Dataset Information

Inferring influenza infection attack rate from seroprevalence data.

ABSTRACT: Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1?40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1?20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3-12, 13-19, 20-29, 30-59 became MN1?40 seropositive, which was much lower than the 90%-100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.

SUBMITTER: Wu JT

PROVIDER: S-EPMC3974861 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Inferring influenza infection attack rate from seroprevalence data.

Wu Joseph T JT Leung Kathy K Perera Ranawaka A P M RA Chu Daniel K W DK Lee Cheuk Kwong CK Hung Ivan F N IF Lin Che Kit CK Lo Su-Vui SV Lau Yu-Lung YL Leung Gabriel M GM Cowling Benjamin J BJ Peiris J S Malik JS

PLoS pathogens 20140403 4

Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1∶40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because differe ...[more]

PMID: 24699693

Similar Datasets

Project description:BACKGROUND:Serial cross-sectional data on antibody levels to the 2009 pandemic H1N1 influenza A virus from a population can be used to estimate the infection attack rates and immunity against future infection in the community. METHODS:From April through December 2009, we obtained 12,217 serum specimens from blood donors (aged 16-59 years), 2520 specimens from hospital outpatients (aged 5-59 years), and 917 specimens from subjects involved in a community pediatric cohort study (aged 5-14 years). We estimated infection attack rates by comparing the proportions of specimens with antibody titers ? 1:40 by viral microneutralization before and after the first wave of the pandemic. Estimates were validated using paired serum samples from 324 individuals that spanned the first wave. Combining these estimates with epidemiologic surveillance data, we calculated the proportion of infections that led to hospitalization, admission to the intensive care unit (ICU), and death. RESULTS:We found that 3.3% and 14% of persons aged 5-59 years had antibody titers ? 1:40 before and after the first wave, respectively. The overall attack rate was 10.7%, with age stratification as follows: 43.4% in persons aged 5-14 years, 15.8% in persons aged 15-19 years, 11.8% in persons aged 20-29 years, and 4%-4.6% in persons aged 30-59 years. Case-hospitalization rates were 0.47%-0.87% among persons aged 5-59 years. Case-ICU rates were 7.9 cases per 100,000 infections in persons aged 5-14 years and 75 cases per 100,000 infections in persons aged 50-59 years, respectively. Case-fatality rates were 0.4 cases per 100,000 infections in persons aged 5-14 years and 26.5 cases per 100,000 infections in persons aged 50-59 years, respectively. CONCLUSIONS:Almost half of all school-aged children in Hong Kong were infected during the first wave. Compared with school children aged 5-14 years, older adults aged 50-59 years had 9.5 and 66 times higher risks of ICU admission and death if infected, respectively.

Project description:BackgroundThe recent spread of avian influenza in wild birds and poultry may be a precursor to the emergence of a 1918-like human pandemic. Therefore, stockpiles of human pre-pandemic vaccine (targeted at avian strains) are being considered. For many countries, the principal constraint for these vaccine stockpiles will be the total mass of antigen maintained. We tested the hypothesis that lower individual doses (i.e., less than the recommended dose for maximum protection) may provide substantial extra community-level benefits because they would permit wider vaccine coverage for a given total size of antigen stockpile.Methods and findingsWe used a mathematical model to predict infection attack rates under different policies. The model incorporated both an individual's response to vaccination at different doses and the process of person-to-person transmission of pandemic influenza. We found that substantial reductions in the attack rate are likely if vaccines are given to more people at lower doses. These results are applicable to all three vaccine candidates for which data are available. As a guide to the magnitude of the effect, we simulated epidemics based on historical studies of immunogenicity. For example, for one of the vaccines for which data are available, the attack rate would drop from 67.6% to 58.7% if 160 out of the total US population of 300 million were given an optimal dose rather than 20 out of 300 million given the maximally protective dose (as promulgated in the US National Pandemic Preparedness Plan). Our results are conservative with respect to a number of alternative assumptions about the precise nature of vaccine protection. We also considered a model variant that includes a single high-risk subgroup representing children. For smaller stockpile sizes that allow vaccine to be offered only to the high-risk group at the optimal dose, the predicted benefits of using the homogenous model formed a lower bound in the presence of a risk group, even when the high-risk group was twice as infective and twice as susceptible.ConclusionsIn addition to individual-level protection (i.e., vaccine efficacy), the population-level implications of pre-pandemic vaccine programs should be considered when deciding on stockpile size and dose. Our results suggest that a lower vaccine dose may be justified in order to increase population coverage, thereby reducing the infection attack rate overall.

