Determining the dynamics of influenza transmission by age.
Ontology highlight
ABSTRACT: 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.
SUBMITTER: White LF
PROVIDER: S-EPMC3997935 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThe 1918 influenza pandemic caused disproportionately high mortality among certain age groups. The mechanisms underlying these differences are not fully understood.ObjectivesTo explore the dynamics of the 1918 pandemic and to identify potential age-specific transmission patterns.MethodsWe examined 1915-1923 daily mortality data in New York City (NYC) and estimated the outbreak duration and initial effective reproductive number (Re ) for each 1-year age cohort.ResultsFour pandemic waves occurred from February 1918 to April 1920. The fractional mortality increase (i.e. ratio of excess mortality to baseline mortality) was highest among teenagers during the first wave. This peak shifted to 25- to 29-year-olds in subsequent waves. The distribution of age-specific mortality during the last three waves was strongly correlated (r = 0·94 and 0·86). With each wave, the pandemic appeared to spread with a comparable early growth rate but then attenuate with varying rates. For the entire population, Re estimates made assuming 2-day serial interval were 1·74 (1·27), 1·74 (1·43), 1·66 (1·25), and 1·86 (1·37), respectively, during the first week (first 3 weeks) of each wave. Using age-specific mortality, the average Re estimates over the first week of each wave were 1·62 (95% CI: 1·55-1·68), 1·68 (1·65-1·72), 1·67 (1·61-1·73), and 1·69 (1·63-1·74), respectively; Re was not significantly different either among age cohorts or between waves.ConclusionsThe pandemic generally caused higher mortality among young adults and might have spread mainly among school-aged children during the first wave. We propose mechanisms to explain the timing and transmission dynamics of the four NYC pandemic waves.
| S-EPMC4082668 | biostudies-literature
Project description:The inference of key infectious disease epidemiological parameters is critical for characterizing disease spread and devising prevention and containment measures. The recent emergence of surveillance records mined from big data such as health-related online queries and social media, as well as model inference methods, permits the development of new methodologies for more comprehensive estimation of these parameters. We use such data in conjunction with Bayesian inference methods to study the transmission dynamics of influenza. We simultaneously estimate key epidemiological parameters, including population susceptibility, the basic reproductive number, attack rate, and infectious period, for 115 cities during the 2003-2004 through 2012-2013 seasons, including the 2009 pandemic. These estimates discriminate key differences in the epidemiological characteristics of these outbreaks across 10 y, as well as spatial variations of influenza transmission dynamics among subpopulations in the United States. In addition, the inference methods appear to compensate for observational biases and underreporting inherent in the surveillance data.
| S-EPMC4352784 | biostudies-other
Project description:Background:It has been found that health-seeking behavior has a certain impact on influenza infection. However, behaviors with/without risk perception on the control of influenza transmission among age groups have not been well quantified. Objectives:The purpose of this study was to assess to what extent, under scenarios of with/without control and preventive/protective behaviors, the age-specific network-driven risk perception influences influenza infection. Materials and methods:A behavior-influenza model was used to estimate the spread rate of age-specific risk perception in response to an influenza outbreak. A network-based information model was used to assess the effect of network-driven risk perception information transmission on influenza infection. A probabilistic risk model was used to assess the infection risk effect of risk perception with a health behavior change. Results:The age-specific overlapping percentage was estimated to be 40%-43%, 55%-60%, and 19%-35% for child, teenage and adult, and elderly age groups, respectively. Individuals perceive the preventive behavior to improve risk perception information transmission among teenage and adult and elderly age groups, but not in the child age group. The population with perceived health behaviors could not effectively decrease the percentage of infection risk in the child age group, whereas for the elderly age group, the percentage of decrease in infection risk was more significant, with a 97.5th percentile estimate of 97%. Conclusion:The present integrated behavior-infection model can help health authorities in communicating health messages for an intertwined belief network in which health-seeking behavior plays a key role in controlling influenza infection.
| S-EPMC5846056 | biostudies-literature
Project description:Characterising viable influenza virus from airborne droplets
| PRJEB32456 | ENA
Project description:Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we present a significant improvement to the efficiency of an individual-based stochastic disease simulation framework commonly used in multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations. The ability to perform efficient simulation allows us to run a batch of simulations and take account of their average in real time. The averaged data are stable and can be used to differentiate spreading patterns that are not readily seen by only conducting a few runs.
| S-EPMC2973967 | biostudies-literature
Project description:Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially-resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we describe a significant improvement in the efficiency of an individual-based stochastic disease simulation framework that has been used for multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations.
