Person:

Goldstein, Edward

Background: An accurate estimate of the total number of cases and severity of illness of an emerging infectious disease is required both to define the burden of the epidemic and to determine the severity of disease. When a novel pathogen first appears, affected individuals with severe symptoms are more likely to be diagnosed. Accordingly, the total number of cases will be underestimated and disease severity overestimated. This problem is manifest in the current epidemic of novel influenza A/H1N1. Methods and Results: We used a simple approach to leverage measures of incident influenza A/H1N1 among a relatively small and well observed group of US, UK, Spanish and Canadian travelers who had visited Mexico to estimate the incidence among a much larger and less well surveyed population of Mexican residents. We estimate that a minimum of 113,000 to 375,000 cases of novel influenza A/H1N1 have occurred in Mexicans during the month of April, 2009. Such an estimate serves as a lower bound because it does not account for underreporting of cases in travelers or for nonrandom mixing between Mexican residents and visitors, which together could increase the estimates by more than an order of magnitude. Conclusions: We find that the number of cases in Mexican residents may exceed the number of confirmed cases by two to three orders of magnitude. While the extent of disease spread is greater than previously appreciated, our estimate suggests that severe disease is uncommon since the total number of cases is likely to be much larger than those of confirmed cases.

Background: During an influenza pandemic, a substantial proportion of transmission is thought to occur in households. We used data on influenza progression in individuals and their contacts collected by the City of Milwaukee Health Department (MHD) to study the transmission of pandemic influenza A/H1N1 virus in 362 households in Milwaukee, WI, and the effects of oseltamivir treatment and chemoprophylaxis. Methods: 135 households had chronological information on symptoms and oseltamivir usage for all household members. The effect of oseltamivir treatment and other factors on the household secondary attack rate was estimated using univariate and multivariate logistic regression with households as the unit of analysis. The effect of oseltamivir treatment and other factors on the individual secondary attack rate was estimated using univariate and multivariate logistic regression with individual household contacts as the unit of analysis, and a generalized estimating equations approach was used to fit the model to allow for clustering within households. Results: Oseltamivir index treatment on onset day or the following day (early treatment) was associated with a 42% reduction (OR: 0.58, 95% CI: 0.19, 1.73) in the odds of one or more secondary infections in a household and a 50% reduction (OR: 0.5, 95% CI: 0.17, 1.46) in the odds of a secondary infection in individual contacts. The confidence bounds are wide due to a small sample of households with early oseltamivir index usage - in 29 such households, 5 had a secondary attack. Younger household contacts were at higher risk of infection (OR: 2.79, 95% CI: 1.50-5.20). Conclusions: Early oseltamivir treatment may be beneficial in preventing H1N1pdm influenza transmission; this may have relevance to future control measures for influenza pandemics. Larger randomized trials are needed to confirm this finding statistically.

Background: Many schools were temporarily closed in response to outbreaks of the recently emerged pandemic influenza A/H1N1 virus. The effectiveness of closing schools to reduce transmission depends largely on student/family behavior during the closure. We sought to improve our understanding of these behaviors. Methodology/Principal Findings: To characterize this behavior, we surveyed students in grades 9–12 and parents of students in grades 5–8 about student activities during a weeklong closure of a school during the first months after the disease emerged. We found significant interaction with the community and other students–though less interaction with other students than during school–with the level of interaction increasing with grade. Conclusions: Our results are useful for the future design of social distancing policies and to improving the ability of modeling studies to accurately predict their impact.

Sizeable quantities of 2009 pandemic influenza A/H1N1 (H1N1pdm) vaccine in the USA became available at the end of 2009 when the autumn wave of the epidemic was declining. At that point, risk factors for H1N1-related mortality for some of the high-risk groups, particularly adults with underlying health conditions, could be estimated. Although those high-risk groups are natural candidates for being in the top priority tier for vaccine allocation, another candidate group is school-aged children through their role as vectors for transmission affecting the whole community. In this paper, we investigate the question of prioritization for vaccine allocation in a declining epidemic between two groups—a group with a high risk of mortality versus a ‘core’ group with a relatively low risk of mortality but fuelling transmission in the community. We show that epidemic data can be used, under certain assumptions on future decline, seasonality and vaccine efficacy in different population groups, to give a criterion when initial prioritization of a population group with a sufficiently high risk of epidemic-associated mortality is advisable over the policy of prioritizing the core group.

Hong Kong experienced two large epidemics of pandemic influenza A(H1N1pdm09). We used regression methods to estimate the excess mortality associated with each epidemic. The first epidemic of H1N1pdm09 peaked in September 2009 and was associated with 2·13 [95% confidence interval (CI): −8·08, 11·82] excess all-cause deaths per 100 000 population. The second epidemic of H1N1pdm09 in early 2011 was associated with 4·72 [95% CI: −0·70, 10·50] excess deaths per 100 000 population. More than half of the estimated excess all-cause deaths were attributable to respiratory causes in each epidemic. The reasons for substantial impact in the second wave remain to be clarified.

We introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate.

There is limited information on the roles of different age groups during pertussis outbreaks. Little is known about vaccine effectiveness against pertussis infection (both clinically apparent and subclinical), which is different from effectiveness against reportable pertussis disease, with the former influencing the impact of vaccination on pertussis transmission in the community. For the 2012 pertussis outbreak in Minnesota, we estimated odds ratios for case counts in pairs of population groups before vs. after the epidemic’s peak. We found children aged 11–12y, 13–14y and 8–10y experienced the greatest rates of depletion of susceptible individuals during the outbreak’s ascent, with all ORs for each of those age groups vs. groups outside this age range significantly above 1, with the highest ORs for ages 11–12y. Receipt of the fifth dose of DTaP was associated with a decreased relative role during the outbreak’s ascent compared to non-receipt [OR 0.16 (0.01, 0.84) for children aged 5, 0.13 (0.003, 0.82) for ages 8–10y, indicating a protective effect of DTaP against pertussis infection. No analogous effect of Tdap was detected. Our results suggest that children aged 8–14y played a key role in propagating this outbreak. The impact of immunization with Tdap on pertussis infection requires further investigation.

The rise of antimicrobial resistance in many pathogens presents a major challenge to the treatment and control of infectious diseases. Furthermore, the observation that drug-resistant strains have risen to substantial prevalence but have not replaced drug-susceptible strains despite continuing (and even growing) selective pressure by antimicrobial use presents an important problem for those who study the dynamics of infectious diseases. While simple competition models predict the exclusion of one strain in favour of whichever is ‘fitter’, or has a higher reproduction number, we argue that in the case of Streptococcus pneumoniae there has been persistent coexistence of drug-sensitive and drug-resistant strains, with neither approaching 100 per cent prevalence. We have previously proposed that models seeking to understand the origins of coexistence should not incorporate implicit mechanisms that build in stable coexistence ‘for free’. Here, we construct a series of such ‘structurally neutral’ models that incorporate various features of bacterial spread and host heterogeneity that have been proposed as mechanisms that may promote coexistence. We ask to what extent coexistence is a typical outcome in each. We find that while coexistence is possible in each of the models we consider, it is relatively rare, with two exceptions: (i) allowing simultaneous dual transmission of sensitive and resistant strains lets coexistence become a typical outcome, as does (ii) modelling each strain as competing more strongly with itself than with the other strain, i.e. self-immunity greater than cross-immunity. We conclude that while treatment and contact heterogeneity can promote coexistence to some extent, the in-host interactions between strains, particularly the interplay between coinfection, multiple infection and immunity, play a crucial role in the long-term population dynamics of pathogens with drug resistance.

65% of fatalities from pH1N1 infections in a large US case series occur in adults with underlying health conditions other than pregnancy, but it appears that only relatively few high-risk adults will get vaccinated during the fall wave of pH1N1 transmission. There are several reasons for this problem; the most important is vaccine shortage. High risk adults (other than pregnant women) were not part of the initial, narrow priority cohort which included pregnant women and children ages 0.5-4; this is despite the fact that some of those high risk groups, such as adults with immunosuppressive conditions and possibly individuals with neurological disorders, have a relative risk for fatality (per capita) higher than pregnant women, and over 28-fold higher than healthy children under the age of 4. With more vaccine becoming available than needed in the initial priority cohort, a broader group which includes high risk adults and individuals under 24 becomes eligible for vaccine in many locations. Nonetheless, due to continuing high demand, high-risk adults face competition for vaccine from healthy individuals under 24; additionally, some locations specifically prioritize school students over high-risk adults. Finally, there is an issue of awareness and a shortage of specific channels that target high risk adults other than pregnant women and facilitate vaccine distribution among them in the US.

We propose a mathematically straightforward method to infer the incidence curve of an epidemic from a recorded daily death curve and time-to-death distribution; the method is based on the Richardson-Lucy deconvolution scheme from optics. We apply the method to reconstruct the incidence curves for the 1918 influenza epidemic in Philadelphia and New York State. The incidence curves are then used to estimate epidemiological quantities, such as daily reproductive numbers and infectivity ratios. We found that during a brief period before the official control measures were implemented in Philadelphia, the drop in the daily number of new infections due to an average infector was much larger than expected from the depletion of susceptibles during that period; this finding was subjected to extensive sensitivity analysis. Combining this with recorded evidence about public behavior, we conclude that public awareness and change in behavior is likely to have had a major role in the slowdown of the epidemic even in a city whose response to the 1918 influenza epidemic is considered to have been among the worst in the U.S.