Ryosuke Omori

An Epidemiological Analysis of the Foot-and-Mouth Disease Epidemic in Miyazaki, Japan, 2010

An epidemic of foot-and-mouth disease occurred in Miyazaki, Japan, beginning in late March 2010. Here, we document the descriptive epidemiological features and investigate the between-farm transmission dynamics. As of 10 July 2010, a total of 292 infected premises have been confirmed with a cumulative incidence for cattle and pig herds of 8.5% and 36.4%, respectively, for the whole of Miyazaki prefecture. Pig herds were more likely to be infected than cattle herds (odds ratio = 4.3 [95% confidence interval (CI): 3.2, 5.7]). Modelling analysis suggested that the relative susceptibility of a cattle herd is 4.2 times greater than a typical pig herd (95% CI: 3.9, 4.5), while the relative infectiousness of a pig herd is estimated to be 8.0 times higher than a cattle herd (95% CI: 5.0, 13.6). The epidemic peak occurred around mid-May, after which the incidence started to decline and the effective reproduction numbers from late May were mostly less than unity, although a vaccination programme in late May could have masked symptoms in infected animals. The infected premises were geographically confined to limited areas in Miyazaki, but sporadic long-distance transmissions were seen within the prefecture. Given that multiple outbreaks in Far East Asian countries have occurred since early 2010, continued monitoring and surveillance is deemed essential.

Coexistence conditions for strains of influenza with immune cross-reaction

The accumulation of cross-immunity in the host population is an important factor driving the antigenic evolution of viruses such as influenza A. Mathematical models have shown that the strength of temporary non-specific cross-immunity and the basic reproductive number are both key determinants for evolutionary branching of the antigenic phenotype. Here we develop deterministic and stochastic versions of one such model. We examine how the time of emergence or introduction of a novel strain affects co-existence with existing strains and hence the initial establishment of a new evolutionary branch. We also clarify the roles of cross-immunity and the basic reproductive number in this process. We show that the basic reproductive number is important because it affects the frequency of infection, which influences the long term immune profile of the host population. The time at which a new strain appears relative to the epidemic peak of an existing strain is important because it determines the environment the emergent mutant experiences in terms of the short term immune profile of the host population. Strains are more likely to coexist, and hence to establish a new clade in the viral phylogeny, when there is a significant time overlap between their epidemics. It follows that the majority of antigenic drift in influenza is expected to occur in the earlier part of each transmission season and this is likely to be a key surveillance period for detecting emerging antigenic novelty.

Disrupting seasonality to control disease outbreaks:the case of koi herpes virus

Common carp accounts for a substantial proportion of global freshwater aquaculture production. Koi herpes virus (KHV), a highly virulent disease affecting carp that emerged in the late 1990s, is a serious threat to this industry. After a fish is infected with KHV, there is a temperature dependent delay before it becomes infectious, and a further delay before mortality. Consequently, KHV epidemiology is driven by seasonal changes in water temperature. Also, it has been proposed that outbreaks could be controlled by responsive management of water temperature in aquaculture setups. We use a mathematical model to analyse the effect of seasonal temperature cycles on KHV epidemiology, and the impact of attempting to control outbreaks by disrupting this cycle. We show that, although disease progression is fast in summer and slow in winter, total mortality over a 2-year period is similar for outbreaks that start in either season. However, for outbreaks that start in late autumn, mortality may be low and immunity high. A single bout of water temperature management can be an effective outbreak control strategy if it is started as soon as dead fish are detected and maintained for a long time. It can also be effective if the frequency of infectious fish is used as an indicator for the beginning of treatment. In this case, however, there is a risk that starting the treatment too soon will increase mortality relative to the case when no treatment is used. This counterproductive effect can be avoided if multiple bouts of temperature management are used. We conclude that disrupting normal seasonal patterns in water temperature can be an effective strategy for controlling koi herpes virus. Exploiting the seasonal patterns, possibly in combination with temperature management, can also induce widespread immunity to KHV in a cohort of fish. However, employing these methods successfully requires careful assessment to ensure that the treatment is started, and finished, at the correct time.

