A Dynamic Model of Poliomyelitis Outbreaks: Learning from the Past to Help Inform the Future
Radboud J. Duintjer Tebbens1,2,
Mark A. Pallansch3,
Olen M. Kew3,
Victor M. Cáceres4,
Roland W. Sutter5 and
Kimberly M. Thompson1
1 KidsRisk Project, Harvard School of Public Health, Boston, MA
2 Department of Mathematics, Delft University of Technology, Delft, the Netherlands
3 Respiratory and Enteric Viruses Branch, Division of Viral and Rickettsial Diseases, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA
4 Polio Eradication Branch, Global Immunization Division, National Immunization Program, Centers for Disease Control and Prevention, Atlanta, GA
5 Department of Immunization, Vaccines and Biologicals, World Health Organization, Geneva, Switzerland
Correspondence to Dr. Kimberly M. Thompson, Harvard School of Public Health, 677 Huntington Avenue, 3rd Floor, Boston, MA 02115 (e-mail: kimt{at}hsph.harvard.edu).
Received for publication June 1, 2004.
Accepted for publication March 25, 2005.
 |
ABSTRACT
|
---|
Policy-makers now face important questions regarding the tradeoffs among different strategies for managing poliomyelitis risks after they succeed with polio eradication. To estimate the potential consequences of reintroductions of polioviruses and the resulting outbreaks, the authors developed a dynamic disease transmission model that can simulate many aspects of outbreaks for different posteradication conditions. In this paper, the authors identify the issues related to prospective modeling of future outbreaks using such a model, including the reality that accurate prediction of conditions and associated model inputs prior to future outbreaks remains challenging. The authors explored the model's behavior in the context of three recent outbreaks resulting from importation of poliovirus into previously polio-free countries and found that the model reproduced reported data on the incidence of cases. The authors expect that this model can provide important insights into the dynamics of future potential poliomyelitis outbreaks and in this way serve as a useful tool for risk assessment.
disease outbreaks; disease transmission; models, statistical; poliomyelitis; poliovirus; risk assessment; vaccination
Abbreviations:
IPV, inactivated polio vaccine; NID, National Immunization Day; OPV, oral polio vaccine
 |
INTRODUCTION
|
---|
Efforts following the 1988 World Health Assembly resolution to eradicate poliomyelitis worldwide (1
) reduced the number of wild polio-endemic countries from 125 in 1988 to six in 2003 (2
). With the formal certification of global poliomyelitis eradication approaching (3
), global, regional, and national decision-makers face important choices among strategies for managing future poliomyelitis risks, including whether to continue vaccination with any of the available vaccines (4
). Apart from the relatively predictable occurrence of vaccine-associated paralytic poliomyelitis with the continued use of oral polio vaccine (OPV), cases of poliomyelitis could occur because of the unintentional reintroduction of wild polioviruses into a population from a laboratory or an inactivated polio vaccine (IPV) manufacturing site (5
), the emergence of circulating vaccine-derived polioviruses with neurovirulence and transmission characteristics similar to those of wild viruses (6
), or bioterrorism. The reasonably well-characterized current frequency and disease burden will "change substantially in the post-certification era, depending on future policy decisions" (7
, p. 42).
Several factors will influence the course of postcertification outbreaks (8
). However, the absence of existing comprehensive dynamic models for poliomyelitis outbreaks limits the ability of researchers and policy-makers to quantitatively understand the interactions that influence the magnitude of outbreaks and the impacts of different strategies. While prospective modeling tools typically deal with the lack of information about actual future conditions by relying on average conditions, model users must recognize that deviations from assumed conditions can lead to substantially different outcomes.
In this paper, we describe and evaluate a mathematical model specifically designed to simulate the spread of polioviruses during an outbreak in a predefined population. We focus on controlled outbreaks and do not study the possibility of reestablished endemic transmission. This transmission model estimates the incidence of poliomyelitis cases over time during an outbreak but does not address the probability of outbreaks. The model uses a large number of inputs that reflect properties of the virus, vaccines, outbreak population and immunity, and immunization response, which give the model flexibility to simulate outbreaks in different plausible future situations. We describe the model and results of simulations of three actual outbreaks in populations previously free of wild poliovirus to demonstrate the model's behavior and identify key inputs that substantially influence the size of outbreaks. We discuss the prospective use of this model as a tool for estimating the burden of disease due to potential future poliomyelitis outbreaks in the context of a larger effort to quantify the risks, costs, and benefits of future poliomyelitis risk management policies.
