the age- and region-specific reproduction numbers are also computed to analyze the differences illustrated in the incidence data.

Results

Incorporation of the age-structure or region-structure allows for robust estimation of parameters, while the basic SIR model provides estimated values beyond the reasonable range with severe fluctuation. The estimated duration of infectious period using age-structured model is around 3.8 and the reproduction number was estimated to be 1.6. The estimated duration of infectious period using region-structured model is around 2.1 and the reproduction number was estimated to be 1.4. The estimated age- and region-specific reproduction numbers are consistent with cumulative incidence for corresponding groups.

Conclusions

Numerical results reveal that the introduction of heterogeneity into the population to represent the general characteristics of dynamics is essential for the robust estimation of parameters.

Optimal test-kit-based intervention strategy of epidemic spreading in heterogeneous complex networks

We propose a deterministic compartmental model of infectious disease that considers the test kits as an important ingredient for the suppression and mitigation of epidemics. A rigorous simulation (with an analytical argument) is provided to reveal the effective reduction of the final outbreak size and the peak of infection as a function of basic reproduction number in a single patch. Furthermore, to study the impact of long and short-distance human migration among the patches, we consider heterogeneous networks where the linear diffusive connectivity is determined by the network link structure. We numerically confirm that implementation of test kits in a fraction of nodes (patches) having larger degrees or betweenness centralities can reduce the peak of infection (as well as the final outbreak size) significantly. A next-generation matrix-based analytical treatment is provided to find out the critical transmission probability in the entire network for the onset of epidemics. Finally, the optimal intervention strategy is validated in two real networks: the global airport network and the transportation network of Kolkata, India.

Marine Natural Products as a Source of Drug Leads against Respiratory Viruses: Structural and Bioactive Diversity

Respiratory viruses, including influenza virus, respiratory syncytial virus, coronavirus, etc., have seriously threatened the human health. For example, the outbreak of severe acute respiratory syndrome coronavirus, SARS, affected a large number of countries around the world. Marine organisms, which could produce secondary metabolites with novel structures and abundant biological activities, are an important source for seeking effective drugs against respiratory viruses. This report reviews marine natural products with activities against respiratory viruses, the emphasis of which was put on structures and antiviral activities of these natural products. This review has described 167 marinederived secondary metabolites with activities against respiratory viruses published from 1981 to 2019. Altogether 102 references are cited in this review article.

2D materials as a diagnostic platform for the detection and sensing of the SARS-CoV-2 virus: a bird's-eye view

Worldwide infections and fatalities caused by the SARS-CoV-2 virus and its variants responsible for COVID-19 have significantly impeded the economic growth of many nations. People in many nations have lost their livelihoods, it has severely impacted international relations and, most importantly, health infrastructures across the world have been tormented. This pandemic has already left footprints on human psychology, traits, and priorities and is certainly going to lead towards a new world order in the future. As always, science and technology have come to the rescue of the human race. The prevention of infection by instant and repeated cleaning of surfaces that are most likely to be touched in daily life and sanitization drives using medically prescribed sanitizers and UV irradiation of textiles are the first steps to breaking the chain of transmission. However, the real challenge is to develop and uplift medical infrastructure, such as diagnostic tools capable of prompt diagnosis and instant and economic medical treatment that is available to the masses. Two-dimensional (2D) materials, such as graphene, are atomic sheets that have been in the news for quite some time due to their unprecedented electronic mobilities, high thermal conductivity, appreciable thermal stability, excellent anchoring capabilities, optical transparency, mechanical flexibility, and a unique capability to integrate with arbitrary surfaces. These attributes of 2D materials make them lucrative for use as an active material platform for authentic and prompt (within minutes) disease diagnosis via electrical or optical diagnostic tools or via electrochemical diagnosis. We present the opportunities provided by 2D materials as a platform for SARS-CoV-2 diagnosis.

Nanomedicine: A Diagnostic and Therapeutic Approach to COVID-19

The SARS-CoV-2 virus is causing devastating morbidity and mortality worldwide. Nanomedicine approaches have a high potential to enhance conventional diagnostics, drugs and vaccines. In fact, lipid nanoparticle/mRNA vaccines are already widely used to protect from COVID-19. In this review, we present an overview of the taxonomy, structure, variants of concern, epidemiology, pathophysiology and detection methods of SARS-CoV-2. The efforts of repurposing, tailoring, and adapting pre-existing medications to battle COVID-19 and the state of vaccine developments are presented. Next, we discuss the broad concepts and limitations of how nanomedicine could address the COVID-19 threat. Nanomaterials are particles in the nanometer scale (10-100 nm) which possess unique properties related to their size, polarity, structural and chemical composition. Nanoparticles can be composed of precious metals (copper, silver, gold), inorganic materials (graphene, silicon), proteins, carbohydrates, lipids, RNA/DNA, or conjugates, combinations and polymers of all of the aforementioned. The advanced biochemical features of these nanoscale particles allow them to directly interact with virions and irreversibly disrupt their structure, which can render a virus incapable of replicating within the host. Virus-neutralizing coats and surfaces impregnated with nanomaterials can enhance personal protective equipment, hand sanitizers and air filter systems. Nanoparticles can enhance drug-based therapies by optimizing uptake, stability, target cell-specific delivery, and magnetic properties. In fact, recent studies have highlighted the potential of nanoparticles in different aspects of the fight against SARS-CoV-2, such as enhancing biosensors and diagnostic tests, drug therapies, designing new delivery mechanisms, and optimizing vaccines. This article summarizes the ongoing research on diagnostic strategies, treatments, and vaccines for COVID-19, while emphasizing the potential of nanoparticle-based pharmaceuticals and vaccines.

