The impact of prior information on estimates of disease transmissibility using Bayesian tools

The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R0 = 1.36-1.46, μ = 2.0-2.7, μ = mean of the SI). The estimates of R₀ and μ for the SARS outbreak ranged from 2.0-4.4 and 7.4-11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample

Estimating the reproductive number in the presence of spatial heterogeneity of transmission patterns

Background: Estimates of parameters for disease transmission in large-scale infectious disease outbreaks are often obtained to represent large groups of people, providing an average over a potentially very diverse area. For control measures to be more effective, a measure of the heterogeneity of the parameters is desirable. Methods: We propose a novel extension of a network-based approach to estimating the reproductive number. With this we can incorporate spatial and/or demographic information through a similarity matrix. We apply this to the 2009 Influenza pandemic in South Africa to understand the spatial variability across provinces. We explore the use of five similarity matrices to illustrate their impact on the subsequent epidemic parameter estimates. Results: When treating South Africa as a single entity with homogeneous transmission characteristics across the country, the basic reproductive number, R0, (and imputation range) is 1.33 (1.31, 1.36). When fitting a new model for each province with no inter-province connections this estimate varies little (1.23-1.37). Using the proposed method with any of the four similarity measures yields an overall R0 that varies little across the four new models (1.33 to 1.34). However, when allowed to vary across provinces, the estimated R0 is greater than one consistently in only two of the nine provinces, the most densely populated provinces of Gauteng and Western Cape. Conclusions: Our results suggest that the spatial heterogeneity of influenza transmission was compelling in South Africa during the 2009 pandemic. This variability makes a qualitative difference in our understanding of the epidemic. While the cause of this fluctuation might be partially due to reporting differences, there is substantial evidence to warrant further investigation

Determining the dynamics of influenza transmission by age

Background: It is widely accepted that influenza transmission dynamics vary by age; however methods to quantify the reproductive number by age group are limited. We introduce a simple method to estimate the reproductive number by modifying the method originally proposed by Wallinga and Teunis and using existing information on contact patterns between age groups. We additionally perform a sensitivity analysis to determine the potential impact of differential healthcare seeking patterns by age. We illustrate this method using data from the 2009 H1N1 Influenza pandemic in Gauteng Province, South Africa. Results: Our results are consistent with others in showing decreased transmission with age. We show that results can change markedly when we make the account for differential healthcare seeking behaviors by age. Conclusions: We show substantial heterogeneity in transmission by age group during the Influenza A H1N1 pandemic in South Africa. This information can greatly assist in targeting interventions and implementing social distancing measures

Communicable Disease Epidemiology and Control: a Global Perspective, 3rd ed.

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Evaluating Watershed Condition: Bottom Up Vs. Top Down Approaches?

Habitat degradation has been identified as one of the major factors affecting the declines of fishes in the Columbia River Basin. The condition of physical habitat and the biotic integrity of stream systems are often directly correlated with substantial alterations to key landscape attributes. As such, numerous approaches to measure watershed condition have been developed. Here, we compare two separate measures of watershed condition: 1) a GIS-based measure of condition, i.e., top down; and 2) a ground based assessment of condition, i.e., bottom up), using field data collected across 1200 sites in the Interior Columbia River Basin under the PIBO Effectiveness Monitoring Project. With our GIS approach, we integrate land management and natural disturbance from watershed upstream of sample reaches into an overall watershed condition score. With our bottom-up approach, we integrate stream temperature data, indices of macroinvertebrate health, and an index of physical habitat condition from reach-level field data into an overall condition score. Our results indicate significant differences in assessments of condition across the two methods, as the GIS approach ranked considerably more watersheds with management activities into a low condition category than found in the bottom-up approach. Conversely, the GIS approach also categorized most watersheds with no or minimal management activities, i.e., reference, as low risk, while the field-based, bottom up approach illustrated a wide range of condition of reference sites due to natural disturbances. Our results suggest GIS-based approaches tended to quantify the ‘risk’ rather than condition within watersheds. The bottom-up approach tended to quantify actual conditions within streams, without consideration of potential risks associated with land management activities. Here, we advocate the most beneficial approach that would be some combination of the two to help guide and prioritize restoration activities to enhance habitat conditions and minimize risk of catastrophic disturbances

