Complexity of the Basic Reproduction Number (R0)

A geometric approach to the impact of immigration of people infected with communicable diseases

We construct a set of new epidemiological thresholds to address the general problem of spreading and containment of a transmissible disease with influx of infected individuals (i.e., when the classic R0 is no longer meaningful). We provide analytical properties of these indices and illustrate their usefulness in a compartmental model of COVID-19 with data taken from Chile showing a good predictive potential when contrasted with the recorded disease behavior. This geometric approach and the associated analytical and numerical results break new ground in that they allow us to quantify the severity of an immigration of infectious individuals into a community, and identification of the key parameters that are capable of changing or reversing the spread of an infectious disease in specific models.

A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo

Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number (R 0 ) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.

Bayesian estimation of the time-varying reproduction number for pulmonary tuberculosis in Iran: A registry-based study from 2018 to 2022 using new smear-positive cases

Introduction

Tuberculosis (TB) is one of the most prevalent infectious diseases in the world, causing major public health problems in developing countries. The rate of TB incidence in Iran was estimated to be 13 per 100,000 in 2021. This study aimed to estimate the reproduction number and serial interval for pulmonary tuberculosis in Iran.

Material and methods

The present national historical cohort study was conducted from March 2018 to March 2022 based on data from the National Tuberculosis and Leprosy Registration Center of Iran's Ministry of Health and Medical Education (MOHME). The study included 30,762 tuberculosis cases and 16,165 new smear-positive pulmonary tuberculosis patients in Iran. We estimated the reproduction number of pulmonary tuberculosis in a Bayesian framework, which can incorporate uncertainty in estimating it. Statistical analyses were accomplished in R software.

Results

The mean age at diagnosis of patients was 52.3 ± 21.2 years, and most patients were in the 35-63 age group (37.1%). Among the data, 9121 (56.4%) cases were males, and 7044 (43.6%) were females. Among patients, 7459 (46.1%) had a delayed diagnosis between 1 and 3 months. Additionally, 3039 (18.8%) cases were non-Iranians, and 2978 (98%) were Afghans. The time-varying reproduction number for pulmonary tuberculosis disease was calculated at an average of 1.06 ± 0.05 (95% Crl 0.96-1.15).

Conclusions

In this study, the incidence and the time-varying reproduction number of pulmonary tuberculosis showed the same pattern. The mean of the time-varying reproduction number indicated that each infected person is causing at least one new infection over time, and the chain of transmission is not being disrupted.

Estimating time-varying cholera transmission and oral cholera vaccine effectiveness in Haiti and Cameroon, 2021-2023

In 2023, cholera affected approximately 1 million people and caused more than 5000 deaths globally, predominantly in low-income and conflict settings. In recent years, the number of new cholera outbreaks has grown rapidly. Further, ongoing cholera outbreaks have been exacerbated by conflict, climate change, and poor infrastructure, resulting in prolonged crises. As a result, the demand for treatment and intervention is quickly outpacing existing resource availability. Prior to improved water and sanitation systems, cholera, a disease primarily transmitted via contaminated water sources, also routinely ravaged high-income countries. Crumbling infrastructure and climate change are now putting new locations at risk - even in high-income countries. Thus, understanding the transmission and prevention of cholera is critical. Combating cholera requires multiple interventions, the two most common being behavioral education and water treatment. Two-dose oral cholera vaccination (OCV) is often used as a complement to these interventions. Due to limited supply, countries have recently switched to single-dose vaccines (OCV1). One challenge lies in understanding where to allocate OCV1 in a timely manner, especially in settings lacking well-resourced public health surveillance systems. As cholera occurs and propagates in such locations, timely, accurate, and openly accessible outbreak data are typically inaccessible for disease modeling and subsequent decision-making. In this study, we demonstrated the value of open-access data to rapidly estimate cholera transmission and vaccine effectiveness. Specifically, we obtained non-machine readable (NMR) epidemic curves for recent cholera outbreaks in two countries, Haiti and Cameroon, from figures published in situation and disease outbreak news reports. We used computational digitization techniques to derive weekly counts of cholera cases, resulting in nominal differences when compared against the reported cumulative case counts (i.e., a relative error rate of 5.67% in Haiti and 0.54% in Cameroon). Given these digitized time series, we leveraged EpiEstim-an open-source modeling platform-to derive rapid estimates of time-varying disease transmission via the effective reproduction number (R t ). To compare OCV1 effectiveness in the two considered countries, we additionally used VaxEstim, a recent extension of EpiEstim that facilitates the estimation of vaccine effectiveness via the relation among three inputs: the basic reproduction number (R 0 ),R t , and vaccine coverage. Here, with Haiti and Cameroon as case studies, we demonstrated the first implementation of VaxEstim in low-resource settings. Importantly, we are the first to use VaxEstim with digitized data rather than traditional epidemic surveillance data. In the initial phase of the outbreak, weekly rolling average estimates ofR t were elevated in both countries: 2.60 in Haiti [95% credible interval: 2.42-2.79] and 1.90 in Cameroon [1.14-2.95]. These values are largely consistent with previous estimates ofR 0 in Haiti, where average values have ranged from 1.06 to 3.72, and in Cameroon, where average values have ranged from 1.10 to 3.50. In both Haiti and Cameroon, this initial period of high transmission preceded a longer period during whichR t oscillated around the critical threshold of 1. Our results derived from VaxEstim suggest that Haiti had higher OCV1 effectiveness than Cameroon (75.32% effective [54.00-86.39%] vs. 54.88% [18.94-84.90%]). These estimates of OCV1 effectiveness are generally aligned with those derived from field studies conducted in other countries. Thus, our case study reinforces the validity of VaxEstim as an alternative to costly, time-consuming field studies of OCV1 effectiveness. Indeed, prior work in South Sudan, Bangladesh, and the Democratic Republic of the Congo reported OCV1 effectiveness ranging from approximately 40% to 80%. This work underscores the value of combining NMR sources of outbreak case data with computational techniques and the utility of VaxEstim for rapid, inexpensive estimation of vaccine effectiveness in data-poor outbreak settings.

Early detection of highly transmissible viral variants using phylogenomics

As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a serious threat to global health. Statistical models of genome sequence evolution may provide a critical tool for early detection of these strains. Using a novel stochastic model that links transmission rates to the entire viral genome sequence, we study the utility of phylogenetic methods that use a phylogenetic tree relating viral samples versus count-based methods that use case counts of variants over time exclusively to detect increased transmission rates and identify candidate causative mutations. We find that phylogenies in particular can detect novel transmission-enhancing variants very soon after their origin and may facilitate the development of early detection systems for outbreak surveillance.

Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach

This article introduces a novel mathematical model analyzing the dynamics of Dengue in the recent past, specifically focusing on the 2023 outbreak of this disease. The model explores the patterns and behaviors of dengue fever in Bangladesh. Incorporating a sinusoidal function reveals significant mid-May to Late October outbreak predictions, aligning with the government's exposed data in our simulation. For different amplitudes (A) within a sequence of values (A = 0.1 to 0.5), the highest number of infected mosquitoes occurs in July. However, simulations project that when βM = 0.5 and A = 0.1, the peak of human infections occurs in late September. Not only the next-generation matrix approach along with the stability of disease-free and endemic equilibrium points are observed, but also a cutting-edge Machine learning (ML) approach such as the Prophet model is explored for forecasting future Dengue outbreaks in Bangladesh. Remarkably, we have fitted our solution curve of infection with the reported data by the government of Bangladesh. We can predict the outcome of 2024 based on the ML Prophet model situation of Dengue will be detrimental and proliferate 25 % compared to 2023. Finally, the study marks a significant milestone in understanding and managing Dengue outbreaks in Bangladesh.