 |
MATERIALS AND METHODS
|
---|
Background on polioviruses and vaccines
Typically, infection with a poliovirus causes no clinical symptoms, but in approximately 1 out of 200 susceptible humans, paralysis occurs (9
13
). As the only known natural reservoir, humans transmit polioviruses mainly via the fecal-oral route in developing countries with poor hygiene and sanitation, as well as via the oral-oral route, which may dominate in developed countries (14
). Infection induces an immune response that leads to serotype-specific protection, with a low degree of cross-immunity (14
). However, reinfection may occur and result in boosted immunity and a period of limited virus shedding. Two widely used vaccines provide effective protection against disease. Most industrialized countries currently use the enhanced-potency IPV (15
). Trivalent OPV continues as the vaccine of choice of the Polio Eradication Initiative (12
). When administered in the proper schedule (three or more doses required, dependent on setting), both vaccines provide lasting individual protection against disease, while OPV appears more efficient at preventing infection by providing better mucosal immunity in the intestinal tract (16
, 17
). The use of live OPV offers the additional benefit of secondary immunization of contacts of vaccine recipients. However, primary seroconversion ("take") rates of enhanced-potency IPV appear higher than those of trivalent OPV in many settings (18
, 19
).
Outbreaks of paralytic poliomyelitis occur in both wild polio-endemic areas and previously polio-free areas (i.e., importation outbreaks that result from an initiating infection acquired elsewhere) (20
). Most conceivable future outbreaks would resemble current importation outbreaks, since they would represent reintroduction of virus into a previously wild polio-free population or a single initiating infection with a vaccine-derived poliovirus.
Poliovirus importations only lead to an outbreak if the virus can establish effective person-to-person transmission and infect enough people to cause paralytic cases. In the initial stage, if carriers infect less than one new susceptible person on average during their infectious period, the outbreak will die out; but if this number (the "net reproductive number") exceeds 1, the outbreak can continue and expand. Dynamic infection/disease transmission models factor in the dependence between the rate of acquiring infections and the susceptible and infectious proportions of a population.
The model
We built on generic transmission models (21
23
) and existing deterministic (13
, 24
27
) and stochastic (28
30
) poliovirus transmission models to develop our poliomyelitis outbreak model, a deterministic, compartmental model that assumes continuously divisible populations in every compartment (complete details are provided in the technical appendix, which is posted on the Journal's website (http://aje.oxfordjournals.org) and is available on request from the authors). Each compartment represents the number of persons in one of 25 age groups with a given infection state (i.e., susceptible, latent, infectious, or removed/recovered) as a function of time. Mathematically, the model consists of a set of nonlinear ordinary differential equations (31
), where the nonlinear term reflects the dependence of the force of infection on the number of infectious persons. A deterministic model assumes that transitions between compartments occur at the average rate. In reality, biologic variability implies that people have different transfer rates, and an actual outbreak represents just one realization of a stochastic process that could result in a wide range of outbreaks. We assumed homogeneous mixing within (sub)populations, implying that an infected person instantly mingles within the entire (sub)population.
We defined persons never exposed to live or killed polioviruses as "fully susceptibles." Given that previously infected or successfully vaccinated persons can still acquire infections, we denoted them as "partially infectibles." We distinguished three groups of partially infectibles: those recently infected with live poliovirus (i.e., OPV, vaccine-derived poliovirus, or wild poliovirus) (group 1), those historically infected with live poliovirus (group 2), and those only IPV-vaccinated (group 3). We considered only those persons who acquired an infection during the outbreak (the "removeds") as being fully protected from reinfection with the outbreak virus; they no longer participate in transmission during the outbreak after completing their infectious period. We assumed that no one began as uninfectible prior to the outbreak, although we assumed that all partially infectibles and removeds remain fully immune to disease (i.e., they can become infected and participate in transmission but do not become paralyzed).
We solved the equations numerically in Mathematica (Wolfram Research, Inc., Champaign, Illinois) for the time period extending from the day of virus introduction through the subsequent 2 years, when the incidence approaches zero because of the increased population immunity resulting from natural infection and the mass immunization response or because of a seasonal trough in R0. We performed one-way analyses based on ranges for the model inputs, as well as a limited number of multiway sensitivity analyses on key inputs.
Model inputs
We based estimates for the model inputs on peer-reviewed studies, available unpublished data, or our own best judgments given the absence of other information. If more than one data set existed for an input, we used the most applicable estimates based on our assessment of the weight of the evidence. The inputs in table 1 represent polio-specific characteristics that do not depend on the attributes of the outbreak, although they may depend on the serotype (in which case the table presents a serotype-average estimate). The basic reproductive number, R0 (the average number of secondary infections caused by one infection introduced into an entirely susceptible population), represents a theoretical summary measure of transmissibility. We based our estimates of R0 on other studies that calculated R0 from pre-vaccine-era data (20
, 32
), and we used an oscillating function to reflect seasonal variations in transmissibility (11
). The estimates differed by population because of variations in contact rates and the survival of polioviruses in different settings.