Project description:BackgroundThe ongoing COVID-19 pandemic hit South America badly with multiple waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverage. This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate (IFR), infection attack rate (IAR) and reproduction number ([Formula: see text]) for twelve most affected South American countries.MethodsWe fit a susceptible-exposed-infectious-recovered (SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities. Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization, Johns Hopkins Coronavirus Resource Center and Our World in Data. We investigate the COVID-19 mortalities in these countries, which could represent the situation for the overall South American region. We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR, IAR and [Formula: see text] of COVID-19 for the South American countries.ResultsWe simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR (varies between 0.303% and 0.723%), IAR (varies between 0.03 and 0.784) and [Formula: see text] (varies between 0.7 and 2.5) for the 12 South American countries. We observe that the severity, dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous. Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America.ConclusionsThis study reveals possible reasons for the two waves of COVID-19 outbreaks in South America. We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.

Project description:Infectious disease transmission is an inherently spatial process in which a host's home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that a uniform local attack rate is typically only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a travel kernel), and a natural history consistent with pandemic influenza; we show that local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, under the less intuitively likely scenario, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The strength and direction of correlation changes sign for other kernel parameter values. We show that simulation models in which it is assumed implicitly that only infectious individuals move are assuming a slightly unusual specific correlation between population density and attack rate. However, we also show that this potential structural bias can be corrected by using the appropriate non-isotropic kernel that maps infectious-only code onto the isotropic dual-mobility kernel. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates. More generally, these results suggest a genuine risk that mechanistic models of high-resolution attack rate data may reach spurious conclusions if the precise implications of spatial force-of-infection assumptions are not first fully characterized, prior to models being fit to data.

Project description:BackgroundIf repeated interventions against multiple outbreaks are not feasible, there is an optimal level of control during the first outbreak. Any control measures above that optimal level will lead to an outcome that may be as sub-optimal as that achieved by an intervention that is too weak. We studied this scenario in more detail.MethodAn age-stratified ordinary-differential-equation model was constructed to study infectious disease outbreaks and control in a population made up of two groups, adults and children. The model was parameterized using influenza as an example. This model was used to simulate two consecutive outbreaks of the same infectious disease, with an intervention applied only during the first outbreak, and to study how cumulative attack rates were influenced by population composition, strength of inter-group transmission, and different ways of triggering and implementing the interventions. We assumed that recovered individuals are fully immune and the intervention does not confer immunity.Results/conclusionThe optimal intervention depended on coupling between the two population sub-groups, the length, strength and timing of the intervention, and the population composition. Population heterogeneity affected intervention strategies only for very low cross-transmission between groups. At more realistic values, coupling between the groups led to synchronization of outbreaks and therefore intervention strategies that were optimal in reducing the attack rates for each subgroup and the population overall coincided. For a sustained intervention of low efficacy, early intervention was found to be best, while at high efficacies, a delayed start was better. For short interventions, a delayed start was always advantageous, independent of the intervention efficacy. For most scenarios, starting the intervention after a certain cumulative proportion of children were infected seemed more robust in achieving close to optimal outcomes compared to a strategy that used a specified duration after an outbreak's beginning as the trigger.