A Numerical Comparison of Petri Net and Ordinary Differential Equation SIR Component Models

Petri nets are a promising modeling framework for epidemiology, including the spread of disease across populations or within an individual. In particular, the Susceptible-Infectious-Recovered (SIR) compartment model is foundational for population epidemiological modeling and has been implemented in several prior Petri net studies. However, the SIR model is generally stated as a system of ordinary differential equations (ODEs) with continuous time and variables, while Petri nets are discrete event simulations. To our knowledge, no prior study has investigated the numerical equivalence of Petri net SIR models to the classical ODE formulation. We introduce crucial numerical techniques for implementing SIR models in the GPenSim package for Petri net simulations. We show that these techniques are critical for Petri net SIR models and show a relative root mean squared error of less than 1% compared to ODE simulations for biologically relevant parameter ranges. We conclude that Petri nets provide a valid framework for modeling SIR-type dynamics using biologically relevant parameter values, provided that the other PN structures we outline are also implemented.

Analysis of the impact of COVID-19 variants and vaccination on the time-varying reproduction number: statistical methods

Introduction

The COVID-19 pandemic has profoundly impacted global health systems, requiring the monitoring of infection waves and strategies to control transmission. Estimating the time-varying reproduction number is crucial for understanding the epidemic and guiding interventions.

Methods

Probability distributions of serial interval are estimated for Pre-Delta and Delta periods. We conducted a comparative analysis of time-varying reproduction numbers, taking into account population immunity and variant differences. We incorporated the regional heterogeneity and age distribution of the population, as well as the evolving variants and vaccination rates over time. COVID-19 transmission dynamics were analyzed with variants and vaccination.

Results

The reproduction number is computed with and without considering variant-based immunity. In addition, values of reproduction number significantly differed by variants, emphasizing immunity's importance. Enhanced vaccination efforts and stringent control measures were effective in reducing the transmission of the Delta variant. Conversely, Pre-Delta variant appeared less influenced by immunity levels, due to lower vaccination rates. Furthermore, during the Pre-Delta period, there was a significant difference between the region-specific and the non-region-specific reproduction numbers, with particularly distinct pattern differences observed in Gangwon, Gyeongbuk, and Jeju in Korea.

Discussion

This research elucidates the dynamics of COVID-19 transmission concerning the dominance of the Delta variant, the efficacy of vaccinations, and the influence of immunity levels. It highlights the necessity for targeted interventions and extensive vaccination coverage. This study makes a significant contribution to the understanding of disease transmission mechanisms and informs public health strategies.

Uncovering the coronavirus outbreak: present understanding and future research paths

INTRODUCTION

The review discusses the pathophysiological mechanisms of SARS-CoV-2, the modes of transmission, and the long-term health consequences of COVID-19, emphasizing the importance of research and successful public health initiatives.

CONTENT

COVID-19 taxonomy, pathophysiology, symptomatology, and epidemiological importance are the key objects of this research paper. This review explains how COVID-19 affects different systems of the body, including respiratory, cardiovascular, and reproductive systems of the human body. It describes the modes of entry of the virus into the cell; more precisely, ACE2 and TMPRSS2 in viral entry. In addition, the present study analyzes the situation of COVID-19 in India regarding vaccine development and the transmission rate related to socioeconomic factors.

SUMMARY

The manifestation of COVID-19 presents a lot of symptoms and post-acute problems, issues which are seriously impacting mental health and physical health as well. The present review summarizes current research into pathogenicity and the mode of virus transmission, together with immunological responses. Coupled with strong vaccination programs, public health initiatives should hold the key to fighting this pandemic.

OUTLOOK

Long-term effects and the development of treatment methods will need further study, as ambiguities on COVID-19 remain. Multidisciplinary collaboration across healthcare sectors in this respect is of paramount importance for the prevention of further spread and protection of public health.

Longitudinal evolution of the HIV effective reproduction number following sequential expansion of treatment as prevention and pre-exposure prophylaxis in British Columbia, Canada: a population-level programme evaluation

BACKGROUND

Treatment as prevention and pre-exposure prophylaxis (PrEP) are key strategies in the control of HIV/AIDS. We aimed to characterise the longitudinal effects of antiretroviral therapy (ART), followed by treatment as prevention and the addition of PrEP, on the HIV effective reproduction number (Re) in British Columbia, Canada.

METHODS

This population-level programme evaluation used data from the Drug Treatment Program of the British Columbia Centre for Excellence in HIV/AIDS (Vancouver, British Columbia, Canada). We also used estimates of HIV incidence and prevalence from the Public Health Agency of Canada, data on the number of new HIV diagnoses per year from the British Columbia Centre for Disease Control, and mortality data from the British Columbia Vital Statistics Agency. Data were obtained from 1985 until 2022, depending on the database source. Outcomes were the annual HIV prevalence, HIV incidence, number of new HIV diagnoses, number of people living with HIV on ART, HIV/AIDS-related and all-cause mortality rates, the HIV incidence-to-all-cause-mortality ratio, and Re. We calculated the modified effective reproduction number (Rme) using two thresholds of viral suppression and compared these values with Re.

FINDINGS

We found a 95% decline in HIV/AIDS-related mortality and a 91% decrease in HIV incidence over the study period. The Re progressively declined from 1996 to 2022; however, from 1996 to 2017, Rme remained stable (>1) when calculated for people living with HIV with unsuppressed viraemia, suggesting that treatment as prevention reduces HIV incidence by decreasing the pool of individuals who are potentially able to transmit the virus. From 2018 to 2022, a decline in the estimated Re and Rme (<1) was observed regardless of whether we considered all people living with HIV or only those who were virologically unsuppressed. This finding suggests that PrEP decreases HIV incidence by reducing the number of susceptible individuals in the community, independently of viral suppression.

INTERPRETATION

Our results show the synergy between generalised treatment as prevention and targeted PrEP in terms of decreasing HIV incidence. These findings support the incorporation of longitudinal monitoring of Re at a programmatic level to identify opportunities for the optimisation of treatment-as-prevention and PrEP programmes.

FUNDING

British Columbia Ministry of Health, Health Canada, Public Health Agency of Canada, Vancouver Coastal Health, Vancouver General Hospital Foundation, Genome British Columbia, and the Canadian Institutes of Health Research.

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

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

Social contact patterns during the early COVID-19 pandemic in Norway: insights from a panel study, April to September 2020

BACKGROUND

During the COVID-19 pandemic, many countries adopted social distance measures and lockdowns of varying strictness. Social contact patterns are essential in driving the spread of respiratory infections, and country-specific measurements are needed. This study aimed to gain insights into changes in social contacts and behaviour during the early pandemic phase in Norway.

METHODS

We conducted an online panel study among a nationally representative sample of Norwegian adults by age and gender. The panel study included six data collections waves between April and September 2020, and 2017 survey data from a random sample of the Norwegian population (including children < 18 years old) were used as baseline. The market research company Ipsos was responsible for carrying out the 2020 surveys. We calculated mean daily contacts, and estimated age-stratified contact matrices during the study period employing imputation of child-to-child contacts. We used the next-generation method to assess the relative reduction of R0 and compared the results to reproduction numbers estimated for Norway during the 2020 study period.

RESULTS

Over the six waves in 2020, 5 938 observations/responses were registered from 1 718 individuals who reported data on 22 074 contacts. The mean daily number of contacts among adults varied between 3.2 (95%CI 3.0-3.4) to 3.9 (95%CI 3.6-4.2) across the data collection waves, representing a 67-73% decline compared to pre-pandemic levels (baseline). Fewer contacts in the community setting largely drove the reduction; the drop was most prominent among younger adults. Despite gradual easing of social distance measures during the survey period, the estimated population contact matrices remained relatively stable and displayed more inter-age group mixing than at baseline. Contacts within households and the community outside schools and workplaces contributed most to social encounters. Using the next-generation method R0 was found to be roughly 25% of pre-pandemic levels during the study period, suggesting controlled transmission.