Dynamics of non-cohabiting sex partnering in sub-Saharan Africa: a modelling study with implications for HIV transmission

Objective To develop an analytical understanding of non-cohabiting sex partnering in sub-Saharan Africa (SSA) using nationally representative sexual behaviour data. Method A non-homogenous Poisson stochastic process model was used to describe the dynamics of non-cohabiting sex. The model was applied to 25 countries in SSA and was fitted to Demographic and Health Survey data. The country-specific mean values and variances of the distributions of number of non-cohabiting partners were estimated. Results The model yielded overall robust fits to the empirical distributions stratified by marital status and sex. The median across all country-specific mean values was highest for unmarried men at 0.574 non-cohabiting partners over the last 12 months, followed by that of unmarried women at 0.337, married men at 0.192 and married women at 0.038. The median of variances was highest for unmarried men at 0.127, followed by married men at 0.057, unmarried women at 0.003 and married women at 0.000. The largest variability in mean values across countries was for unmarried men (0.103–1.206), and the largest variability in variances was among unmarried women (0.000–1.994). Conclusions Non-cohabiting sex appears to be a random ‘opportunistic’ phenomenon linked to situations that may facilitate it. The mean values and variances of number of partners in SSA show wide variation by country, marital status and sex. Unmarried individuals have larger mean values than their married counterparts, and men have larger mean values than women. Unmarried individuals appear to play a disproportionate role in driving heterogeneity in sexual networks and possibly epidemiology of sexually transmitted infections.

How Is Vaccine Effectiveness Scaled by the Transmission Dynamics of Interacting Pathogen Strains with Cross- Protective Immunity?

Background Many novel vaccines can cover only a fraction of all antigenic types of a pathogen. Vaccine effectiveness (VE) in the presence of interactions between vaccine strains and others is complicated by the interacting transmission dynamics among all strains. The present study investigated how the VE estimates measured in the field, based on estimated odds ratio or relative risks, are scaled by vaccination coverage and the transmission dynamics in the presence of cross-protective immunity between two strains, i.e. vaccine and non-vaccine strains. Methodology/Principal Findings Two different types of epidemiological models, i.e. with and without re-infection by the same antigenic type, were investigated. We computed the relative risk of infection and the odds ratio of vaccination, the latter of which has been measured by indirect cohort method as applied to vaccine effectiveness study of Streptococcus pneumoniae. The VE based on the relative risk was less sensitive to epidemiological dynamics such as cross-protective immunity and vaccination coverage than the VE calculated from the odds ratio, and this was especially the case for the model without re-infection. Vaccine-induced (cross-protective) immunity against a non-vaccine strain appeared to yield the highest impact on the VE estimate calculated from the odds ratio of vaccination. Conclusion It is essential to understand the transmission dynamics of non-vaccine strains so that epidemiological methods can appropriately measure both the direct and indirect population impact of vaccination. For pathogens with interacting antigenic types, the most valid estimates of VE, that are unlikely to be biased by the transmission dynamics, may be obtained from longitudinal prospective studies that permit estimation of the VE based on the relative risk of infection among vaccinated compared to unvaccinated individuals.

Real-time Investigation of Measles Epidemics with Estimate of Vaccine Efficacy

As part of measles elimination effort, evaluation of the vaccination program and real-time assessment of the epidemic dynamics constitute two important tasks to improve and strengthen the control. The present study aimed to develop an epidemiological modeling method which can be applied to estimating the vaccine efficacy at an individual level while conducting the timely investigation of the epidemic. The multivariate renewal process model was employed to describe the temporal evolution of infection by vaccination history, jointly estimating the time-dependent reproduction number and the vaccine efficacy. Analyzing the enhanced surveillance data of measles in Aichi prefecture, Japan from 2007-08, the vaccine efficacy was estimated at 96.7% (95% confidence interval: 95.8, 97.4). Using an age structured model, the vaccine efficacy among those aged from 5-19 years was shown to be smaller than that among those from 0-4 years. The age-dependent vaccine efficacy estimate informs the age-groups to be targeted for revaccination. Because the estimation method can rest on readily available epidemiological data, the proposed model has a potential to be integrated with routine surveillance.

The determinant of periodicity in Mycoplasma pneumoniae incidence: an insight from mathematical modelling

Until the early 1990s, incidences of Mycoplasma pneumoniae (MP) infection showed three to five year epidemic cycles in multiple countries, however, the mechanism for the MP epidemic cycle has not been understood. Here, we investigate the determinant of periodicity in MP incidence by employing a mathematical model describing MP transmission dynamics. Three candidates for the determinant of periodicity were evaluated: school-term forcing, minor variance in the duration of immunity, and epidemiological interference between MP serotypes. We find that minor variation in the duration of immunity at the population level must be considered essential for the MP epidemic cycle because the MP cyclic incidence pattern did not replicate without it. Minor variation, in this case, is a less dispersed distribution for the duration of immunity than an exponential distribution. Various lengths of epidemic cycles, including cycles typically found in nature, e.g. three to five year cycles, were also observed when there was minor variance in the duration of immunity. The cyclic incidence pattern is robust even if there is epidemiological interference due to cross-immune protection, which is observed in the epidemiological data as negative correlation between epidemics per MP serotype.

Theoretical basis to measure the impact of short-lasting control of an infectious disease on the epidemic peak.