View this table:
[in this window]
[in a new window]
|
TABLE 1. Generic model inputs* for a mathematical model designed to simulate the spread of polioviruses during a posteradication outbreak in a predefined population
|
|

View larger version (19K):
[in this window]
[in a new window]
|
FIGURE 2. Example of a prospectively modeled outbreak occurring after cessation of poliomyelitis vaccination in a hypothetical country. This model assumes a low-income country with R0 = 13 and a population of 100 million 5 years after cessation of all polio immunizations and 10 years after stopping supplemental immunization activities. Detection occurs as soon as the cumulative incidence reaches one paralytic case, and the delay from detection to response is 70 days. The response scenarios assume two immunization rounds at a 30-day interval covering 90% of all children under age 5 years in 3 days. The "no response" curve reaches a peak of over 1,700 cases on day 197. tOPV, trivalent oral polio vaccine; mOPV, monovalent oral polio vaccine.
|
|

View larger version (28K):
[in this window]
[in a new window]
|
FIGURE 1. Weekly incidence of paralytic poliomyelitis in the 1996 outbreak in Albania, as reported by Prevots et al. (33 ), and modeled results. NID, National Immunization Day.
|
|
We defined the relative susceptibility of partially infectibles in group i as the probability that a partially infectible person in group i acquires infection divided by the probability that a fully susceptible person acquires infection in an identical situation. We similarly defined relative infectiousness as the relative ability to transmit an infection.
On the basis of data availability and other attributes, we chose three outbreaks with different attributes, including two wild poliovirus importation outbreaks (Albania and the Netherlands) and one circulating vaccine-derived poliovirus outbreak (Dominican Republic), that occurred in developed (Netherlands) and developing (Dominican Republic and Albania) countries, using OPV (Albania and Dominican Republic) and IPV (Netherlands) and involving serotypes 1 (Albania and Dominican Republic) and 3 (Netherlands).
Table 2 lists model inputs for the Albanian outbreak and the assumed initial population immunity profiles. The large, well-documented outbreak that occurred in Albania in 1996 (138 paralytic cases) involved almost the entire country (33
). All virus isolates belonged to one lineage (34
), strongly indicating that a single virus introduction led to the outbreak. Lacking conclusive information about the date of virus introduction, we assumed it had occurred approximately 2 months before the first paralytic case. The fact that the index patient showed onset of paralysis within 2 weeks of a preventive National Immunization Day (NID) in April and May 1996 targeted only at young children (34
) supports our belief that the introduction happened before this NID.
The importation of a type 1 circulating vaccine-derived poliovirus from Haiti in the spring of 2000 resulted in the first reported case in the Dominican Republic outbreak on July 12, 2000 (35
39
). Authorities reported a total of 13 confirmed cases and 13 polio-compatible cases, with the last confirmed case showing paralysis onset on January 25, 2001. Reported cases occurred only in children under age 15 years, all scattered in low-coverage communities in five provinces along the North-South axis of the country, demonstrating substantial heterogeneity in immunity in the population. To capture the clear confinement of the outbreak, we defined the outbreak population as a homogeneous group consisting of residents of the five provinces where the reported cases occurred. We made the key assumption that vaccine-derived polioviruses with the capacity to cause outbreaks possess the same transmissibility and neurovirulence characteristics as wild polioviruses, consistent with laboratory studies (35
, 40
, 41
). We assumed that the introduction occurred during May 2000, based on extrapolation of the observed genetic changes in the VP1 region of the poliovirus genome among the outbreak isolates back to a common origin and assuming a constant mutation rate.
Finally, we modeled the large poliomyelitis outbreak that occurred in the Netherlands in 19921993, which affected almost exclusively members of specific religious communities (42
46
). The Netherlands relies exclusively on IPV for routine immunization and consistently reaches approximately 97 percent coverage (42
); however, substantial numbers of members of Orthodox Reformed churches refuse vaccination for religious reasons, leading to very low coverage in those subpopulations. The wild poliovirus type 3 outbreak in 19921993 resulted in 71 cases (61 paralytic cases, including two deaths) between September 17, 1992, and February 19, 1993 (42
). Cases were distributed approximately evenly among age groups up to age 40 years, with three patients being older than 40. Since the approximately 300,000 members of religious communities in the Netherlands live in a socially and geographically close-knit network (42
), we modeled the Dutch population as two subpopulations with distinct population immunity profiles. To estimate the transmission rates, we assumed that 99 percent of potentially infectious contacts for any member of the subpopulation of 300,000 occurred within this subpopulation and 1 percent involved members of the other subpopulation.
 |
RESULTS
|
---|
Simulation of the three recent outbreaks
Figure 1 shows the actual reported incidence of paralytic poliomyelitis and the results of the simulation of the Albanian outbreak, with all inputs set at their base case values. Assuming that the virus introduction occurred in mid-February and that the virus survived the spring NID, we find very good correspondence of the model with the reported incidence during most stages of the outbreak. The simulated incidence reaches its maximum during the same week as the peak of reported cases, with 12 simulated cases versus 15 reported cases. The simulation predicts a cumulative incidence up to the week before the response that matches the 113 actual reported cases but slightly overestimates the incidence after the response (31 simulated cases vs. 25 reported cases).