CONCLUSION

Social contacts declined significantly in the months following the March 2020 lockdown, aligning with implementation of stringent social distancing measures. These findings contribute valuable empirical information into the social behaviour in Norway during the early pandemic, which can be used to enhance policy-relevant models for addressing future crises when mitigation measures might be implemented.

The airborne transmission of viruses causes tight transmission bottlenecks

The transmission bottleneck describes the number of viral particles that initiate an infection in a new host. Previous studies have used genome sequence data to suggest that transmission bottlenecks for influenza and SARS-CoV-2 involve few viral particles, but the general principles of virus transmission are not fully understood. Here we show that, across a broad range of circumstances, tight transmission bottlenecks are a simple consequence of the physical process of airborne viral transmission. We use mathematical modelling to describe the physical process of the emission and inhalation of infectious particles, deriving the result that that the great majority of transmission bottlenecks involve few viral particles. While exceptions to this rule exist, the circumstances needed to create these exceptions are likely very rare. We thus provide a physical explanation for previous inferences of bottleneck size, while predicting that tight transmission bottlenecks prevail more generally in respiratory virus transmission.

Revisiting the Modularity-Disease transmission Link: Uncovering the importance of intra-modular structure

Studies have shown that the internal structure of modules is hardly important for the spread of epidemics. However, most of these studies have assumed that intra-module connectivity and inter-module connectivity do not affect each other. In reality, changes in the internal structure of modules may affect inter-module links and thus change the modularity of the entire network. Therefore, we have developed a theoretical network model with adjustable modularity to investigate the impact of this situation on disease transmission. Our findings indicate that the intra-module structure plays a crucial role in disease outbreaks. Changes in intra-module structure lead to significant numerical changes in peak prevalence and duration of disease. This implies that the potential impact of changes in exposure patterns within modules should also be considered when investigating the exact impact of modular social networks on disease burden.

MODELS: a six-step framework for developing an infectious disease model

Since the COVID-19 pandemic began, a plethora of modeling studies related to COVID-19 have been released. While some models stand out due to their innovative approaches, others are flawed in their methodology. To assist novices, frontline healthcare workers, and public health policymakers in navigating the complex landscape of these models, we introduced a structured framework named MODELS. This framework is designed to detail the essential steps and considerations for creating a dependable epidemic model, offering direction to researchers engaged in epidemic modeling endeavors.

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

BACKGROUND

Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the "last line of defense" for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies.

OBJECTIVE

This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves.

METHODS

A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients.

RESULTS

Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic.

CONCLUSION

The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach

BACKGROUND

Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data.

OBJECTIVE

We aim to compare GPS colocated versus Bluetooth beacon-derived proximity contact data for their impacts on transmission models' results under community and types of diseases.

METHODS

We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence.

RESULTS

Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001).

CONCLUSIONS

We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.

Workplace interventions to reduce the risk of SARS-CoV-2 infection outside of healthcare settings

BACKGROUND

Although many people infected with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) experience no or mild symptoms, some individuals can develop severe illness and may die, particularly older people and those with underlying medical problems. Providing evidence-based interventions to prevent SARS-CoV-2 infection has become more urgent with the potential psychological toll imposed by the coronavirus disease 2019 (COVID-19) pandemic. Controlling exposures to occupational hazards is the fundamental method of protecting workers. When it comes to the transmission of viruses, workplaces should first consider control measures that can potentially have the most significant impact. According to the hierarchy of controls, one should first consider elimination (and substitution), then engineering controls, administrative controls, and lastly, personal protective equipment. This is the first update of a Cochrane review published 6 May 2022, with one new study added.

OBJECTIVES

To assess the benefits and harms of interventions in non-healthcare-related workplaces aimed at reducing the risk of SARS-CoV-2 infection compared to other interventions or no intervention.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, Web of Science Core Collections, Cochrane COVID-19 Study Register, World Health Organization (WHO) COVID-19 Global literature on coronavirus disease, ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform, and medRxiv to 13 April 2023.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and non-randomised studies of interventions. We included adult workers, both those who come into close contact with clients or customers (e.g. public-facing employees, such as cashiers or taxi drivers), and those who do not, but who could be infected by coworkers. We excluded studies involving healthcare workers. We included any intervention to prevent or reduce workers' exposure to SARS-CoV-2 in the workplace, defining categories of intervention according to the hierarchy of hazard controls (i.e. elimination; engineering controls; administrative controls; personal protective equipment).

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Our primary outcomes were incidence rate of SARS-CoV-2 infection (or other respiratory viruses), SARS-CoV-2-related mortality, adverse events, and absenteeism from work. Our secondary outcomes were all-cause mortality, quality of life, hospitalisation, and uptake, acceptability, or adherence to strategies. We used the Cochrane RoB 2 tool to assess risk of bias, and GRADE methods to evaluate the certainty of evidence for each outcome.

MAIN RESULTS

We identified 2 studies including a total of 16,014 participants. Elimination-of-exposure interventions We included one study examining an intervention that focused on elimination of hazards, which was an open-label, cluster-randomised, non-inferiority trial, conducted in England in 2021. The study compared standard 10-day self-isolation after contact with an infected person to a new strategy of daily rapid antigen testing and staying at work if the test is negative (test-based attendance). The trialists hypothesised that this would lead to a similar rate of infections, but lower COVID-related absence. Staff (N = 11,798) working at 76 schools were assigned to standard isolation, and staff (N = 12,229) working at 86 schools were assigned to the test-based attendance strategy. The results between test-based attendance and standard 10-day self-isolation were inconclusive for the rate of symptomatic polymerase chain reaction (PCR)-positive SARS-CoV-2 infection (rate ratio (RR) 1.28, 95% confidence interval (CI) 0.74 to 2.21; 1 study; very low-certainty evidence). The results between test-based attendance and standard 10-day self-isolation were inconclusive for the rate of any PCR-positive SARS-CoV-2 infection (RR 1.35, 95% CI 0.82 to 2.21; 1 study; very low-certainty evidence). COVID-related absenteeism rates were 3704 absence days in 566,502 days-at-risk (6.5 per 1000 working days) in the control group and 2932 per 539,805 days-at-risk (5.4 per 1000 working days) in the intervention group (RR 0.83, 95% CI 0.55 to 1.25). We downgraded the certainty of the evidence to low due to imprecision. Uptake of the intervention was 71% in the intervention group, but not reported for the control intervention. The trial did not measure our other outcomes of SARS-CoV-2-related mortality, adverse events, all-cause mortality, quality of life, or hospitalisation. We found seven ongoing studies using elimination-of-hazard strategies, six RCTs and one non-randomised trial. Administrative control interventions We found one ongoing RCT that aims to evaluate the efficacy of the Bacillus Calmette-Guérin (BCG) vaccine in preventing COVID-19 infection and reducing disease severity. Combinations of eligible interventions We included one non-randomised study examining a combination of elimination of hazards, administrative controls, and personal protective equipment. The study was conducted in two large retail companies in Italy in 2020. The study compared a safety operating protocol, measurement of body temperature and oxygen saturation upon entry, and a SARS-CoV-2 test strategy with a minimum activity protocol. Both groups received protective equipment. All employees working at the companies during the study period were included: 1987 in the intervention company and 1798 in the control company. The study did not report an outcome of interest for this systematic review. Other intervention categories We did not find any studies in this category.

