OPEN a ACCESS Freely available 0 n Q online Did the Transmission Potential Did Modeling Overestimate the Transmission Potential Modeling Overestimate of (H1N1-2009)? for of Pandemic Pandemic Size Estimation Estimation for (H1N1-2009)? Sample Sample Size Post-Epidemic Studies Post-Epidemic Seroepidemiological Seroepidemiological Studies 1,2,3 4,5 4,6 Hiroshi Nishiura *, Gerardo Chowell Castillo-Chavez Hiroshi Gerardo Carlos Castillo-Chavez4'6 Nishiura1'2'3*, ChoweII4'5,, Carlos 11 PRESTO, Japan, 2 Theoretical Epidemiology, The Netherlands, of Public Science and and Technology 2TheoreticaI of Utrecht, 3 School School of Public Health, PRESTO, Japan Saitama, Japan, Utrecht, Utrecht, Utrecht, The Netherlands, 3 Health, Japan Science Technology Agency, Agency, Saitama, Epidemiology, University University of The Mathematical and Center, School School of Evolution and Social Social Change, Change, Arizona Arizona The University of Hong 4Mathematical and Computational Sciences of Human Human Evolution and China, 4 Center, University of Hong Kong, Kong, Hong Hong Kong, Kong, China, Computational Modeling Modeling Sciences State University, United States States of of America, 5 Fogarty International National Institutes of United States State Center, Institutes of Health, States of of America, America, Arizona, United America, 5 Center, National Health, Bethesda, Bethesda, Maryland, University, Tempe, Tempe, Arizona, Fogarty International Maryland, United 6 Santa Fe 6Santa Fe Institute, Santa Fe, New Mexico, United States States of of America America Institute, Santa Fe, New Mexico, United Abstract Abstract studies before and and after after the of H1N1-2009 þÿH IN I 2009 are useful for Background: before the epidemic wave of are useful for estimating eprdemrc wave Background Seroepidemiological Seroepidemiological studies estimating attack rates rates with a a potential validate estimates of the number R rn modeling population with to validate early of the reproduction R, in studies. population attack potential to reproduction number, early estimates modeling studies Methodology/Principal final epidemic the proportion of in a population who become Since the the final size the of individuals rndrvrduals rn a become eprdemrc size, proportion population who Methodology/Prlnclpal Findings: Flndlngs Since infected during an epidemic, rs not not the the result result of of a a binomial brnomral sampling because infection rnfectron events events are not independent infected are not eprdemrc is process because independent during an sampling process of each each other, other we we the use use of an an asymptotic drstrrbutron of the size to 95% confidence of propose the of of the final final size to compute confidence propose compute approximate approximate 95% asymptotic distribution intervals of of the the observed observed final size This allows the of the observed final sizes based on intervals final size. This allows the comparison of the observed final sizes against predictions on the the comparison predictions based against þÿþÿ I I5 1.40 þÿ40 I and 1.90), þÿ90) I which also yields modeling and which also formulae for for determining sample sizes sizes for for future future (R = 1.15, simple formulae modeling study study (R yields simple determining sample studies. We We examine examine of eleven eleven published of H1N1-2009 þÿHN1-2009 I that seroepidemiological aa total total of studies of that took took published seroepidemiological seroepidemiological studies. seroepidemiological studies after observing the peak incidence rn a a number number of countries. countries Observed rn srx place in of Observed seropositive six studies studies appear appear place after peak incidence seropositive proportions proportions in observing the to be be smaller smaller than than that þÿ I40 four of the srx studies sampled serum less than month after to that predicted from R = 1.40; four of the six studies sampled serum less than one one month after the the predicted from incidence The comparison of the observed final sizes þÿþÿ I I5and and 1.90 þÿ90 I reveals reveals that all eleven eleven studies reported The of the observed final sizes against that all studies reported peak peak incidence. comparison against R = 1.15 not to be be significantly the prediction þÿ I I5 but but final sizes rn nine studies rndrcate appear not to from the with R = 1.15, final sizes in nine studies indicate appear predrctron with significantly deviating devratrng from overestrmatron the value value R = 1.90 þÿ90 I is rs used. used overestimation ifrf the Conclusions sizes of published studies were of model model Conclusions: Sample of were too too small small toto assess assess the the valrdrty validity of Sample sizes published seroepidemiological seroeprdemrologrcal studies þÿ90 I was used We We recommend recommend the use of the rn determining predictions when R = 1.90 was used. the use of the proposed approach in the sample sample predictions except except when proposed approach determining the size of post-epidemic studies 95% confidence confidence interval of size of calculating the 95% interval of observed observed final final srze size, and and post eprdemrc seroepidemiological seroeprdemrologrcal studies, calculating the relevant instead of of the use of methods methods that on a brnomral conducting hypothesis the use of that rely a binomial proportion. proportion conducting relevant hypothesis testing testing instead rely on Cltatlon Nrshrura H, H Chowell Chowell G Castillo-Chavez Castrllo Chavez C (2011) Drd Modeling Overestrmate the Transmrssron Potentral of Pandemic Pandemrc Citation: Nishiura G, the Transmission Potential of (H1N1-2009)? Sample Srze Size (2011) Did (H1N1 2009)7 Sample Modelrng Overestimate Estrmatron for Post-Epidemic Post Eprdemrc Seroepidemiological Studres PLoS ONE ONE 6(3): e17908 dor 10 1371/journal pone 0017908 Estimation for PLoS doi:10.1371/journal.pone.0017908 6(3) e17908. Seroeprdemrologrcal Studies. Editor Alessandro Indrana University at Bloomington, Unrted States States of of America Amerrca Editor: Alessandro Vespignani, Vesprgnanr Indiana Un|vers|ty at Bloomrngton United Received December 14 2010; 2010 Accepted 15 2011; 2011 Published Published March 24, 24 2011 2011 Received December 14, March Accepted February February 15, © 2011 2011 Nishiura Nrshrura et et al. al This Thrs is rs an an artrcle distributed drstrrbuted under the the terms terms of the the Creatrve Commons Attrrbutron Lrcense whrch Copyright: ß open-access article under of Creative Commons Attribution License, which permits Copyright open access permrts unrestrrcted use drstrrbutron and reproduction rn any medrum the original author and and source source are credrted unrestricted use, distribution, and provided are credited. reproductron in any medium, provrded the orrgrnal author HN was was the Japan Science and and Technology PRESTO program. GC received received financial Liberal Arts Funding: supported Japan Science Technology Agency GC financial support support from from the the College of of the the Liberal Arts Funding: HN supported by the Agency PRESTO program. and Sciences Sciences of of Arizona Arizona State University. National Science Science Foundation Foundation Grant DMS DMS - 0502349), U.S. Department of Defense Defense (NSA - Grant and State (NSF - Grant Grant H98230-06-1-0097), 0502349), U.S. H98230-06-1-0097), University. National Department of the Alfred Alfred T. Sloan Sloan Foundation Foundation and the the Office Office of of the the Provost Provost of Arizona Arizona State University CCC's research. research. The had no no role in in study the and of State The funders funders had role study design, data data University support support CCC’s collection and analysis, decision to publish, or preparation of the the manuscript. collection and to analysis, decision publish, or preparation of manuscript. - - - Interests: The authors authors have have declared declared that no no interests exist. Competing The that competing exist. Competing Interests: competing interests E-mail: [email protected] [email protected] * E-mail: * the population attack attack rate estimating rate (i.e. (i.e. infected infected fraction fraction of of aa estimating the here also also referred as the referred toto as the final final size size oror the the population) [4], [4], here of infected infected individuals in a a population at proportion of individuals in the end end of of anan proportion population at the epidemic. In addition, population-wide seroepidemiological seroepidemiological sursuraddition, population-Wide epidemic. In are useful for in realveys are useful for monitoring epidemiological dynamics in realveys monitoring epidemiological dynamics effectiveness of time, of certain certain interventions interventions [5], and time, assessing assessingeffectiveness [5], and of vaccination determining prioritization prioritization strategies strategies of vaccination during the the course of an course of an epidemic epidemic (e.g. (e.g. identifying identifying subpopulations subpopulations that that should should be at particular times be vaccinated vaccinated at times during anan ongoing epidemic) epidemic) [6,7]. [6,7]. Both and epidemiological Both serological studies have have serological and epidemiological modeling studies increased our of the the transmission increased our understanding transmission dynamics of understanding of dynamics of from the of the the H1N1-2009 H1N1-2009 from the beginning of pandemic [4,8]. In [4,8]. In the reproduction number, the average R, defined defined asas the average number, R, particular, the number of secondary cases number of generated by by aa single primary case case secondary cases generated single primary Introduction Introduction Influenza caused the the first first influenza influenza of Influenza A (H1N1-2009) caused pandemic of A(H1N1-2009) pandemic the twenty-first century A substantial substantial fraction of the the World the fraction of world century [1]. A has probably been infected infected already with this this virus, population has virus, but but probably been already with direct estimation estimation of the the infected infected fraction of the the population is not not aa direct of fraction of feasible by on available þÿ case data data feasible available epidemiological by relying only only on epidemiological ‘case’ surveillance data consisting of confirmed confirmed cases or influenza(e.g. data cases or influenza(e.g. surveillance consisting of like illness illness cases). In particular, influenza influenza known to involve like is known to involve cases). In infections and disease disease severity tends to to be selfasymptomatic [2], be selfasymptomatic infections [2], and severity tends individuals who often do do not not limiting healthy individuals who often require limiting among among medical attention. due to to the the non-specific nature of medical attention. Moreover, of Moreover, due non-specific nature influenza-like illness insufficient to confirm or symptoms, influenza-like illness is insufficient to confirm or symptoms, exclude the diagnosis of influenza influenza exclude the [3]. Therefore, Therefore, seroepidemiodiagnosis of seroepidemiostudies before before and and after after an an Wave are crucial for logical studies epidemic are crucial for epidemic wave PLoS ONE ONE || www.plosone.org PLoS www.plosone.org 1 March 2011 2011 6 | Issue Issue 3 3 | e17908 March | Volume Volume 6 e17908 Sample for Post-Epidemic Studies Size for Sample Size Post-Epidemic Serological Serological Studies throughout of [9], was estimated using its entire entire course