Both the geographic distribution and the number of poliomyelitis cases due to circulating vaccine-derived polioviruses appeared much more limited in the Dominican Republic outbreak than in the Albanian outbreak (although inadequate surveillance in the Dominican Republic prior to detection of the outbreak suggests the possibility of missed cases). The small number of cases and uncertainty about the true magnitude of the outbreak limited our ability to accurately define the outbreak population. The simulation results (shown in the technical appendix) contain some notable differences compared with the reported numbers of confirmed and polio-compatible cases (i.e., 31 of 46 cases occurred after the first NID in the model, but only five of 26 reported cases occurred after the first NID). Furthermore, the model predicts a much lower incidence in the first weeks than was reported. The virus introduction potentially occurred at the other end of the plausible range for this input (i.e., approximately 6 weeks earlier), but when we assume an earlier virus introduction the model incidence dramatically overestimates the reported numbers. Alternatively, a somewhat lower R0 and/or rate of paralytic cases per infection for the strain of vaccine-derived virus in this outbreak as compared with wild polioviruses could explain the difference. Finally, the random path of the virus through this highly heterogeneous population (first in a small number of very low-coverage communities, where it caused the majority of cases, and then in the general population) ultimately must have determined the observed kinetics of this small outbreak. Given the lack of detailed population immunity data, our average-based model produced a mediocre representation.
In the Dutch outbreak (see technical appendix), we again found heterogeneity in the population to be an important consideration. However, in this case we could more adequately model the religious communities as a subpopulation, because the outbreak involved them specifically and good data existed about their size and vaccination status. As with the reported numbers, cases in the religious subpopulation dominate the simulated model incidence, while the high levels of population immunity and the low contact rate between the two subpopulations prevent any substantial outbreak in the general population. Unlike the simulations of the other two outbreaks, this model appears to simulate the observed incidence very well in the early stages. The timing of the peak corresponds well to the peak in reported incidence, and the total of 59 model-predicted poliomyelitis cases up to week 60 (last reported case) compares well with the 71 reported cases.
Sensitivity analysis
Using the total number of outbreak cases as the outcome measure, we performed one-way sensitivity analyses on inputs for each of the modeled outbreaks based on the ranges shown in tables 1 and 2 for the Albanian outbreak and similar ranges for the other two outbreaks (see tables in technical appendix). The sensitivity analyses identified several key uncertain inputs, including the duration of infectiousness, the relative infectiousness and relative susceptibility of the most prevalent type of partially infectibles, R0, and the time between virus introduction and response. Furthermore, the date of introduction and the peak day of seasonal transmission both interacted importantly with each other, with R0, and with its amplitude, and in some instances we observed nonmonotonic behavior of the model output as a function of these inputs.
Prospective model
In developing a modeling tool for characterizing potential future outbreaks, we recognize the inherent uncertainty in outcome projections given limited information and the reality that in fact many possible futures exist. However, we believe, on the basis of insights from our extensive synthesis of the literature and experience from modeling three historical outbreaks, that poliovirus transmission models provide helpful tools for studying potential outbreaks after eradication. We offer a generic prospective model that we believe might help in assessing the relative impact of various factors, including the prior vaccination policy (including no vaccination), coverage, and the timeliness and intensity of the outbreak response. Given that different baseline conditions exist, we believe that prospective modeling should stratify countries according to income level (an imperfect but effective surrogate for critical factors that influence key model inputs). Tables 1 and 3 provide the "average" inputs that we believe represent the best starting points for modeling potential future outbreaks.
View this table:
[in this window]
[in a new window]
|
TABLE 3. Inputs for a prospective model* designed to simulate the spread of polioviruses during a posteradication outbreak in a predefined population
|
|
Table 3 omits suggested typical inputs for the date of virus introduction relative to the seasonal peak, since these remain unknown prospectively. We anticipate difficulties in estimating the time between virus introduction and outbreak detection, because in past outbreaks the date of virus introduction often remained unknown and the time to detection depends on many conditions (13
). Our approach estimates the time at detection from the prospective outbreak model itself by using detection triggers (e.g., the occurrence of a certain number of clinical cases) that represent different surveillance systems (table 3). We assume that routine immunization coverage remains stable from the present to the time of the outbreak, independent of the vaccine used. We also implicitly assume unlimited access to vaccine for response, presumably either from ongoing production or from a stockpile. With specific guidelines for the strategy for responding to poliomyelitis outbreaks after eradication still developing, table 3 includes two demonstrative response strategies. Response 1 involves three NID rounds beginning 45 days after detection, and response 2 involves two rounds beginning 70 days after detection.