Quantifying Temporal Variability in Stream Habitat Data: Implications for Restoration and Monitoring

Quantifying natural and anthropogenic-induced levels of temporal variability is essential for robust trend analyses and for evaluating the effectiveness of restoration activities or changed management actions. Here, we used data collected as part of the Pacfish/Infish Biological Effectiveness Monitoring Project to evaluate the extent of temporal variability in instream habitat collected at the reach scale. We integrated habitat data collected yearly (2001-2009) at 50 sites experiencing a range of management activities into our analyses to better understand the consistency of temporal variability in watersheds with inherently different landscape characteristics and disturbance regimes. We initially decomposed variance estimates to remove site-to-site variability, sampling error, and year effects and use the remaining variance as a measure of site-specific temporal variability. We then relate this temporal variability to landscape, management, and climate attributes at multiple scales to better understand which characteristics result in more or less variability in habitat attributes at specific sites. Our results suggest temporal variability differs significantly across individual sites and attributes within sites, indicating our ability to detect significant changes as a result of management changes and/or restoration efforts are context dependent. The spatial scale of landscape attributes, e.g., stream buffer vs. catchment, related to temporal variability also varied across individual attributes. Our efforts highlight the importance of considering site specific measures of temporal variability as they relate to specific restoration and management goals

Searching for Sharp Drops in the Incidence of Pandemic A/H1N1 Influenza by Single Year of Age

BACKGROUND During the 2009 H1N1 pandemic (pH1N1), morbidity and mortality sparing was observed among the elderly population; it was hypothesized that this age group benefited from immunity to pH1N1 due to cross-reactive antibodies generated from prior infection with antigenically similar influenza viruses. Evidence from serologic studies and genetic similarities between pH1N1 and historical influenza viruses suggest that the incidence of pH1N1 cases should drop markedly in age cohorts born prior to the disappearance of H1N1 in 1957, namely those at least 52-53 years old in 2009, but the precise range of ages affected has not been delineated. METHODS AND FINDINGS To test for any age-associated discontinuities in pH1N1 incidence, we aggregated laboratory-confirmed pH1N1 case data from 8 jurisdictions in 7 countries, stratified by single year of age, sex (when available), and hospitalization status. Using single year of age population denominators, we generated smoothed curves of the weighted risk ratio of pH1N1 incidence, and looked for sharp drops at varying age bandwidths, defined as a significantly negative second derivative. Analyses stratified by hospitalization status and sex were used to test alternative explanations for observed discontinuities. We found that the risk of laboratory-confirmed infection with pH1N1 declines with age, but that there was a statistically significant leveling off or increase in risk from about 45 to 50 years of age, after which a sharp drop in risk occurs until the late fifties. This trend was more pronounced in hospitalized cases and in women and was independent of the choice in smoothing parameters. The age range at which the decline in risk accelerates corresponds to the cohort born between 1951-1959 (hospitalized) and 1953-1960 (not hospitalized). CONCLUSIONS The reduced incidence of pH1N1 disease in older individuals shows a detailed age-specific pattern consistent with protection conferred by exposure to influenza A/H1N1 viruses circulating before 1957.The project described was supported by the National Institute Of General Medical Sciences [Award Number U54GM088558], http://www.nigms.nih. gov/. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute Of General Medical Sciences or the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Outbreak of Rift Valley fever affecting veterinarians and farmers in South Africa, 2008

