Duration Of Infectious Period Research Articles

Estimation of the herd-level basic reproduction number for African swine fever in Vietnam, 2019.

African swine fever (ASF) is a notifiable viral disease of pigs and wild boars that causes severe economic losses to the swine industry. The pig industry in Vietnam was recently attacked by the ASF virus (ASFV) for the first time in history. However, we lack information regarding the transmissibility of ASF within indoor production systems communities, such as those in Vietnam. Therefore, we aimed to estimate the basic reproduction number (R0) for ASF during the early stages of transmission between farms in indoor production system communities from local and national data in Vietnam. The linear regression model approach for the susceptible infectious method was used in this study to estimate the transmission rate and, consequently, the R0 value. The R0 values between-farm of ASF ranged from 1.41 to 10.8 in different scenarios of infectious period duration, from 15 to 30 days at the national and local levels. These results help to understand the scale and speed of ASF infection in Vietnam and to provide a scientific basis to implement control measures to restrict the spread of ASFV in other locations.

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

BackgroundThe reproduction number is one of the most crucial parameters in determining disease dynamics, providing a summary measure of the transmission potential. However, estimating this value is particularly challenging owing to the characteristics of epidemic data, including non-reproducibility and incompleteness.MethodsIn this study, we propose mathematical models with different population structures; each of these models can produce data on the number of cases of the influenza A(H1N1)pdm09 epidemic in South Korea. These structured models incorporating the heterogeneity of age and region are used to estimate the reproduction numbers at various terminal times. Subsequently, the age- and region-specific reproduction numbers are also computed to analyze the differences illustrated in the incidence data.ResultsIncorporation of the age-structure or region-structure allows for robust estimation of parameters, while the basic SIR model provides estimated values beyond the reasonable range with severe fluctuation. The estimated duration of infectious period using age-structured model is around 3.8 and the reproduction number was estimated to be 1.6. The estimated duration of infectious period using region-structured model is around 2.1 and the reproduction number was estimated to be 1.4. The estimated age- and region-specific reproduction numbers are consistent with cumulative incidence for corresponding groups.ConclusionsNumerical results reveal that the introduction of heterogeneity into the population to represent the general characteristics of dynamics is essential for the robust estimation of parameters.

The value of decreasing the duration of the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Finding medications or vaccines that may decrease the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could potentially reduce transmission in the broader population. We developed a computational model of the U.S. simulating the spread of SARS-CoV-2 and the potential clinical and economic impact of reducing the infectious period duration. Simulation experiments found that reducing the average infectious period duration could avert a median of 442,852 [treating 25% of symptomatic cases, reducing by 0.5 days, reproductive number (R0) 3.5, and starting treatment when 15% of the population has been exposed] to 44.4 million SARS-CoV-2 cases (treating 75% of all infected cases, reducing by 3.5 days, R0 2.0). With R0 2.5, reducing the average infectious period duration by 0.5 days for 25% of symptomatic cases averted 1.4 million cases and 99,398 hospitalizations; increasing to 75% of symptomatic cases averted 2.8 million cases. At $500/person, treating 25% of symptomatic cases saved $209.5 billion (societal perspective). Further reducing the average infectious period duration by 3.5 days averted 7.4 million cases (treating 25% of symptomatic cases). Expanding treatment to 75% of all infected cases, including asymptomatic infections (R0 2.5), averted 35.9 million cases and 4 million hospitalizations, saving $48.8 billion (societal perspective and starting treatment after 5% of the population has been exposed). Our study quantifies the potential effects of reducing the SARS-CoV-2 infectious period duration.