Figure 2 provides an example of a potential future outbreak based on the prospective model for a hypothetical low-income country with 100 million inhabitants in the fifth year after cessation of polio vaccination for response 2, with either monovalent OPV or trivalent OPV as the vaccine used for immunization response.
 |
DISCUSSION
|
---|
We developed a dynamic disease transmission model aimed at simulating the spread of poliovirus infection after reintroduction of virus into a wild polio-free population. Given that any outbreak represents only one of many possible realizations of a stochastic process, we cannot expect an average-based model to perfectly reproduce the same numbers as those reported, although we should expect it to reasonably match the kinetics of an outbreak. In this sense, the Albanian and Dutch outbreak models produced close matches of the reported epidemiologic data with plausible model input values, but inadequate data about heterogeneity in the Dominican Republic population made modeling that outbreak more difficult. On the basis of review and synthesis of the literature and our experience from modeling these outbreaks, we identified and estimated inputs for a prospective model for poliomyelitis outbreaks. We hope the prospective model will serve as a useful tool in exploring future policies related to management of poliomyelitis risks (e.g., in assessing the impacts of different outbreak and response scenarios as illustrated in figure 2 or effective routine immunization coverage thresholds required to prevent outbreaks) and help identify key characteristics of outbreaks to provide better focus for future data collection efforts (e.g., more accurate information on the time between virus introduction and detection would improve confidence in other inputs chosen for the Albanian and Dutch outbreak models). Surveillance data provide critical information, and we suggest that sustained monitoring of situations that create the types of subpopulations in which outbreaks may occur represents an important opportunity to potentially preempt future outbreaks. Decisions regarding future use of IPV would benefit from additional data that could reduce uncertainties about the relative susceptibility and infectiousness of IPV vaccinees, which drive the Dutch outbreak model. Finally, since one-way sensitivity analysis gives only a crude ranking of the importance of inputs and different sensitivities may arise in other situations (e.g., prospectively), more advanced sensitivity and uncertainty analyses could also provide important insights.
To our knowledge, our model incorporates the most advanced analyses of poliovirus transmission dynamics yet developed; however, we note several important limitations. This model, like any model, remains limited by the quality of the information that goes into it. For the prospective model, the a-priori choice of the size of an outbreak population determines the maximum potential outbreak magnitude, and modeling countries as homogeneous populations implies more rapidly growing outbreaks than would occur with more heterogeneous mixing (47
). The model does not incorporate the influence of heterogeneous mixing between age groups, partly because of difficulties in obtaining such data. Although heterogeneous mixing between age groups possibly played a role in the Dominican Republic outbreak, where all reported cases occurred in children (35
), the age distribution in the other two outbreaks does not suggest more transmission among children than among adults (33
, 42
). The lack of reported paralytic cases in adults in the Dominican Republic outbreak may reflect the high level of population immunity among adults who experienced frequent exposure to wild or OPV viruses before the discontinuation of NIDs in 1996, or possibly the absence of routine surveillance of adults (48
). Inclusion of adults in future reporting may become increasingly important as the time since the last wild virus isolation in a country grows. The assumption of continuously divisible populations demands cautious interpretation of absolute numbers, especially with low incidence. For example, the model could sustain transmission with less than one (partially) infected person (i.e., a physical impossibility) in each age group at the end of an outbreak that could resurge in the next peak season.
The three retrospective outbreak models demonstrate the use of situation-specific information (outbreak virus serotype, response, season) to help inform the modeling process. Using this model as a prospective tool to evaluate the consequences of different poliomyelitis risk management policies in future outbreaks requires the use of generic inputs in place of the situation-specific inputs, or sets of scenarios that represent the spectrum of possible conditions prospectively. We expect that our average-based prospective model will perform best in situations of widespread virus dissemination within a population (e.g., the Albania outbreak), when local heterogeneity and randomness average out. However, we did not test the model on outbreaks in very large populations, and therefore inferences from the prospective model for such situations must remain cautious. Analysts should develop specific models for those situations in which heterogeneous mixing exerts an important impact (47
) and use appropriate inputs to prospectively model particular (i.e., "non-average") scenarios of interest.
In the context of prospective modeling, the times between virus introduction and detection and between detection and response emerge as critical inputs (8
) for characterizing the impact of potential responses. The quality of surveillance clearly influences the timeliness of detection; therefore, when using prospective models, investigators will need to carefully consider future changes in the surveillance network. This model can estimate the length of time until a threshold number of paralytic poliomyelitis cases or infections occurs and model any appropriate dependence on the type and quality of surveillance. Clearly, when developing response policies, investigators will need to consider the trade-offs associated with different strategies, and this model may help in the prediction of outbreak dynamics as a function of different response times and sizes, although its assumption of a predefined population means it cannot model a response that does not target entire (sub)populations at once. Until comprehensive outbreak response guidelines exist, our prospective model requires assumptions regarding the response that may not later prove consistent with the protocol.
Finally, when evaluating future outbreaks and responses, the question of availability of vaccine becomes very important, especially in countries that might cease all polio vaccination. In the Dutch outbreak, a vaccine shortage led to a restricted response (42
), and inadequate supplies could similarly affect future responses. Assuming that a polio vaccine stockpile will exist, its size, location, and content will limit the number of available response options. With increasing numbers of susceptible persons in the future, the existence of adequate response capabilities represents a crucial issue in mitigating the important risks that potential outbreaks pose.
 |
ACKNOWLEDGMENTS
|
---|
Radboud Duintjer Tebbens and Dr. Kimberly Thompson acknowledge receiving financial support for their work from the Centers for Disease Control and Prevention under grant U50/CCU300860.
