Unraveling R0: considerations for public health applications

Global disparities in COVID-19 vaccine booster dose (VBD) acceptance and hesitancy: An updated narrative review

The global deployment of COVID-19 vaccine booster dose (VBD) has been recognized as a promising therapeutic alliance to provide repeated immunity against the arrival of new variants. Despite scientific evidence supports the effectiveness of periodic doses, COVID-19 vaccine booster reluctance continues to thrive. This narrative review aimed to examine global COVID-19 vaccine booster dose (VBD) acceptance and summarize an up-to-date assessment of potential antecedents associated with VBD acceptance. A comprehensive search was performed in several reputable databases such as Medline (via PubMed), Scopus, Google scholar, and Web of Science from June 10th, 2023, to August 1st, 2023. All relevant descriptive and observational studies on COVID-19 VBD acceptance and hesitancy were included in this review. A total of fifty-eight (58) studies were included, with Asia representing the highest count with thirty-one (53%) studies, Europe with eleven (19 %), the United States with nine (16 %), and other regions (Africa and multi-ethnic) with seven (12 %). Worldwide, the pooled COVID-19 VBD acceptance rate was 77.09 % (95 % CI: 76.28-78.18), VBD willingness (n) = 164189, and the total sample (N) = 212,990. The highest and the lowest VBD acceptance rate was reported in Europe and American regions, respectively, 85.38 % (95 % CI: 85.02-85.73, (n) = 32,047, (N = 37,533) vs. 66.92 % (95 % CI: 66.56-67.4), (n) = 29335, (N) = 43,832. However, Asia and multi-ethnic areas reported moderately high VBD acceptance rate 79.13 % (95 % CI: 78.77-79.23, (n) = 93,994, (N) = 11,8779) and 72.16 % (95 % CI: 71.13-72.93, (n) = 9276, (N) = 12,853), respectively. The most common and key antecedents of COVID-19 VBD acceptance and hesitancy across the countries were "equal safety", "efficacy", "effectiveness", "post-vaccination side effects", "community protection" "family protection", "risk-benefit ratio", "booster necessity", "trust", and "variants control". Disparities in the uptake of COVID-19 VBD were observed globally, with the highest rates found in Europe, and the lowest rates in American regions. Multiple potential antecedents including safety, efficacy, and post-vaccination side effects were associated with VBD acceptance and hesitancy.

Spatial analysis of Dengue through the reproduction numbers relating to socioeconomic features: Case studies on two Brazilian urban centers

The study of the propagation of infectious diseases in urban centers finds a close connection with their population's social characteristics and behavior. This work performs a spatial analysis of dengue cases in urban centers based on the basic reproduction numbers, R0, and incidence by planning areas (PAs), as well as their correlations with the Human Development Index (HDI) and the number of trips. We analyzed dengue epidemics in 2002 at two Brazilian urban centers, Belo Horizonte (BH) and Rio de Janeiro (RJ), using PAs as spatial units. Our results reveal heterogeneous spatial scenarios for both cities, with very weak correlations between R0 and both the number of trips and the HDI; in BH, the values of R0 show a less spatial heterogeneous pattern than in RJ. For BH, there are moderate correlations between incidence and both the number of trips and the HDI; meanwhile, they weakly correlate for RJ. Finally, the absence of strong correlations between the considered measures indicates that the transmission process should be treated considering the city as a whole.

Forecasting the effects of vaccination on the COVID-19 pandemic in Malaysia using SEIRV compartmental models

OBJECTIVES

This study aimed to develop susceptible-exposed-infectious-recovered-vaccinated (SEIRV) models to examine the effects of vaccination on coronavirus disease 2019 (COVID-19) case trends in Malaysia during Phase 3 of the National COVID-19 Immunization Program amidst the Delta outbreak.

METHODS

SEIRV models were developed and validated using COVID-19 case and vaccination data from the Ministry of Health, Malaysia, from June 21, 2021 to July 21, 2021 to generate forecasts of COVID-19 cases from July 22, 2021 to December 31, 2021. Three scenarios were examined to measure the effects of vaccination on COVID-19 case trends. Scenarios 1 and 2 represented the trends taking into account the earliest and latest possible times of achieving full vaccination for 80% of the adult population by October 31, 2021 and December 31, 2021, respectively. Scenario 3 described a scenario without vaccination for comparison.

RESULTS

In scenario 1, forecasted cases peaked on August 28, 2021, which was close to the peak of observed cases on August 26, 2021. The observed peak was 20.27% higher than in scenario 1 and 10.37% lower than in scenario 2. The cumulative observed cases from July 22, 2021 to December 31, 2021 were 13.29% higher than in scenario 1 and 55.19% lower than in scenario 2. The daily COVID-19 case trends closely mirrored the forecast of COVID-19 cases in scenario 1 (best-case scenario).

CONCLUSIONS

Our study demonstrated that COVID-19 vaccination reduced COVID-19 case trends during the Delta outbreak. The compartmental models developed assisted in the management and control of the COVID-19 pandemic in Malaysia.

Herd immunity, vaccination and moral obligation

The public health benefits of herd immunity are often used as the justification for coercive vaccine policies. Yet, 'herd immunity' as a term has multiple referents, which can result in ambiguity, including regarding its role in ethical arguments. The term 'herd immunity' can refer to (1) the herd immunity threshold, at which models predict the decline of an epidemic; (2) the percentage of a population with immunity, whether it exceeds a given threshold or not; and/or (3) the indirect benefit afforded by collective immunity to those who are less immune. Moreover, the accumulation of immune individuals in a population can lead to two different outcomes: elimination (for measles, smallpox, etc) or endemic equilibrium (for COVID-19, influenza, etc). We argue that the strength of a moral obligation for individuals to contribute to herd immunity through vaccination, and by extension the acceptability of coercion, will depend on how 'herd immunity' is interpreted as well as facts about a given disease or vaccine. Among other things, not all uses of 'herd immunity' are equally valid for all pathogens. The optimal conditions for herd immunity threshold effects, as illustrated by measles, notably do not apply to the many pathogens for which reinfections are ubiquitous (due to waning immunity and/or antigenic variation). For such pathogens, including SARS-CoV-2, mass vaccination can only be expected to delay rather than prevent new infections, in which case the obligation to contribute to herd immunity is much weaker, and coercive policies less justifiable.