| S-EPMC2808187 | biostudies-literature
Project description:In this paper, we report the epidemic characteristics of the three co-circulating influenza viruses (i.e., A/H1N1, A/H3N2, and B) in two tropical African cities-Kampala and Entebbe, Uganda-over an eight-year period (2008-2015). Using wavelet methods, we show that influenza epidemics recurred annually during the study period. In most months, two or more influenza viruses co-circulated at the same time. However, the epidemic timing differed by influenza (sub)type. Influenza A/H3N2 caused epidemics approximately every 2 years in both cities and tended to alternate with A/H1N1 or B. Influenza A/H1N1 and B produced smaller but more frequent epidemics and biennial epidemics of these two viruses tended to be synchronous. In addition, epidemics of A/H3N2 were more synchronized in the two cities (located ca.37?km apart) than that of A/H1N1 or influenza B.
| S-EPMC6368486 | biostudies-literature
Project description:During August 2012-November 2014, we conducted a case ascertainment study to investigate household transmission of influenza virus in Managua, Nicaragua. We collected up to 5 respiratory swab samples from each of 536 household contacts of 133 influenza virus-infected persons and assessed for evidence of influenza virus transmission. The overall risk for influenza virus infection of household contacts was 15.7% (95% CI 12.7%-19.0%). Oseltamivir treatment of index patients did not appear to reduce household transmission. The mean serial interval for within-household transmission was 3.1 (95% CI 1.6-8.4) days. We found the transmissibility of influenza B virus to be higher than that of influenza A virus among children. Compared with households with <4 household contacts, those with >4 household contacts appeared to have a reduced risk for infection. Further research is needed to model household influenza virus transmission and design interventions for these settings.
| S-EPMC6154158 | biostudies-literature
Project description:Seasonal influenza outbreaks remain an important public health concern, causing large numbers of hospitalizations and deaths among high-risk groups. Understanding the dynamics of individual transmission is crucial to design effective control measures and ultimately reduce the burden caused by influenza outbreaks. In this study, we analyzed surveillance data from Kamigoto Island, Japan, a semi-isolated island population, to identify the drivers of influenza transmission during outbreaks. We used rapid influenza diagnostic test (RDT)-confirmed surveillance data from Kamigoto island, Japan and estimated age-specific influenza relative illness ratios (RIRs) over eight epidemic seasons (2010/11 to 2017/18). We reconstructed the probabilistic transmission trees (i.e., a network of who-infected-whom) using Bayesian inference with Markov-chain Monte Carlo method and then performed a negative binomial regression on the inferred transmission trees to identify the factors associated with onwards transmission risk. Pre-school and school-aged children were most at risk of getting infected with influenza, with RIRs values consistently above one. The maximal RIR values were 5.99 (95% CI 5.23, 6.78) in the 7-12 aged-group and 5.68 (95%CI 4.59, 6.99) in the 4-6 aged-group in 2011/12. The transmission tree reconstruction suggested that the number of imported cases were consistently higher in the most populated and busy districts (Tainoura-go and Arikawa-go) ranged from 10-20 to 30-36 imported cases per season. The number of secondary cases generated by each case were also higher in these districts, which had the highest individual reproduction number (Reff: 1.2-1.7) across the seasons. Across all inferred transmission trees, the regression analysis showed that cases reported in districts with lower local vaccination coverage (incidence rate ratio IRR = 1.45 (95% CI 1.02, 2.05)) or higher number of inhabitants (IRR = 2.00 (95% CI 1.89, 2.12)) caused more secondary transmissions. Being younger than 18 years old (IRR = 1.38 (95%CI 1.21, 1.57) among 4-6 years old and 1.45 (95% CI 1.33, 1.59) 7-12 years old) and infection with influenza type A (type B IRR = 0.83 (95% CI 0.77, 0.90)) were also associated with higher numbers of onwards transmissions. However, conditional on being infected, we did not find any association between individual vaccination status and onwards transmissibility. Our study showed the importance of focusing public health efforts on achieving high vaccine coverage throughout the island, especially in more populated districts. The strong association between local vaccine coverage (including neighboring regions), and the risk of transmission indicate the importance of achieving homogeneously high vaccine coverage. The individual vaccine status may not prevent onwards transmission, though it may reduce the severity of infection.
| S-EPMC10068240 | biostudies-literature
Project description:Influenza virus evades prevailing natural and vaccine-induced immunity by accumulating antigenic change in the haemagglutinin surface protein. Linking amino acid substitutions in haemagglutinin epitopes to epidemiology has been problematic because of the scarcity of data connecting these scales. We use experiments on equine influenza virus to address this issue, quantifying how key parameters of viral establishment and shedding increase the probability of transmission with genetic distance between previously immunizing virus and challenge virus. Qualitatively similar patterns emerge from analyses based on antigenic distance and from a published human influenza study. Combination of the equine data and epidemiological models allows us to calculate the effective reproductive number of transmission as a function of relevant genetic change in the virus, illuminating the probability of influenza epidemics as a function of immunity.
| S-EPMC3800096 | biostudies-literature