course of infection infection estimated throughout its [9], was using epidemiological data during the early stages of the pandemic. data the of the One epidemiological during early stages pandemic. One of R is its potential to provide early of the the important features of of R its potential to provide and important features early and crude predictions crude of the the expected final epidemic size [10]. For predictions of expected final epidemic size [10]. For instance, estimate for is the frequently cited initial initial estimate for H1N1-2009 H1N1-2009 instance, the frequently cited R = 1.40 [8], R=1.40 and the the final final size size equation of any [8], and equation of any homogeneously homogeneously model an mixing (with initially fully susceptible population) mixing model (with an initially fully susceptible population) that 51.1% 51.1% of the the population would infection predicts of experience predicts that population would experience infection the end end of of the the epidemic next several by section). Nevertheless, several by the epidemic (see (seenext section).Nevertheless, studies have that the infected seroepidemiological studies have suggested that the infected seroepidemiological suggested fraction was to be be smaller smaller than 51.1% 51.1% result that that fraction was likely than [11], likely to [11], aa result has led led researchers researchers to speculate on additional has to additional (often speculate on (often unforeseen) unforeseen) mechanisms or factors the transmission transmission mechanisms or factors influencing dynamics. influencing the dynamics. Hence, play aa key key role validating studies play role in in validating Hence, seroepidemiological seroepidemiological studies crude predictions R. the crude based on on R. Further, whenever the observed observed Further, whenever predictions based (sample) than based on R, the of final size size is smaller smaller than that that based on the use use of R, (sample) final seroepidemiological provide indirect evidence of studies may indirect evidence of the the seroepidemiologicalstudies may provide positive particular public effect of of particular health interventions. interventions. positive effect public health A glance at the the literature literature shows that that various A shows various seroepidemiological glance at seroepidemiological studies published so far far have have adopted binomial studies sampling process published so adopted aa binomial sampling process to quantify uncertainty ‘proportion’ to the of the the of infected infected quantify the uncertainty of þÿ proportion of individuals the confidence confidence intervals of individuals (e.g. intervals of (e.g. [12,13]). [12,13]). Accordingly, Accordingly, the the proportion have also also been been derived derived from binomial distribution the from aa binomial distribution proportion have exact or one of the the using or approximate methods [6,14,15]. of using exact approximate methods [6,14,15]. Perhaps Perhaps one main reasons reasons for widespread use of the the binomial in this this main for of binomial proportion proportion in Widespreaduse context can be attributed attributed to a a well-known well-known and simple context can be to and formula for for simple formula the sample size determination determination of the the binomial binomial the of proportion sample size proportion [16]. [16]. it should should be noted noted that that H1N1-2009 transmitted Nevertheless, be is transmitted H1N1-2009 Nevertheless, it from human human to human, and the the risk risk of of infection infection in one one individual from to in individual human, and on other individuals individuals in the the same same unit. This This depends other in population unit. depends on population highlights need to account for the so-called ‘‘dependent the need to account for the so-called highlights the þÿ dependent happening’’ [17,18]. Moreover, an observed final size size represents represents aa an observed final Moreover, þÿhappening[17,18]. single among all all possible possible sample paths of of the the stochastic realization realization single stochastic among sample paths epidemic, to explicitly account for for demographic need to epidemic, indicating indicating aa need explicitly account demographic stochasticity. issues for aa formal formal framework framework for for These issues call call for stochasticity. These determining post-epidemic seroepidemiological seroepidemiological the sample size of of post-epidemic determining the sample size studies. studies. The purpose of present study is toto introduce an approximate The purpose of the the present introduce an study is approximate method the computation of the method for for the computation of the uncertainty bound of of the the final final uncertainty bound epidemic which also permits us to simple methods methods for for also permits us to discuss discuss simple size, which epidemic size, sample We reanalyze reanalyze published published datasets datasets of of postpostsize calculations. calculations. We sample size peak seroepidemiological studies of of H1N1-2009 H1N1-2009 and explicitly test test and explicitly peak seroepidemiologicalstudies if R for for H1N1-2009 indicated aa biased biased estimate estimate if early estimates of of R indicated H1N1-2009 early estimates of final epidemic of the the final size. epidemic size. Materials and Materials and Methods Methods Seroepidemiological data Seroepidemiological data As to motivate our As aa way way to motivate our study, we we start start by by presenting presenting summary summary study, results of of the studies of Table results the seroepidemiological of H1N1-2009. Table 11 H1N1-2009. seroepidemiologicalstudies summarizes a total total of seroepidemiological studies studies that that were were summarizes a of eleven eleven seroepidemiological conducted after conducted after observing peak peak incidence incidence of of H1N1-2009 H1N1-2009 in in observing various If the the epidemic various populations populations [6,7,11–15,19–22]. curve [6,7,11-15,19-22] If epidemic curve revealed revealed aa multimodal multimodal distribution distribution with with clearly clearly distinct distinct peaks, peaks, the the datasets can can either post-peak datasets either be be after after the the first first wave wave (e.g. (e.g. England England post-peak our interest to to London London and [14], but we we restrict restrict our interest and the the West West Midland, Midland, [14] but because other areas areas were or after the second wave because other were far far less less affected) affected) or after the second wave The majority of studies studies sampled (e.g. USA [13]). majority of sampled serum serum from from (e.g. USA [13]). The at clinics clinics or hospital laboratory, registered patients at or blood blood donors, donors, hospital laboratory, registeredpatients defined cohort except for aa defined cohort population in in Singapore and aa except for population Singapore [22] [22] and , Table 1. Post-peak studies of of pandemic influenza Table 1. (H1N1-2009) (H1N1-2009) among Post-peak seroepidemiological pandemic influenza population. seroepidemiological studies among aa general general population. location Survey Survey location Subjects{ Subjects] Sample Sample size{ size] before Prop Prop before (%){ (%)i Prop after Prop after (%){ (%)i Sampling Sampling period{ period] After After Country Country peak1 peak§ Vac¥ Vac¥ Lab Lab method" method1] Australia Australia [19] [19] New South South Wales Wales New Clinical chemistry Clinical chemistry laboratories laboratories 1247 1247 12.8 12.8 28.6 28.6 09 Aug–Sep 09 Aug-Sep Yes Yes No No H|240 HI$40 Canada [15] Canada [15] British Columbia Columbia British Patient sen/ice Patient service center center 1127 1127 *7.5 *7.5 46.0 46.0 10 May May 10 Yes Yes Yes Yes H|240 & HI$40 & MN232 MN$32 China (1) [11] China [11] Beijing Beijing Blood donors donors and and Blood Patients Patients 710 710 *7.5 *7.5 13.8 13.8 Nov-Dec 09 Nov–Dec 09 No No Yes Yes H|240 HI$40 China (2) [6] China [6] Hong Hong Kong Kong Blood donors, Blood donors, cohort pediatric pediatric cohort 2913 2913 3.3 3.3 14.0 14.0 Nov-Dec 09 Nov–Dec 09 Yes Yes No No MN240 MN$40 Germany [7] Germany [7] Frankfurt Frankfurt adults Hospitalized Hospitalized adults 225 225 *7.5 *7.5 12.0 12.0 Nov 09 Nov 09 No No No No H|240 HI$40 India [20] India [20] Pune Pune School children children & School & general general population population 5047 5047 0.9 0.9 15.5 15.5 09 Sep–Oct Sep-Oct 09 No No No No H|240 HI$40 Japan [21] Japan [21] entire Japan entire Japan individuals Healthy Healthy individuals 6035 6035 7.6 7.6 40.3 40.3 10 Jul–Sep 10 Jul-Sep Yes Yes Yes Yes H|240 HI$40 New Zealand Zealand [12] New [12] Auckland Auckland region region Registered Registered patients patients 1147 1147 11.9 11.9 30.3 30.3 Nov 09-Mar Nov 09–Mar 10 Yes Yes Yes Yes H|240 HI$40 Singapore [22] Singapore [22] Singapore Singapore Adult cohort cohort Adult 727 727 2.6 2.6 13.5 13.5 Oct 09 09 Oct Yes Yes No No HI ($4 HI (24 fold fold rise) UK [14] UK [14] England Patients Patients accessing accessing health care care health 275 275 14.5 14.5 22.5 22.5 09 Sep Sep 09 No No No No H|232 HI$32 USA [13] USA [13] Pittsburgh Pittsburgh Clinical laboratories laboratories Clinical 846 846 6.0 6.0 21.5 21.5 Nov 09 Nov 09 No No Yes Yes H|240 HI$40 { size and and sampling period refer refer to to those those after after observing the peak incidence incidence of H1N1-2009. H1N1-2009. For several several studies Subjects, sample size of For studies examining the [Subjects, observing the examining pre-existing pre-existing immunity, immunity, the same or additional additional before the the 2009 2009 pandemic were were at different different time periods, but but are are not included included in this same or samples investigated time not in this Table. Table. samples before investigated at iEstimated before and and after after observing an epidemic was in the used itit as Estimated proportions When age-standardized estimate estimate was given the original we used as the the proportions seropositive seropositive before observing an epidemic peak. When given in original study, we mean. population population mean. *Three studies studies did did not not estimate estimate the proportion before the the 2009 2009 pandemic, and and we we assume that 7.5% 7.5% of immune based on *Three the assume that of the the population was initially based on aa proportion seropositive seropositive before population was initially immune { crude average other studies. studies. crude average among among other 1 §After column if the the sampling took place longer than month after observing the highest incidence incidence of cases. cases. After peak column represents than 1 month after of represents if sampling took observing the ¥ ¥Vaccination column if a a population-wide vaccination of H1N1-2009 H1N1-2009 took place prior Vaccination column represents campaign took to the the sampling. represents if population-wide vaccination campaign of prior to " 11 methods to determine determine inbibition and MN, microneutralization Laboratory methods to seropositivity; assay assay. MN, microneutralization seropositivity; HI, hemagglutination hemagglutination inbibition assay and assay. doi:10.1371/journal.pone.0017908.t001 doi:10.1371/journal.pone.0017908.t001 PLoS ONE ONE || www.plosone.org PLoS www.plosone.org 22 March 2011 2011 6 | Issue Issue 3 3 | e17908 March | Volume Volume 6 e17908 Sample for Post-Epidemic Studies Size for Sample Size Post-Epidemic Serological Serological Studies sample of Japanese population population of study volunteers of the the general sample of study volunteers general _]apanese [21]. Only the Japanese study has not been published the has not been in English; [21]. Only _]apanesestudy published in English; the based National Epidemiological of the data data are are based on on National Surveillance of Epidemiological Surveillance Vaccine-Preventable Diseases which are annually to Vaccine-Preventable Diseases which are conducted to annually conducted understand the epidemiological number of understand the of a a number of epidemiological dynamics dynamics of infectious diseases, infectious at least least 5,400 diseases,involving 5,400 non-randomly involving at non-randomly sampled sampled individuals across all in individuals across all age-groups in each each year and covering 24 age-groups year and covering 24 prefectures per prefecture) prefecture) among aa total individuals total of of 49 49 prefectures (225 (225 individuals per among prefectures Japan. Other published serological Other published were prefecturesacross across_]apan. serologicalsurveys surveys were not included in because they were conducted before the not included in Table Table 1, were conducted before the 1, because they observed or because on confined observed epidemic because they focused on aa confined epidemic peak peak or they focused healthcare workers or population workers or military personnel) population (e.g. (e.g. healthcare military personnel) [5,23– [5,23but a a few few of of them them have have been discussed elsewhere elsewhere 27], been discussed [4]. 