SARS-CoV-2 Transmission From People Without COVID-19 Symptoms

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiology of coronavirus disease 2019 (COVID-19), is readily transmitted person to person. Optimal control of COVID-19 depends on directing resources and health messaging to mitigation efforts that are most likely to prevent transmission, but the relative importance of such measures has been disputed. To assess the proportion of SARS-CoV-2 transmissions in the community that likely occur from persons without symptoms. This decision analytical model assessed the relative amount of transmission from presymptomatic, never symptomatic, and symptomatic individuals across a range of scenarios in which the proportion of transmission from people who never develop symptoms (ie, remain asymptomatic) and the infectious period were varied according to published best estimates. For all estimates, data from a meta-analysis was used to set the incubation period at a median of 5 days. The infectious period duration was maintained at 10 days, and peak infectiousness was varied between 3 and 7 days (-2 and +2 days relative to the median incubation period). The overall proportion of SARS-CoV-2 was varied between 0% and 70% to assess a wide range of possible proportions. Level of transmission of SARS-CoV-2 from presymptomatic, never symptomatic, and symptomatic individuals. The baseline assumptions for the model were that peak infectiousness occurred at the median of symptom onset and that 30% of individuals with infection never develop symptoms and are 75% as infectious as those who do develop symptoms. Combined, these baseline assumptions imply that persons with infection who never develop symptoms may account for approximately 24% of all transmission. In this base case, 59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms. Under a broad range of values for each of these assumptions, at least 50% of new SARS-CoV-2 infections was estimated to have originated from exposure to individuals with infection but without symptoms. In this decision analytical model of multiple scenarios of proportions of asymptomatic individuals with COVID-19 and infectious periods, transmission from asymptomatic individuals was estimated to account for more than half of all transmissions. In addition to identification and isolation of persons with symptomatic COVID-19, effective control of spread will require reducing the risk of transmission from people with infection who do not have symptoms. These findings suggest that measures such as wearing masks, hand hygiene, social distancing, and strategic testing of people who are not ill will be foundational to slowing the spread of COVID-19 until safe and effective vaccines are available and widely used.

Inferred duration of infectious period of SARS-CoV-2: rapid scoping review and analysis of available evidence for asymptomatic and symptomatic COVID-19 cases

ObjectivesOur objective was to review the literature on the inferred duration of the infectious period of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and provide an...

Designing a modelling structure for the grapevine downy mildew pathosystem

A process-based simulation model for the grapevine-downy mildew pathosystem was developed in order to quantitatively synthesize the literature available and to provide a tool to guide strategic decisions for disease management. The model includes: i) the main processes involved in the disease dual epidemics on leaves and clusters, from inoculum mobilisation to disease multiplication on foliage, and to infection of clusters; and ii) host dynamics, i.e. crop development, growth, and physiological and disease-induced senescence. A numerical evaluation was performed to investigate the response of the model to changes of the main epidemiological parameters, i.e. the basic infection rate corrected for the removals (RcOPT), the duration of latency period (LP), the duration of infectious period (IP), and the rate of primary infections (P). Increasing values of RcOPT and IP, and decreasing values of LP resulted in a faster increase of the epidemic on both foliage and clusters, while decreasing values of P delayed epidemics. The simulated dynamics of epidemics on foliage and clusters conformed to patterns of dual epidemics observed for downy mildew. The model can be useful for investigating the effect of strategic disease management tools such as the use of resistant varieties or to investigate the behaviour of the pathosystem under scenarios of climate change.

How Valid Are Assumptions About Re-emerging Smallpox? A Systematic Review of Parameters Used in Smallpox Mathematical Models.