Influenza A virus NS1 induces degradation of sphingosine 1-phosphate lyase to obstruct the host innate immune response

The type I interferon (IFN)-mediated innate immune response is one of the central obstacles influenza A virus (IAV) must overcome in order to successfully replicate within the host. We have previously shown that sphingosine 1-phosphate (S1P) lyase (SPL) enhances IKKϵ-mediated type I IFN responses. Here, we demonstrate that the nonstructural protein 1 (NS1) of IAV counteracts the SPL-mediated antiviral response by inducing degradation of SPL. SPL was ubiquitinated and downregulated upon IAV infection or NS1 expression, whereas NS1-deficient IAV failed to elicit SPL ubiquitination or downregulation. Transiently overexpressed SPL increased phosphorylation of IKKϵ, resulting in enhanced expression of type I IFNs. However, this induction was markedly inhibited by IAV NS1. Collectively, this study reveals a novel strategy employed by IAV to subvert the type I IFN response, providing new insights into the interplay between IAV and host innate immunity.

Distribution and evolution of H1N1 influenza A viruses with adamantanes-resistant mutations worldwide from 1918 to 2019

H1N1 influenza is a kind of acute respiratory infectious disease that has a high socioeconomic and medical burden each year around the world. In the past decades, H1N1 influenza viruses have exhibited high resistance to adamantanes, which has become a serious issue. To understand the up-to-date distribution and evolution of H1N1 influenza viruses with adamantanes-resistant mutations, we conducted a deep analysis of 15875 M2 protein and 8351 MP nucleotides sequences. Results of the distribution analyses showed that 77.32% of H1N1 influenza viruses harbored-resistance mutations of which 73.52% were S31N, And the mutant variants mainly appeared in North America and Europe and H1N1 influenza viruses with S31N mutation became the circulating strains since 2009 all over the world. In addition, 80.65% of human H1N1 influenza viruses and 74.61% of swine H1N1 influenza viruses exhibited adamantanes resistance, while the frequency was only 1.86% in avian H1N1 influenza viruses. Studies from evolutionary analyses indicated that the avian-origin swine H1N1 influenza viruses replaced the classical human H1N1 influenza viruses and became the circulating strains after 2009; The interspecies transmission among avian, swine, and human strains over the past 20 years contributed to the 2009 swine influenza pandemic. Results of our study clearly clarify the historical drug resistance level of H1N1 influenza viruses around the world and demonstrated the evolution of adamantanes-resistant mutations in H1N1 influenza viruses. Our findings emphasize the necessity for monitoring the adamantanes susceptibility of H1N1 influenza viruses and draw attention to analyses of the evolution of drug-resistant H1N1 influenza variants.

Estimation of novel coronavirus (COVID-19) reproduction number and case fatality rate: A systematic review and meta-analysis

BACKGROUND AND AIMS

Realizing the transmission potential and the magnitude of the coronavirus disease 2019 (COVID-19) aids public health monitoring, strategies, and preparation. Two fundamental parameters, the basic reproduction number (R 0) and case fatality rate (CFR) of COVID-19, help in this understanding process. The objective of this study was to estimate the R 0 and CFR of COVID-19 and assess whether the parameters vary in different regions of the world.

METHODS

We carried out a systematic review to find the reported estimates of the R 0 and the CFR in articles from international databases between January 1 and August 31, 2020. Random-effect models and Forest plots were implemented to evaluate the mean effect size of R 0 and the CFR. Furthermore, R 0 and CFR of the studies were quantified based on geographic location, the tests/thousand population, and the median population age of the countries where the studies were conducted. To assess statistical heterogeneity among the selected articles, the I 2 statistic and the Cochran's Q test were used.

RESULTS

Forty-five studies involving R 0 and 34 studies involving CFR were included. The pooled estimation of R 0 was 2.69 (95% CI: 2.40, 2.98), and that of the CFR was 2.67 (2.25, 3.13). The CFR in different regions of the world varied significantly, from 2.49 (2.08, 2.94) in Asia to 3.40 (2.81, 4.04) in North America. We observed higher mean CFR values for the countries with lower tests (3.15 vs 2.16) and greater median population age (3.13 vs 2.27). However, R 0 did not vary significantly in different regions of the world.

CONCLUSIONS

An R 0 of 2.69 and a CFR of 2.67 indicate the severity of the COVID-19. Although R 0 and CFR may vary over time, space, and demographics, we recommend considering these figures in control and prevention measures.

Evolution of A(H1N1) pdm09 influenza virus masking by glycosylation

Introduction: As the pathogen that caused the first influenza virus pandemic in this century, the swine-origin A(H1N1) pdm09 influenza virus has caused continuous harm to human public health. The evolution of hemagglutinin protein glycosylation sites, including the increase in number and positional changes, is an important way for influenza viruses to escape host immune pressure. Based on the traditional influenza virus molecular monitoring, special attention should be paid to the influence of glycosylation evolution on the biological characteristics of virus antigenicity, transmission and pathogenicity. The epidemiological significance of glycosylation mutants should be analyzed as a predictive tool for early warning of new outbreaks and pandemics, as well as the design of vaccines and drug targets.Areas covered: We review on the evolutionary characteristics of glycosylation on the HA protein of the A(H1N1)pdm09 influenza virus in the last ten years.Expert opinion: We discuss the crucial impact of evolutionary glycosylation on the biological characteristics of the virus and the host immune responses, summarize studies revealing different roles of glycosylation play during host adaptation. Although these studies show the significance of glycosylation evolution in host-virus interaction, much remains to be discovered about the mechanism.

Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks

Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model.