AUTHORS' CONCLUSIONS

We are uncertain whether a test-based attendance policy affects rates of PCR-positive SARS-CoV-2 infection (any infection; symptomatic infection) compared to standard 10-day self-isolation amongst school and college staff. A test-based attendance policy may result in little to no difference in absenteeism rates compared to standard 10-day self-isolation. The non-randomised study included in our updated search did not report any outcome of interest for this Cochrane review. As a large part of the population is exposed in the case of a pandemic, an apparently small relative effect that would not be worthwhile from the individual perspective may still affect many people, and thus become an important absolute effect from the enterprise or societal perspective. The included RCT did not report on any of our other primary outcomes (i.e. SARS-CoV-2-related mortality and adverse events). We identified no completed studies on any other interventions specified in this review; however, eight eligible studies are ongoing. More controlled studies are needed on testing and isolation strategies, and working from home, as these have important implications for work organisations.

Characteristics of the Sexual Networks of Men Who Have Sex With Men in Montréal, Toronto, and Vancouver: Insights from Canada's 2022 Mpox Outbreak

BACKGROUND

The 2022-2023 global mpox outbreak disproportionately affected gay, bisexual, and other men who have sex with men (GBM). We investigated differences in GBM's sexual partner distributions across Canada's 3 largest cities and over time, and how they shaped transmission.

METHODS

The Engage Cohort Study (2017-2023) recruited GBM via respondent-driven sampling in Montréal, Toronto, and Vancouver (n = 2449). We compared reported sexual partner distributions across cities and periods: before COVID-19 (2017-2019), pandemic (2020-2021), and after lifting of restrictions (2021-2023). We used Bayesian regression and poststratification to model partner distributions. We estimated mpox's basic reproduction number (R0) using a risk-stratified compartmental model.

RESULTS

Pre-COVID-19 pandemic distributions were comparable: fitted average partners (past 6 months) were 10.4 (95% credible interval: 9.4-11.5) in Montréal, 13.1 (11.3-15.1) in Toronto, and 10.7 (9.5-12.1) in Vancouver. Sexual activity decreased during the pandemic and increased after lifting of restrictions, but remained below prepandemic levels. Based on reported cases, we estimated R0 of 2.4 to 2.7 and similar cumulative incidences (0.7%-0.9%) across cities.

CONCLUSIONS

Similar sexual partner distributions may explain comparable R0 and cumulative incidence across cities. With potential for further recovery in sexual activity, mpox vaccination and surveillance strategies should be maintained.

Systematic review of empiric studies on lockdowns, workplace closures, and other non-pharmaceutical interventions in non-healthcare workplaces during the initial year of the COVID-19 pandemic: benefits and selected unintended consequences

BACKGROUND

We conducted a systematic review aimed to evaluate the effects of non-pharmaceutical interventions within non-healthcare workplaces and community-level workplace closures and lockdowns on COVID-19 morbidity and mortality, selected mental disorders, and employment outcomes in workers or the general population.

METHODS

The inclusion criteria included randomized controlled trials and non-randomized studies of interventions. The exclusion criteria included modeling studies. Electronic searches were conducted using MEDLINE, Embase, and other databases from January 1, 2020, through May 11, 2021. Risk of bias was assessed using the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool. Meta-analysis and sign tests were performed.

RESULTS

A total of 60 observational studies met the inclusion criteria. There were 40 studies on COVID-19 outcomes, 15 on anxiety and depression symptoms, and five on unemployment and labor force participation. There was a paucity of studies on physical distancing, physical barriers, and symptom and temperature screening within workplaces. The sign test indicated that lockdown reduced COVID-19 incidence or case growth rate (23 studies, p < 0.001), reproduction number (11 studies, p < 0.001), and COVID-19 mortality or death growth rate (seven studies, p < 0.05) in the general population. Lockdown did not have any effect on anxiety symptoms (pooled standardized mean difference = -0.02, 95% CI: -0.06, 0.02). Lockdown had a small effect on increasing depression symptoms (pooled standardized mean difference = 0.16, 95% CI: 0.10, 0.21), but publication bias could account for the observed effect. Lockdown increased unemployment (pooled mean difference = 4.48 percentage points, 95% CI: 1.79, 7.17) and decreased labor force participation (pooled mean difference = -2.46 percentage points, 95% CI: -3.16, -1.77). The risk of bias for most of the studies on COVID-19 or employment outcomes was moderate or serious. The risk of bias for the studies on anxiety or depression symptoms was serious or critical.

CONCLUSIONS

Empiric studies indicated that lockdown reduced the impact of COVID-19, but that it had notable unwanted effects. There is a pronounced paucity of studies on the effect of interventions within still-open workplaces. It is important for countries that implement lockdown in future pandemics to consider strategies to mitigate these unintended consequences.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration # CRD42020182660.

Value of information dynamics in Disease X vaccine clinical trials

BACKGROUND

Solutions have been proposed to accelerate the development and rollout of vaccines against a hypothetical disease with epidemic or pandemic potential called Disease X. This may involve resolving uncertainties regarding the disease and the new vaccine. However the value for public health of collecting this information will depend on the time needed to perform research, but also on the time needed to produce vaccine doses. We explore this interplay, and its effect on the decision on whether or not to perform research.

METHOD

We simulate numerically the emergence and transmission of a disease in a population using a susceptible-infected-recovered (SIR) compartmental model with vaccination. Uncertainties regarding the disease and the vaccine are represented by parameter prior distributions. We vary the date at which vaccine doses are available, and the date at which information about parameters becomes available. We use the expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) to measure the value of information.

RESULTS

As expected, information has less or no value if it comes too late, or (equivalently) if it can only be used too late. However we also find non trivial dynamics for shorter durations of vaccine development. In this parameter area, it can be optimal to implement vaccination without waiting for information depending on the respective durations of dose production and of clinical research.

CONCLUSION

We illustrate the value of information dynamics in a Disease X outbreak scenario, and present a general approach to properly take into account uncertainties and transmission dynamics when planning clinical research in this scenario. Our method is based on numerical simulation and allows us to highlight non trivial effects that cannot otherwise be investigated.

Structural network characteristics affect epidemic severity and prediction in social contact networks

Understanding and mitigating epidemic spread in complex networks requires the measurement of structural network properties associated with epidemic risk. Classic measures of epidemic thresholds like the basic reproduction number (R0) have been adapted to account for the structure of social contact networks but still may be unable to capture epidemic potential relative to more recent measures based on spectral graph properties. Here, we explore the ability of R0 and the spectral radius of the social contact network to estimate epidemic susceptibility. To do so, we simulate epidemics on a series of constructed (small world, scale-free, and random networks) and a collection of over 700 empirical biological social contact networks. Further, we explore how other network properties are related to these two epidemic estimators (R0 and spectral radius) and mean infection prevalence in simulated epidemics. Overall, we find that network properties strongly influence epidemic dynamics and the subsequent utility of R0 and spectral radius as indicators of epidemic risk.

Multi-feature SEIR model for epidemic analysis and vaccine prioritization

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

What Influence Could the Acceptance of Visitors Cause on the Epidemic Dynamics of a Reinfectious Disease?: A Mathematical Model

The globalization in business and tourism becomes crucial more and more for the economical sustainability of local communities. In the presence of an epidemic outbreak, there must be such a decision on the policy by the host community as whether to accept visitors or not, the number of acceptable visitors, or the condition for acceptable visitors. Making use of an SIRI type of mathematical model, we consider the influence of visitors on the spread of a reinfectious disease in a community, especially assuming that a certain proportion of accepted visitors are immune. The reinfectivity of disease here means that the immunity gained by either vaccination or recovery is imperfect. With the mathematical results obtained by our analysis on the model for such an epidemic dynamics of resident and visitor populations, we find that the acceptance of visitors could have a significant influence on the disease's endemicity in the community, either suppressive or supportive.

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure

The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.