Globally eradicated in 1980, smallpox is listed as a category A bioterrorism agent. If smallpox were to re-emerge, it may be due to an act of bioterrorism or a laboratory accident, and the impact is likely to be severe. Preparedness against smallpox is subject to more uncertainty than other infectious diseases because it is eradicated, there is uncertainty about population immunity, and the current global health workforce has no practical experience or living memory of smallpox. In the event of re-emergence of smallpox, mathematical modeling plays a crucial role in improving the evidence base to inform preparedness, mitigation, and response activities. However, the predictions of mathematical models about outbreak magnitude and impact depend critically on the assumptions and disease parameters used. We aimed to identify modeling studies that would be applicable to re-emerging smallpox and to evaluate consistency and the certainty of the evidence published about the key parameters used. We conducted a systematic review using PRISMA criteria, of assumptions used in modeling studies on duration of latent, prodromal, and infectious period, as well as the choice of the basic reproduction number (R0) for re-emerging smallpox. We performed a literature search using PubMED, Scopus, Web of Science, and EMBASE and included peer-reviewed articles that focused on smallpox models, stated at least three of the aforementioned parameters and published in English. A total of 42 studies were selected for inclusion. There was general agreement on the duration of latent and prodromal periods, being 11-12 d (88%) and 3 d (59%), respectively. The duration of the infectious period varied from 4 to 20 d. Most models assumed 16 d (19%), 12 d (16.7%), and 8.6 d (12%) of infectiousness. In 25/34 studies, R0 ranged between 3 and 5, generally lower than the R0 calculated from past outbreaks. Models of smallpox re-emergence also tend to use the same limited available historical data sources but assume a wide range of different estimates for key parameters. Models use very optimistic assumptions of decreased population immunity, despite high uncertainty about duration and magnitude of post-vaccination immunity. This review reveals a paradox. A substantial proportion of the modern population is unvaccinated, never exposed to boosting from wild-type smallpox, or immunocompromised; furthermore, vaccine-induced immunity wanes over time. Failure to consider these factors in a model will lead to underestimating the true impact of a re-emergent smallpox epidemic in the contemporary population.

Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens.

Genetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs quickly after resistance is deployed, resulting in significant crop losses and a continuing need to develop new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability. These strategies mainly rely on varying different combinations of resistance sources across time (crop rotations) and space. The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental comparison of the efficiency (i.e. ability to reduce disease impact) and durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of landscape-scale deployment strategies presents major logistical challenges. Therefore, we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above, including both qualitative resistance (i.e. major genes) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model, implemented in the R package landsepi, provides a new and useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters. This article describes the model and its parameterisation for rust diseases of cereal crops, caused by fungi of the genus Puccinia. To illustrate the model, we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance. The comparison of the four major deployment strategies described above will be the objective of future studies.

Quantifying the epidemic spread of Ebola virus (EBOV) in Sierra Leone using phylodynamics

Measuring epidemic parameters early in an outbreak is essential to inform control efforts. Using the viral genome sequence and collection date from 78 infections in the 2014 Ebola virus outbreak in Sierra Leone, we estimate key epidemiological parameters such as infectious period duration (approximately 71 hours) and date of the first case in Sierra Leone (approximately April 25th). We also estimate the effective reproduction number, Re, (approximately 1.26), which is the number of secondary infections effectively caused by an infected individual and accounts for public health control measures. This study illustrates that phylodynamics methods, applied during the initial phase of an outbreak on fewer and more easily attainable data, can yield similar estimates to count-based epidemiological studies.

Quantitative transmission characteristics of different H5 low pathogenic avian influenza viruses in Muscovy ducks

EU annual serosurveillance programs show that domestic duck flocks have the highest seroprevalence of H5 antibodies, demonstrating the circulation of notifiable avian influenza virus (AIV) according to OIE, likely low pathogenic (LP). Therefore, transmission characteristics of LPAIV within these flocks can help to understand virus circulation and possible risk of propagation. This study aimed at estimating transmission parameters of four H5 LPAIV (three field strains from French poultry and decoy ducks, and one clonal reverse-genetics strain derived from one of the former), using a SIR model to analyze data from experimental infections in SPF Muscovy ducks. The design was set up to accommodate rearing on wood shavings with a low density of 1.6ducks/m2: 10 inoculated ducks were housed together with 15 contact-exposed ducks. Infection was monitored by RNA detection on oropharyngeal and cloacal swabs using real-time RT-PCR with a cutoff corresponding to 2–7 EID50. Depending on the strain, the basic reproduction number (R0) varied from 5.5 to 42.7, confirming LPAIV could easily be transmitted to susceptible Muscovy ducks. The lowest R0 estimate was obtained for a H5N3 field strain, due to lower values of transmission rate and duration of infectious period, whereas reverse-genetics derived H5N1 strain had the highest R0. Frequency and intensity of clinical signs were also variable between strains, but apparently not associated with longer infectious periods. Further comparisons of quantitative transmission parameters may help to identify relevant viral genetic markers for early detection of potentially more virulent strains during surveillance of LPAIV.