27], but The sample size of of the the eleven eleven seroepidemiological The which studies, which sample size seroepidemiologicalstudies, recorded to 6035 recorded post-peak from 225 6035 225 to post-peak seroprevalence, seroprevalence, ranged ranged from individuals. studies examined examined the first individuals. Eight seroprevalence before the first Eight studies seroprevalencebefore the proportion of the the population wave, estimating with preprewave, estimating the proportion of population with VVhere indicated, the sample size existing indicated, the existing immunity immunity (Table (Table 1). 1). Where sample size estimation of those those studies studies relied relied on on estimation of aa binomial binomial proportion proportion [12– [1214,19]. The post-peak varied substantially with, 14,19]. The post-peak sampling sampling period period varied substantially with, for six studies sampling the post-peak serum more than for example, six studies the serum more than 1 1 example, sampling post-peak month after Five month after the the peak incidence. Five studies studies clearly stated that that a a peak incidence. clearly stated vaccination H1N 1-2009 had had population-wide campaign population-wide vaccination campaign against against H1N1-2009 taken place to sampling. The laboratory in taken laboratory method method employed place prior prior to sampling. The employed in these studies studies was was based on on inhibition these based hemagglutination assays hemagglutination inhibition assays (HI) or microneutralization with eight studies setting the or microneutralization assays assays (MN) with eight studies setting the threshold level at at HI$40. HI240. It is practically difficult seropositive level It practically very very difficult seropositivethreshold to determine determine the end end of of an an and thus, we the to the epidemic, regard thus, we epidemic, and regard the observed increase in in seroprevalence after the observed increase (i.e. seroprevalence after the seroprevalence (i.e. seroprevalence minus that that before before the the peak) as an an estimate of of the the fraction fraction of peak estimate of peak minus peak) as infected individuals the epidemic. We used the the ageinfected individuals during used during the epidemic. We agestandardized final size size estimate estimate for an an entire standardized final for entire population when population when in the the original instead of of using crude estimates estimates of of the the given given in original study study instead using crude fraction. The 2009 involved health seropositive The pandemic involved public health 2009 pandemic seropositivefraction. public transmission and spatial interventions, (e.g. interventions, heterogeneous heterogeneous transmission (e.g. age age and spatial and seasonality, as the the first to stimulate stimulate heterogeneities) first step aa but, as heterogeneities)and seasonality,but, step to relevant discussion on this subject, the a relevant discussion on this the present study adopts subject, present study adopts a without homogeneously time-dependent homogeneously mixing mixing assumption assumption without time-dependent dydynamics. we focus on on the difference difference between the namics. Specifically, focus the between the Specifically, we observed final sizes sizes for for an an entire population and the the predictions of observed final entire population and predictions of final size size yielded the modeling the data data in in final by the Thus, the yielded by modeling approach. approach. Thus, Table 1 1 are are here under under the the assumption of a a well-mixed Table analyzed well-mixed analyzed here assumption of It should should be noted noted that, in the the absence absence of of any timepopulation. be that, in population. It any timethe final size is is known known to depend on the dependent final size to the dependent factors, the depend only only on number under the the homogeneous reproduction number R, under mixing assumphomogeneous assumption [9,10]. tion [9,10]. the earliest earliest studies studies in in Mexico the estimation estimation of Following the Mexico [8,28], of [8,28], the R was was conducted the data in different R conducted using the early epidemic growth data in different using early epidemic growth locations across the world world estimates in 2009 locations across the (yielding published estimates in 2009 (yielding published some reassessed The estimated in different different [29–38], reassessed [39]). estimated R, R, in [29-38], some [39]). The and subpopulations, þÿ less than than 1’’ þÿ1 epidemic from ‘‘less epidemic settings settingsand subpopulations, ranged ranged from to greater than 2 [28,29,35]. The definition definition of R also varied [40] of R also varied [40] to greater than [28,29,35]. The from study to to study. One One study, for for example, incorporated the the from of seasonal seasonal variations variations in the the force of infection infection impact of in force of [33]. [33]. Among the earliest earliest estimate estimate of of R was derived from from the the early of these, R was derived these, the early phase phase of the pandemic the Spring in Mexico Mexico the using various 2009 in pandemic during the Spring 2009 using various methods the posterior modeling methods [8]. Using aa Bayesian posterior method, the Bayesian method, median of of R R (and the 95% 95% credible credible intervals) estimated at at 1.40 1.40 median was estimated (and the intervals)was Since the median (1.15, 1.90) [8]. Since the posterior median crudely represents (1.15, 1.90) crudely represents of estimates estimates in in other other published and because because the the mid-point studies, and mid-point of published studies, lower and and upper bounds to the the range of R R in in lower roughly correspond to upper bounds range of other studies studies (with R,2), we focus on on an estimate of R derived other focus an estimate of R derived R<2), we from an an of cases cases in an an outbreak in La La Gloria, from exponential in outbreak in Gloria, exponential growth of Mexico. we not only assess the prediction based on R Mexico. Thus, not the based on R = 1.40, Thus, we 1.40, only assess but also but also on the lower lower and bounds of of R. R. Note Note that that the lower on the and upper the lower upper bounds (1.15) is smaller than the posterior median of R obtained is smaller than the median of R obtained (1.15) posterior using methods in study coalescent other methods in the the same same using other study including including aa coalescent population genetic analysis (R: (R = 1.22). estimate of of RR for for Given an an estimate population genetic analysis 1.22). Given an initially fully susceptible population, and and assuming assuming that that the the an initially fully susceptible population, initial number of is sufficiently smaller than than the the total total initial number of infectives infectives sufficiently smaller population size, the final epidemic size r satisfies the final size satisfies p population size, epidemic bound bound ^ r), 1{r~exp({ R 1-p=<=XP(-Rn), which referred which is referred to the final to asas the final size size equation Both sides sides of of equation [10]. [10]. Both the probability that an individual equation represent the probability that an individual escapes equation (1) (1) represent escapes infection throughout of anan epidemic. epidemic. Since presence infection the course course of Since the the presence throughout the of pre-existing immunity has yet yet toto be be clarified clarified atat the beginning of of of pre-existing the beginning immunity has the 2009 use to calculate the the pandemic, we we use equation (1) to calculate the predicted 2009 pandemic, equation (1) predicted final size. Iteratively 1.40 final epidemic solving (1) for RR being being 1.15, 1.15, 1.40 epidemic size. Iteratively solving (1) for and final size size r and 76.7%, respectively. and 1.90, the final 51.1% and 1.90, the 24.9%, 51.1% 76.7%, respectively. p is 24.9%, We test these forecasts against against the observed final final sizes sizes given given in in We test these forecasts the observed Table 1. For For this this reason, itit is Table 1. is essential essential toto compute uncertainty reason, compute uncertainty bounds 95% confidence confidence bounds (e.g. interval) of the the observed observed final final sizes sizes in in (e.g. 95% interval) of seroepidemiological studies. seroepidemiologicalstudies. bound for binomial Uncertainty for aa binomial proportion proportion Uncertainty bound As As to discussing aa prelude prelude to discussing the the uncertainty uncertainty bound bound of of final final size, size, wewe a a binomial binomial proportion, proportion, which has been used in in published which has been widely widely used published seroepidemiological seroepidemiological studies shown shown in in Table Table 1. 