Project description:BackgroundThe first wave of SARS-CoV-2 infection in Chile occurred during the cold season reaching a peak by the end of June 2020, with 80 % of the cases concentrated in its capital, Santiago. The main objective of this study was to estimate the attack rate during this first wave of SARS-CoV-2 in a large, densely populated city with more than seven million inhabitants. Since the number of confirmed cases provides biased information due to individuals' potential self-selection, mostly related to asymptomatic patients and testing access, we measured antibodies against SARS-CoV-2 to assess infection prevalence during the first wave in the city, as well as estimate asymptomatic cases, and infection fatality ratio. To our knowledge this is one of the few population-based cross-sectional serosurvey during the first wave in a highly affected emerging country. The challenges of pandemic response in urban settings in a capital city like Santiago, with heterogeneous subpopulations and high mobility through public transportation, highlight the necessity of more accurate information regarding the first waves of new emerging diseases.MethodsFrom April 24 to June 21, 2020, 1326 individuals were sampled from a long-standing panel of household representatives of Santiago. Immunochromatographic assays were used to detect IgM and IgG antibody isotypes.ResultsSeroprevalence reached 6.79 % (95 %CI 5.58 %-8.26 %) in the first 107 days of the pandemic, without significant differences among sex and age groups; this figure indicates an attack rate 2.8 times higher than the one calculated with registered cases. It also changes the fatality rate estimates, from a 2.33 % case fatality rate reported by MOH to an estimated crude 1.00 % (CI95 % 0.97-1.03) infection fatality rate (adjusted for test performance 1.66 % [CI95 % 1.61-1.71]). Most seropositive were symptomatic (81,1 %).ConclusionsDespite the high number of cases registered, mortality rates, and the stress produced over the health system, the vast majority of the people remained susceptible to potential new epidemic waves. We contribute to the understanding of the initial spread of emerging epidemic threats. Consequently, our results provide better information to design early strategies that counterattack new health challenges in urban contexts.

Project description:In an emerging influenza pandemic, estimating severity (the probability of a severe outcome, such as hospitalization, if infected) is a public health priority. As many influenza infections are subclinical, sero-surveillance is needed to allow reliable real-time estimates of infection attack rate (IAR) and severity.We tested 14,766 sera collected during the first wave of the 2009 pandemic in Hong Kong using viral microneutralization. We estimated IAR and infection-hospitalization probability (IHP) from the serial cross-sectional serologic data and hospitalization data. Had our serologic data been available weekly in real time, we would have obtained reliable IHP estimates 1 wk after, 1-2 wk before, and 3 wk after epidemic peak for individuals aged 5-14 y, 15-29 y, and 30-59 y. The ratio of IAR to pre-existing seroprevalence, which decreased with age, was a major determinant for the timeliness of reliable estimates. If we began sero-surveillance 3 wk after community transmission was confirmed, with 150, 350, and 500 specimens per week for individuals aged 5-14 y, 15-19 y, and 20-29 y, respectively, we would have obtained reliable IHP estimates for these age groups 4 wk before the peak. For 30-59 y olds, even 800 specimens per week would not have generated reliable estimates until the peak because the ratio of IAR to pre-existing seroprevalence for this age group was low. The performance of serial cross-sectional sero-surveillance substantially deteriorates if test specificity is not near 100% or pre-existing seroprevalence is not near zero. These potential limitations could be mitigated by choosing a higher titer cutoff for seropositivity. If the epidemic doubling time is longer than 6 d, then serial cross-sectional sero-surveillance with 300 specimens per week would yield reliable estimates when IAR reaches around 6%-10%.Serial cross-sectional serologic data together with clinical surveillance data can allow reliable real-time estimates of IAR and severity in an emerging pandemic. Sero-surveillance for pandemics should be considered.

Dataset Information

Inferring influenza infection attack rate from seroprevalence data.

Publications

Inferring influenza infection attack rate from seroprevalence data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets