Estimation of the infection attack rate of mumps in an outbreak among college students using paired serology

Mumps virus is a highly transmissible pathogen that is effectively controlled in countries with high vaccination coverage. Nevertheless, outbreaks have occurred worldwide over the past decades in vaccinated populations. Here we analyse an outbreak of mumps virus genotype G among college students in the Netherlands over the period 2009–2012 using paired serological data. To identify infections in the presence of preexisting antibodies we compared mumps specific serum IgG concentrations in two consecutive samples (n=746), whereby the first sample was taken when students started their study prior to the outbreaks, and the second sample was taken 2–5 years later. We fit a binary mixture model to the data. The two mixing distributions represent uninfected and infected classes. Throughout we assume that the infection probability increases with the ratio of antibody concentrations of the second to first sample. The estimated infection attack rate in this study is higher than reported earlier (0.095 versus 0.042). The analyses yield probabilistic classifications of participants, which are mostly quite precise owing to the high intraclass correlation of samples in uninfected participants (0.85, 95%CrI: 0.82−0.87). The estimated probability of infection increases with decreasing antibody concentration in the pre-outbreak sample, such that the probability of infection is 0.12 (95%CrI: 0.10−0.13) for the lowest quartile of the pre-outbreak samples and 0.056 (95%CrI: 0.044−0.068) for the highest quartile. We discuss the implications of these insights for the design of booster vaccination strategies

Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2

Estimating the differences in the incubation-period, serial-interval, and generation-interval distributions of SARS-CoV-2 variants is critical to understanding their transmission. However, the impact of epidemic dynamics is often neglected in estimating the timing of infection-for example, when an epidemic is growing exponentially, a cohort of infected individuals who developed symptoms at the same time are more likely to have been infected recently. Here, we reanalyze incubation-period and serial-interval data describing transmissions of the Delta and Omicron variants from the Netherlands at the end of December 2021. Previous analysis of the same dataset reported shorter mean observed incubation period (3.2 d vs. 4.4 d) and serial interval (3.5 d vs. 4.1 d) for the Omicron variant, but the number of infections caused by the Delta variant decreased during this period as the number of Omicron infections increased. When we account for growth-rate differences of two variants during the study period, we estimate similar mean incubation periods (3.8 to 4.5 d) for both variants but a shorter mean generation interval for the Omicron variant (3.0 d; 95% CI: 2.7 to 3.2 d) than for the Delta variant (3.8 d; 95% CI: 3.7 to 4.0 d). The differences in estimated generation intervals may be driven by the "network effect"-higher effective transmissibility of the Omicron variant can cause faster susceptible depletion among contact networks, which in turn prevents late transmission (therefore shortening realized generation intervals). Using up-to-date generation-interval distributions is critical to accurately estimating the reproduction advantage of the Omicron variant

Transmission and Control of African Horse Sickness in The Netherlands: A Model Analysis

African horse sickness (AHS) is an equine viral disease that is spread by Culicoides spp. Since the closely related disease bluetongue established itself in The Netherlands in 2006, AHS is considered a potential threat for the Dutch horse population. A vector-host model that incorporates the current knowledge of the infection biology is used to explore the effect of different parameters on whether and how the disease will spread, and to assess the effect of control measures. The time of introduction is an important determinant whether and how the disease will spread, depending on temperature and vector season. Given an introduction in the most favourable and constant circumstances, our results identify the vector-to-host ratio as the most important factor, because of its high variability over the country. Furthermore, a higher temperature accelerates the epidemic, while a higher horse density increases the extent of the epidemic. Due to the short infectious period in horses, the obvious clinical signs and the presence of non-susceptible hosts, AHS is expected to invade and spread less easily than bluetongue. Moreover, detection is presumed to be earlier, which allows control measures to be targeted towards elimination of infection sources. We argue that recommended control measures are euthanasia of infected horses with severe clinical signs and vector control in infected herds, protecting horses from midge bites in neighbouring herds, and (prioritized) vaccination of herds farther away, provided that transport regulations are strictly applied. The largest lack of knowledge is the competence and host preference of the different Culicoides species present in temperate regions

Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020.

A novel coronavirus (2019-nCoV) is causing an outbreak of viral pneumonia that started in Wuhan, China. Using the travel history and symptom onset of 88 confirmed cases that were detected outside Wuhan in the early outbreak phase, we estimate the mean incubation period to be 6.4 days (95% credible interval: 5.6–7.7), ranging from 2.1 to 11.1 days (2.5th to 97.5th percentile). These values should help inform 2019-nCoV case definitions and appropriate quarantine durations

Spatiotemporal Analysis of the 2014 Ebola Epidemic in West Africa

<div><p>In 2014–2016, Guinea, Sierra Leone and Liberia in West Africa experienced the largest and longest Ebola epidemic since the discovery of the virus in 1976. During the epidemic, incidence data were collected and published at increasing resolution. To monitor the epidemic as it spread within and between districts, we develop an analysis method that exploits the full spatiotemporal resolution of the data by combining a local model for time-varying effective reproduction numbers with a gravity-type model for spatial dispersion of the infection. We test this method in simulations and apply it to the weekly incidences of confirmed and probable cases per district up to June 2015, as reported by the World Health Organization. Our results indicate that, of the newly infected cases, only a small percentage, between 4% and 10%, migrates to another district, and a minority of these migrants, between 0% and 23%, leave their country. The epidemics in the three countries are found to be similar in estimated effective reproduction numbers, and in the probability of importing infection into a district. The countries might have played different roles in cross-border transmissions, although a sensitivity analysis suggests that this could also be related to underreporting. The spatiotemporal analysis method can exploit available longitudinal incidence data at different geographical locations to monitor local epidemics, determine the extent of spatial spread, reveal the contribution of local and imported cases, and identify sources of introductions in uninfected areas. With good quality data on incidence, this data-driven method can help to effectively control emerging infections.</p></div

Observed incidence over time per district, classified by origin of cases.

<p>Weekly observed incidence in all districts of Guinea (green), Sierra Leone (blue) and Liberia (red), based on median posterior values averaged over ten augmented data sets. A distinction is made between locally generated cases (lightly coloured bar), imported cases from another district in the same country (dark tip of same colour), and imported cases from another country (dark tip of different colour). Districts are ordered by time of first observed case; in six districts in Guinea not a single case has been observed. Note that the y-axis scale depends on the maximal number of observed cases per week in a district, ranging from 1 (Dinguiraye, Guinea) to 618 (Montserrado, Liberia).</p

Expected number of migrated cases between districts as a function of underreporting fraction in Guinea, based on median posterior values, with five repetitions per underreporting level, for Guinea (green), Sierra Leone (blue) and Liberia (red).

<p>Median value (dark symbol), median value averaged over five repetitions (dark line), interquartile range (light line), and 2.5% and 97.5% percentiles (light symbols).</p

Number of introduced cases per district distributed over possible origin districts, based on median posterior values averaged over ten augmented data sets.

<p>Districts are ordered per country by time of first observed case. District columns add up to the total number of observed introduced cases in that district, which can be higher than 1 due to multiple introductions and due to multiple cases per introduction. Colours indicate distinct categories: 1 or more introduced cases (red), between 0.1 and 1 (dark orange), between 0.01 and 0.1 (orange), between 0.001 and 0.01 (yellow), between 0 and 0.001 (light yellow), and 0 (white). The latter category means that this introduction is impossible, because the destination was never infected or the source was not infected at the time of introduction in the destination.</p

Estimated effective reproduction numbers over time per district.

<p>Effective reproduction numbers <i>R</i>_<i>e</i> in all districts of Guinea (green), Sierra Leone (blue) and Liberia (red) over time; median values (black dots), interquartile credible interval (dark coloured bars), and 95% credible interval (light coloured bars). Districts are ordered by time of first observed case; in six districts in Guinea not a single case has been observed.</p