1. Let Let X studies X be be aa binomial binomial random random variable variable for for size n, and let =X/X/n n be sample let r be the the sample sample proportion proportion positive. positive. n, and p = sample size The most confidence interval of The most well-knovvn, well-known, parsimonious, parsimonious, confidence interval of the the binomial binomial proportion, proportion, employs employs aa normal normal approximation approximation toto binomial also referred binomial distribution, which isis also referred toto asas the the Wald Wald distribution, which confidence interval. The confidence interval. The 100(1-2a)% confidence interval interval for for the the 100(1-2oc)%confidence as sample proportion r written as p is written sample proportion first consider the confidence confidence interval of first consider the interval of ^+za r biz. rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ^ (1{^ r r) Q nn , ð2Þ <2> where denotes 1-a 1-oc quantile of the the standard standard normal distribution where zzua denotes normal distribution quantile of = þÿ rules of of thumb’’ þÿthumb suggest for aoc = 0.025). The ‘‘rules suggest that that (e.g. -1.96 for a<21.96 (e.g. zþÿzo, 0.025). The the normal normal as the approximation works well well as long asas np>5 nr.5 and and n(1n(1approximation works long the rules rules of of thumb thumb do not r).5, but the do not always work work out out well well [41]. p)>5, but always [41]. The score interval The computation of of the the Wilson Wilson score interval isis aa better better not difficult and yields alternative, which isis not computationally difficult and yields better better alternative, which computationally of associated associated uncertainty coverage of Here, we we focus focus onon the the coverage uncertainty [42,43]. [42,43]. Here, Wald confidence interval in the the present Wald confidence interval in present study, study, because because we we extend extend its principle to the the computation of 95% confidence its principle to of the the 95% confidence interval interval of of the final size. the final epidemic epidemic size. The idea behind the Wald The idea behind the Wald confidence confidence interval interval comes comes from from the Wald test for that the inverting the Wald test for r. the null null hypothesis hypothesis p. Suppose Suppose that is tested to detect relevant H tested where where one one wishes wishes to detect aa relevant 0:r = r Hozp po0 is alternative where the value of alternative H :r?r , where r is the proposed value of the the Hlzpaépo, po 1 0 0 In the the case case of the the prediction with proportion. In of with R: R = 1.40, po r0 might proportion. be set at at 0.511 0.511 that the be set (assuming the final final size size follows follows aa binomial binomial (assuming that The Wald statistic to distribution). The Wald statistic to be be compared normal compared toto aa normal distribution distribution is given given by by = ^{r0 r 5-90 , s:e:(^ r) s.e.(fJ) þÿ ð3Þ (3) standard error of r, s.e.(^ r) is the the standard error of by the the square square p, approximated by s.e.(f)) root term in root term in (2). The sample size estimation estimation of The of aa binomial binomial proportion proportion can can also also sample size if we m denote of error, a employ In fact, if we let let m denote the the margin of a error, employ (3). In where where = PLoS ONE ONE || www.plosone.org PLoS www.plosone.org ð1Þ (1) 3 March 2011 2011 6 | Issue Issue 3 3 | e17908 March | Volume Volume 6 e17908 Sample for Post-Epidemic Studies Size for Sample Size Post-Epidemic Serological Serological Studies summary of that which of sampling error that quantifies summary sampling error quantifies uncertainty, uncertainty, which corresponds to half the width of a confidence interval for the to half the width of a confidence interval for the corresponds proportion margin of of more than then a a desired desired margin of error error of no no more than m m p, then proportion r, means means of in that population satisfies satisfies ([10]): of cases cases in that population ([10]): r { -ln ln 1{ 1{q : R~ r R= ð4Þ (4) r)ƒm: zz,s.e.(fJ) Sm. a s:e:(^ ^(1{^ 1 r) r A A za @ zi nn _ 2 ƒm S m2: _ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 22 u u s r 2 2 ur33(1(1{r)z r(1{r) ln 1{ (1- )2ln2 u m 1{q s:e:(r)~u : 2 S_e_(p)= u t r N N rz(1{r) p+(1-p)ln ln 1{ 1{q ð5Þ (5) )+(5) Solving for n n gives Solving equation equation (5) (5) for gives z 2 n§ "2 a ^ (1{^ r r), (§)2i>(1-/3), m ð6Þ <6> ð10Þ (10) pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi!2 ^ (1{^ r r)zzb (^ rzm)(1{^ r{m) : m 2_ ð7Þ (7) za/f»<1-f»>+z,fm/<f»+m><1-f»-m> za and (7), it is seen seen that the the sample size n n based on Comparing that based on Comparing (6) (6) and (7), it sample size to the case case for a a of 50% 50% in (7) (6) the for power of in (6) corresponds corresponds to power (7) (i.e. (i.e. zZ5b = z50.5 0.5 = 0). 0)= 1-L) 1-EIN Given of the the generation generation time time are are now now known known for for Given that that q and the the CV CV of q and the Wald interval can H1N1-2009, Wald confidence confidence interval can employ (10) (10) for for H1N1-2009, the employ computing corresponding confidence interval, interval, for for the 95% confidence computing the corresponding 95% and for the minimum hypothesis for estimating estimating the minimum sample sample size size hypothesis testing testing and One should required for post-epidemicseroepidemiological post-epidemic seroepidemiological studies. studies. One should required for bear in mind that estimate isis nevertheless nevertheless conservative conservative (i.e. (i.e. bear in mind that the the error error estimate to be the method likely to be underestimated), underestimated), because because (i) the method isis based based onon likely normal normal approximation, we ignore time-dependent dynamics dynamics approximation, (ii) (ii) we ignore time-dependent health interventions, including public health and (iii) we ignore ignore heterogeheterogeinterventions, and including public (iii) we neous transmission neous transmission (see Discussion Discussion for for (ii) (ii) and and (iii)). (iii)). .N N isis the the (see size in in the the above above expressions. to replace population size we wish wish to replace .N N by by population expressions.IfIf we size n, the binomial of nn has has to sample n, the binomial sampling error of to be be accounted accounted sample size sampling error in the the calculation calculation of the the variance. in of variance. In In the the case case of of simple simple random random the resulting error is the sum sampling, resulting standard standard error is given by the sum of of the the sampling, the given by of two two respective variance of independent processes, processes, i.e. i.e. respectivevariance independent well-known formula for estimating the minimum minimum size n n aa well-known formula for sample estimating the sample size for a a binomial Since the the eventual eventual unknown for binomial proportion. r p is unknown proportion. Since before the survey, one may set r = 0.511 or use aa before the actual actual one set or use p=0.5ll survey, may estimate. It should should be noted noted that published It be that published seroprevalence seroprevalence estimate. does not not account for Type II error error equation explicitly for (i.e. equation (6) (6) does explicitly account Type II (i.e. of the the test) to the power in power of incorporate power in Hence, to power test) [44]. [44]. Hence, incorporate the the sample one can the calculating can alternatively size, one calculating the sample size, alternatively employ employ the formula following ([45]): following formula ([45]): n§ "2 ð9Þ (9) The estimated estimated R l.l5 to l.90 in The R (e.g. (e.g. in in the the range of 1.15 to 1.90 in Mexico) Mexico) isis not not range of in aa fully fully susceptible population, the basic reproduction number R0 R0 in the basic reproduction number susceptiblepopulation, but satisfies R0 = R/(1-q) [9]. Using Using the the estimator estimator of R in the but satisfies R0=R/(1-q) of R in (9), (9), the standard error in can be as standard error in (8) be rewritten rewritten as (8) can By error, both sides sides and and using the approximate standard error, By squaring squaring both using the approximate standard we have we have 2 1-% : rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n(N{n) zs:e:2 (r; n) N3 +s.e.2(p;n) bound for for a a final final epidemic size Uncertainty epidemic size Uncertainty bound An explicit derivation of final size distribution, An of final size which employs distribution, which explicit derivation employs aa recursive has been been carried carried out the so-called so-called recursive equation, out through equation, has through the Sellke construction construction in a a series of of stochastic stochastic Sellke in series epidemic modeling epidemic studies [46,47]. In addition, number of stochastic stochastic studies of modeling addition, aa number [46,47]. In modeling studies in in the the context context of large have examined examined the studies of the large populations populations have distribution of the the final final epidemic size via the central central asymptotic of via the asymptotic distribution epidemic size limit theorem theorem Within stochastic modeling it limit [48,49]. aa stochastic modeling framework, framework, it [48,49]. Within known that an an outbreak declines to to extinction extinction without is known that outbreak declines without causing causing aa with a a probability of extinction outbreaks large large epidemic epidemic with probability of extinction pp (small (small outbreaks are referred to to as as minor minor epidemic). occurs with are referred with epidemic). AA major epidemic epidemic occurs An standard error of the final size of probability 1-p. An approximate standard error of the final size of l-lb. the major based on on the asymptotic result of of the the result major epidemic epidemic based asymptotic convergence convergence the final final size size distribution distribution the is ([50,51]): ([50,51]): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 2 u 2 2 ur(1{r)zR2 s0 r(1{r) pu-p> u m t , s:e:(r)~ S-ff-(P): » N ½1{R(1{r)2 p(1-p)+R2(;) an approximate variance of of the the binomial binomial n(N-n)/N3 is an approximate variance the standard sampling error, and and s.e.(r;n) is the standard error error of of final final size size when when sampling error, s.e.(p;n)is the sampling error linked the linked toto nn isis ignored(i.e. ignored(i.e. what what we we replace replace .N N by by nn sampling error in equation The introduction introduction of sampling error also in of also applies applies toto equation (10)). (l0)). The sampling error the standard standard error of the the error of the binomial binomial proportion proportion in in (2), (2), but but this this term term is is then negligibly negligibly usually for very very large large .N N (because n(N-n)/N3 is then usually ignored for (becausen(.N-n)/_/V3 under an an small) assumption that that the the randomly selected individuals individuals small) under assumption randomly selected the entire entire sufficiently population. Thus, we use use only only Thus, we sufficiently represent represent the in the the following involves non-negligible s.e.(r;n) following analyses. fraction s.e.(p;n)in analyses.IfIf nn involves non-negligiblefraction of .N one use the above above expression of N (e.g. .5%), one may use the expression (l(11) introduce (e.g.