An Epidemic Model for Multi-Intervention Outbreaks

Modeling is an important tool to utilize at the beginning of an infectious disease outbreak, as it allows estimation of parameters - such as the basic reproduction number, R 0 -that can be used to postulate how the outbreak may continue to spread. However, there exist many challenges that need to be accounted for, such as an unknown first case date, retrospective reporting of 'probable' cases, changing dynamics between case count and death count trends, and the implementation of multiple control efforts and their delayed or diminished effects. Using the near-daily data provided from the recent outbreak of Sudan ebolavirus in Uganda as a case study, we create a model and present a framework aimed at overcoming these aforementioned challenges. The impact of each challenge is examined by comparing model estimates and fits throughout our framework. Indeed, we found that allowing for multiple fatality rates over the course of an outbreak generally resulted in better fitting models. On the other hand, not knowing the start date of an outbreak appeared to have large and non-uniform effects on parameter estimates, particularly at the beginning stages of an outbreak. While models that did not account for the decaying effect of interventions on transmission underestimated R 0 , all decay models run on the full dataset yielded precise R 0 estimates, demonstrating the robustness of R 0 as a measure of disease spread when examining data from the entire outbreak.

Emerging data inputs for infectious diseases surveillance and decision making

Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.

Deaths from undetected COVID-19 infections as a fraction of COVID-19 deaths can be used for early detection of an upcoming epidemic wave

With countries across the world facing repeated epidemic waves, it becomes critical to monitor, mitigate and prevent subsequent waves. Common indicators like active case numbers may not be sensitive enough in the presence of systemic inefficiencies like insufficient testing or contact tracing. Test positivity rates are sensitive to testing strategies and cannot estimate the extent of undetected cases. Reproductive numbers estimated from logarithms of new incidences are inaccurate in dynamic scenarios and not sensitive enough to capture changes in efficiencies. Systemic fatigue results in lower testing, inefficient tracing and quarantining thereby precipitating the onset of the epidemic wave. We propose a novel indicator for detecting the slippage of test-trace efficiency based on the number of deaths/hospitalizations resulting from known and hitherto unknown infections. This can also be used to forecast an epidemic wave that is advanced or exacerbated due to a drop in efficiency in situations where the testing has come down drastically and contact tracing is virtually nil as is prevalent currently. Using a modified SEIRD epidemic simulator we show that (i) Ratio of deaths/hospitalizations from an undetected infection to total deaths converges to a measure of systemic test-trace inefficiency. (ii) This index forecasts the slippage in efficiency earlier than other known metrics. (iii) Mitigation triggered by this index helps reduce peak active caseload and eventual deaths. Deaths/hospitalizations accurately track the systemic inefficiencies and detect latent cases. Based on these results we make a strong case that administrations use this metric in the ensemble of indicators. Further, hospitals may need to be mandated to distinctly register deaths/hospitalizations due to previously undetected infections. Thus the proposed metric is an ideal indicator of an epidemic wave that poses the least socio-economic cost while keeping the surveillance robust during periods of pandemic fatigue.

Modelling quarantine effects on SARS-CoV-2 epidemiological dynamics in Chilean communes and their relationship with the Social Priority Index

Background

An epidemiological model (susceptible, un-quarantined infected, quarantined infected, confirmed infected (SUQC)) was previously developed and applied to incorporate quarantine measures and calculate COVID-19 contagion dynamics and pandemic control in some Chinese regions. Here, we generalized this model to incorporate the disease recovery rate and applied our model to records of the total number of confirmed cases of people infected with the SARS-CoV-2 virus in some Chilean communes.

Methods

In each commune, two consecutive stages were considered: a stage without quarantine and an immediately subsequent quarantine stage imposed by the Ministry of Health. To adjust the model, typical epidemiological parameters were determined, such as the confirmation rate and the quarantine rate. The latter allowed us to calculate the reproduction number.

Results

The mathematical model adequately reproduced the data, indicating a higher quarantine rate when quarantine was imposed by the health authority, with a corresponding decrease in the reproduction number of the virus down to values that prevent or decrease its exponential spread. In general, during this second stage, the communes with the lowest social priority indices had the highest quarantine rates, and therefore, the lowest effective viral reproduction numbers. This study provides useful evidence to address the health inequity of pandemics. The mathematical model applied here can be used in other regions or easily modified for other cases of infectious disease control by quarantine.

Modeling mobility, risk, and pandemic severity during the first year of COVID

During the COVID-19 pandemic, most US states have taken measures of varying strength, enforcing social and physical distancing in the interest of public safety. These measures have enabled counties and states, with varying success, to slow down the propagation and mortality of the disease by matching the propagation rate to the capacity of medical facilities. However, each state's government was making its decisions based on limited information and without the benefit of being able to look retrospectively at the problem at large and to analyze the commonalities and the differences among the states and the counties across the country. We developed models connecting people's mobility, socioeconomic, and demographic factors with severity of the COVID pandemic in the US at the County level. These models can be used to inform policymakers and other stakeholders on measures to be taken during a pandemic. They also enable in-depth analysis of factors affecting the relationship between mobility and the severity of the disease. With the exception of one model, that of COVID recovery time, the resulting models accurately predict the vulnerability and severity metrics and rank the explanatory variables in the order of statistical importance. We also analyze and explain why recovery time did not allow for a good model.

Why COVID-19 modelling of progression and prevention fails to translate to the real-world

Mathematical models were used widely to inform policy during the COVID pandemic. However, there is a poor understanding of their limitations and how they influence decision-making. We used systematic review search methods to find early modelling studies that determined the reproduction number and analysed its use and application to interventions and policy in the UK. Up to March 2020, we found 42 reproduction number estimates (39 based on Chinese data: R₀ range 2.1-6.47). Several biases affect the quality of modelling studies that are infrequently discussed, and many factors contribute to significant differences in the results of individual studies that go beyond chance. The sources of effect estimates incorporated into mathematical models are unclear. There is often a lack of a relationship between transmission estimates and the timing of imposed restrictions, which is further affected by the lag in reporting. Modelling studies lack basic evidence-based methods that aid their quality assessment, reporting and critical appraisal. If used judiciously, models may be helpful, especially if they openly present the uncertainties and use sensitivity analyses extensively, which need to consider and explicitly discuss the limitations of the evidence. However, until the methodological and ethical issues are resolved, predictive models should be used cautiously.

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.