Background. During 2008, Rift Valley fever (RVF) virus re-emerged in South Africa as focal outbreaks in several provinces. Aims. To investigate an outbreak affecting cattle farmers and farm workers, and the staff and students of a veterinary school, assess the prevalence of infection during the outbreak, document the clinical presentation of cases, and identify potential risk factors. Methods. We conducted a cross-sectional serological survey of exposed veterinarians and farmers, who were examined to determine the presence of current or recent illness. Blood specimens were collected for virus isolation, nucleic acid detection and serology. A subset was interviewed using a standardised questionnaire to obtain data on recent exposures and risk factors for infection. Results. Of 53 participants potentially exposed to infected domestic ruminants, 15% had evidence of recent infection and 4% evidence of past exposure to the RVF virus. The prevalence of acute infection was 21% in veterinarians compared with 9% in farmers and farm workers. After a mean incubation period of 4.3 days, the most frequent symptoms experienced included myalgia (100%), headache (88%) and malaise (75%). No asymptomatic cases were identified. Transmission by direct contact with infected animals was the major risk factor in these professional groups. Performing animal autopsies was significantly associated with acute infection (risk ratio 16.3, 95% confidence interval 2.3 - 114.2). Conclusions. Increased risks associated with veterinary practices highlight a need for the use of personal protective equipment, and identify veterinarians as a primary target group for future vaccination. Results. Of 53 participants potentially exposed to infected domestic ruminants, 15% had evidence of recent infection whilst 4% of past exposure to the RVF virus. The prevalence of acute infection was higher in veterinarians (21%) in comparison to farmers and farm workers (9%). After a mean incubation period of 4.3 days, the most frequent symptoms experienced included myalgia (100%), headache (88%) and malaise (75%). No asymptomatic cases were identified. Transmission by direct contact with infected animals was identified as the major risk factor in these professional groups. Performing animal autopsies was significantly associated with acute infection (risk ratio 16.3, 95% CI 2.3-114.2). Conclusions. Increased risks associated with veterinary practices highlight a need for the use of personal protective equipment, and identify veterinarians as a primary target group for future vaccination

Searching for Sharp Drops in the Incidence of Pandemic A/H1N1 Influenza by Single Year of Age

Background: During the 2009 H1N1 pandemic (pH1N1), morbidity and mortality sparing was observed among the elderly population; it was hypothesized that this age group benefited from immunity to pH1N1 due to cross-reactive antibodies generated from prior infection with antigenically similar influenza viruses. Evidence from serologic studies and genetic similarities between pH1N1 and historical influenza viruses suggest that the incidence of pH1N1 cases should drop markedly in age cohorts born prior to the disappearance of H1N1 in 1957, namely those at least 52–53 years old in 2009, but the precise range of ages affected has not been delineated. Methods and Findings: To test for any age-associated discontinuities in pH1N1 incidence, we aggregated laboratory-confirmed pH1N1 case data from 8 jurisdictions in 7 countries, stratified by single year of age, sex (when available), and hospitalization status. Using single year of age population denominators, we generated smoothed curves of the weighted risk ratio of pH1N1 incidence, and looked for sharp drops at varying age bandwidths, defined as a significantly negative second derivative. Analyses stratified by hospitalization status and sex were used to test alternative explanations for observed discontinuities. We found that the risk of laboratory-confirmed infection with pH1N1 declines with age, but that there was a statistically significant leveling off or increase in risk from about 45 to 50 years of age, after which a sharp drop in risk occurs until the late fifties. This trend was more pronounced in hospitalized cases and in women and was independent of the choice in smoothing parameters. The age range at which the decline in risk accelerates corresponds to the cohort born between 1951–1959 (hospitalized) and 1953–1960 (not hospitalized). Conclusions: The reduced incidence of pH1N1 disease in older individuals shows a detailed age-specific pattern consistent with protection conferred by exposure to influenza A/H1N1 viruses circulating before 1957

Typhoid Fever and Invasive Nontyphoid Salmonellosis, Malawi and South Africa

To determine the prevalence of invasive nontyphoid salmonellosis and typhoid fever in Malawi and South Africa, we compared case frequency and patient age distribution. Invasive nontyphoid salmonellosis showed a clear bimodal age distribution; the infection developed in women at a younger age than in men. Case frequency for typhoid fever was lower than for salmonellosis