>5%), may l) oror introduce the so-called so-called finite correction factor the finite population correction factor (FPC) (FPC) for for the the calculation of the the error calculation of error [52]. [52]. Given an observed final % confidence Given an observed final size size r, the 100(1-2a)% confidence p, the 100(1-Qoc) interval for calculated as interval for r as p is calculated where where vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 22 u u ^ s r 2 2 3 ui>3(1-i>)+ ^ (1{^ ^(1{^ r r) ln 1{ r)z ur m 1{q ^+za u r : 2 biz. u. t ^ r n ^ z(1{^ n r r) ln 1{ 1{q ð8Þ (8) A i»<1-f»>21n2(1-§ ð12Þ (12) ;»+(1-;»)1n(1-&I)l where r now the observed observed final size size and and possibly the where represents final possibly the p now represents the solution to to (1) in case case of an an unique positive of initially positive solution (l) in initially fully susceptible susceptible R is the the reproduction number number while and s U denote denote population. R while m pl and the mean mean and standard standard deviation of the the generation time (and the and deviation of generation time (and thus, the coefficient coefficient of variation variation and .N the population s/m of (CV)), N is the U/pl is the (CV)), and size. This This approximation has has been been evaluated evaluated elsewhere If size. elsewhere [50,51]. [50,5l]. If of the the the aa proportion population is initially immune, the proportion qq of initially immune, number R estimated estimated from an an reproduction number R from exponential exponential growth growth PLoS ONE ONE || www.plosone.org PLoS www.plosone.org ð11Þ (11) that we an unbiased estimate of and aa known Suppose we have have an unbiased estimate of qq and known CV CV Supposethat of the the generation time (e.g. of (e.g. from from separate To compare compare generation time separate datasets). datasets).To the observed observed final size r the prediction based the final size against the based onon R: R = 1.40, l.40, p against 0.5l l, with would be be 0.511, with the the Wald Wald statistic statistic compared compared toto aa normal normal r po0 would distribution distribution given by by 4 March 2011 2011 6 | Issue Issue 3 3 | e17908 March | Volume Volume 6 e17908 Sample for Post-Epidemic Studies Size for Sample Size Post-Epidemic Serological Serological Studies Results Results ^ {r0 r vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi : 2 u u 3 ^ r s 2 2 ur ^ (1{^ (1{^ r)z r r) ln 1{ -f»>+ u^i>3(1 1{q m u 2 u ^ r t ^z(1{^ nn r r) ln 1{ 1{q _ Confidence intervals Confidence intervals ð13Þ (13) Table the of eleven eleven seroepidemiseroepidemiTable 2 summarizes the empirical results of 2 summarizes empirical results ological proportion infected ological studies of H1N1-2009. The sample sample proportion £1 f»<1-i»>21n2(1 ranged from 4.5% to 38.5%. The smallest three final sizes resulted - studies of H1N1-2009. infected The ranged from 4.5% to 38.5%. The smallest three final sizes resulted from samples within 11 month month after after observing peak incidence, incidence, and and the the from sampleswithin observingpeak largest three involved aa population-wide population-wide vaccination vaccination campaignprior campaign prior three involved largest fl-l-(1-fJ)ln(1-£I>T pffiffiffi Let n. The sample Let n(r)~s:e:(r) The minimum minimum size which which explicitly v(p)=S.e.(p)/H. sample size explicitly accounts for is calculated from accounts for only error calculated from only Type Type II error z 22 Zua n(^ r)22 : n§ "2 m V(/»> (M) to Whereas the 95% confidence confidence interval of the the binomial binomial to the the survey. the 95% interval of survey. VVhereas narrow with the standard standard errors proportion was was narrow with the errors ranging from from 0.6% 0.6% proportion ranging to 95% confidence interval of final size was much much broader broader to 1.6%, the 95% confidence interval of final size was 1.6%, the ranging from 6.6% to 76.9%, which led to include 0% within the from 6.6% to which led to include 0% within the 76.9%, ranging confidence limits of seropositive confidence limits of seropositive in in nine nine studies, studies, calling for ad-hoc ad-hoc calling for truncation truncation (or calling for anan alternative alternative method method of of computation that (or calling for computation that include the F F distribution). may include the The broader broader uncertainty uncertainty bound bound may distribution). The from the model-based final final size than the binomial proportion proportion can can be be from the model-based size than the binomial as follows. the smallest analytically demonstrated as follows. First, First, the smallest standard standard analytically demonstrated error in (12) seen when the CV CV of the generation error in when the of the time is 0, 0, i.e., i.e., (12) is seen generation time ð14Þ <14 > A 1 _ If account for we have If we we account for both both Type and II II errors, we have errors, Type II and zzav(/>)+2,ev(/>+m) r)zzb n(^ rzm) 2 a n(^ : n§ nz m m A A _ ð15Þ (15) vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u ^A33 (1{^ 11/ r) r ^+za u r u 2 : i>iZ<» t ^ r nn r ^z(1{^ r) ln 1{ 1{q - It should should be be noted noted that that the the method used to to account account for the power It method used for the power can examine the the range of r,1-q-m the (equation only because the p< 1-q-mbecause (equation (15)) (15))can only examine range of standard error of final final size size includes includes the logarithmic approximate error of the approximate standard logarithmic function. function. Application Application ð16Þ (16) ;»+(1-p)1n(1-&I)) Because 0#r#1 and 0#q#1, have Because we have 05,051 and 0SqS1, we and illustration illustration and ^ r §1: 21. ^ r ^ r z(1{^ r ) ln 1{ fJ+(1-f>)ln 1{q To highlight the importance of explicitly for the the To highlight the importance of explicitly accounting accounting for variance of the the final final size size distribution, the following two exercises variance of following two exercises distribution, the are we examine are performed. examine post-peak First, we performed. First, post-peak seroepidemiological seroepidemiological studies of of H1N1-2009, the 95% 95% confidence confidence intervals studies intervals H1N1-2009, comparing comparing the two binomial and asymptotic generated methods; proportion methods; binomial generated by by two proportion and asymptotic final size distribution. distribution. For this reason, when the final size For this when calculating reason, calculating the we the data data as as if if they were uncertainty regard generated bounds, we uncertainty bounds, regard the they were generated from binomial or the final size of of a a from aa binomial process or the final epidemic process epidemic size For simplicity, assume that homogeneously population. For we assume that homogeneously mixing mixing population. simplicity, we we have an an unbiased estimate estimate of of the the proportion of population we have unbiased proportion of population with with based on the observed pre-existing on the observed seropositive pre-existing immunity immunity based seropositive to the the epidemic in Table Table 1. We consider proportion wave in 1. We consider proportion prior prior to epidemic wave of the the observed observed final size, to the the uncertainty final which corresponds size, which uncertainty of corresponds to difference in infected infected fraction before and and after after observing the peak difference in fraction before peak observing the incidence. we test the significance of the the observed observed incidence. Subsequently, test the Subsequently,we significance of final size size against model predictions 51.1% and 76.7% 76.7% final and 24.0%, 51.1% against model predictions (i.e. (i.e. 24.0%, based on on 1.40 and The mean and based R = 1.15, 1.40 and 1.90, respectively). The mean and R=1.15, 1.90, respectively). standard deviation of the the generation time are are fixed at at 2.7 and 1.1 1.1 standard deviation of fixed 2.7 and generation time the CV CV is 0.41) on contact days, based on contact tracing so, the days, respectively respectively(and (and so, 0.41) based data in in the the Netherlands Netherlands To address address the uncertainty data [40]. the with [40]. To uncertainty with to the the shape and scale scale of of the the generation time distribution, respect distribution, respect to shape and generation time we also consider consider hypothesis of two two other scenarios scenarios in in which we also testing of other which hypothesistesting the CV CV constant and the is 0 (i.e. generation 11 (i.e. (i.e. aa constant generation time) and (i.e. distributed exponentially generation exponentially distributed generation time). time). as of the the selected Second, as sensitivity selected empirical sensitivity analysis analysis of we the desired desired minimum size of of illustrations, present minimum sample illustrations, we present the sample size final size by the approximate standard standard final epidemic epidemic size by employing the error of the the final final size. Examining various various of error error error of size. margins of from 0% 0% to to 50% 50% with with R 1.40 and and 1.90 1.90 and and ranging from R being being 1.15, 1.15, 1.40 the CV CV of of the the generation time ranging from from 0 0 to to 1, the above above the 1, the generation time mentioned formulae and (15) are used with mentioned formulae (14) used with significance (14) and (15) are significance level at at a oc=0.05 for the the latter latter the power is set set at at level = 0.05 and, for formula, the is formula, power for this the 12b = 0.80. Moreover, for this sensitivity analysis the proporMoreover, 1-/3=0.80. sensitivity analysis proportion of of the the population with with pre-existing at tion fixed at pre-existing immunity immunity qq is fixed which corresponds to the the mean based on on 7.5%, mean based eight published 7.5%, which corresponds to studies in Table 1. Subsequently, we also examine the studies in Table 1. also examine the Subsequently, we of the the minimum minimum size required as as sensitivity sample aa function function sensitivity of sample size of R R and and q. of q. PLoS ONE ONE || www.plosone.org PLoS www.plosone.org ð17Þ (17) 1-é it is that Therefore, is proven proven that Therefore, it vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u ^3 (1{^ ^ (1{^ r r r) r) u : u 2 § n t ^ r n ^ n r z(1{^ r ) ln 1{ i>+(1-i>)ln 1{q 2 Q ð18Þ (18) 1-£TI)) The equality holds when 1. The when pr = 1. equality holds = Hypothesis testing Hypothesis testing of the time at Assuming CV CV of the generation generation time at 0.41, 0.41, six six serological serological Assuming studies appeared to have have yielded studies yielded significantly smaller final final sizes sizes appeared to significantly smaller than that that predicted by 1.40 (Table than by R: R = 1.40 (Table 2). Nevertheless, four four of of 2). Nevertheless, the six six studies studies sampled the serum within within 11 month month after after observing observing sampled serum and four of the studies with peak incidence, four of the remaining five studies with incidence, and peak remaining five result serum than month after the insignificant result sampled serum longer than 1 month after the insignificant sampled longer association between peak (no between the the significant significant test test result result peak (no significant association and sampling the peak; and within 11 month month after after the peak; p=0.24, p = 0.24, þÿFisher Fisher’ss sampling within exact in four the six studies with exact test). four of of the six studies with significantly significantly test). Populations Populations in smaller final unvaccinated smaller final sizes sizes were were unvaccinated prior toto sampling, and prior sampling, and three of the the five studies with three of five studies with insignificant results results involved involved vaccination Taken vaccination prior prior toto the the survey survey (p = 0.57, þÿFisher Fisher’s). s). Taken (p=0.57, of the six studies together, five of the six studies with with significantly smaller smaller final final together, five sizes sampled serum serum within after peak sizes within 11 month month after peak incidence incidence oror examined unvaccinated examined unvaccinated population, while while all all the the five five remaining studies with test results conducted studies with insignificant test results conducted sampling longer longer than than 11 month month after after the the peak peak oror the the population population involved involved vaccination VVhen vaccination (p = 0.55, þÿFisher Fisher’s). s). When comparing observed observed final final =0.55, sizes against R: results of all studies were not found sizes R = 1.15, results of all studies were not found toto be be 