Nipah virus: a potential pandemic agent in the context of the current severe acute respiratory syndrome coronavirus 2 pandemic

For centuries, zoonotic diseases have been responsible for various outbreaks resulting in the deaths of millions of people. The best example of this is the current coronavirus disease 2019 (COVID-19) pandemic. Like severe acute respiratory syndrome coronavirus, Nipah virus is another deadly virus which has caused several outbreaks in the last few years. Though it causes a low number of infections, disease severity results in a higher death rate. In the context of the recent COVID-19 pandemic, we speculate that many countries will be unable to deal with the sudden onset of such a viral outbreak. Thus, further research and attention to the virus are needed to address future outbreaks.

COVID-19: pathogenesis, advances in treatment and vaccine development and environmental impact-an updated review

Diseases negatively impact the environment, causing many health risks and the spread of pollution and hazards. A novel coronavirus, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has led to a recent respiratory syndrome epidemic in humans. In December 2019, the sudden emergence of this new coronavirus and the subsequent severe disease it causes created a serious global health threat and hazards. This is in contrast to the two aforementioned coronaviruses, SARS-CoV-2 (in 2002) and middle east respiratory syndrome coronavirus MERS-CoV (in 2012), which were much more easily contained. The World Health Organization (WHO) dubbed this contagious respiratory disease an "epidemic outbreak" in March 2020. More than 80 companies and research institutions worldwide are working together, in cooperation with many governmental agencies, to develop an effective vaccine. To date, six authorized vaccines have been registered. Up till now, no approved drugs and drug scientists are racing from development to clinical trials to find new drugs for COVID-19. Wild animals, such as snakes, bats, and pangolins are the main sources of coronaviruses, as determined by the sequence homology between MERS-CoV and viruses in these animals. Human infection is caused by inhalation of respiratory droplets. To date, the only available treatment protocol for COVID-19 is based on the prevalent clinical signs. This review aims to summarize the current information regarding the origin, evolution, genomic organization, epidemiology, and molecular and cellular characteristics of SARS-CoV-2 as well as the diagnostic and treatment approaches for COVID-19 and its impact on global health, environment, and economy.

Statistical Estimation of the Reproductive Number From Case Notification Data

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.

Estimating the relative probability of direct transmission between infectious disease patients

BACKGROUND

Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases.

METHODS

Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates.

RESULTS

In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany.

CONCLUSIONS

Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

Using demographic data to understand the distribution of H1N1 and COVID-19 pandemics cases among federal entities and municipalities of Mexico

BACKGROUND

The novel coronavirus disease (COVID-19) pandemic is the second global health emergency the world has faced in less than two decades, after the H1N1 Influenza pandemic in 2009-2010. Spread of pandemics is frequently associated with increased population size and population density. The geographical scales (national, regional or local scale) are key elements in determining the correlation between demographic factors and the spread of outbreaks. The aims of this study were: (a) to collect the Mexican data related to the two pandemics; (b) to create thematic maps using federal and municipal geographic scales; (c) to investigate the correlations between the pandemics indicators (numbers of contagious and deaths) and demographic patterns (population size and density).

METHODS

The demographic patterns of all Mexican Federal Entities and all municipalities were taken from the database of "Instituto Nacional de Estadística y Geografía" (INEGI). The data of "Centro Nacional de Programas Preventivos y Control de Enfermedades" (CENAPRECE) and the geoportal of Mexico Government were also used in our analysis. The results are presented by means of tables, graphs and thematic maps. A Spearman correlation was used to assess the associations between the pandemics indicators and the demographic patterns. Correlations with a p value < 0.05 were considered significant.

RESULTS

The confirmed cases (ccH1N1) and deaths (dH1N1) registered during the H1N1 Influenza pandemic were 72.4 thousand and 1.2 thousand respectively. Mexico City (CDMX) was the most affected area by the pandemic with 8,502 ccH1N1 and 152 dH1N1. The ccH1N1 and dH1N1 were positively correlated to demographic patterns; p-values higher than the level of marginal significance were found analyzing the % ccH1N1 and the % dH1N1 vs the population density. The COVID-19 pandemic data indicated 75.0 million confirmed cases (ccCOVID-19) and 1.6 million deaths (dCOVID-19) worldwide, as of date. The CDMX, where 264,330 infections were recorded, is the national epicenter of the pandemic. The federal scale did not allow to observe the correlation between demographic data and pandemic indicators; hence the next step was to choose a more detailed geographical scale (municipal basis). The ccCOVID-19 and dCOVID-19 (municipal basis) were highly correlated with demographic patterns; also the % ccCOVID-19 and % dCOVID-19 were moderately correlated with demographic patterns.

CONCLUSION

The magnitude of COVID-19 pandemic is much greater than the H1N1 Influenza pandemic. The CDMX was the national epicenter in both pandemics. The federal scale did not allow to evaluate the correlation between exanimated demographic variables and the spread of infections, but the municipal basis allowed the identification of local variations and "red zones" such as the delegation of Iztapalapa and Gustavo A. Madero in CDMX.