Doubling time of infectious diseases

The concept of doubling time has been increasingly used since the onset of the coronavirus disease 2019 (COVID-19) pandemic, but its characteristics are not well understood, especially as applied to infectious disease epidemiology. The present study aims to be a practical guide to monitoring the doubling time of infectious diseases. Via simulation exercise, we clarify the epidemiological characteristics of doubling time, allowing possible interpretations. We show that the commonly believed relationship between the doubling time and intrinsic growth rate in population ecology does not strictly apply to infectious diseases, and derive the correct relationship between the two. We examined the impact of varying (i) the growth rate, (ii) the starting point of counting cumulative number of cases, and (iii) the length of observation on statistical estimation of doubling time. It was difficult to recover values of growth rate from doubling time, especially when the growth rate was small. Starting time period is critical when the statistical estimation of doubling time occurs during the course of an epidemic. The length of observation was critical in determining the overall magnitude of doubling time, and when only the latest 1-2 weeks' data were used, the resulting doubling time was very short, regardless of the intrinsic growth rate r. We suggest that doubling time estimates of infectious disease epidemics should at a minimum be accompanied by descriptions of (i) the starting time at which the cumulative count is initiated and (ii) the length of observation.

Statistical methods used to combine the effective reproduction number, R(t), and other related measures of COVID-19 in the UK

In the recent COVID-19 pandemic, a wide range of epidemiological modelling approaches were used to predict the effective reproduction number, R(t), and other COVID-19-related measures such as the daily rate of exponential growth, r(t). These candidate models use different modelling approaches or differing assumptions about spatial or age-mixing, and some capture genuine uncertainty in scientific understanding of disease dynamics. Combining estimates using appropriate statistical methodology from multiple candidate models is important to better understand the variation of these outcome measures to help inform decision-making. In this paper, we combine estimates for specific UK nations/regions using random-effects meta-analyses techniques, utilising the restricted maximum-likelihood (REML) method to estimate the heterogeneity variance parameter, and two approaches to calculate the confidence interval for the combined estimate: the standard Wald-type and the Knapp and Hartung (KNHA) method. As estimates in this setting are derived using model predictions, each with varying degrees of uncertainty, equal-weighting is favoured over the standard inverse-variance weighting to avoid potential up-weighting of models providing estimates with lower levels of uncertainty that are not fully accounting for inherent uncertainties. Both equally-weighted models using REML alone and REML+KNHA approaches were found to provide similar variation for R(t) and r(t), with both approaches providing wider, and therefore more conservative, confidence intervals around the combined estimate compared to the standard inverse-variance weighting approach. Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures. This in turn allows timely and informed decision-making based on all available information.

Data driven time-varying SEIR-LSTM/GRU algorithms to track the spread of COVID-19

COVID-19 is an infectious disease caused by a newly discovered coronavirus, which has become a worldwide pandemic greatly impacting our daily life and work. A large number of mathematical models, including the susceptible-exposed-infected-removed (SEIR) model and deep learning methods, such as long-short-term-memory (LSTM) and gated recurrent units (GRU)-based methods, have been employed for the analysis and prediction of the COVID-19 outbreak. This paper describes a SEIR-LSTM/GRU algorithm with time-varying parameters that can predict the number of active cases and removed cases in the US. Time-varying reproductive numbers that can illustrate the progress of the epidemic are also produced via this process. The investigation is based on the active cases and total cases data for the USA, as collected from the website "Worldometer". The root mean square error, mean absolute percentage error and r₂ score were utilized to assess the model's accuracy.

Global variation in early epidemic growth rates and reproduction number of seasonal influenza

INTRODUCTION

Little is known about global variation in early epidemic growth rates and effective reproduction numbers (R_e) of seasonal influenza. We aimed to estimate global variation in R_e of influenza type A and B during a single period.

METHODS

Country influenza detection time series from September 2017 through January 2019 were obtained from an international database. Type A and B epidemics by country were selected based on R_e estimates for a five-week moving window advanced by week. Associations of R_e with absolute latitude, Human Development Index, percent of the population aged <15 years and percent living rurally in each country were assessed.

RESULTS

Time series were included for 119 of 169 available countries. There were 100 countries with influenza A and 79 with B epidemics. Median R_e for both influenza A and B epidemics was 1.23 (ranges: A 1.10, 1.60; B 1.06, 1.58). R_e of influenza B, but not A, was independently associated with absolute latitude, increasing by 0.022 (95% CI 0.002, 0.043) per 10 degrees.

CONCLUSIONS

R_e of influenza A and B were similar. Only R_e of influenza B was associated with country characteristics; increasing with distance from the equator. The approach may be suitable for continuous R_e surveillance.

What Is the Impact of Early and Subsequent Epidemic Characteristics on the Pre-delta COVID-19 Epidemic Size in the United States?

It is still uncertain how the epidemic characteristics of COVID-19 in its early phase and subsequent waves contributed to the pre-delta epidemic size in the United States. We identified the early and subsequent characteristics of the COVID-19 epidemic and the correlation between these characteristics and the pre-delta epidemic size. Most (96.1% (49/51)) of the states entered a fast-growing phase before the accumulative number of cases reached (30). The days required for the number of confirmed cases to increase from 30 to 100 was 5.6 (5.1−6.1) days. As of 31 March 2021, all 51 states experienced at least 2 waves of COVID-19 outbreaks, 23.5% (12/51) experienced 3 waves, and 15.7% (8/51) experienced 4 waves, the epidemic size of COVID-19 was 19,275−3,669,048 cases across the states. The pre-delta epidemic size was significantly correlated with the duration from 30 to 100 cases (p = 0.003, r = −0.405), the growth rate of the fast-growing phase (p = 0.012, r = 0.351), and the peak cases in the subsequent waves (K1 (p < 0.001, r = 0.794), K2 (p < 0.001, r = 0.595), K3 (p < 0.001, r = 0.977), and K4 (p = 0.002, r = 0.905)). We observed that both early and subsequent epidemic characteristics contribute to the pre-delta epidemic size of COVID-19. This identification is important to the prediction of the emerging viral infectious diseases in the primary stage.