The impacts of spatial-temporal heterogeneity of human-to-human contacts on the extinction probability of infectious disease from branching process model

In this study, we focus on the impacts of spatial-temporal heterogeneity of human-to-human contacts on the spread of infectious diseases and develop a multi-type branching process model by introducing random human-to-human contact mode into a structured population. We provide the general formulas of the generation size, extinction probability, and basic reproduction number of the proposed branching process model. The result shows that the natural temporal heterogeneity (i.e. random contacts over time) can lead to a higher extinction probability while remains the same basic reproduction number and generation size. This is also numerically verified by choosing the real contact distributions from different circumstances of four countries. In addition, we observe a non-monotonic pattern of the differences, against the transmission probability and the mean contact rate, between the extinction probabilities under the constant and random contact patterns. Given the spatial heterogeneity, we show that it can contribute to the increase of basic reproduction number, but also increase the extinction probability of the infectious disease. This study adds novel insights to the course of the impact of heterogeneity on the transmission dynamics and also provides additional evidence for the limited role of reproduction numbers.

Varying Flavobacterium psychrophilum shedding dynamics in three bacterial coldwater disease-susceptible salmonid (Family Salmonidae) species

Flavobacterium psychrophilum causes bacterial coldwater disease (BCWD) and is responsible for substantial losses in farm and hatchery-reared salmonids (Family Salmonidae). Although F. psychrophilum infects multiple economically important salmonids and is transmitted horizontally, the extent of knowledge regarding F. psychrophilum shedding rates and duration is limited to rainbow trout (Oncorhynchus mykiss). Concurrently, hundreds of F. psychrophilum sequence types (STs) have been described using multilocus sequence typing (MLST), and evidence suggests that some variants have distinct phenotypes, including differences in host associations. Whether shedding dynamics differ among F. psychrophilum variants and/or salmonids remains unknown. Thus, three F. psychrophilum isolates (e.g., US19, US62, and US87) in three MLST STs (e.g., ST13, ST277, and ST275) with apparent host associations for coho salmon (O. kisutch), Atlantic salmon (Salmo salar), or rainbow trout were intramuscularly injected into each respective fish species. Shedding rates of live and dead fish were determined by quantifying F. psychrophilum loads in water via quantitative PCR. Both live and dead Atlantic and coho salmon shed F. psychrophilum, as did live and dead rainbow trout. Regardless of salmonid species, dead fish shed F. psychrophilum at higher rates (e.g., up to ~108-1010 cells/fish/hour) compared to live fish (up to ~107-109 cells/fish/hour) and for a longer duration (5-35 days vs 98 days); however, shedding dynamics varied by F. psychrophilum variant and/or host species, a matter that may complicate BCWD management. Findings herein expand knowledge on F. psychrophilum shedding dynamics across multiple salmonid species and can be used to inform future BCWD management strategies.IMPORTANCEFlavobacterium psychrophilum causes bacterial coldwater disease (BCWD) and rainbow trout fry syndrome, both of which cause substantial losses in farmed and hatchery-reared salmon and trout populations worldwide. This study provides insight into F. psychrophilum shedding dynamics in rainbow trout (Oncorhynchus mykiss) and, for the first time, coho salmon (O. kisutch) and Atlantic salmon (Salmo salar). Findings revealed that live and dead fish of all fish species shed the bacterium. However, dead fish shed F. psychrophilum at higher rates than living fish, emphasizing the importance of removing dead fish in farms and hatcheries. Furthermore, shedding dynamics may differ according to F. psychrophilum genetic variant and/or fish species, a matter that may complicate BCWD management. Overall, study results provide deeper insight into F. psychrophilum shedding dynamics and will guide future BCWD management strategies.

Simulating the impact of optimized prevention and control measures on the transmission of monkeypox in the United States: A model-based study

This study aimed to develop a modified susceptible-exposed-infected-recovered (SEIR) model to evaluate monkeypox epidemics in the United States and explore more optimized prevention and control measures. To further assess the impact of public health measures on the transmission of monkeypox, different intervention scenarios were developed based on the classic SEIR model, considering reducing contact, enhancing vaccination, diagnosis delay, and environmental transmission risk, respectively. We evaluated the impact of different measures by simulating their spread in different scenarios. During the simulation period, 8709 people were infected with monkeypox. The simulation analysis showed that: (1) the most effective measures to control monkeypox transmission during the early stage of the epidemic were reducing contact and enhancing vaccination, with cumulative infections at 51.20% and 41.90% of baseline levels, respectively; (2) shortening diagnosis time would delay the peak time of the epidemic by 96 days; and (3) the risk of environmental transmission of monkeypox virus was relatively low. This study indirectly proved the effectiveness of the prevention and control measures, such as reducing contact, enhancing vaccination, shortening diagnosis time, and low risk of environmental transmission, which also provided an important reference and containment experience for nonepidemic countries.

Disparities and effectiveness of COVID-19 vaccine policies in three representative European countries

OBJECTIVE

The aim of this study was to examine the Coronavirus disease 2019(COVID-19) vaccine policies disparities and effectiveness in Germany, Denmark and Bulgaria, with a view to providing lessons for global vaccination and response to possible outbreak risks.

METHODS

This study analyzed big data through public information on the official websites of the Ministries of Health of the European Union, Germany, Denmark and Bulgaria and the official websites of the World Health Organization. We systematically summarized the COVID-19 vaccine policies of the three countries, and selected the following six indicators for cross-cutting vaccination comparisons: COVID-19 vaccine doses administered per 100 people, COVID-19 vaccination rate, the share of people with fully vaccinated, the share of people only partly vaccinated, cumulative confirmed COVID-19 cases per million, cumulative confirmed COVID-19 deaths per million. Meanwhile, we selected the following four indicators for measuring the effectiveness of COVID-19 vaccine policy implementation: daily cases per million, daily deaths per million, the effective reproduction rate (Rt), the moving-average case fatality rate (CFR).

RESULTS

Although these three EU countries had the same start time for vaccination, and the COVID-19 vaccine supply was coordinated by the EU, there are still differences in vaccination priorities, vaccination types, and vaccine appointment methods. Compared to Germany and Denmark, Bulgaria had the least efficient vaccination efforts and the worst vaccination coverage, with a vaccination rate of just over 30% as of June 2023, and the maximum daily deaths per million since vaccination began in the country was more than three times that of the other two countries. From the perspective of implementation effect, vaccination has a certain effect on reducing infection rate and death rate, but the spread of new mutant strains obviously aggravates the severity of the epidemic and reduces the effectiveness of the vaccine. Among them, the spread of the Omicron mutant strain had the most serious impact on the three countries, showing an obvious epidemic peak.

CONCLUSIONS

Expanding vaccination coverage has played a positive role in reducing COVID-19 infection and mortality rates and stabilizing Rt. Priority vaccination strategies targeting older people and at-risk groups have been shown to be effective in reducing COVID-19 case severity and mortality in the population. However, the emergence and spread of new variant strains, and the relaxation of epidemic prevention policies, still led to multiple outbreaks peaking. In addition, vaccine hesitancy, mistrust in government and ill-prepared health systems are hampering vaccination efforts. Among the notable ones are divergent types of responses to vaccine safety issue could fuel mistrust and hesitancy around vaccination. At this stage, it is also necessary to continue to include COVID-19 vaccination in priority vaccination plans and promote booster vaccination to prevent severe illness and death. Improving the fairness of vaccine distribution and reducing the degree of vaccine hesitancy are the focus of future vaccination work.

The basic reproduction number (R0) of ebola virus disease: A systematic review and meta-analysis

BACKGROUND

Ebola virus disease (Ebola) is highly pathogenic, transmissible, and often deadly, with debilitating consequences. Superspreading within a cluster is also possible. In this study, we aim to document Ebola basic reproduction number (R0): the average number of new cases associated with an Ebola case in a completely susceptible population.