Whole-genome sequencing of SARS-CoV-2 reveals the detection of G614 variant in Pakistan

Since its emergence in China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide including Pakistan. During the pandemic, whole genome sequencing has played an important role in understanding the evolution and genomic diversity of SARS-CoV-2. Although an unprecedented number of SARS-CoV-2 full genomes have been submitted in GISAID and NCBI, data from Pakistan is scarce. We report the sequencing, genomic characterization, and phylogenetic analysis of five SARS-CoV-2 strains isolated from patients in Pakistan. The oropharyngeal swabs of patients that were confirmed positive for SARS-CoV-2 through real-time RT-PCR at National Institute of Health, Pakistan, were selected for whole-genome sequencing. Sequencing was performed using NEBNext Ultra II Directional RNA Library Prep kit for Illumina (NEW ENGLAND BioLabs Inc., MA, US) and Illumina iSeq 100 instrument (Illumina, San Diego, US). Based on whole-genome analysis, three Pakistani SARS-CoV-2 strains clustered into the 20A (GH) clade along with the strains from Oman, Slovakia, United States, and Pakistani strain EPI_ISL_513925. The two 19B (S)-clade strains were closely related to viruses from India and Oman. Overall, twenty-nine amino acid mutations were detected in the current study genome sequences, including fifteen missense and four novel mutations. Notably, we have found a D614G (aspartic acid to glycine) mutation in spike protein of the sequences from the GH clade. The G614 variant carrying the characteristic D614G mutation has been shown to be more infectious that lead to its rapid spread worldwide. This report highlights the detection of GH and S clade strains and G614 variant from Pakistan warranting large-scale whole-genome sequencing of strains prevalent in different regions to understand virus evolution and to explore their genetic diversity.

Prioritizing antiviral drugs against SARS-CoV-2 by integrating viral complete genome sequences and drug chemical structures

The outbreak of a novel febrile respiratory disease called COVID-19, caused by a newfound coronavirus SARS-CoV-2, has brought a worldwide attention. Prioritizing approved drugs is critical for quick clinical trials against COVID-19. In this study, we first manually curated three Virus-Drug Association (VDA) datasets. By incorporating VDAs with the similarity between drugs and that between viruses, we constructed a heterogeneous Virus-Drug network. A novel Random Walk with Restart method (VDA-RWR) was then developed to identify possible VDAs related to SARS-CoV-2. We compared VDA-RWR with three state-of-the-art association prediction models based on fivefold cross-validations (CVs) on viruses, drugs and virus-drug associations on three datasets. VDA-RWR obtained the best AUCs for the three fivefold CVs, significantly outperforming other methods. We found two small molecules coming together on the three datasets, that is, remdesivir and ribavirin. These two chemical agents have higher molecular binding energies of − 7.0 kcal/mol and − 6.59 kcal/mol with the domain bound structure of the human receptor angiotensin converting enzyme 2 (ACE2) and the SARS-CoV-2 spike protein, respectively. Interestingly, for the first time, experimental results suggested that navitoclax could be potentially applied to stop SARS-CoV-2 and remains to further validation.

Nowcasting epidemics of novel pathogens: lessons from COVID-19

Epidemic nowcasting broadly refers to assessing the current state by understanding key pathogenic, epidemiologic, clinical and socio-behavioral characteristics of an ongoing outbreak. Its primary objective is to provide situational awareness and inform decisions on control responses. In the event of large-scale sustained emergencies, such as the COVID-19 pandemic, scientists need to constantly update their aims and analytics with respect to the rapidly evolving emergence of new questions, data and findings in order to synthesize real-time evidence for policy decisions. In this Perspective, we share our views on the functional aims, rationale, data requirements and challenges of nowcasting at different stages of an epidemic, drawing on the ongoing COVID-19 experience. We highlight how recent advances in the computational and laboratory sciences could be harnessed to complement traditional approaches to enhance the scope, timeliness, reliability and utility of epidemic nowcasting.

A novel comprehensive metric to assess effectiveness of COVID-19 testing: Inter-country comparison and association with geography, government, and policy response

Testing and case identification are key strategies in controlling the COVID-19 pandemic. Contact tracing and isolation are only possible if cases have been identified. The effectiveness of testing should be assessed, but a single comprehensive metric is not available to assess testing effectiveness, and no timely estimates of case detection rate are available globally, making inter-country comparisons difficult. The purpose of this paper was to propose a single, comprehensive metric, called the COVID-19 Testing Index (CovTI) scaled from 0 to 100, derived from epidemiological indicators of testing, and to identify factors associated with this outcome. The index was based on case-fatality rate, test positivity rate, active cases, and an estimate of the detection rate. It used parsimonious modeling to estimate the true total number of COVID-19 cases based on deaths, testing, health system capacity, and government transparency. Publicly reported data from 165 countries and territories that had reported at least 100 confirmed cases by June 3, 2020 were included in the index. Estimates of detection rates aligned satisfactorily with previous estimates in literature (R2 = 0.44). As of June 3, 2020, the states with the highest CovTI included Hong Kong (93.7), Australia (93.5), Iceland (91.8), Cambodia (91.3), New Zealand (90.6), Vietnam (90.2), and Taiwan (89.9). Bivariate analyses showed the mean CovTI in countries with open public testing policies (66.9, 95% CI 61.0-72.8) was significantly higher than in countries with no testing policy (29.7, 95% CI 17.6-41.9) (p<0.0001). A multiple linear regression model assessed the association of independent grouping variables with CovTI. Open public testing and extensive contact tracing were shown to significantly increase CovTI, after adjusting for extrinsic factors, including geographic isolation and centralized forms of government. The correlation of testing and contact tracing policies with improved outcomes demonstrates the validity of this model to assess testing effectiveness and also suggests these policies were effective at improving health outcomes. This tool can be combined with other databases to identify other factors or may be useful as a standalone tool to help inform policymakers.

Estimation of secondary household attack rates for emergent SARS-CoV-2 variants detected by genomic surveillance at a community-based testing site in San Francisco

BACKGROUND

Sequencing of the SARS-CoV-2 viral genome from patient samples is an important epidemiological tool for monitoring and responding to the pandemic, including the emergence of new mutations in specific communities.

METHODS

SARS-CoV-2 genomic sequences were generated from positive samples collected, along with epidemiological metadata, at a walk-up, rapid testing site in the Mission District of San Francisco, California during November 22-December 2, 2020 and January 10-29, 2021. Secondary household attack rates and mean sample viral load were estimated and compared across observed variants.