BackgroundWhile many pandemic preparedness plans have promoted disease control effort to lower and delay an epidemic peak, analytical methods for determining the required control effort and making statistical inferences have yet to be sought. As a first step to address this issue, we present a theoretical basis on which to assess the impact of an early intervention on the epidemic peak, employing a simple epidemic model.MethodsWe focus on estimating the impact of an early control effort (e.g. unsuccessful containment), assuming that the transmission rate abruptly increases when control is discontinued. We provide analytical expressions for magnitude and time of the epidemic peak, employing approximate logistic and logarithmic-form solutions for the latter. Empirical influenza data (H1N1-2009) in Japan are analyzed to estimate the effect of the summer holiday period in lowering and delaying the peak in 2009.ResultsOur model estimates that the epidemic peak of the 2009 pandemic was delayed for 21 days due to summer holiday. Decline in peak appears to be a nonlinear function of control-associated reduction in the reproduction number. Peak delay is shown to critically depend on the fraction of initially immune individuals.ConclusionsThe proposed modeling approaches offer methodological avenues to assess empirical data and to objectively estimate required control effort to lower and delay an epidemic peak. Analytical findings support a critical need to conduct population-wide serological survey as a prior requirement for estimating the time of peak.

HIV and herpes simplex virus type 2 epidemiological synergy: misguided observational evidence? A modelling study

Objectives To investigate whether observational studies of HIV and herpes simplex virus type 2 (HSV-2) infections have the capacity to assess the HIV/HSV-2 epidemiological synergy. Methods An individual-based Monte Carlo model was used to simulate HIV/HSV-2 epidemics in two scenarios: no HIV/HSV-2 biological interaction and HSV-2 seropositivity enhancing HIV acquisition. Cross-sectional observational studies were simulated by sampling individuals from the population to assess resulting crude and adjusted ORs of the HIV/HSV-2 association. Meta-analyses were conducted to estimate the pooled mean ORs. Impact of under-reporting of sexual behaviour and miscapture of high-risk individuals was assessed through sensitivity analyses. Results Assuming no HIV/HSV-2 biological interaction, the crude HIV/HSV-2 OR ranged between 1.38 and 9.93, with a pooled mean of 6.45 (95% CI 5.81 to 7.17). Adjustment for the number of sexual partners over last year, over lifetime and for both partner numbers simultaneously reduced the mean OR to 5.45 (95% CI 4.90 to 6.06), 3.70 (95% CI 3.32 to 4.12) and 3.54 (95% CI 3.17 to 3.94), respectively. Assuming HIV/HSV-2 biological interaction, the crude OR ranged between 3.44 and 9.95, with a pooled mean of 8.05 (95% CI 7.14 to 9.07). The adjustments reduced the mean OR to 7.00 (95% CI 6.21 to 7.90), 3.76 (95% CI 3.32 to 4.25) and 3.68 (95% CI 3.25 to 4.17), respectively. Under-reporting of partners reduced the confounder-adjustment effects. Miscapture of high-risk individuals considerably lowered the estimated ORs. Conclusions It is difficult to control for sexual-behaviour confounding in observational studies. The observed HIV/HSV-2 association appears more consistent with two infections sharing the same mode of transmission, rather than with HSV-2 enhancing HIV acquisition.

Sexual network drivers of Hiv and herpes simplex virus type 2 transmission

Objectives: HIV and herpes simplex virus type 2 (HSV-2) infections are sexually transmitted and propagate in sexual networks. Using mathematical modeling, we aimed to quantify effects of key network statistics on infection transmission, and extent to which HSV-2 prevalence can be a proxy of HIV prevalence. Design/methods: An individual-based simulation model was constructed to describe sex partnering and infection transmission, and was parameterized with representative natural history, transmission, and sexual behavior data. Correlations were assessed on model outcomes (HIV/HSV-2 prevalences) and multiple linear regressions were conducted to estimate adjusted associations and effect sizes. Results: HIV prevalence was one-third or less of HSV-2 prevalence. HIV and HSV-2 prevalences were associated with a Spearmans rank correlation coefficient of 0.64 (95% confidence interval: 0.58–0.69). Collinearities among network statistics were detected, most notably between concurrency versus mean and variance of number of partners. Controlling for confounding, unmarried mean/variance of number of partners (or alternatively concurrency) were the strongest predictors of HIV prevalence. Meanwhile, unmarried/married mean/variance of number of partners (or alternatively concurrency), and clustering coefficient were the strongest predictors of HSV-2 prevalence. HSV-2 prevalence was a strong predictor of HIV prevalence by proxying effects of network statistics. Conclusion: Network statistics produced similar and differential effects on HIV/HSV-2 transmission, and explained most of the variation in HIV and HSV-2 prevalences. HIV prevalence reflected primarily mean and variance of number of partners, but HSV-2 prevalence was affected by a range of network statistics. HSV-2 prevalence (as a proxy) can forecast a populations HIV epidemic potential, thereby informing interventions.