METHODS

We undertook a systematic review and meta-analysis. We searched PubMed, EMBASE, and Web of Science for studies published between 1976 and February 27, 2023. We also manually searched the reference lists of the reviewed studies to identify additional studies. We included studies that reported R0 during Ebola outbreaks in Africa. We excluded studies that reported only the effective reproduction number (Rt). Abstracting data from included studies was performed using a pilot-tested standard form. Two investigators reviewed the studies, extracted the data, and assessed quality. The pooled R0 was determined by a random-effects meta-analysis. R0 was stratified by country. We also estimated the theoretically required immunization coverage to reach herd-immunity using the formula of (1-1/R0) × 100 %.

RESULTS

The search yielded 2042 studies. We included 53 studies from six African countries in the systematic review providing 97 Ebola mean R0 estimates. 27 (with 46 data points) studies were included in the meta-analysis. The overall pooled mean Ebola R0 was 1.95 (95 % CI 1.74-2.15), with high heterogeneity (I2 = 99.99 %; τ2 = 0.38; and p < 0.001) and evidence of small-study effects (Egger's statistics: Z = 4.67; p < 0.001). Mean Ebola R0 values ranged from 1.2 to 10.0 in Nigeria, 1.1 to 7 in Guinea, 1.14 to 8.33 in Sierra Leone, 1.13 to 5 in Liberia, 1.2 to 5.2 in DR Congo, 1.34 to 2.7 in Uganda, and from 1.40 to 2.55 for all West African countries combined. Pooled mean Ebola R0 was 9.38 (95 % CI 4.16-14.59) in Nigeria, 3.31 (95 % CI 2.30-4.32) in DR Congo, 2.0 (95 % CI 1.25-2.76) in Uganda, 1.83 (95 % CI 1.61-2.05) in Liberia, 1.73 (95 % CI 1.47-2.0) in Sierra Leonne, and 1.44 (95 % CI 1.29-1.60) in Guinea. In theory, 50 % of the population needs to be vaccinated to achieve herd immunity, assuming that Ebola vaccine would be 100 % effective.

CONCLUSIONS

Ebola R0 varies widely across countries. Ebola has a much wider R0 range than is often claimed (1.3-2.0). It is possible for an Ebola index case to infect more than two susceptible individuals.

Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

Monitoring the effective reproduction number [Formula: see text] of a rapidly unfolding pandemic in real-time is key to successful mitigation and prevention strategies. However, existing methods based on case numbers, hospital admissions or fatalities suffer from multiple measurement biases and temporal lags due to high test positivity rates or delays in symptom development or administrative reporting. Alternative methods such as web search and social media tracking are less directly indicating epidemic prevalence over time. We instead record age-stratified anonymous contact matrices at a daily resolution using a longitudinal online-offline survey in Hungary during the first two waves of the COVID-19 pandemic. This approach is innovative, cheap, and provides information in near real-time for estimating [Formula: see text] at a daily resolution. Moreover, it allows to complement traditional surveillance systems by signaling periods when official monitoring infrastructures are unreliable due to observational biases.

Differential contagiousness of respiratory disease across the United States

The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, R0. This number depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we used Bayesian inference and the next-generation matrix approach to estimate region-specific R0 values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. We focused on MSA populations after finding that these populations were more uniformly impacted by COVID-19 than state populations. Our maximum a posteriori (MAP) estimates for R0 range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases. ONE-SENTENCE SUMMARY: Initial contagiousness of Coronavirus Disease 2019 varied over a 4-fold range across urban areas of the United States.

Work Attendance with Acute Respiratory Illness Before and During COVID-19 Pandemic, United States, 2018-2022

Both SARS-CoV-2 and influenza virus can be transmitted by asymptomatic, presymptomatic, or symptomatic infected persons. We assessed effects on work attendance while ill before and during the COVID-19 pandemic in the United States by analyzing data collected prospectively from persons with acute respiratory illnesses enrolled in a multistate study during 2018-2022. Persons with previous hybrid work experience were significantly less likely to work onsite on the day before through the first 3 days of illness than those without that experience, an effect more pronounced during the COVID-19 pandemic than during prepandemic influenza seasons. Persons with influenza or COVID-19 were significantly less likely to work onsite than persons with other acute respiratory illnesses. Among persons with positive COVID-19 test results available by the second or third day of illness, few worked onsite. Hybrid and remote work policies might reduce workplace exposures and help reduce spread of respiratory viruses.

Bayesian monitoring of COVID-19 in Sweden

In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure. From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health. Significance: Using public data from Swedish patient registries we develop a national-scale computational model of COVID-19. The parametrized model produces valuable weekly predictions of healthcare demands at the regional level and validates well against several different sources. We also obtain critical epidemiological insights into the disease progression, including, e.g., reproduction number, immunity and disease fatality estimates. The success of the model hinges on our novel use of filtering techniques which allows us to design an accurate data-driven procedure using data exclusively from healthcare demands, i.e., our approach does not rely on public testing and is therefore very cost-effective.

Academic and healthcare efforts from Cessation to complete resumption of professional football tournaments during COVID-19 pandemic: A narrative review

The Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus led to over 626 million infections and 6.5 million deaths worldwide and forced to cancel or postpone several sporting events. Effective control techniques are therefore urgently required to avoid COVID-19 spread at these local and global events. This narrative review addressed the healthcare and research efforts on the intersections between COVID-19 and major professional sports leagues worldwide, with special reference to the FIFA World Cup football 2022. This explained how the broader transformation of COVID-19 from being a potential risk to an urgent pandemic public health emergency, caused the world of Football to halt between February and March 2020. This review could add to the growing literature on the importance of scientific research in understanding the relationship between mass sports events and COVID-19 trajectory, concerning studies conducted globally and particularly for the recommencement of major professional football competitions. The information outlined in the article may help sports organizations understand the risks associated with sports and their settings and improve their preparedness for future events under unprecedented circumstances. There were tremendous global healthcare and research efforts to deal with this unprecedented pandemic. The successful FIFA World Cup football tournament was an indicator of the success of these efforts.

Computational fluid dynamics modeling of aerosol particle transport through lung airway mucosa

Delivery of aerosols to the lung can treat various lung diseases. However, the conducting airways are coated by a protective mucus layer with complex properties that make this form of delivery difficult. Mucus is a non-Newtonian fluid and is cleared from the lungs over time by ciliated cells. Further, its gel-like structure hinders the diffusion of particles through it. Any aerosolized treatment of lung diseases must penetrate the mucosal barrier. Using computational fluid dynamics, a model of the airway mucus and periciliary layer was constructed to simulate the transport of impacted aerosol particles. The model predicts the dosage fraction of particles of a certain size that penetrate the mucus and reach the underlying tissue, as well as the distance downstream of the dosage site where tissue concentration is maximized. Reactions that may occur in the mucus are also considered, with simulated data for the interaction of a model virus and an antibody.

Comparison of COVID-19 vaccine policies and their effectiveness in Korea, Japan, and Singapore

BACKGROUND

This study aimed to analyze coronavirus disease 2019 (COVID-19)vaccine policies and effectiveness in Korea, Japan, and Singapore, thereby providing empirical experience for vaccination and response to similar public health emergencies.

METHODS

The study systematically summarized the COVID-19 vaccine policies in Korea, Japan, and Singapore through public information from the Our World in Data website and the official websites of the Ministries of Health in these three countries.Total vaccinations, COVID-19 vaccination rates, rates of fully vaccinated, rates of boostervaccinated, and total confifirmed cases were selected for cross-sectional comparison of COVID-19 vaccination in these three countries. Combining the basic characteristics of these three countries, daily cases per million, daily deaths per million, and the effective reproduction rate were calculated to measure the effectiveness of COVID-19 vaccine policies implementation in each of these three countries RESULTS: The countermeasures against the COVID-19 in Korea, Japan, and Singapore, although seemingly different on the surface, have all taken an aggressive approach. There are large similarities in the timing of the start of COVID-19 vaccination, the type of vaccine, how vaccine appointments are made, and whether vaccination are free, and all had high vaccination rates. A systematic comparison of the anti-epidemic practices in the three East Asian countries revealed that all three countries experienced more than one outbreak spike due to the spread of new mutant strains after the start of mass vaccination with COVID-19 vaccination, but that vaccination played a positive role in reducing the number of deaths and stabilizing the effective reproduction rate.