RESULTS

A total of 12,124 tests were performed yielding 1,099 positives. From these, 811 high quality genomes were generated. Certain viral lineages bearing spike mutations, defined in part by L452R, S13I, and W152C, comprised 54.9% of the total sequences from January, compared to 15.7% in November. Household contacts exposed to "West Coast" variants were at higher risk of infection compared to household contacts exposed to lineages lacking these variants (0.357 vs 0.294, RR=1.29; 95% CI:1.01-1.64). The reproductive number was estimated to be modestly higher than other lineages spreading in California during the second half of 2020. Viral loads were similar among persons infected with West Coast versus non-West Coast strains, as was the proportion of individuals with symptoms (60.9% vs 64.1%).

CONCLUSIONS

The increase in prevalence, relative household attack rates, and reproductive number are consistent with a modest transmissibility increase of the West Coast variants; however, additional laboratory and epidemiological studies are required to better understand differences between these variants.

SUMMARY

We observed a growing prevalence and elevated attack rate for "West Coast" SARS-CoV-2 variants in a community testing setting in San Francisco during January 2021, suggesting its modestly higher transmissibility.

Systems and Clinical Pharmacology of COVID-19 Therapeutic Candidates: A Clinical and Translational Medicine Perspective

Over 50 million people have been infected with the SARS-CoV-2 virus, while around 1 million have died due to COVID-19 disease progression. COVID-19 presents flu-like symptoms that can escalate, in about 7-10 days from onset, into a cytokine storm causing respiratory failure and death. Although social distancing reduces transmissibility, COVID-19 vaccines and therapeutics are essential to regain socioeconomic normalcy. Even if effective and safe vaccines are found, pharmacological interventions are still needed to limit disease severity and mortality. Integrating current knowledge and drug candidates (approved drugs for repositioning among >35 candidates) undergoing clinical studies (>3000 registered in ClinicalTrials.gov), we employed Systems Pharmacology approaches to project how antivirals and immunoregulatory agents could be optimally evaluated for use. Antivirals are likely to be effective only at the early stage of infection, soon after exposure and before hospitalization, while immunomodulatory agents should be effective in the later-stage cytokine storm. As current antiviral candidates are administered in hospitals over 5-7 days, a long-acting combination that targets multiple SARS-CoV-2 lifecycle steps may provide a long-lasting, single-dose treatment in outpatient settings. Long-acting therapeutics may still be needed even when vaccines become available as vaccines are likely to be approved based on a 50% efficacy target.

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.

The value of infectious disease modeling and trend assessment: a public health perspective

INTRODUCTION

Disease outbreaks of acquired immunodeficiency syndrome, severe acute respiratory syndrome, pandemic H1N1, H7N9, H5N1, Ebola, Zika, Middle East respiratory syndrome, and recently COVID-19 have raised the attention of the public over the past half-century. Revealing the characteristics and epidemic trends are important parts of disease control. The biological scenarios including transmission characteristics can be constructed and translated into mathematical models, which can help to predict and gain a deeper understanding of diseases.

AREAS COVERED

This review discusses the models for infectious diseases and highlights their values in the field of public health. This information will be of interest to mathematicians and clinicians, and make a significant contribution toward the development of more specific and effective models. Literature searches were performed using the online database of PubMed (inception to August 2020).

EXPERT OPINION

Modeling could contribute to infectious disease control by means of predicting the scales of disease epidemics, indicating the characteristics of disease transmission, evaluating the effectiveness of interventions or policies, and warning or forecasting during the pre-outbreak of diseases. With the development of theories and the ability of calculations, infectious disease modeling would play a much more important role in disease prevention and control of public health.

Genetic diversity and genomic epidemiology of SARS-CoV-2 in Morocco

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), declared as a pandemic due to its rapid spread worldwide. In this study, we investigate the genetic diversity and genomic epidemiology of SARS-CoV-2, using 22 virus genome sequences reported by three different laboratories in Morocco till June 7,2020, as well as 40,366 virus genomes from all around the world. The SARS-CoV-2 genomes from Moroccan patients revealed 62 mutations, of which 30 were mis-sense mutations. The mutations Spike_D614G and NSP12_P323L were present in all the 22 analyzed sequences, followed by N_G204R and N_R203K, which occurred in 9 among the 22 sequences. The mutations NSP10_R134S, NSP15_D335N, NSP16_I169L, NSP3_L431H, NSP3_P1292L and Spike_V6F occurred once in Moroccan sequences, with no record in other sequences worldwide. Phylogenetic analyses revealed that Moroccan SARS-CoV-2 genomes included 9 viruses belonging to Clade 20A, 9 to Clade 20B and 2 to Clade 20C, suggesting that the epidemic spread in Morocco did not display a predominant SARS-CoV-2 route. Therefore, multiple and unrelated introductions of SARS-CoV-2 into Morocco through different routes have occurred, giving rise to the diversity of virus genomes in the country. Further, in all probability, the SARS-CoV-2 circulated in a cryptic way in Morocco, starting from January 15, 2020 before the first case was officially discovered on March 2, 2020.