The authors thank Dr. Bruce Aylward, Dr. Arnold Bosman, Dr. Steve Cochi, Dr. Walt Dowdle, Dr. Paul Fine, Dr. Howard Gary, Dr. Hamid Jafari, Denise Johnson, Bob Keegan, Dr. Mauricio Landaverde, Dr. Tracy Lieu, Dr. Marc Lipsitch, Dr. Anton van Loon, Dr. Steve McLaughlin, Dr. Paul Oostvogel, Dr. Maria Cristina Pedreira, Dr. Becky Prevots, Dr. Nalinee Sangrujee, Dr. Harrie van der Avoort, and Dr. Lara Wolfson for helpful insights, discussions, and comments.
The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the World Health Organization.
Conflict of interest: none declared.
 |
References
|
---|
- World Health Assembly, World Health Organization. Global eradication of poliomyelitis by the year 2000. (Resolution 41.28). Geneva, Switzerland: World Health Organization, 1988.
- World Health Organization. Global Polio Eradication Initiative: progress 2003. Geneva, Switzerland: World Health Organization, 2004. (Report no. WHO/Polio/01.04).
- Smith J, Leke R, Adams A, et al. Certification of polio eradication: process and lessons learned. Bull World Health Organ 2004;82:249.[ISI][Medline]
- Sangrujee N, Duintjer Tebbens RJ, Cáceres VM, et al. Policy decision options during the first 5 years following certification of polio eradication. MedGenMed 2003;5:35. (Electronic article).
- Dowdle WR, Wolff C, Sanders R, et al. Will containment of wild poliovirus in laboratories and inactivated poliovirus vaccine production sites be effective for global certification? Bull World Health Organ 2004;82:5962.[ISI][Medline]
- Kew OM, Wright PF, Agol VI, et al. Circulating vaccine-derived polioviruses: current state of knowledge. Bull World Health Organ 2004;82:1623.[ISI][Medline]
- Aylward RB, Cochi SL. Framework for evaluating the risks of paralytic poliomyelitis after global interruption of wild poliovirus transmission. Bull World Health Organ 2004;82:406.[ISI][Medline]
- Fine PEM, Oblapenko G, Sutter RW. Polio control after certification: major issues outstanding. Bull World Health Organ 2004;82:4752.[ISI][Medline]
- Poliomyelitis in the United States: introduction of a sequential vaccination schedule of inactivated poliovirus vaccine followed by oral poliovirus vaccine. Recommendations of the Advisory Committee on Immunization Practices. MMWR Morb Mortal Wkly Rep 1997;46.
- Dowdle WR, Birmingham ME. The biologic principles of poliovirus eradication. J Infect Dis 1997;175(suppl 1):S28692.[ISI][Medline]
- Nathanson N, Martin JR. The epidemiology of poliomyelitis: enigmas surrounding its appearance, epidemicity, and disappearance. Am J Epidemiol 1979;110:67292.[ISI][Medline]
- Sutter RW, Kew OM, Cochi SL. Poliovirus vaccinelive. In: Plotkin SA, Orenstein WA, eds. Vaccines. Philadelphia, PA: W B Saunders Company, 2004:651705.
- Fine PE, Sutter RW, Orenstein WA. Stopping a polio outbreak in the post-eradication era. Dev Biol (Basel) 2001;105:12947.[Medline]
- Melnick JL. Poliovirus and other enteroviruses. In: Evans AS, Kaslow RA, eds. Viral infections of humans: epidemiology and control. New York, NY: Plenum Medical Book Company, 1997:583663.
- Plotkin SA, Vidor E. Poliovirus vaccineinactivated. In: Plotkin SA, Orenstein WA, eds. Vaccines. Philadelphia, PA: W B Saunders Company, 2004:62549.
- Ghendon YZ, Sanakoyeva II. Comparison of the resistance of the intestinal tract to poliomyelitis virus (Sabin's strains) in persons after naturally and experimentally acquired immunity. Acta Virol (Praha) 1961;5:26573.
- Onorato IM, Modlin JF, McBean MA, et al. Mucosal immunity induced by enhanced-potency inactivated and oral polio vaccines. J Infect Dis 1991;163:16.[ISI][Medline]
- Patriarca PA, Wright PF, John TJ. Factors affecting the immunogenicity of oral poliovirus vaccine in developing countries: review. Rev Infect Dis 1991;13:92639.[ISI][Medline]
- Sutter RW, Cáceres VM, Más Lago P. The role of routine immunization in the post-certification era. Bull World Health Organ 2004;82:318.[ISI][Medline]
- Patriarca PA, Sutter RW, Oostvogel PM. Outbreaks of paralytic poliomyelitis, 19761995. J Infect Dis 1997;175(suppl 1):S16572.[ISI][Medline]
- Serfling R. Historical review of epidemic theory. Hum Biol 1952;24:14566.[Medline]
- Elveback LR, Fox JP, Ackerman E, et al. An influenza simulation model for immunization studies. Am J Epidemiol 1976;103:15265.[Abstract]
- Anderson RM, May RM. Infectious diseases of humans: dynamics and control. New York, NY: Oxford University Press, 1991.