Assessing the potential impact of Salmonella vaccines in an endemically infected dairy herd

Salmonella spp. in cattle contribute to bacterial foodborne disease for humans. Reduction of Salmonella prevalence in herds is important to prevent human Salmonella infections. Typical control measures are culling of infectious animals, vaccination, and improved hygiene management. Vaccines have been developed for controlling Salmonella transmission in dairy herds; however, these vaccines are imperfect and a variety of vaccine effects on susceptibility, infectiousness, Salmonella shedding level, and duration of infectious period were reported. To assess the potential impact of imperfect Salmonella vaccines on prevalence over time and the eradication criterion, we developed a deterministic compartmental model with both replacement (cohort) and lifetime (continuous) vaccination strategies, and applied it to a Salmonella Cerro infection in a dairy farm. To understand the uncertainty of prevalence and identify key model parameters, global parameter uncertainty and sensitivity analyses were performed. The results show that imperfect Salmonella vaccines reduce the prevalence of Salmonella Cerro. Among three vaccine effects that were being considered, decreasing the length of the infectious period is most effective in reducing the endemic prevalence. Analyses of contour lines of prevalence or the critical reproduction ratio illustrate that, reducing prevalence to a certain level or zero can be achieved by choosing vaccines that have either a single vaccine effect at relatively high effectiveness, or two or more vaccine effects at relatively low effectiveness. Parameter sensitivity analysis suggests that effective control measures through applying Salmonella vaccines should be adjusted at different stages of infection. In addition, lifetime (continuous) vaccination is more effective than replacement (cohort) vaccination. The potential application of the developed vaccination model to other Salmonella serotypes related to foodborne diseases was also discussed. The presented study may be used as a tool for guiding the development of Salmonella vaccines.

A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data

We propose a transmission model to estimate the main characteristics of influenza transmission in households. The model details the risks of infection in the household and in the community at the individual scale. Heterogeneity among subjects is investigated considering both individual susceptibility and infectiousness. The model was applied to a data set consisting of the follow-up of influenza symptoms in 334 households during 15 days after an index case visited a general practitioner with virologically confirmed influenza. Estimating the parameters of the transmission model was challenging because a large part of the infectious process was not observed: only the dates when new cases were detected were observed. For each case, the data were augmented with the unobserved dates of the start and the end of the infectious period. The transmission model was included in a 3-levels hierarchical structure: (i) the observation level ensured that the augmented data were consistent with the observed data, (ii) the transmission level described the underlying epidemic process, (iii) the prior level specified the distribution of the parameters. From a Bayesian perspective, the joint posterior distribution of model parameters and augmented data was explored by Markov chain Monte Carlo (MCMC) sampling. The mean duration of influenza infectious period was estimated at 3.8 days (95 per cent credible interval, 95 per cent CI [3.1,4.6]) with a standard deviation of 2.0 days (95 per cent CI [1.1,2.8]). The instantaneous risk of influenza transmission between an infective and a susceptible within a household was found to decrease with the size of the household, and established at 0.32 person day(-1) (95 per cent CI [0.26,0.39]); the instantaneous risk of infection from the community was 0.0056 day(-1) (95 per cent CI [0.0029,0.0087]). Focusing on the differences in transmission between children (less than 15 years old) and adults, we estimated that the former were more likely to transmit than adults (posterior probability larger than 99 per cent), but that the mean duration of the infectious period was similar in children (3.6 days, 95 per cent CI [2.3,5.2]) and adults (3.9 days, 95 per cent CI [3.2,4.9]). The posterior probability that children had a larger community risk was 76 per cent and the posterior probability that they were more susceptible than adults was 79 per cent.