Cross-Sectional Study on Awareness and Knowledge of COVID-19 Among Senior pharmacy Students

Extraordinary actions have been implemented in an effort to control the rapid spread of the ongoing COVID-19 epidemic in Egypt. People's adherence to control measures is influenced by their knowledge, attitudes and practices towards the disease. Therefore, in the present study we assessed pharmacy senior students' knowledge, attitudes and practices towards the COVID-19 pandemic. An online questionnaire was created and it consisted of 12 questions testing their knowledge about COVID-19 clinical characteristics, transmission routes and prevention and control steps. Among senior pharmacy students (n = 238), 70% were females and 63% were living in greater Cairo. Their main source of information included social media (70%), published articles (48%) and television (48%). The overall correct knowledge score was 83%. Most of the students displayed a good COVID-19 knowledge level (72.5% of the students). The students were least informed when trying to answer questions about hyper-coagulation, as a major cause for death in patients with severe COVID-19, and about the timings on the necessity to wear masks. Assessment of students' attitudes and practices towards COVID-19 reflected that 87% of them were confident that health care teams and scientists could win the fight against the virus. In addition, 72% of students agreed that COVID-19 will be controlled successfully. The greater the students' knowledge, the more confident they felt that COVID-19 will be controlled successfully (OR 2.2, 95% confidence interval [CI] 1.03-4.72). Good behavioral practice towards COVID-19 control was confirmed when 87% of students answered that they didn't go out to any crowded place. Females were 3.6 times (95% confidence interval [CI] 1.03-3.11) more likely to avoid going out than males. Bad behavioral practice became evident when approximately 50% of students admitted that they did not wear masks when they left their house. Therefore, more efforts should be taken to protect future pharmacists from this pandemic.

Sensor-based localization of epidemic sources on human mobility networks

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.

Tracking the source of an epidemic outbreak is of crucial importance as it allows for identification of communities where control efforts should be focused for both short and long-term management and control of the disease. However, such identification is often problematic, time-consuming, and data-intensive. Recently network-based analysis approaches have been established for source detection to account for complex modern spreading, driven substantially by human mobility. Here we develop a probabilistic framework for waterborne disease, that allows investigators to infer the community or the region sparking an outbreak based on a sparse surveillance network. The framework can integrate prior information on the likelihood of a community being the source, for instance as a function of population size or hygiene conditions. Furthermore, we assign an accuracy measure to the resulting source estimate, which is crucial for its practical usability. We test the method in the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province with promising results. Moreover, we outline a series of guidelines in terms of data needs and preliminary operations to implement the proposed framework in practice.

Retrospective prediction of the epidemic trend of COVID-19 in Wuhan at four phases

The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in December 2019 and was basically under control in April 2020 in Wuhan. To explore the impact of intervention measures on the COVID-19 epidemic, we established susceptible-exposed-infectious-recovered (SEIR) models to predict the epidemic characteristics of COVID-19 at four different phases (beginning, outbreak, recession, and plateau) from January 1st to March 30th, 2020. We found that the infection rate rapidly grew up to 0.3647 at Phase II from 0.1100 at Phase I and went down to 0.0600 and 0.0006 at Phase III and IV, respectively. The reproduction numbers of COVID-19 were 10.7843, 13.8144, 1.4815, and 0.0137 at Phase I, II, III, and IV, respectively. These results suggest that intensive interventions, including compulsory home isolation and rapid improvement of medical resources, can effectively reduce the COVID-19 transmission. Furthermore, the predicted COVID-19 epidemic trend by our models was close to the actual epidemic trend in Wuhan. Our phase-based SEIR models demonstrate that intensive intervention measures can effectively control COVID-19 spread even without specific medicines and vaccines against this disease.

Data Analytics and Mathematical Modeling for Simulating the Dynamics of COVID-19 Epidemic—A Case Study of India

The global explosion of the COVID-19 pandemic has created worldwide unprecedented health and economic challenges which stimulated one of the biggest annual migrations globally. In the Indian context, even after proactive decisions taken by the Government, the continual growth of COVID-19 raises questions regarding its extent and severity. The present work utilizes the susceptible-infected-recovered-death (SIRD) compartment model for parameter estimation and fruitful prediction of COVID-19. Further, various optimization techniques such as particle swarm optimization (PSO), gradient (G), pattern search (PS) and their hybrid are employed to solve the considered model. The simulation study endorse the efficiency of PSO (with or without G) and G+PS+G over other techniques for ongoing pandemic assessment. The key parametric values including characteristic time of infection and death and reproduction number have been estimated as 60 days, 67 days and 4.78 respectively by utilizing the optimum results. The model assessed that India has passed its peak duration of COVID-19 with more than 81% recovery and only a 1.59% death rate. The short duration analysis (15 days) of obtained results against reported data validates the effectiveness of the developed models for ongoing pandemic assessment.

A host-based whole genome sequencing study reveals novel risk loci associated with severity of influenza A(H1N1)pdm09 infection

Influenza A(H1N1)pdm09 virus has remained in a seasonal circulation since being recognized in 2009. Although it followed a mild course in most patients, in others it caused a series of severe clinical illnesses. Epidemiologic studies have implicated that host factors have a major influence on the disease severity of influenza A(H1N1)pdm09 infection. However, an understanding of relevant genetic variations and the underlying mechanisms is still limited. In this present study, we used a host-based whole genome sequencing (WGS) method to comprehensively explore the genetic risk loci associated with severity of influenza A(H1N1)pdm09 infection. From the common single-nucleotide variants (SNVs) analysis, we identified the abnormal nominally significant (P < 1 × 10⁻⁴) common SNVs enriched in PTBP3 gene. The results of rare functional SNVs analysis supported that there were several novel candidate genes might confer risk of severe influenza A(H1N1)pdm09 diseases, such as FTSJ3, CPVL, BST2, NOD2 and MAVS. Moreover, our results of gene set based analysis indicated that the HIF-1 transcription factor and IFN-γ pathway might play an important role in the underlying mechanism of severe influenza A(H1N1)pdm09. These findings will increase our knowledge about biological mechanism underlying the severe influenza A(H1N1)pdm09 and facilitate to design novel personalized treatments.