- Chen CJ, Lin TM, You SL. Epidemiological aspects of a poliomyelitis outbreak in Taiwan, 1982. Ann Acad Med Singapore 1984;13:14955.[Medline]
- Cvjetanovic B, Grab B, Dixon H. Epidemiological models of poliomyelitis and measles and their application in the planning of immunization programmes. Bull World Health Organ 1982;60:40522.[ISI][Medline]
- Eichner M, Hadeler KP. Deterministic models for the eradication of poliomyelitis: vaccination with the inactivated (IPV) and attenuated (OPV) polio virus vaccine. Math Biosci 1995;127:14966.[CrossRef][ISI][Medline]
- Fine PE, Carneiro IA. Transmissibility and persistence of oral poliovirus vaccine viruses: implications for the Global Poliomyelitis Eradication Initiative. Am J Epidemiol 1999;150:100121.[Abstract]
- Eichner M, Dietz K. Eradication of poliomyelitis: when can one be sure that polio virus transmission has been terminated? Am J Epidemiol 1996;143:81622.[Abstract]
- Elveback L, Ackerman E, Gatewood L, et al. Stochastic two-agent epidemic simulation models for a community of families. Am J Epidemiol 1971;93:26780.[ISI][Medline]
- Eichner M, Hadeler KP, Dietz K. Stochastic models for the eradication of poliomyelitis: minimum population size for polio virus persistence. In: Isham V, Medley GF, eds. Models for infectious human diseases: their structure and relation to data. New York, NY: Cambridge University Press, 1996:31527.
- Boyce WE, DiPrima RC. Elementary differential equations and boundary value problems. New York, NY: John Wiley and Sons, Inc, 1992.
- Fine PEM, Carneiro IAM. Transmissibility and persistence of oral poliovirus vaccine viruses: implications for the Global Poliomyelitis Eradication Initiative. London, United Kingdom: Infectious Disease Epidemiology Unit, Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, 1998:186.
- Prevots DR, Ciofi degli Atti ML, Sallabanda A, et al. Outbreak of paralytic poliomyelitis in Albania, 1996: high attack rate among adults and apparent interruption of transmission following nationwide mass vaccination. Clin Infect Dis 1998;26:41925.[ISI][Medline]
- Fiore L, Genovese D, Diamanti E, et al. Antigenic and molecular characterization of wild type 1 poliovirus causing outbreaks of poliomyelitis in Albania and neighboring countries in 1996. J Clin Microbiol 1998;36:191218.[Abstract/Free Full Text]
- Kew O, Morris-Glasgow V, Landaverde M, et al. Outbreak of poliomyelitis in Hispaniola associated with circulating type 1 vaccine-derived poliovirus. Science 2002;296:3569.[Abstract/Free Full Text]
- Oficina Nacional de Estadística, República Dominicana. Censo nacional de poblacion y vivienda 1993. (In Spanish). Santo Domingo, Dominican Republic: Oficina Nacional de Estadística, 2003.
- Pan American Health Organization. National polio immunization campaign in the Dominican Republic. EPI Newslett 2000;22:3.
- Pan American Health Organization. Haiti and the Dominican Republic join efforts to control polio and measles on the Island of Hispaniola. EPI Newslett 2002;24:56.
- Landaverde M, Venczel L, de Quadros C. Brote de poliomielitis en Haití y la República Dominicana debido a un virus derivado de la vacuna antipoliomielítica oral. (In Spanish). Rev Pánam Salud Pública 2001;9:2724.[Medline]
- Shimizu H, Thorley B, Paladin FJ, et al. Circulation of type 1 vaccine-derived poliovirus in the Philippines in 2001. J Virol 2004;78:1351221.[Abstract/Free Full Text]
- Yang C, Naguib T, Yang S, et al. Circulation of endemic type 2 vaccine-derived poliovirus in Egypt from 1983 to 1993. J Virol 2003;77:836677.[Abstract/Free Full Text]
- Oostvogel P, van Wijngaarden J, van der Avoort HG, et al. Poliomyelitis outbreak in an unvaccinated community in the Netherlands, 19923. Lancet 1994;344:66570.[CrossRef][ISI][Medline]
- Conyn-Van Spaendonck MA, de Melker HE, Abbink F, et al. Immunity to poliomyelitis in the Netherlands. Am J Epidemiol 2001;153:20714.[Abstract/Free Full Text]
- Rümke H, Oostvogel PM, van Steenis G, et al. Poliomyelitis in the Netherlands: a review of population immunity and exposure between the epidemics in 1978 and 1992. Epidemiol Infect 1995;115:28998.[ISI][Medline]
- Guijt GJ. Beschikbaarheid van het polio vaccin tijdens de epidemie 9293. (In Dutch). Infect Bull 1993;4:2213.