CONCLUSIONS

This study comparatively analyzed the COVID-19 vaccine policies and their effects in South Korea, Japan, and Singapore, and found that there is a common set of logical combinations behind the seemingly different strategies of these three countries. Therefore, in the process of combating COVID-19, countries can learn from the successful experience of combating the epidemic and continue to strengthen the implementation of vaccination programs, as well as adjusting public perceptions to reduce the level of vaccine hesitancy, enhance the motivation for vaccination, and improve the coverage of COVID-19 vaccine based on different cultural factors, which remains the direction for future development.

Time series forecasting of COVID-19 infections and deaths in Alpha and Delta variants using LSTM networks

Since the beginning of the rapidly spreading COVID-19 pandemic, several mutations have occurred in the genetic sequence of the virus, resulting in emerging different variants of concern. These variants vary in transmissibility, severity of infections, and mortality rate. Designing models that are capable of predicting the future behavior of these variants in the societies can help decision makers and the healthcare system to design efficient health policies, and to be prepared with the sufficient medical devices and an adequate number of personnel to fight against this virus and the similar ones. Among variants of COVID-19, Alpha and Delta variants differ noticeably in the virus structures. In this paper, we study these variants in the geographical regions with different size, population densities, and social life styles. These regions include the country of Iran, the continent of Asia, and the whole world. We propose four deep learning models based on Long Short-Term Memory (LSTM), and examine their predictive power in forecasting the number of infections and deaths for the next three, next five, and next seven days in each variant. These models include Encoder Decoder LSTM (ED-LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional LSTM (Conv-LSTM), and Gated Recurrent Unit (GRU). Performance of these models in predictions are evaluated using the root mean square error, mean absolute error, and mean absolute percentage error. Then, the Friedman test is applied to find the leading model for predictions in all conditions. The results show that ED-LSTM is generally the leading model for predicting the number of infections and deaths for both variants of Alpha and Delta, with the ability to forecast long time intervals ahead.

Antibody escape, the risk of serotype formation, and rapid immune waning: Modeling the implications of SARS-CoV-2 immune evasion

As the COVID-19 pandemic progresses, widespread community transmission of SARS-CoV-2 has ushered in a volatile era of viral immune evasion rather than the much-heralded stability of "endemicity" or "herd immunity." At this point, an array of viral strains has rendered essentially all monoclonal antibody therapeutics obsolete and strongly undermined the impact of vaccinal immunity on SARS-CoV-2 transmission. In this work, we demonstrate that antibody escape resulting in evasion of pre-existing immunity is highly evolutionarily favored and likely to cause waves of short-term transmission. In the long-term, invading strains that induce weak cross-immunity against pre-existing strains may co-circulate with those pre-existing strains. This would result in the formation of serotypes that increase disease burden, complicate SARS-CoV-2 control, and raise the potential for increases in viral virulence. Less durable immunity does not drive positive selection as a trait, but such strains may transmit at high levels if they establish. Overall, our results draw attention to the importance of inter-strain cross-immunity as a driver of transmission trends and the importance of early immune evasion data to predict the trajectory of the pandemic.

Peak risk of SARS-CoV-2 infection within 5 s of face-to-face encounters: an observational/retrospective study

The link between aerosol dynamics and viral exposure risk is not fully understood, particularly during movement and face-to-face interactions. To investigate this, we employed Particle Trace Velocimetry with a laser sheet and a high-speed camera to measure microparticles from a human mannequin's mouth. The average peak time in the non-ventilated condition (expiratory volume, 30 L; passing speed, 5 km/h) was 1.33 s (standard deviation = 0.32 s), while that in the ventilated condition was 1.38 s (standard deviation = 0.35 s). Our results showed that the peak of viral exposure risk was within 5 s during face-to-face encounters under both ventilated and non-ventilated conditions. Moreover, the risk of viral exposure greatly decreased in ventilated conditions compared to non-ventilated conditions. Based on these findings, considering a risk mitigation strategy for the duration of 5 s during face-to-face encounters is expected to significantly reduce the risk of virus exposure in airborne transmission.

Large-scale sporting events during the COVID-19 pandemic: insights from the FIFA World Cup 2022 in Qatar with an analysis of patterns of COVID-19 metrics

The 2022 FIFA World Cup (FIFA-WC) held in Qatar presented unique challenges, given the potential for rapid transmission of coronavirus disease 2019 (COVID-19) among over 1.4 million international fans attending the event. This study aimed to investigate the impact of the FIFA-WC 2022 on COVID-19 cases, deaths, and reproduction rate (R0) in Qatar. Additionally, it sought to understand the implications of hosting large-scale events during a pandemic without COVID-19 restrictive measures, providing critical insights for future decision-making. Data from "Our World in Data" were analysed for three distinct periods: one week before the FIFA-WC (week-preWC), the four weeks of the event (week-1WC to week-4WC), and one week after (week-postWC). The results revealed a significant increase in COVID-19 cases during week-3WC and week-4WC (compared to week-preWC) in Qatar, followed by a subsequent decrease during the week-postWC. Notably, Qatar experienced a more pronounced surge in positive cases than the global trend. Regarding COVID-19-related deaths, Qatar's peak occurred during week-2WC, while globally deaths peaked from week-3WC to week-postWC. Nevertheless, Qatar's death toll remained relatively low compared to the global trend throughout the event. The findings highlight that the FIFA-WC 2022 in Qatar demonstrated the feasibility of organizing large-scale sporting events during a pandemic with appropriate measures in place. They emphasize the importance of high vaccination coverage, continuous monitoring, and effective collaboration between event organizers, healthcare authorities, and governments. As such, the event serves as a valuable model for future gatherings, underlining the significance of evidence-based decision-making and comprehensive public health preparedness.

Assessments of epidemic spread in aquaculture: comparing different scenarios of infectious bacteria incursion through spatiotemporal hybrid modeling

The sustainable development of the aquaculture sector is at risk due to the significant challenges posed by many emerging infectious diseases. While disease prevention and control measures are becoming increasingly critical, there is a dearth of studies on the epidemiological aspects of disease transmission in aquatic ecosystems. This study aims to forecast the spread of a bacterial disease between fish farms in two regions, Romsdalsfjord in Norway and Gujwa in South Korea by applying a DTU-DADS-Aqua spatiotemporal hybrid simulation model. The simulation model assessed the pattern of disease transmission between fish farms under different degrees of transmission power based on the distance between farms (ScalingInf), host susceptibility (RelSusceptibility), the origin site of disease, and the capacity of culling fish. The distance between fish farms was found to have significant associations with disease transmission. In most simulation conditions, the disease transmission between different bay management areas (BMAs) was not evident in Romsdalsfjord. In the Guwja region, where there are relatively narrow distances between fish farms, the spread of infectious disease was greatly affected by ScalingInf. The impact of RelSusceptibility on disease transmission patterns is a critical factor to consider in simulation modeling. When RelSusceptibility ranges from 0.5-1, there is little impact on the likelihood of disease transmission. Conversely, lower ranges (0.2 and 0.05) of RelSusceptibility result in a significant decrease in the area affected by the spread of disease. Eradication measures could control the patterns of infectious disease transmission, but the effectiveness of the depopulation strategy can be dramatically changed depending on the geographical environment. In conclusion, through a comparative analysis of the disease transmission and management scenarios, this study demonstrates the potential use of existing simulation models in predicting the spread of infectious diseases under different epidemiological circumstances and quarantine actions.

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.