Early mandated social distancing is a strong predictor of reduction in peak daily new COVID-19 cases

OBJECTIVES

Mandated social distancing has been applied globally to reduce the spread of coronavirus disease 2019 (COVID-19). However, the beneficial effects of this community-based intervention have not been proven or quantified for the COVID-19 pandemic.

STUDY DESIGN

This is a regional population-level observational study.

METHODS

Using publicly available data, we examined the effect of timing of mandated social distancing on the rate of COVID-19 cases in 119 geographic regions, derived from 41 states within the United States and 78 other countries. The highest number of new COVID-19 cases per day recorded within a geographic unit was the primary outcome. The total number of COVID-19 cases in regions where case numbers had reached the tail end of the outbreak was an exploratory outcome.

RESULTS

We found that the highest number of new COVID-19 cases per day per million persons was significantly associated with the total number of COVID-19 cases per million persons on the day before mandated social distancing (β = 0.66, P < 0.0001). These findings suggest that if mandated social distancing is not initiated until the number of existing COVID-19 cases has doubled, the eventual peak would result in 58% more COVID-19 cases per day. Subgroup analysis on those regions where the highest number of new COVID-19 cases per day has peaked showed increase in β values to 0.85 (P < 0.0001). The total number of cases during the outbreak in a region was strongly predicted by the total number of COVID-19 cases on the day before mandated social distancing (β = 0.97, P < 0.0001).

CONCLUSIONS

Initiating mandated social distancing when the numbers of COVID-19 cases are low within a region significantly reduces the number of new daily COVID-19 cases and perhaps also reduces the total number of cases in the region.

Juice of Citrullus lanatus var. citroides (wild watermelon) inhibits the entry and propagation of influenza viruses in vitro and in vivo

Vaccines and various anti-influenza drugs are clinically used to prevent and treat influenza infections. However, with the antigenic mismatch of vaccines and the emergence of drug-resistant viral strains, new approaches for treating influenza are warranted. This study focused on natural foods as potential candidates for the development of new treatment options for influenza infections. The screening of plants from the Cucurbitaceae family revealed that the juice of Citrullus lanatus var. citroides (wild watermelon) had the strongest ability to inhibit the replication of influenza virus in Madin-Darby canine kidney cells. The results of a time-of-addition assay indicated that wild watermelon juice (WWMJ) inhibits the adsorption and late stages of viral replication, suggesting that WWMJ contains multiple constituents with effective anti-influenza activity. A viral adsorption analysis showed that WWMJ reduces the amount of viral RNA in the cells at 37°C but not at 4°C, confirming that WWMJ inhibits viral entry into the host cells at 37°C. These results suggest that a mechanism other than the inhibition of viral attachment is involved in the anti-influenza action of WWMJ, which is perhaps responsible for a reduction in internalization of the virus. Administration of WWMJ into the nasal mucosa of BALB/c mice infected with the A/PR/8/34 mouse-adapted influenza virus was seen to significantly improve the survival rate. The findings of this study, therefore, demonstrate the anti-influenza potential of WWMJ in vitro and in vivo, thereby suggesting the candidature of WWMJ as a functional food product that can be used to develop anti-influenza agents and drugs.

Comparison of COVID-19 Health Risks With Other Viral Occupational Hazards

The European Commission periodically classifies viruses on their occupational hazards to define the level of protection that workers are entitled to claim. Viruses belonging to Groups 3 and 4 can cause severe human disease and hazard to workers, as well as a spreading risk to the community. However, there is no effective prophylaxis or treatment available for Group 4 viruses. European trade unions and the Commission are negotiating the classification of the COVID-19 virus along these 2 categories. This article weighs the reasons to classify it in Group 3 or 4 while comparing its risks to those of the most significant viruses classified in these 2 categories. COVID-19 characteristics justify its classification in Group 4. Contaminated workers in contact with the public play an important role in disseminating the virus. In hospitals and nursing homes, they increase the overall case fatality rate. By strongly protecting these workers and professionals, the European Union would not only improve health in work environments, but also activate a mechanism key to reducing the COVID-19 burden in the general population. Admittedly, the availability of a new vaccine or treatment would change this conclusion, which was reached in the middle of the first pandemic.

Integrated environment-occupant-pathogen information modeling to assess and communicate room-level outbreak risks of infectious diseases

Microbial pathogen transmission within built environments is a main public health concern. The pandemic of coronavirus disease 2019 (COVID-19) adds to the urgency of developing effective means to reduce pathogen transmission in mass-gathering public buildings such as schools, hospitals, and airports. To inform occupants and guide facility managers to prevent and respond to infectious disease outbreaks, this study proposed a framework to assess room-level outbreak risks in buildings by modeling built environment characteristics, occupancy information, and pathogen transmission. Building information modeling (BIM) is exploited to automatically retrieve building parameters and possible occupant interactions that are relevant to pathogen transmission. The extracted information is fed into an environment pathogen transmission model to derive the basic reproduction numbers for different pathogens, which serve as proxies of outbreak potentials in rooms. A web-based system is developed to provide timely information regarding outbreak risks to occupants and facility managers. The efficacy of the proposed method was demonstrated by a case study, in which building characteristics, occupancy schedules, pathogen parameters, as well as hygiene and cleaning practices are considered for outbreak risk assessment. This study contributes to the body of knowledge by computationally integrating building, occupant, and pathogen information modeling for infectious disease outbreak assessment, and communicating actionable information for built environment management.

Modeling epidemic spreading through public transit using time-varying encounter network

Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people's preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying "influential passengers" using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading.

Estimating the time-varying reproduction number of COVID-19 with a state-space method

After slowing down the spread of the novel coronavirus COVID-19, many countries have started to relax their confinement measures in the face of critical damage to socioeconomic structures. At this stage, it is desirable to monitor the degree to which political measures or social affairs have exerted influence on the spread of disease. Though it is difficult to trace back individual transmission of infections whose incubation periods are long and highly variable, estimating the average spreading rate is possible if a proper mathematical model can be devised to analyze daily event-occurrences. To render an accurate assessment, we have devised a state-space method for fitting a discrete-time variant of the Hawkes process to a given dataset of daily confirmed cases. The proposed method detects changes occurring in each country and assesses the impact of social events in terms of the temporally varying reproduction number, which corresponds to the average number of cases directly caused by a single infected case. Moreover, the proposed method can be used to predict the possible consequences of alternative political measures. This information can serve as a reference for behavioral guidelines that should be adopted according to the varying risk of infection.

Molecular epidemiology in the HIV and SARS-CoV-2 pandemics

PURPOSE OF REVIEW

The aim of this review was to compare and contrast the application of molecular epidemiology approaches for the improved management and understanding of the HIV versus SARS-CoV-2 epidemics.

RECENT FINDINGS

Molecular biology approaches, including PCR and whole genome sequencing (WGS), have become powerful tools for epidemiological investigation. PCR approaches form the basis for many high-sensitivity diagnostic tests and can supplement traditional contact tracing and surveillance strategies to define risk networks and transmission patterns. WGS approaches can further define the causative agents of disease, trace the origins of the pathogen, and clarify routes of transmission. When coupled with clinical datasets, such as electronic medical record data, these approaches can investigate co-correlates of disease and pathogenesis. In the ongoing HIV epidemic, these approaches have been effectively deployed to identify treatment gaps, transmission clusters and risk factors, though significant barriers to rapid or real-time implementation remain critical to overcome. Likewise, these approaches have been successful in addressing some questions of SARS-CoV-2 transmission and pathogenesis, but the nature and rapid spread of the virus have posed additional challenges.

SUMMARY

Overall, molecular epidemiology approaches offer unique advantages and challenges that complement traditional epidemiological tools for the improved understanding and management of epidemics.

A model to rate strategies for managing disease due to COVID-19 infection

Considering looming fatality and economic recession, effective policy making based on ongoing COVID-19 pandemic is an urgent and standing issue. Numerous issues for controlling infection have arisen from public discussion led by medical professionals. Yet understanding of these factors has been necessarily qualitative and control measures to correct unfavorable trends specific to an infection area have been lacking. The logical implement for control is a large scale stochastic model with countless parameters lacking robustness and requiring enormous data. This paper presents a remedy for this vexing problem by proposing an alternative approach. Machine learning has come to play a widely circulated role in the study of complex data in recent times. We demonstrate that when machine learning is employed together with the mechanistic framework of a mathematical model, there can be a considerably enhanced understanding of complex systems. A mathematical model describing the viral infection dynamics reveals two transmissibility parameters influenced by the management strategies in the area for the control of the current pandemic. Both parameters readily yield the peak infection rate and means for flattening the curve, which is correlated to different management strategies by employing machine learning, enabling comparison of different strategies and suggesting timely alterations. Treatment of population data with the model shows that restricted non-essential business closure, school closing and strictures on mass gathering influence the spread of infection. While a rational strategy for initiation of an economic reboot would call for a wider perspective of the local economics, the model can speculate on its timing based on the status of the infection as reflected by its potential for an unacceptably renewed viral onslaught.

Accounting for cross-immunity can improve forecast accuracy during influenza epidemics

Previous exposure to influenza viruses confers cross-immunity against future infections with related strains. However, this is not always accounted for explicitly in mathematical models used for forecasting during influenza outbreaks. We show that, if an influenza outbreak is due to a strain that is similar to one that has emerged previously, then accounting for cross-immunity explicitly can improve the accuracy of real-time forecasts. To do this, we consider two infectious disease outbreak forecasting models. In the first (the "1-group model"), all individuals are assumed to be identical and cross-immunity is not accounted for. In the second (the "2-group model"), individuals who have previously been infected by a related strain are assumed to be less likely to experience severe disease, and therefore recover more quickly, than immunologically naive individuals. We fit both models to estimated case notification data (including symptomatic individuals as well as laboratory-confirmed cases) from Japan from the 2009 H1N1 influenza pandemic, and then generate synthetic data for a future outbreak by assuming that the 2-group model represents the epidemiology of influenza infections more accurately. We use the 1-group model (as well as the 2-group model for comparison) to generate forecasts that would be obtained in real-time as the future outbreak is ongoing, using parameter values estimated from the 2009 epidemic as informative priors, motivated by the fact that without using prior information from 2009, the forecasts are highly uncertain. In the scenario that we consider, the 1-group model only produces accurate outbreak forecasts once the peak of the epidemic has passed, even when the values of important epidemiological parameters such as the lengths of the mean incubation and infectious periods are known exactly. As a result, it is necessary to use the more epidemiologically realistic 2-group model to generate accurate forecasts. Accounting for cross-immunity driven by exposures in previous outbreaks explicitly is expected to improve the accuracy of epidemiological modelling forecasts during influenza outbreaks.

Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model

The current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing dynamic models. In this paper, a novel stochastic model was proposed aiming to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We found that: (1) instead of aberration, there was a remarkable amount of asymptomatic virus carriers, (2) a virus carrier with symptoms was approximately twice more likely to pass the disease to others than that of an asymptomatic virus carrier, (3) the transmission rate reduced significantly since the implementation of control measures in Mainland China, and (4) it was expected that the epidemic outbreak would be contained by early March in the selected provinces and cities in China.