- Rijksinstituut voor Volksgezondheid en Milieu (RIVM). National kompas volksgezonheid. (In Dutch). Bilthoven, the Netherlands: RIVM, 2003. (http://www.rivm.nl/). (Accessed November 13, 2003).
- Koopman J. Modeling infection transmission. Annu Rev Public Health 2004;25:30326.[CrossRef][ISI][Medline]
- Hull BP, Dowdle WR. Poliovirus surveillance: building the global Polio Laboratory Network. J Infect Dis 1997;175(suppl 1):S11316.[ISI][Medline]
- Modlin JF, Halsey NA, Thoms ML, et al. Humoral and mucosal immunity in infants induced by three sequential inactivated poliovirus vaccine-live attenuated oral poliovirus vaccine immunization schedules. Baltimore Area Polio Vaccine Study Group. J Infect Dis 1997;175(suppl 1):s22834.[ISI][Medline]
- Alexander JP Jr, Gary HE Jr, Pallansch MA. Duration of poliovirus excretion and its implications for acute flaccid paralysis surveillance: a review of the literature. J Infect Dis 1997;175(suppl 1):S17682.[ISI][Medline]
- Robertson SE. Poliomyelitis. (Immunological basis for immunization series, no. 6). Geneva, Switzerland: World Health Organization, 1993. (Report no. WHO/EPI/Gen/93.16).
- Gelfland HM, LeBlanc DR, Fox JP, et al. Studies on the development of natural immunity to poliomyelitis in Louisiana. II. Description and analysis of episodes of infection observed in study group households. Am J Hyg 1957;65:36785.[ISI][Medline]
- Buonagurio DA, Coleman JW, Patibandla SA, et al. Direct detection of Sabin poliovirus vaccine strains in stool specimens of first-dose vaccinees by a sensitive reverse transcription-PCR method. J Clin Microbiol 1999;37:2839.[Abstract/Free Full Text]
- Samoilovich E, Roivainen M, Titov LP, et al. Serotype-specific mucosal immune response and subsequent poliovirus replication in vaccinated children. J Med Virol 2003;71:27480.[CrossRef][ISI][Medline]
- Kaul D, Ogra P. Mucosal responses to parenteral and mucosal vaccines. Dev Biol Stand 1998;95:1416.[Medline]
- Chen RT, Hausinger S, Dajani AS, et al. Seroprevalence of antibody against poliovirus in inner-city preschool children. JAMA 1996;275:163945.[Abstract]
- Horstmann DM, Paul JR. The incubation period in human poliomyelitis and its implications. JAMA 1947;135:1114.[ISI]
- Population Division, United Nations. World population prospects population database: the 2002 revision population database. New York, NY: United Nations, 2003. (http://esa.un.org/unpp/index.asp?panel=2). (Accessed July 31, 2003).
- Más Lago P, Bravo JR, Andrus JK, et al. Lesson from Cuba: mass campaign administration of trivalent oral poliovirus vaccine and seroprevalence of poliovirus neutralizing antibodies. Bull World Health Organ 1994;72:2215.[ISI][Medline]
- Vaccine Assessment and Monitoring Team, Department of Immunization, Vaccines and Biologicals, World Health Organization. Third dose of polio vaccine: reported estimates of Pol3 coverage. Geneva, Switzerland: World Health Organization, 2004. (http://www.who.int/vaccines/globalsummary/timeseries/tscoveragepol3.htm).
- Bernier RH. Some observations on poliomyelitis lameness surveys. Rev Infect Dis 1984;6(suppl 2):S3715.[ISI][Medline]
- Squarcione S, Germinario C, Iandolo E, et al. Seroimmunity to poliomyelitis in an Albanian immigrant population. Vaccine 1992;10:8536.[CrossRef][ISI][Medline]
- PoliomyelitisNetherlands. MMWR Morb Mortal Wkly Rep 1992;41:7758.[Medline]
- United Nations Children's Fund (UNICEF). The state of the world's children 2003. New York, NY: UNICEF, 2003. (http://www.unicef.org/sowc03/).
- World Development Indicators Database, World Bank. World Bank list of economies (July 2002). Washington, DC: World Bank, 2002. (www.csirwebistad.org/pdf/classi.pdf).
- McBean AM, Thoms ML, Albrecht P, et al. Serologic response to oral polio vaccine and enhanced-potency inactivated polio vaccines. Am J Epidemiol 1988;128:61528.[Abstract]
- Cáceres VM, Sutter RW. Sabin monovalent oral polio vaccines: review of past experiences and their potential use after polio eradication. Clin Infect Dis 2001;33:53141.[CrossRef][ISI][Medline]