Epidemiological characteristics and transmissibility of HPV infection: A long-term retrospective study in Hokkien Golden Triangle, China, 2013-2021

OBJECTIVE

Multiple human papillomavirus (HPV)-associated diseases have put a significant disease burden on the world. Therefore, we conducted a study to explore the epidemiological characteristics of HPV and the transmissibility of its genotypes.

METHODS

HPV testing data was collected from Hospital. A transmission dynamics model of HPV was constructed to simulate and compare the transmissibility of different HPV genotypes, which was quantitatively described by the basic reproduction number (R0).

RESULTS

The collected HPV subjects were mainly from Xiamen City, Zhangzhou City and Quanzhou City, together, they are known as the Hokkien golden triangle. There were variations in the distribution of HPV infections by age groups. Among all HPV genotypes, 13 of them had R0 > 1, with 10 of them being high-risk types. The top five were HPV56, 18, 58, 52 and 53, among which, HPV56, 18, 58 and 42 were of high risk, whereas HPV53 was not, and the R0 values for the five were 3.35 (CI: 0.00-9.99), 3.20 (CI: 0.00-6.46), 3.19 (CI: 1.27-6.94), 3.19 (CI: 1.01-8.42) and 2.99 (CI: 0.00-9.39), respectively. In addition, HPV52 had R0 > 1 for about 51 months, which had the longest duration.

CONCLUSION

Most high-risk HPV types in the Hokkien golden triangle could transmit among the population. Therefore, there is a need of further optimization for developing HPV vaccines and better detection methods in the region.

Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks

Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.

Phylogenies increase power to detect highly transmissible viral genome variants

As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a significant threat to global health. Viral genome sequences, combined with statistical models of sequence evolution, may provide a critical tool for early detection of these strains. Using a novel statistical model that links transmission rates to the entire viral genome sequence, we study the power of phylogenetic methods-using a phylogenetic tree relating viral samples-and count-based methods-using case-counts of variants over time-to detect increased transmission rates, and to identify causative mutations. We find that phylogenies in particular can detect novel variants very soon after their origin, and may facilitate the development of early detection systems for outbreak surveillance.

Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea

OBJECTIVES

The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator-namely, the number of close contacts per patient with confirmed COVID-19.

METHODS

From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data's characteristics.

RESULTS

In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults.

CONCLUSIONS

The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.

Association of climatic variables with risk of transmission of influenza in Guangzhou, China, 2005-2021

BACKGROUND

Climatic variables constitute important extrinsic determinants of transmission and seasonality of influenza. Yet quantitative evidence of independent associations of viral transmissibility with climatic factors has thus far been scarce and little is known about the potential effects of interactions between climatic factors on transmission.

OBJECTIVE

This study aimed to examine the associations of key climatic factors with risk of influenza transmission in subtropical Guangzhou.

METHODS

Influenza epidemics were identified over a 17-year period using the moving epidemic method (MEM) from a dataset of N = 295,981 clinically- and laboratory-confirmed cases of influenza in Guangzhou. Data on eight key climatic variables were collected from China Meteorological Data Service Centre. Generalized additive model combined with the distributed lag non-linear model (DLNM) were developed to estimate the exposure-lag-response curve showing the trajectory of instantaneous reproduction number (R_t) across the distribution of each climatic variable after adjusting for depletion of susceptible, inter-epidemic effect and school holidays. The potential interaction effects of temperature, humidity and rainfall on influenza transmission were also examined.

RESULTS

Over the study period (2005-21), 21 distinct influenza epidemics with varying peak timings and durations were identified. Increasing air temperature, sunshine, absolute and relative humidity were significantly associated with lower R_t, while the associations were opposite in the case of ambient pressure, wind speed and rainfall. Rainfall, relative humidity, and ambient temperature were the top three climatic contributors to variance in transmissibility. Interaction models found that the detrimental association between high relative humidity and transmissibility was more pronounced at high temperature and rainfall.

CONCLUSION

Our findings are likely to help understand the complex role of climatic factors in influenza transmission, guiding informed climate-related mitigation and adaptation policies to reduce transmission in high density subtropical cities.

Modeling vaccination coverage during the 2022 central Ohio measles outbreak: a cross-sectional study

Background

Of the eight large (>50 cases) US postelimination outbreaks, the first and last occurred in Ohio. Ohio's vaccination registry is incomplete. Community-level immunity gaps threaten more than two decades of measles elimination in the US. We developed a statistical model, VaxEstim, to rapidly estimate the early-phase vaccination coverage and immunity gap in the exposed population during the 2022 Central Ohio outbreak.

Methods

We used reconstructed daily incidence (from publicly available data) and assumptions about the distribution of the serial interval, or the time between symptom onset in successive measles cases, to estimate the effective reproduction number (i.e., the average number of secondary infections caused by an infected individual in a partially immune population). We estimated early-phase measles vaccination coverage by comparing the effective reproduction number to the basic reproduction number (i.e., the average number of secondary infections caused by an infected individual in a fully susceptible population) while accounting for vaccine effectiveness. Finally, we estimated the early-phase immunity gap as the difference between the estimated critical vaccination threshold and vaccination coverage.

Findings

VaxEstim estimated the early-phase vaccination coverage as 53% (95% credible interval, 21%-77%), the critical vaccination threshold as 93%, and the immunity gap as 42% (95% credible interval, 18%-74%).

Interpretation

This study estimates a significant immunity gap in the exposed population during the early phase of the 2022 Central Ohio measles outbreak, suggesting a robust public health response is needed to identify the susceptible community and develop community-specific strategies to close the immunity gap.

Funding

This work was supported in part by the National Institute of General Medical Sciences, National Institutes of Health; the UK Medical Research Council (MRC); the Foreign, Commonwealth and Development Office; the National Institute for Health Research (NIHR) Health Protection Research Unit in Modelling Methodology; Imperial College London, and the London School of Hygiene & Tropical Medicine, Community Jameel; the EDCTP2 programme, supported by the EU; and the Sergei Brin Foundation.

Pathobiological analysis of african swine fever virus contact-exposed pigs and estimation of the basic reproduction number of the virus in Vietnam

BACKGROUND

African swine fever (ASF), caused by African swine fever virus (ASFV), is a fatal disease affecting wild and domestic pigs. Since China reported the first ASF outbreak in August 2018, ASFV has swept over the neighbouring Asian countries. However, studies involving experimental pig-to-pig ASFV transmission in Vietnam are lacking. The main objective of this experimental study was to demonstrate the pathobiological characteristics of ASFV contact-exposed pigs and estimate their basic reproduction number (R₀) in Vietnam. Fifteen pigs were randomly divided into two groups: experimental (n = 10) and negative control (n = 5) groups. One pig in the experimental group was intramuscularly inoculated with ASFV strain from Vietnam in 2020 and housed with the uninoculated pigs during the study period (28 days).

RESULTS

The inoculated pig died 6 days post-inoculation, and the final survival rate was 90.0%. We started observing viremia and excretion of ASFV 10 days post-exposure in contact-exposed pigs. Unlike the surviving and negative control pigs, all necropsied pigs showed severe congestive splenomegaly and moderate-to-severe haemorrhagic lesions in the lymph nodes. The surviving pig presented with mild haemorrhagic lesions in the spleen and kidneys. We used Susceptible-Infectious-Removed models for estimating R₀. The R₀ values for exponential growth (EG) and maximum likelihood (ML) were calculated to be 2.916 and 4.015, respectively. In addition, the transmission rates (β) were estimated to be 0.729 (95% confidence interval [CI]: 0.379-1.765) for EG and 1.004 (95% CI: 0.283-2.450) for ML.

CONCLUSIONS

This study revealed pathobiological and epidemiological information in about pig-to-pig ASFV transmission. Our findings suggested that culling infected herds within a brief period of time may mitigate the spread of ASF outbreaks.