1.15, against different. studies indicated indicated that the observed significantly different. Eight studies that the observed final sizes were smaller than final sizes were significantly smaller than that that predicted by by R: 1.90. Varying CV of the generation time R = 1.90. Varying the the CV of the time from from 00 toto 11 with CV=0= 0 did with R: R = 1.40, the significance significance levels levels with with CV did not not vary vary 1.40, the from those of of CV the results from those = 0.41, but but the results with with CV=1 CV = 1 indicate indicate CV=0.41, 5 March 2011 2011 6 | Issue Issue 3 3 | e17908 March | Volume Volume 6 e17908 Sample for Post-Epidemic Studies Size for Sample Size Post-Epidemic Serological Serological Studies Table 2. and the post-peak seroepidemiological studies studies of (H1N1-2009). Table 2. Uncertainty bounds and hypothesis of the of influenza influenza (H1N1-2009). post-peak seroepidemiological Uncertainty bounds hypothesis testing testing of Country Country Sample Sample size{ sizeT Prop Prop infected infected (%){ (%)T 95% CI 95% CI of of binomial binomial prop (%){ prop (%)1 95% CI 95% CI of final of final size (%){ size (%)i After After peak1 peak§ Vac¥ Vac¥ P-values$ P-values$ fR 1.40 1.40 1.15 1.15 1.90 1.90 1.40 1.40 1.40 1.40 0.41 0.41 0.41 0.41 0.41 0.41 0 0 1 1 T CV Australia [19] Australia [19] 1247 1247 15.8 15.8 13.8, 17.9 13.8, 17.9 0, 50.2 0, 50.2 Yes Yes No No *0.02 *0.02 0.30 0.30 *,0.01 **0.010.07 *0.01 *0.01 0.07 0.07 Canada Canada [15] [15] 1127 1127 38.5 38.5 35.7, 41.4 35.7, 41.4 16.5, 60.6 16.5, 60.6 Yes Yes Yes Yes 0.13 0.13 0.89 0.89 *,0.01 *0.110.21 0.11 0.11 0.21 0.21 China China (1) (1) [11] [11] 710 710 6.3 6.3 4.5, 8.1 4.5, 8.1 0, 46.8 0, 46.8 No No Yes Yes *0.02 *0.02 0.18 0.18 *,0.01 **0.010.05 *0.01 *0.01 0.05 0.05 China (2) China (2) [6] [6] 2913 2913 10.7 10.7 11.8 9.6, 9.6, 11.8 67.8 0, 0, 67.8 Yes Yes No No 0.08 0.08 0.31 0.31 *0.01 *0.01 0.07 0.07 0.15 0.15 Germany [7] Germany [7] 225 225 4.5 4.5 7.3 1.8, 1.8, 7.3 56.0 0, 0, 56.0 No No No No *0.04 *0.04 0.22 0.22 **0.030.09 *,0.01 *0.03 *0.03 0.09 0.09 India [20] India [20] 5047 5047 14.6 14.6 15.6 13.6, 13.6, 15.6 30.1 0, 0, 30.1 No No No No *0.10*** *,0.01 0.10 0.10 *** *,0.01 ** *,0.01 **,0.01 Japan [21] Japan [21] 6035 6035 32.7 32.7 33.9 31.5, 31.5, 33.9 19.8, 45.6 19.8, 45.6 Yes Yes Yes Yes *0.88***0.02 *,0.01 0.88 0.88 ***0.02 *,0.01 **0.02 *,0.01 *0.02 *0.02 New Zealand Zealand [12] New [12] 1147 1147 18.4 18.4 20.6 16.1, 16.1, 20.6 81.4 0, 0, 81.4 Yes Yes Yes Yes 0.15 0.15 0.42 0.42 *0.03 *0.03 0.13 0.13 0.23 0.23 Singapore [22] Singapore [22] 727 727 10.9 10.9 13.1 8.6, 8.6, 13.1 94.4 0, 0, 94.4 Yes Yes No No 0.17 0.17 0.37 0.37 0.06 0.06 0.15 0.15 0.24 0.24 UK [14] UK [14] 275 275 8.0 8.0 11.2 4.8, 4.8, 11.2 35.3 0, 0, 35.3 No No No No *0.11***0.01 0.11 *,0.01 0.11 ***0.01 *,0.01 **0.01 *,0.01 *0.01 *0.01 USA [13] USA [13] 846 846 15.5 15.5 17.9 13.1, 13.1, 17.9 100.0 0, 0, 100.0 No No Yes Yes 0.32 0.32 0.21 0.21 0.31 0.31 0.37 0.37 0.45 0.45 { size refers refers to to the the number number of of enrolled enrolled to measure measure the seroprevalence after observing an epidemic infected is given Sample size subjects the epidemic peak. is the proportion [Sample subjects to seroprevalence after observing an peak. Proportion Proportion infected given by by the proportion after observing minus the the proportion before the the peak in Table 1. after Table 1. observing peak peak minus proportion before peak in 195% confidence intervals (CI) show show lower lower and and upper confidence intervals of of the the proportion. 95% CI binomial is derived derived from normal approximation 95% confidence intervals intervals The 95% CI of of binomial proportion from aa normal approximation upper confidence proportion. The proportion is { to binomial binomial while the the 95% 95% CI CI of of final final size size is similarly derived from from the Wald method method result of to distribution, the Wald employing asymptotic convergence result of final final size size distribution. distribution. distribution, while similarly derived employing asymptotic convergence §After column if the the sampling took place month after observing the highest incidence of cases. cases. After peak represents than 11 month after of peak column represents if sampling took place longer longer than observing the highest incidence ¥Vaccination column if a a population-wide vaccination of H1N1-2009 H1N1-2009 took place Vaccination column represents campaign took to the the sampling. represents if population-wide vaccination campaign of place prior prior to sampling. $ are based on on two-sided Wald test test employing the approximate standard error of final final epidemic p-values are based two-sided Wald error of size. $p-values employing the approximate standard epidemic size. the estimated number in Mexico which we would like to test our the coefficient of variation of the R, variation of the generation time. Significant Significant R, the estimated reproduction CV, the coefficient of reproduction number in Mexico against against which we would like to test our hypothesis; hypothesis; CV, generation time. difference indicated mark followed followed difference is indicated by by by * mark by p-value. p-value. doi:10.1371/journal.pone.0017908.t002 doi:10.1371/journal.pone.0017908.t002 1 ¥ * that only three observed final sizes were were smaller that observed final sizes significantly only three significantly smaller than that that predicted R: 1.40. than by R = 1.40. predicted by = with with pre-existingimmunity pre-existing immunity qq (with (with fixed fixed RR = 1.40). the 1.40).Interestingly, Interestingly, the minimum size hits the value around minimum sample size hits the largest value around qq=0.20. = 0.20. For For sample largest size estimation estimation Sample Sample size CV = 0. 0. This example, yielded the the largestsample largest sample size size with with CV This 0.212 yielded example, qq = 0.212 can be and second can be inspected by taking first first and second derivatives derivatives of of (16) (16) with with inspectedby to q to: respect the CV= CV = 0), 0), leading leading to: respect to q (with (with the = shows the the minimum minimum sizes required Figure sample for postpostFigure 11 shows sample sizes required for studies to to test test the final final size size against epidemic the epidemic seroepidemiological seroepidemiologicalstudies against R: = 1.15, 1.40 and and 1.90 1.90 with with CV CV being 0.41 and and 1. 1. Whereas VVhereas R being 0, 1.15, 1.40 0, 0.41 median lower and and upper size of of empirical median (and (and lower upper quartiles) quartiles) sample sample size studies in in Table Table 1 1 was was such sample sizes can can studies 1127 only 1127 (710, (710, 2913), 2913), such sample sizes only a difference difference from the the prediction of R: 1.90 at at a a explicitly from prediction of R = 1.90 explicitly prove prove a of error error 5%. To To argue the significant difference from margin 5%. the from margin of argue significant difference based on on R=1.40 the identical identical of error error prediction based R = 1.40 with with the margin of and with with varying CV of of the the generation time 0.41 0.41 (range: we and 0, 1), varying CV generation time (range: 0, 1), we need 8665 8665 (range: individuals at the the power of ideally at 7215, 15947) ideally need (range: 7215, 15947) individuals power of 50% and and 16121 individuals at the of 80%. 50% 29680) individuals at the power of 80%. 16121 (13423, (13423, 29680) power At the the margin of of error error these numbers numbers are reduced to 2167 At 10%, are reduced to 2167 10%, these and 3715 3715 (3093, As R R gets closer (1804, (1804, 3987) 3987) and (3093, 6841), 6841), respectively. respectively.As gets closer to the lower lower and as as the variance of the the to the uncertainty bound, bound, and the variance of time becomes relative to the the generation becomes larger relative to the mean, the mean, generation time larger minimum size required increases. increases. minimum sample sample size examines the the sensitivity of the the minimum size to to QA examines Figure minimum sample Figure 2A sensitivityof sample size the reproduction number number R. the R. Ignoring pre-existingimmunity pre-existing immunity (q (q= 0), R = 2 with with the the CV CV of ofthe time 0.41 0.41 (0, at least R the generation least generationtime (0, 1) 1) requires requiresat individuals at power of 50% 50% and and 317 317 (281, 201 (177, 201 at of (177, 320) 320) individuals power (281, 500) 500) individuals at power of 80%. 80%. As As R R is reduced reduced and approaches the individuals at and power of approaches the critical level, much much greater sizes are are For instance, critical required. For greater sample sample sizes the minimum minimum size for for R: than 2-fold 1.2 is more 2-fold higher the sample R = 1.2 more than sample size than that that required for for R R: = 1.4. 1.4. Figure illustrates the the relationship than 2B illustrates Figure 2B relationship between minimum minimum size and and the the proportion of ofthe between sample the population sample size qmax ~1{ ^ r , ^ r 1_ 1{exp ^{1 r f1m.x=1- . ð19Þ (19) @> _ which the which is the most difficult situation most difficult situation in in which which the the hypothesistesting hypothesis testing the against predicted final final size size requires collect anan against the predicted requires usus toto collect leads the the unrealistically large number number of of blood blood samples. samples. qqmax max leads unrealistically large denominator of the the approximate standard error in to be 0. denominator of standard error in (16) to be 0. approximate (16) Discussion Discussion We have introduced introduced We have to compute aa framework framework to the uncertainty uncertainty compute the size that the Wald epidemic that employs Wald epidemic size employs the an the absence approximation, an approach motivated by by the absence of of aa approach motivated available to estimate estimate the readily available methodology to the sample size of of postpostmethodology sample size Published epidemic studies. Published seroepidemiologepidemic seroepidemiological seroepidemiologicalstudies. seroepidemiological studies studies of of H1N1-2009 H1N 1-2009 so so far the confidence ical far have have computed computed the confidence interval of the the observed observed final size as interval of final size as ifif itit were were aa binomial binomial the data proportion. However, However, the data generating generating process process behind behind the the proportion. infectious diseases involves dynamics of infectious diseases involves dependence between between dynamics of dependence infected individuals infected individuals [17], which does does not not lead lead toto aa binomial binomial [17], which the observed size represents proportion. Moreover, Moreover, the observed final final size single proportion. represents aa single stochastic realization all stochastic realization among all possible possible sample sample paths (i.e. (i.e. all all among bounds bounds = = PLoS ONE ONE || www.plosone.org PLoS www.plosone.org = 6 of of the the final final March 2011 2011 6 | Issue Issue 3 3 | e17908 March | Volume Volume 6 e17908 Sample Size for for Post-Epidemic Studies Sample Size Post-Epidem ic Serological Serological Studies 8000 Q 7000 8000 ` I _ Q & number ~ ' § 0000 | % 3 3 E _._1,% ` _ 4000 g þÿ E ` ~ 5000 ID . 4000 3 ` E 3 E 1 ` ` E E _ 1 3 .E E 2000 '_ _ ` 2000 __ _ 1000 . -_ ` _ _ _ 0 _ _ ........ _ 5 10 15 20 25 30 35 40 45 50 0 5 10 15 Margin oferr0r(%) 3000 I 7000 Coefficient ' ofthe generation time ~ #041 þÿ° 5000 __ 4000 I 0 41 - - ' -0 oo ---- 5000 | _ I 1 4000 - | 1 2 _ E 3 3000 ~ þÿ ` 3 ` ' \ . tx ` ` `` 1000 . " _Q 2000 E . _ E -'-100 E 1 E 2000 þÿ me genefanon ~ þÿ O. ' þÿ 3000 50 Var'at'°'l oflhe i 3 2 1 ` 45 an _ , E E - þÿ Q ' 3 -0.00 ---- 1 ua þÿ 2 þÿ° 1 2 ° 40 of Cogfflcem ' 1 30000 ---1.00 ' E 35 (%) E f-~ ; ° 30 error 2 l 7000 I @6000 25 of ` l of variation g g " ' _ E 20 Margin 8000 3 ........ 0 0 2 (B=0,50) þÿ ~_ . ._ 149 ...... ` 1 .E - 1000 3 _._1_g) ` ' 3000 15 *1.40 ,` " : 1 E - .` - I g ~` þÿ l § E § I Q g ` 3000 § 6000 3 ` N E E l1,4[) -1 ---- § . ,_ _` \ 5000 | -1.15 ---- numbef E 5 7000 \ A . Reproducuon x 1 | Q ` _ ' `_ "`_` . ` - 1000 _" ~_`_ _ . _ _ ."` _ 0 0 0 5 10 15 20 25 30 35 40 45 50 0 Margin oferror(%) 5 10 15 20 Margin 25 of error 30 35 40 45 50 (%) 1. Minimum Minimum sizes required for post-epidemic studies of final size as Figure sample studies of final size as aa function function of of the the margin Figure 1. sample sizes required for post-epidemic seroepidemiological seroepidemiological margin the reproduction and the the coefficient coefficient of variation variation of the & B) Sample size error, the number, and of of the generation time. (A & size with with three three different different error, number, reproduction generation time. numbers as a a function function of the the margin of error. error. an estimation formula based Type error alone reproduction as of (A) employs estimation formula based Type I error alone (at aoc=0.05), = 0.05), while while (B) reproduction numbers margin of employs an accounts for both both Type and II errors errors and 12b of error error random around accounts for (at aoc=0.05 = 0.05 and = 0.80). The The margin represents sampling around which which the the reported error, represents random reported Type I and 1-;'3=0.80). margin of sampling error, would include include the the true true Since (A) is a a special case case of (B) (with 1.40 in in (A) is also also shown line in in (B). The percentage percentage. of = 0.50), RR== 1.40 shown as as dotted dotted line The (with b percentage would percentage. Since /}=0.50), coefficient of variation variation of the the generation time and and the of population are fixed coefficient of (CV) of the proportion with pre-existing fixed atat 40.7% 40.7% and and 7.5%, 7.5%, proportion of population with generation time pre-existing immunity immunity are & D) Sample size size with with three three different different coefficients of variation as a a function of the of error. accounts for respectively. (C & coefficients of variation as function of the margin error. (C) accounts for Type Type I error error alone alone margin of while (D) accounts accounts for both both Type and II errors errors and 12b number and of population (a = 0.05), while for (a = 0.05 and = 0.80). The The reproduction and the the proportion with (oc=0.05), (ac=0.05 reproduction number proportion of population with Type I and 1-fi=0.80). are fixed at 1.40 1.40 and and 7.5%, CV = =00 corresponds constant pre-existing fixed at to aa constant generation time, whereas whereas CV=1 CV = 1 represents an 7.5%, respectively. corresponds to represents an pre-existing immunity immunity are respectively. CV generation time, distributed time. In In (B) and and (D), several several lines lines are are due to to impossibility account for in the exponentially generation truncated, due to account for larger margins of error error in the truncated, exponentially distributed generation time. impossibility to margins of estimation formula. estimation formula. doi:10.1371/journal.pone.0017908.g001 studies published seroepidemiological published toto date date did did not not necessarily necessarily seroepidemiologicalstudies an of prediction based overestimation an overestimation of based onon R=1.40, R = 1.40, and and all the sizes did did not moreover, all the observed observed fir1al final sizes not reveal reveal significant significant moreover, the lower l.l5. Published deviation from deviation from prediction With with the lower limit limit R: R = 1.15. Published studies that the bound R: 1.90 seroepidemiological studies agree that the upper bound R = 1.90 seroepidemiological agree upper other published estimates of of R>2 (and published estimates R.2 [29,30]) was was likely anan (and thus, other still speculate that R: 1.40 may overestimation overestimation [39]. One One may may still R = 1.40 may Well well speculatethat be an all of the final sizes overestimation observed were be an overestimation (because all of the observed final sizes were (because of the the epidemic), us to possible probabilistic trajectories to possibleprobabilistic trajectories of epidemic), requiring us consider stochastic the data. data. To To account account for these variations consider stochastic variations in in the for these the approximate standard standard error of the the final We issues, employed error of final issues, we employed the size given as result of of a a size aa convergence result homogeneously given as convergence homogeneously mixing stochastic calculation of the the standard standard error stochastic epidemic model. The The calculation of error epidemic model. to be be simple to compute are was shown to was shown programs are simple to compute (spreadsheet (spreadsheetprograms the proposed of final sufficient). bound of final sufficient). By applying the proposed uncertainty bound size to to influenza influenza also shown that all all the the have also shown that size (H1N1-2009), we have (HlNl-2009), We PLoS ONE ONE || www.plosone.org PLoS www.plosone.org indicate indicate 77 March 2011 6 | Issue Issue 3 March 2011 | Volume Volume 6 3 | e17908 e17908 Sample for Post-Epidemic Studies Size for Sample Size Post-Epidemic Serological Serological Studies 1000000 1000000 A 100000 Coefficient B Coefficient of variation ofthe generation time 100000 A _ _ 2 _ _ g 1.00 þÿ mo _ - § OO _ (D Z 1000 " § 1000 g ' _ 1.00 tx . _____OAOO ` þÿ" _, . "` ,» ' ` _ _ § _ E _ iordl þÿ , //I, þÿ ,f I þÿ sv / . _G 5 _ -/,' 10000 _ 2 time generation _ 5 -0,41 §10000 variationofthe / A § of ' /` _ g . ._ __ ' § é _ ` . 10 10 ~ _ T . 1 1 10 20 30 Reproduction 40 50 0 60 5 10 15 20 25 30 35 40 45 50 with þÿDl' ¬- ¬XlSIlnQ PFODOFUOIT lmlT1Uiilty(%) number 2. Sensitivity of minimum minimum size for for post-epidemic studies to number and Figure sample seroepidemiological studies to the the reproduction number and the the sample size post-epidemic reproduction Figure 2. Sensitivity of seroepidemiological of population with minimum size with three different different coefficients proportion of with pre-existing The minimum sample with three coefficients of of variation variation (CVs) as aa (A). The (CVs) as proportion population sample size pre-existing immunity. immunity. (A). function of the the reproduction number. (B). The minimum minimum size with with three CVs as as a a function of the of population function of sample three CVs function of the proportion with pre-existing (B). The reproduction number. sample size proportion of population with pre-existing ln (A), the proportion of population with pre-existing is fixed at 0, and the 10% immunity. fixed at the estimates estimates correspond correspond toto the the margin of error error of of 10% (A), the 0, and proportion of population with immunity. In pre-existing immunity immunity is margin of and Type and II ll errors errors at a oc=0.05 and 12b In (B), the reproduction number is fixed Hxed at at 1.40, and the and at = 0.05 and = 0.50, respectively. the estimates estimates correspond to the the (B), the 1.40, and reproduction number correspond to Type I and 1-/}=0.50, respectively. In of error error of 10% 10% and and Type and II ll errors errors at a oc=0.05 and 12b margin of Type II and at = 0.05 and = 0.50, respectively. margin of 1-/}=0.50, respectively. doi:10.1371/journal.pone.0017908.g002 doi:10.1371/journal.pone.0017908.g002 conservative conservative uncertainty bounds. The The proposed proposed method method has has aa uncertainty bounds. potential for for explicitly discussing aa posteriori posteriori effectiveness effectiveness of of potential explicitly discussing interventions the direct direct comparison observed final interventions through through the of observed final sizes sizes comparison of in different in different settings. Hence, Hence, we we believe believe that that the the proposed proposed settings. calculation of the the 95% calculation of 95% confidence confidence interval interval will will greatly help greatly help this area area of should also also be progressing this of research. research. ItIt should be noted noted that that the the progressing use of the the proposed an use of proposed uncertainty uncertainty bounds bounds plays plays an important role role important especially for influenza influenza transmission transmission with with R<2 R,2 (Figure (Figure 2A). especiallyfor 2A). Our illustration of the the proposed Our illustration of proposed method method posed posed four four technical technical the computation the uncertainty challenges for the of the bound of of final final challengesfor computation of uncertainty bound the coefficient coefficient of variation size; of variation of of the the generation time has has toto be be size; (i) the generation time the proportion known, (ii) proportion of of pre-existing pre-existing immunity immunity before before anan known, (ii) the the bounds, epidemic critically influences influences the bounds, (iii) (iii) sampling of several several epidemic critically sampling of studies took an seroepidemiological took place place shortly after an epidemic seroepidemiological studies shortly after epidemic and (iv) peak and vaccination and and other other public public health health interventions interventions peak (iv) vaccination the course course of an the observed during the of an epidemic can can modify the observed final final size. As the present critical need size. As for for (i), present study study demonstrates demonstrates aa critical need toto (i), the estimate the variance of the the generation estimate the variance of time in in addition addition toto the the generation time mean. That is, the distribution of mean. That the distribution of the the generation generation time time plays plays aa key key role also in role not not only in in estimating estimating RR [53,54] but also in characterizing characterizing the the [53,54] but variance of final size. With variance of final epidemic With respect respect toto (ii), although we we did did epidemic size. not to the not include include seroepidemiological studies prior prior to the 2009 2009 seroepidemiological studies have shown shown that that such we have such aa survey survey of of qq isis aa pandemic [24,25,27], [24,25,27], we to determine the sample after the key to determine the size after the epidemic epidemic [55]. In key sample size [55]. In smaller than than 51.1%), the sample sizes of of published smaller but the published seroepide51.1%), but sample sizes seroepidestudies turned turned out out to to be be too too small small to to answer answer this question. miological this miological studies question. formulae for variance variance of of the the final size distribution distribution Although for final size (i.e. Although formulae (i.e. the square root of which which we as an an standard the of we regarded approximate square root regarded as approximate standard has been been known known stochastic error) among stochastic modeling experts error) has among modeling experts [50], [50], the present extended its use use to the the computation of the the 95% 95% the its to present study study extended computation of confidence interval of the the observed observed final size size by the confidence interval of final by replacing replacing the number its estimator. estimator. This also also led us to consider consider reproduction by This led us to reproduction number by its Wald test test and sample size estimation. estimation. What the aa parsimonious and What the parsimonious Wald sample size for present post-epidemic present study study suggests suggests for post-epidemic seroepidemiological seroepidemiological studies is to to employ the proposed formula to calculate calculate the studies (12) the employ the proposed formula (12) to 95% confidence confidence interval and (14) or to help determine the 95% interval and (15) the (14) or (15) to help determine size for for seroepidemiological For the the latter, the sample For latter, the sample size seroepidemiological surveys. surveys. of (14) useful: following simplification of might be be useful: (14) might sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ^ r s 2 2 3 (1{^ ^ ^ r )z ln 1{ r (1{^ r ) r 1{q m n(^ r)2 ~ : ð20Þ n§ nz (20) ^ r s:e:(^ þÿ '(r)) ^ s:e:(^ r ) r z(1{^ r ) ln 1{ s.e.