Impacto de la Covid-19 sobre la población migrante residente en Argentina a la luz de las dificultades que obstaculizan su acceso a la salud

Resumen Este artículo persigue un doble propósito. En primer lugar, presentar los resultados de un trabajo exploratorio que indaga si las personas migrantes se vieron más afectadas que las nacionales por la Covid-19 en Argentina. Para ello, se evalúa el comportamiento de indicadores como el nivel de testeos, contagios y decesos por Covid-19 entre la población nativa y migrante, con base en datos inéditos solicitados a la Dirección Nacional de Epidemiología e Información Estratégica del Ministerio de Salud de la Nación, para el período de enero de 2020 y mayo de 2021. En segundo lugar, este artículo busca aproximar un análisis interpretativo de las posibles causas que pueden haber influido en la afectación diferencial encontrada sobre la población migrante. Con este fin, y bajo el supuesto de que la población migrante se encuentra en una situación de desventaja estructural, se trabaja con procesamientos propios de la Encuesta Nacional Migrante de Argentina 2020, así como con un vasto conjunto de estudios académicos complementarios, presentando de manera integral las dificultades directas e indirectas que enfrenta la población migrante para acceder al sistema de salud, un derecho reconocido en la norma incondicionalmente.

A surrogate Bayesian framework for a SARS-CoV-2 data driven stochastic model

Dynamic compartmentalized data (DCD) and compartmentalized differential equations (CDEs) are key instruments for modeling transmission of pathogens such as the SARS-CoV-2 virus. We describe an effi-cient nowcasting algorithm for modeling transmission of SARS-CoV-2 with uncertainty quantification for the COVID-19 impact. A key concern for transmission of SARS-CoV-2 is under-reporting of cases, and this is addressed in our data-driven model by providing an estimate for the detection rate. Our novel top-down model is based on CDEs with stochastic constitutive parameters obtained from the DCD using Bayesian inference. We demonstrate the robustness of our algorithm for simulation studies using synthetic DCD, and nowcasting COVID-19 using real DCD from several regions across five continents.

Exact Bayesian inference of epidemiological parameters from mortality data: application to African swine fever virus

Pathogens such as African swine fever virus (ASFV) are an increasing threat to global livestock production with implications for economic well-being and food security. Quantification of epidemiological parameters, such as transmission rates and latent and infectious periods, is critical to inform efficient disease control. Parameter estimation for livestock disease systems is often reliant upon transmission experiments, which provide valuable insights in the epidemiology of disease but which may also be unrepresentative of at-risk populations and incur economic and animal welfare costs. Routinely collected mortality data are a potential source of readily available and representative information regarding disease transmission early in outbreaks. We develop methodology to conduct exact Bayesian parameter inference from mortality data using reversible jump Markov chain Monte Carlo incorporating multiple routes of transmission (e.g. within-farm secondary and background transmission from external sources). We use this methodology to infer epidemiological parameters for ASFV using data from outbreaks on nine farms in the Russian Federation. This approach improves inference on transmission rates in comparison with previous methods based on approximate Bayesian computation, allows better estimation of time of introduction and could readily be applied to other outbreaks or pathogens.

A review on COVID-19 transmission, epidemiological features, prevention and vaccination

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused hundreds of millions of infections and millions of deaths over past two years. Currently, many countries have still not been able to take the pandemic under control. In this review, we systematically summarized what we have done to mitigate the COVID-19 pandemic, from the perspectives of virus transmission, public health control measures, to the development and vaccination of COVID-19 vaccines. As a virus most likely coming from bats, the SARS-CoV-2 may transmit among people via airborne, faecal-oral, vertical or foodborne routes. Our meta-analysis suggested that the R₀ of COVID-19 was 2.9 (95% CI: 2.7–3.1), and the estimates in Africa and Europe could be higher. The median R_t could decrease by 23–96% following the nonpharmacological interventions, including lockdown, isolation, social distance, and face mask, etc. Comprehensive intervention and lockdown were the most effective measures to control the pandemic. According to the pooled R₀ in our meta-analysis, there should be at least 93.3% (95% CI: 89.9–96.2%) people being vaccinated around the world. Limited amount of vaccines and the inequity issues in vaccine allocation call for more international cooperation to achieve the anti-epidemic goals and vaccination fairness.

Third waves of the COVID-19 pandemic: Prominence of initial public health Interference

Since the first news of a coronavirus-related pneumonia outbreak in December 2019, the virus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), which causes COVID-19, has spread worldwide, with more than 100 million people infected in over 210 countries and two million people dying as of today. In the UK (B.1.1.7), South Africa (B.1.351), Brazil (P.1), and India (B.1.617), independent SARS-CoV-2 lineages have recently been established. The virus will access these variants via the angiotensin-converting enzyme-2 (ACE2) receptor due to several mutations in the immune-dominant spike protein. SARS-CoV-2 has caused substantial morbidity and mortality, as well as significant strain on public health systems and the global economy, due to the severity and intensity at which it has spread. COVID-19 vaccines have shown to be highly successful in clinical trials and can be used to fight the pandemic. The COVID-19 pandemic's environmental trends change at breakneck speed, making predictions based on traditional epidemiological knowledge particularly speculative. Following the first outbreak, the second wave of COVID-19 swept across the globe, infecting various countries. A third wave of coronavirus infection has already been experienced in a few countries. Many of us have said, "When this is over," but what exactly does that mean? Since the virus's first-, second-, and third-order effects manifest over various time periods, the pandemic will not be considered 'over' until the 'third phase' of the COVID-19 pandemic has passed. It is the best time to take preventative steps and immunize ourselves with vaccines in order to prepare for the predicted third wave of COVID-19 in some countries. To effectively suppress and monitor the COVID-19 pandemic, early and timely measures with improved social distancing policies should be enforced. We must continue critical public health efforts to suppress transmission and reduce mortality while working toward the rollout of a safe and efficient vaccine, and we must have the patience to listen, learn, improve, innovate, and evolve.

Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks

The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook—involving more than 500,000 respondents from 64 countries—showing that there is a “one-to-one” relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease—sharing epidemiological features with COVID-19—that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks.