(f>) 1{q A . Q = t/i>3(1-i>)+ (9)i>(1-i>)2ln2(1-%) _ fJ+(1-f>)ln(1£)) The standard standard error calculated the specified The error s.e.(^ r) is calculated by s.e.(f)) by using using the specified confidence interval twice the margin of of error) and and the confidence interval (i.e. the the (i.e. twice confidence level (i.e. nominal For instance, if if confidence level coverage probability). (i.e. nominal coverage probability). For the margin of of error error 5% and and the the confidence confidence level is 95%, the the is 5% level 95%, the standard error 0.05/1.96 the standard standard error 0.025. Similarly, the standard error is 0.05/1.96 = 0.025. error 0.030 and and 0.020 at the the confidence confidence levels of 90% 90% and and 99%, is 0.030 levels of 0.020 at 99%, It is worth worth stressing that the the purpose of post-epidemic respectively. of post-epidemic respectively.It stressingthat purpose studies is not not to test test the observed observed seroepidemiological necessarily the seroepidemiologicalstudies necessarilyto final size against but includes real-time final size predicted value, but includes real-time against aa predicted of an an and various various considerations of public monitoring of epidemic considerations of epidemic and health interventions. As long as as there is no alternative health interventions. As there no better better alternative method for computing the the uncertainty, the the proposed method for proposed approach approach should also be be used used for those other to calculate should also for those other purposes to calculate purposes addition to the the estimation estimation of should be addition to of qq itself, itit should be noted noted that that our our an that the the pre-existing adopted an assumption pre-existing immunity immunity assumption that offered offered aa complete protection from from infection infection (i.e. all-or-nothing complete protection (i.e. all-or-nothing If the the pre-existingimmunity protection). If pre-existing immunity isis imperfect and and described described the so-called by the so-called leaky leaky protection protection (e.g. (e.g. partial partial reductions reductions in in by contact and susceptibility per contact and in in infectiousness infectiousness upon susceptibility per upon infection), those quantifications those will be be required in in addition addition toto the the estimation estimation quantifications will of the the proportion the initially of proportion of of the initially immune immune population. Issues Issues (iii) and (iv) technical and pose further further technical challenges toto precisely precisely estimate estimate (iv) pose challenges of seroprevalence studies. Given Given uncertainty bounds bounds of in empirical studies. seroprevalencein method method = PLoS ONE ONE || www.plosone.org PLoS www.plosone.org 88 March 2011 2011 6 | Issue Issue 3 3 | e17908 March | Volume Volume 6 e17908 Sample for Post-Epidemic Studies Size for Sample Size Post-Epidemic Serological Serological Studies that of is given that the the observation observation of incidence incidence is in every discrete time time given in every discrete unit, way forward may be to employ a forward be to a parsimonious unit, aa possible possible way may employ parsimonious discrete time model (e.g. or chain discrete time stochastic stochastic model or chain (e.g. branching branching process process binomial model) to binomial which may well enable enable us us to draw draw the the 95% 95% model) [56], [56], which may well confidence interval in by conditioning confidence interval in a a given reporting interval given reporting interval by conditioning the to intervals. Proposing the distribution distribution to previous previous reporting reporting intervals. Proposing simple simple methods to part of future methods to address address these these issues issues is part of our our future studies. studies. Our relied the Our method method relied on on the homogeneous and homogeneousmixing mixing assumption assumptionand ignored time dependent factors that that include include seasonality and public ignored time dependent factors seasonalityand public health interventions. In this this sense, the proposed health interventions. In the proposed uncertainty is sense, uncertainty is regarded an underestimate, the time-dependent as an because the underestimate, because regarded as tirne-dependent variations in potential increase variance variations in the the transmission transmission can increase the the variance potential can of the the final final size distribution, and also also because because of size heterogeneous distribution, and heterogeneous transmission (e.g. also increase transmission can also increase (e.g. age-dependent age-dependent mixing) mixing) can variance an could variance (e.g. epidemic with extremely (e.g. an epidemic with extremely high high assortativity assortativity could multimodal final size size distribution distribution for an entire population generate final for an entire population generate multimodal If an an intervention focused only on of cases cases or if [57]). intervention is focused aa portion or if [57]). If only on portion of disease-induced deaths occur occur in non-negligible not the disease-induced deaths in only order, not non-negligible order, only the variance but for final size relation (our variance but also also the the formulae formulae for the the final size relation (our have to be reassessed in the equation (1)) have to be reassessed [58–60]. Moreover, in the Moreover, equation [5f%60]. of strong deterministic has presence of modeling presence strong seasonality, seasonality,aa deterministic modeling study study has demonstrated limited of R alone in demonstrated aa very predictive performance of R alone in very limited predictive performance the final final epidemic size [61,62]. Given that that seroepianticipating anticipating the epidemic size [6l,62]. Given seroepistudies tend tend to to stratify demiological by age-group (to demiological studies stratify population population by age-group (to the of the risk of and capture the age-dependency of the risk of infection), and capture age-dependency infection), that the the final final size size of of age-structured can be considering models can be considering that age-structured models different from that that of of homogeneous further work different from work homogeneous population population [63], [63], further could at at least least incorporate the could by employing incorporate heterogeneous heterogeneousmixing mixing by employing the similar result of the the final final size size distribution distribution existing convergence result of existing similar convergence using multitype epidemic model (e. (e.g. model). An An using aa multitype epidemic model g. age-structured age-structured model). elegant formula for the asymptotic final size distribution of formula for the final size distribution of elegant asymptotic multitype epidemic models has has been been derived derived by by Ball Ball and and Clancy Clancy multitype epidemic models [64], yielding aa variance variance matrix matrix (which similar to but aa little little to but [64], yielding (which isis similar more complicated than that discussed in the present study). more than that discussed in the complicated present study). Nevertheless, itit should be noted noted that that the the elements elements of of the the nextnextshould be Nevertheless, the reproduction generation matrix (or (or the reproduction matrix) matrix) would would be be included included generation matrix as the of the the final final size equation for for multitype multitype models models as the solution solution of size equation those cannot cannot be estimator of [64,65], and those be simply replaced by by the the estimator of RR [64,65] and simply replaced using final size was done in the the Ppresent study using8 usin 8 final size (i.e. i.e. as as was done in resent stud Y usin homogeneous model), and thus, the computation computation of of 95% 95% and thus, the homogeneous model), confidence interval may may well well require require full full quantification quantification of of the the confidence interval next-generation matrix matrix (in to observation of of final final sizes sizes for for addition to observation next-generation (in addition each type). each type). Each issues should be addressed addressed in in the the Each of of the the abovementioned abovementioned issues should be the context of empirical future, ideally ideally in in the context of applications. Until Until that that future, empirical applications. time, rather than than relying relying onon aa binomial binomial proportion, proportion, wewe time, rather recommend the use use of the approach recommend the of the introduced in in this this study study ifif approach introduced the to determine the the goal is to determine the sample size of of post-epidemic post-epidemic goal is sample size calculate the seroepidemiological the 95% 95% confidence confidence studies, toto calculate seroepidemiological studies, interval of observed observed final conduct relevant interval of final size, size, oror toto conduct relevant hypothesis hypothesis testing. testing. , Author Contributions Author Contributions Conceived and designed the experiments: HN. Performed Conceived and Performed the the experiments: experiments: designedthe experiments:HN. HN. Analyzed the data: data: HN GC. Contributed Contributed materials/ HN. Analyzed the HN GC. reagents/materials/analysis reagents/ analysis tools: HN. HN. Wrote the paper: HN GC GC C-CC. C-CC. tools: Wrote the paper: HN References References 1. Neumann Neumann Noda T, Kawaoka Y and pandemic of 1. G, Noda Y (2009) Emergence and pandemic potential T, Kawaoka (2009)Emergence potential of H1N1 influenza virus. Nature Nature 459: 931–939. 931-939. swine-origin influenza virus. 459: swine-origin H1N1 Carrat Lemaitre Cauchemez et al. al. (2008) 2. Carrat F, Vergu M, S, et NM, Lemaitre M, Cauchemez Vergu E, Ferguson Ferguson NM, (2008) Time lines lines of of infection infection and disease disease in in human human influenza: review of of volunteer Time and influenza: aa review volunteer studies. Am 167: 775–785. 775-785. challenge challenge studies. AmJJ Epidemiol 167: 3. 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