The spreading of a disease across a population is affected by the compliance with behavioral restrictions, enforced by governments to slow the diffusion of an epidemic. In this study, we use a large-scale survey to relate compliance with behavioral rules to individual level of disease risk perception. We asses that absence of risk awareness is associated with a set of harmful behaviors (namely, non-compliance with: social distancing, use of facial masks and adoption of any prevention measures) that can accelerate the diffusion of an epidemic. Through a mathematical model, we study how epidemic dynamics, and in particular hospitalization burden, is affected by the presence of different fractions of the total population who do not correctly perceive the disease risk and, accordingly, adopt harmful behaviors. Moreover, we study how different social contact structures among individuals modulate the effect on epidemic spreading of a fixed population fraction with null risk perception. Our findings highlight that a fixed percentage of people with null risk awareness has a lower impact on epidemic size in social structures characterized by communities and heterogeneity in contacts among individuals. The spreading of a disease across a population is affected by the compliance with behavioral restrictions, enforced by governments to slow the diffusion of an epidemic. In this study, we use a large-scale survey to relate compliance with behavioral rules to individual level of disease risk perception. We asses that absence of risk awareness is associated with a set of harmful behaviors (namely, non-compliance with: social distancing, use of facial masks and adoption of any prevention measures) that can accelerate the diffusion of an epidemic. Through a mathematical model, we study how epidemic dynamics, and in particular hospitalization burden, is affected by the presence of different fractions of the total population who do not correctly perceive the disease risk and, accordingly, adopt harmful behaviors. Moreover, we study how different social contact structures among individuals modulate the effect on epidemic spreading of a fixed population fraction with null risk perception. Our findings highlight that a fixed percentage of people with null risk awareness has a lower effectiveness on epidemic size in social structures characterized by communities and heterogeneity in contacts among individuals. However, in these same social structures, larger fractions of risk-denying population cause an enhanced effect on epidemic size.

A stochastic contact network model for assessing outbreak risk of COVID-19 in workplaces

The COVID-19 pandemic has drastically shifted the way people work. While many businesses can operate remotely, a large number of jobs can only be performed on-site. Moreover as businesses create plans for bringing workers back on-site, they are in need of tools to assess the risk of COVID-19 for their employees in the workplaces. This study aims to fill the gap in risk modeling of COVID-19 outbreaks in facilities like offices and warehouses. We propose a simulation-based stochastic contact network model to assess the cumulative incidence in workplaces. First-generation cases are introduced as a Bernoulli random variable using the local daily new case rate as the success rate. Contact networks are established through randomly sampled daily contacts for each of the first-generation cases and successful transmissions are established based on a randomized secondary attack rate (SAR). Modification factors are provided for SAR based on changes in airflow, speaking volume, and speaking activity within a facility. Control measures such as mask wearing are incorporated through modifications in SAR. We validated the model by comparing the distribution of cumulative incidence in model simulations against real-world outbreaks in workplaces and nursing homes. The comparisons support the model's validity for estimating cumulative incidences for short forecasting periods of up to 15 days. We believe that the current study presents an effective tool for providing short-term forecasts of COVID-19 cases for workplaces and for quantifying the effectiveness of various control measures. The open source model code is made available at github.com/abhineetgupta/covid-workplace-risk.

Early estimates of the incidence trend and the reproductive number of the monkeypox epidemic in Brazil

Background

We aimed to calculate the weekly growth of the incidence and the effective reproductive number (Rt) of the 2022 Monkeypox epidemic during its introduction in Brazil.

Method

We described the case distribution in the country and calculated the incidence trend and the Rt in the four geographical states with the highest case reports. By using two regression approaches, count model and the Prais-Winsten, we calculated the relative incidence increase. Moreover, we estimated the Rt for the period between the 24th and the 50th days after the first official report, using a serial interval reported in another population and two alternative values (± 3 days).

Results

Up to August 22, 3.896 Monkeypox cases were confirmed in Brazil. The weekly incidence increases were between 37.5% (95% CI: 20.7% - 56,6%) and 82.1% (95% CI: 59.5%–107.8%), and all estimates of Rt were significantly higher than 1 in the four states analyzed.

Conclusions

The Monkeypox outbreak in Brazil is a significant public health emergency that requires coordinated public health strategies such as testing, contact tracing, and vaccination.

Global control of COVID-19: good vaccines may not suffice

The COVID-19 pandemic has unveiled health and socioeconomic inequities around the globe. Effective epidemic control requires the achievement of herd immunity, where susceptible individuals are conferred indirect protection by being surrounded by immunized individuals. The proportion of people that need to be vaccinated to obtain herd immunity is determined through the herd immunity threshold. However, the number of susceptible individuals and the opportunities for contact between infectious and susceptible individuals influence the progress of an epidemic. Thus, in addition to vaccination, control of a pandemic may be difficult or impossible to achieve without other public health measures, including wearing face masks and social distancing. This article discusses the factors that may contribute to herd immunity and control of COVID-19 through the availability of effective vaccines and describes how vaccine effectiveness in the community may be lower than that expected. It also discusses how pandemic control in some countries and populations may face vaccine accessibility barriers if market forces strongly regulate the new technologies available, according to the inverse care law.

Prescription opioid treatment for non-cancer pain and initiation of injection drug use: large retrospective cohort study

OBJECTIVE

To assess the association between long term prescription opioid treatment medically dispensed for non-cancer pain and the initiation of injection drug use (IDU) among individuals without a history of substance use.

DESIGN

Retrospective cohort study.

SETTING

Large administrative data source (containing information for about 1.7 million individuals tested for hepatitis C virus or HIV in British Columbia, Canada) with linkage to administrative health databases, including dispensations from community pharmacies.

PARTICIPANTS

Individuals age 11-65 years and without a history of substance use (except alcohol) at baseline.

MAIN OUTCOME MEASURES

Episodes of prescription opioid use for non-cancer pain were identified based on drugs dispensed between 2000 and 2015. Episodes were classified by the increasing length and intensity of opioid use (acute (lasting <90 episode days), episodic (lasting ≥90 episode days; with <90 days' drug supply and/or <50% episode intensity), and chronic (lasting ≥90 episode days; with ≥90 days' drug supply and ≥50% episode intensity)). People with a chronic episode were matched 1:1:1:1 on socioeconomic variables to those with episodic or acute episodes and to those who were opioid naive. IDU initiation was identified by a validated administrative algorithm with high specificity. Cox models weighted by inverse probability of treatment weights assessed the association between opioid use category (chronic, episodic, acute, opioid naive) and IDU initiation.

RESULTS

59 804 participants (14 951 people from each opioid use category) were included in the matched cohort, and followed for a median of 5.8 years. 1149 participants initiated IDU. Cumulative probability of IDU initiation at five years was highest for participants with chronic opioid use (4.0%), followed by those with episodic use (1.3%) and acute use (0.7%), and those who were opioid naive (0.4%). In the inverse probability of treatment weighted Cox model, risk of IDU initiation was 8.4 times higher for those with chronic opioid use versus those who were opioid naive (95% confidence interval 6.4 to 10.9). In a sensitivity analysis limited to individuals with a history of chronic pain, cumulative risk for those with chronic use (3.4% within five years) was lower than the primary results, but the relative risk was not (hazard ratio 9.7 (95% confidence interval 6.5 to 14.5)). IDU initiation was more frequent at higher opioid doses and younger ages.

CONCLUSIONS

The rate of IDU initiation among individuals who received chronic prescription opioid treatment for non-cancer pain was infrequent overall (3-4% within five years) but about eight times higher than among opioid naive individuals. These findings could have implications for strategies to prevent IDU initiation, but should not be used as a reason to support involuntary tapering or discontinuation of long term prescription opioid treatment.

Inferring the effect of interventions on COVID-19 transmission networks

Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. We assume a Watts–Strogatz small-world network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We show that NPIs drastically reduced random contacts in the transmission network, increased network clustering, and resulted in a previously unappreciated transition from an exponential to a constant regime of new cases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory.

The Approach of Pregnant Women to Vaccination Based on a COVID-19 Systematic Review

Background and Objectives: Pregnant women are more likely to develop a more severe course of COVID-19 than their non-pregnant peers. There are many arguments for the safety and efficacy of COVID-19 vaccines in pregnant women. The aim of this study is to conduct a systematic review concerning the approach of pregnant women towards vaccination against COVID-19, with particular regard to determinants of vaccination acceptance. Materials and Methods: Articles were reviewed in which the aim was to evaluate—via a survey or questionnaire—the acceptance and decision to undergo vaccination against COVID-19. The articles were subjected to review according to recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (PRISMA). Results: In various studies, the percentage of pregnant women accepting the COVID-19 vaccine was between 29.7% and 77.4%. The strongest factors co-existing with the acceptance of the COVID-19 vaccination in pregnancy were trust in the importance and effectiveness of the vaccine, explicit communication about the safety of COVID-19 vaccines for pregnant women, acceptance of other vaccinations such as those for influenza, belief in the importance of vaccines/mass vaccination in one’s own country, anxiety about COVID-19, trust in public health agencies/health science, as well as compliance to mask guidelines. The remaining factors were older age, higher education, and socioeconomic status. Conclusions: This review allowed us to show that geographic factors (Asian, South American countries) and pandemic factors (different threats and risks from infection) significantly influence the acceptance of vaccines. The most significant factors affecting acceptance are those related to public awareness of the risk of infection, vaccine safety, and the way in which reliable information about the need and safety of vaccines is provided. Professional and reliable patient information by obstetricians and qualified medical personnel would significantly increase the level of confidence in vaccination against COVID-19.

Cost of neonatal abstinence syndrome: an economic analysis of English national data held in the National Neonatal Research Database

OBJECTIVE

To determine the incidence of neonatal abstinence syndrome (NAS) across neonatal units, explore healthcare utilisation and estimate the direct cost to the NHS.

DESIGN

Population cohort study.

SETTING

NHS neonatal units, using data held in the National Neonatal Research Database.

PARTICIPANTS

Infants born between 2012 and 2017, admitted to a neonatal unit in England, receiving a diagnosis of NAS (n=6411).

MAIN OUTCOME MEASURES

Incidence, direct annual cost of care (£, 2016-2017 prices), duration of neonatal unit stay (discharge HR), predicted additional cost of care, and odds of receiving pharmacotherapy.

RESULTS

Of 524 334 infants admitted during the study period, 6411 had NAS. The incidence (1.6/1000 live births) increased between 2012 and 2017 (β=0.07, 95% CI (0 to 0.14)) accounting for 12/1000 admissions and 23/1000 cot days nationally. The direct cost of care was £62 646 661 over the study period. Almost half of infants received pharmacotherapy (n=2631; 49%) and their time-to-discharge was significantly longer (median 18.2 vs 5.1 days; adjusted HR (aHR) 0.16, 95% CI (0.15 to 0.17)). Time-to-discharge was longer for formula-fed infants (aHR 0.73 (0.66 to 0.81)) and those discharged to foster care (aHR 0.77 (0.72 to 0.82)). The greatest predictor of additional care costs was receipt of pharmacotherapy (additional mean adjusted cost of £8420 per infant).

CONCLUSIONS

This population study highlights the substantial cot usage and economic costs of caring for infants with NAS on neonatal units. A shift in how healthcare systems provide routine care for NAS could benefit infants and families while alleviating the burden on services.

COVID-19 and the flu: data simulations and computational modelling to guide public health strategies

BACKGROUND

Pandemics threaten lives and economies. This article addresses the global threat of the anticipated overlap of COVID-19 with seasonal-influenza.

OBJECTIVES

Scientific evidence based on simulation methodology is presented to reveal the impact of a dual outbreak, with scenarios intended for propagation analysis. This article aims at researchers, clinicians of family medicine, general practice and policy-makers worldwide. The implications for the clinical practice of primary health care are discussed. Current research is an effort to explore new directions in epidemiology and health services delivery.

METHODS

Projections consisted of machine learning, dynamic modelling algorithms and whole simulations. Input data consisted of global indicators of infectious diseases. Four simulations were run for '20% versus 60% flu-vaccinated populations' and '10 versus 20 personal contacts'. Outputs consisted of numerical values and mathematical graphs. Outputs consisted of numbers for 'never infected', 'vaccinated', 'infected/recovered', 'symptomatic/asymptomatic' and 'deceased' individuals. Peaks, percentages, R0, durations are reported.

RESULTS

The best-case scenario was one with a higher flu-vaccination rate and fewer contacts. The reverse generated the worst outcomes, likely to disrupt the provision of vital community services. Both measures were proven effective; however, results demonstrated that 'increasing flu-vaccination rates' is a more powerful strategy than 'limiting social contacts'.

CONCLUSIONS

Results support two affordable preventive measures: (i) to globally increase influenza-vaccination rates, (ii) to limit the number of personal contacts during outbreaks. The authors endorse changing practices and research incentives towards multidisciplinary collaborations. The urgency of the situation is a call for international health policy to promote interdisciplinary modern technologies in public health engineering.

Hundred Days of Teleconsultations and Their Usefulness in the Management of COVID-19: Experience of the COVID-19 National Call Center in Bolivia

Background: There is currently little scientific evidence on the usefulness of implementing strategies against COVID-19 remotely with the help of telemedicine. Objective: Evaluate whether teleconsultation is helpful as an instrument of mediated care in the monitoring and follow-up of individuals with high suspicion of COVID-19 through early detection by the Call Center COVID-19 of the Ministry of Health and Sports, Bolivia. Methodology: Descriptive and cross-sectional observational study of patients captured by the Call Center-COVID-19, who were monitored and followed up in their homes through teleconsultations carried out by the National TeleHealth Program, remotely through information and communication technologies throughout the Bolivian territory during the first 100 days of its implementation. Results: A total of 3,278 patients were studied, recruited between March 16 and June 23, 2020; 49.4% were women, with an overall mean age of 37.5 years (standard deviation [SD] 15.2). The mean detection time was 7.6 days (SD 6.92); 93.8% required home isolation, and only 6.2% were transferred for hospitalization. The mean follow-up time for all patients was 6.7 days (SD 4.87; range 2-38). A total of 75.6% were discharged as recovered patients, and 1.9% died. Conclusions: Early detection of individuals with suspected COVID-19 was achieved, knowing their clinical evolution until their recovery or death. Teleconsultations showed good outcomes at discharge and low fatal outcomes. From these results, it can be inferred that teleconsultation is a valuable tool in the monitoring, evaluation, and follow-up of patients. The Ministry of Health and Sports through Call Center-COVID-19 reinforced the Epidemiological Surveillance System as a passive search tool for possible suspected cases at the national level and decongesting other services in charge of this task.

[Evaluation of interventions during the COVID-19 pandemic: development of a model based on subpopulations with different contact rates]

Si bien se han realizado múltiples intentos de modelar matemáticamente la pandemia de la enfermedad por coronavirus 2019 (COVID-19), causada por SARS-CoV-2, pocos modelos han sido pensados como herramientas interactivas accesibles para usuarios de distintos ámbitos. El objetivo de este trabajo fue desarrollar un modelo que tuviera en cuenta la heterogeneidad de las tasas de contacto de la población e implementarlo en una aplicación accesible, que permitiera estimar el impacto de posibles intervenciones a partir de información disponible. Se desarrolló una versión ampliada del modelo susceptible-expuesto-infectado-resistente (SEIR), denominada SEIR-HL, que asume una población dividida en dos subpoblaciones, con tasas de contacto diferentes. Asimismo, se desarrolló una fórmula para calcular el número básico de reproducción (R₀) para una población dividida en n subpoblaciones, discriminando las tasas de contacto de cada subpoblación según el tipo o contexto de contacto. Se compararon las predicciones del SEIR-HL con las del SEIR y se demostró que la heterogeneidad en las tasas de contacto puede afectar drásticamente la dinámica de las simulaciones, aun partiendo de las mismas condiciones iniciales y los mismos parámetros. Se empleó el SEIR-HL para mostrar el efecto sobre la evolución de la pandemia del desplazamiento de individuos desde posiciones de alto contacto hacia posiciones de bajo contacto. Finalmente, a modo de ejemplo, se aplicó el SEIR-HL al análisis de la pandemia de COVID-19 en Argentina; también se desarrolló un ejemplo de uso de la fórmula del R₀. Tanto el SEIR-HL como una calculadora del R₀ fueron implementados informáticamente y puestos a disposición de la comunidad.

Nine-month Trend of Time-Varying Reproduction Numbers of COVID-19 in West of Iran

BACKGROUND

The basic reproduction number (R0) is an important concept in infectious disease epidemiology and the most important parameter to determine the transmissibility of a pathogen. This study aimed to estimate the nine-month trend of time-varying R of COVID-19 epidemic using the serial interval (SI) and Markov Chain Monte Carlo in Lorestan, west of Iran.

STUDY DESIGN

Descriptive study.

METHODS

This study was conducted based on a cross-sectional method. The SI distribution was extracted from data and log-normal, Weibull, and Gamma models were fitted. The estimation of time-varying R0, a likelihood-based model was applied, which uses pairs of cases to estimate relative likelihood.

RESULTS

In this study, Rt was estimated for SI 7-day and 14-day time-lapses from 27 February-14 November 2020. To check the robustness of the R0 estimations, sensitivity analysis was performed using different SI distributions to estimate the reproduction number in 7-day and 14-day time-lapses. The R0 ranged from 0.56 to 4.97 and 0.76 to 2.47 for 7-day and 14-day time-lapses. The doubling time was estimated to be 75.51 days (95% CI: 70.41, 81.41).

CONCLUSION

Low R0 of COVID-19 in some periods in Lorestan, west of Iran, could be an indication of preventive interventions, namely quarantine and isolation. To control the spread of the disease, the reproduction number should be reduced by decreasing the transmission and contact rates and shortening the infectious period.

Characterizing Norovirus Transmission from Outbreak Data, United States

Norovirus is the leading cause of acute gastroenteritis outbreaks in the United States. We estimated the basic (R₀) and effective (R_e) reproduction numbers for 7,094 norovirus outbreaks reported to the National Outbreak Reporting System (NORS) during 2009–2017 and used regression models to assess whether transmission varied by outbreak setting. The median R₀ was 2.75 (interquartile range [IQR] 2.38–3.65), and median R_e was 1.29 (IQR 1.12–1.74). Long-term care and assisted living facilities had an R₀ of 3.35 (95% CI 3.26–3.45), but R₀ did not differ substantially for outbreaks in other settings, except for outbreaks in schools, colleges, and universities, which had an R₀ of 2.92 (95% CI 2.82–3.03). Seasonally, R₀ was lowest (3.11 [95% CI 2.97–3.25]) in summer and peaked in fall and winter. Overall, we saw little variability in transmission across different outbreaks settings in the United States.

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

OBJECTIVE

To assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus.

METHODS

We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0.

FINDINGS

We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R0, across countries. An intermediate level of youth and GINI inequality are associated with high R0, (n-shape relationships), while high city population and high social media use are associated with high R0. Pollution, temperature, and humidity did not have strong relationships with R0 but were positive.

CONCLUSION

Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.

Face masks against COVID-19: Standards, efficacy, testing and decontamination methods

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the novel coronavirus disease 2019 (COVID-19), has caused a global pandemic on a scale not seen for over a century. Increasing evidence suggests that respiratory droplets and aerosols are likely the most common route of transmission for SARS-CoV-2. Since the virus can be spread by presymptomatic and asymptomatic individuals, universal face masking has been recommended as a straightforward and low-cost strategy to mitigate virus transmission. Numerous governments and public health agencies around the world have advocated for or mandated the wearing of masks in public settings, especially in situations where social distancing is not possible. However, the efficacy of wearing a mask remains controversial. This interdisciplinary review summarizes the current, state-of-the-art understanding of mask usage against COVID-19. It covers three main aspects of mask usage amid the pandemic: quality standards for various face masks and their fundamental filtration mechanisms, empirical methods for quantitatively determining mask integrity and particle filtration efficiency, and decontamination methods that allow for the reuse of traditionally disposable N95 and surgical masks. The focus is given to the fundamental physicochemical and engineering sciences behind each aspect covered in this review, providing novel insights into the current understanding of mask usage to curb COVID-19 spread.

Ethical Considerations in Chemotherapy and Vaccines in Cancer Patients in Times of the COVID-19 Pandemic

The COVID-19 situation is a worldwide health emergency with strong implications in clinical oncology. In this viewpoint, we address two crucial dilemmas from the ethical dimension: (1) Is it ethical to postpone or suspend cancer treatments which offer a statistically significant benefit in quality of life and survival in cancer patients during this time of pandemic?; (2) Should we vaccinate cancer patients against COVID-19 if scientific studies have not included this subgroup of patients? Regarding the first question, the best available evidence applied to the ethical principles of Beauchamp and Childress shows that treatments (such as chemotherapy) with clinical benefit are fair and beneficial. Indeed, the suspension or delay of such treatments should be considered malefic. Regarding the second question, applying the doctrine of double-effect, we show that the potential beneficial effect of vaccines in the population with cancer (or those one that has had cancer) is much higher than the potential adverse effects of these vaccines. In addition, there is no better and less harmful known solution.

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.

Demystifying R Naught: Understanding What Does it Hide?

Since the onset of the pandemic in Wuhan city, China, forecasting and projections of the pandemic are the areas of interest for the investigators, and the basic reproduction rate R0 always stayed the favorite tool. The basic reproduction number (R0) is either ratio or rate or the basic reproductive rate. This dimensionless number was calculated in the past to describe the contagiousness or transmissibility of infectious agents for many communicable diseases. Its importance in the context of COVID-19 is not less, it tells us about the public health measures to be undertaken for disease prevention, and how the transmission of COVID-19 will be affected or eliminated. R0 is affected by several biological, sociobehavioral, and environmental factors which decide agent transmission. R0 is estimated by using complex mathematical models, the results of which are easily distorted, misjudged, and misused. R0 is not a biological constant for an agent or pathogen, it is a rate over time. It can measure the disease severity and also gives an estimate about the herd immunity required for the reversal of epidemic. R0 cannot be altered through vaccination campaigns though it can tell us about the relationship between the population's immune status and epidemic curve. Modeled R0 values are dependent on the model structures and assumptions made. Some R0 values reported in the scientific literature are likely outdated as assumptions are frequently changing in the current pandemic. R0 must be predicted and applied with great caution as this basic metric is far from simple.

Real time monitoring of the evolution of an epidemic regarded as a physical relaxation process

The emergence of an epidemic evokes the need to monitor its spread and assess and validate any mitigation measures enacted by governments and administrative bodies in real time. We present here a method based on previous models of relaxation in fractal structures to observe and quantify this spread and the response of affected populations and governing bodies, and apply it to COVID-19 as a case study. This method provides means to simultaneously track in real time quantities such as the mortality and the recovery rates as well as the number of new infections caused by an infected person. With sufficient data, this method enables thorough monitoring and assessment of an epidemic without ad-hoc assumptions regarding the evolution of the pandemic in the future.

The Basic Reproduction Number as a Loop Gain Matrix

The COVID-19 pandemic and the disordered reactions of most governments made the importance of mathematical modelling and model-based predictions evident, even outside the scientific community. The basic reproduction number [Formula: see text] quickly entered the common jargon, as a concise but effective tool to communicate the spreading power of a disease and estimate, at least roughly, the possible outcomes of the epidemic. However, while [Formula: see text] is easily defined for simple models, its proper definition is more subtle for larger, state-of-the-art models. Here we show that it is nothing else than the spectral radius of the gain matrix of a linear system, and that this matrix generalizes [Formula: see text] in the computation of the vector-valued final epidemic size and epidemic threshold, in a large class of finite-dimensional SIR-like models.

An SIER model to estimate optimal transmission rate and initial parameters of COVD-19 dynamic in Sri Lanka

COVID-19 global outbreak has been significantly damaging the human well-being, life style of people and the global economy. It is clear that the entire world is moving into a dangerous phase of this epidemic at the moment. With absence of a preventive vaccine, the governments across world implement, monitor and manage various public health and social distancing measures to control the spread of this extremely contagious disease and it is found that most of these responses have been critical results of numerous mathematical and decision support models. In this study, SEIR compartment structure is used to model the COVID-19 transmission in Sri Lanka. Reported cases data during the first 80 days of the outbreak is used to model the time dependent transmission rate of the disease. Optimal transmission rates and initial size of the exposed and infected sizes of the populations are then estimated matching between clinically identified cases to model based simulated outcomes.

Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly in Manaus, the capital of Amazonas state in northern Brazil. The attack rate there is an estimate of the final size of the largely unmitigated epidemic that occurred in Manaus. We use a convenience sample of blood donors to show that by June 2020, 1 month after the epidemic peak in Manaus, 44% of the population had detectable immunoglobulin G (IgG) antibodies. Correcting for cases without a detectable antibody response and for antibody waning, we estimate a 66% attack rate in June, rising to 76% in October. This is higher than in São Paulo, in southeastern Brazil, where the estimated attack rate in October was 29%. These results confirm that when poorly controlled, COVID-19 can infect a large proportion of the population, causing high mortality.

Estimating and explaining the spread of COVID-19 at the county level in the USA

The basic reproduction number, R0, determines the rate of spread of a communicable disease and therefore gives fundamental information needed to plan public health interventions. Using mortality records, we estimated the rate of spread of COVID-19 among 160 counties and county-aggregates in the USA at the start of the epidemic. We show that most of the high among-county variance is explained by four factors (R2 = 0.70): the timing of outbreak, population size, population density, and spatial location. For predictions of future spread, population density and spatial location are important, and for the latter we show that SARS-CoV-2 strains containing the G614 mutation to the spike gene are associated with higher rates of spread. Finally, the high predictability of R0 allows extending estimates to all 3109 counties in the conterminous 48 states. The high variation of R0 argues for public health policies enacted at the county level for controlling COVID-19.

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.