Modelling the impact of vaccination and sexual behaviour adaptations on mpox cases in the USA during the 2022 outbreak.

BACKGROUND: The 2022 mpox outbreak has infected over 30 000 people in the USA, with cases declining since mid-August. Infections were commonly associated with sexual contact between men. Interventions to mitigate the outbreak included vaccination and a reduction in sexual partnerships. Understanding the contributions of these interventions to decreasing cases can inform future public health efforts. METHODS: We fit a dynamic network transmission model to mpox cases reported by Washington DC through 10 January 2023. This model incorporated both vaccine administration data and reported reductions in sexual partner acquisition by gay, bisexual or other men who have sex with men (MSM). The model output consisted of daily cases over time with or without vaccination and/or behavioural adaptation. RESULTS: We found that initial declines in cases were likely caused by behavioural adaptations. One year into the outbreak, vaccination and behavioural adaptation together prevented an estimated 84% (IQR 67% to 91%) of cases. Vaccination alone averted 79% (IQR 64% to 88%) of cases and behavioural adaptation alone averted 25% (IQR 10% to 42%) of cases. We further found that in the absence of vaccination, behavioural adaptation would have reduced the number of cases, but would have prolonged the outbreak. CONCLUSIONS: We found that initial declines in cases were likely caused by behavioural adaptation, but vaccination averted more cases overall and was key to hastening outbreak conclusion. Overall, this indicates that outreach to encourage individuals to protect themselves from infection was vital in the early stages of the mpox outbreak, but that combination with a robust vaccination programme hastened outbreak conclusion.

Age Structure Eliminates the Impact of Coinfection on Epidemic Dynamics in a Freshwater Zooplankton System.

AbstractParasites often coinfect host populations and, by interacting within hosts, might change the trajectory of multiparasite epidemics. However, host-parasite interactions often change with host age, raising the possibility that within-host interactions between parasites might also change, influencing the spread of disease. We measured how heterospecific parasites interacted within zooplankton hosts and how host age changed these interactions. We then parameterized an epidemiological model to explore how age effects altered the impact of coinfection on epidemic dynamics. In our model, we found that in populations where epidemiologically relevant parameters did not change with age, the presence of a second parasite altered epidemic dynamics. In contrast, when parameters varied with host age (based on our empirical measures), there was no longer a difference in epidemic dynamics between singly infected and coinfected populations, indicating that variable age structure within a population eliminates the impact of coinfection on epidemic dynamics. Moreover, infection prevalence of both parasites was lower in populations where epidemiologically relevant parameters changed with age. Given that host population age structure changes over time and space, these results indicate that age effects are important for understanding epidemiological processes in coinfected systems and that studies focused on a single age group could yield inaccurate insights.

Using infection prevalence, seroprevalence and case report data to estimate chlamydial infection incidence.

OBJECTIVES: To measure the effectiveness of chlamydia control strategies, we must estimate infection incidence over time. Available data, including survey-based infection prevalence and case reports, have limitations as proxies for infection incidence. We therefore developed a novel method for estimating chlamydial incidence. METHODS: We linked a susceptible infectious mathematical model to serodynamics data from the National Health and Nutritional Examination Survey, as well as to annual case reports. We created four iterations of this model, varying assumptions about how the method of infection clearance (via treatment seeking, routine screening or natural clearance) relates to long-term seropositivity. Using these models, we estimated annual infection incidence for women aged 18-24 and 25-37 years in 2014. To assess model plausibility, we also estimated natural clearance for the same groups. RESULTS: Of the four models we analysed, the model that best explained the empirical data was the one in which longer-lasting infections, natural clearance and symptomatic infections all increased the probability of long-term seroconversion. Using this model, we estimated 5910 (quartile (Q)1, 5330; Q3, 6500) incident infections per 100 000 women aged 18-24 years and 2790 (Q1, 2500; Q3, 3090) incident infections per 100 000 women aged 25-37 years in 2014. Furthermore, we estimated that natural clearance rates increased with age. CONCLUSIONS: Our method can be used to estimate the number of chlamydia infections each year, and thus whether infection incidence increases or decreases over time and after policy changes. Furthermore, our results suggest that clearance via medical intervention may lead to short-term or no seroconversion, and the duration of untreated chlamydial infection may vary with age, underlining the complexity of chlamydial infection dynamics.

Estimated Incidence and Prevalence of Gonorrhea in the United States, 2006-2019.

BACKGROUND: We extend recent work estimating incidence and prevalence of gonococcal infections among men and women aged 15 to 39 years in the United States in 2018 by applying the same modeling framework to estimate gonococcal incidence and prevalence during 2006 to 2019. METHODS: The model is informed by cases from the Nationally Notifiable Disease Surveillance System, data from the National Survey of Family Growth, and data on other factors known to impact gonococcal incidence and prevalence. We use Monte Carlo simulation to account for uncertainty in input parameters. Results are reported as median annual per-capita incidence and prevalence; uncertainty intervals are characterized by the 25th and 75th simulated percentiles. RESULTS: There were 1,603,473 (1,467,801-1,767,779) incident cases of gonorrhea estimated in 2019. Per-capita incidence increased 32%, from 1101 (1002-1221) to 1456 (1333-1605) infections per 100,000 persons. This trend in per-capita incidence had 3 phrases: an initial decline during 2006 to 2009, a plateau through 2013, and a rapid increase of 66% through 2019. Men aged 25 to 39 years experienced the greatest increase in incidence (125%, 541 [467-651] to 1212 infections [1046-1458] per 100,000 men). Women aged 25 to 39 years had the lowest incidence in 2019, with 1040 infections (882-1241) per 100,000 women. Prevalence increased more slowly among those aged 25 to 39 years versus 15 to 24 years. The incidence ratio comparing men with women aged 25 to 39 years increased from 0.76 to 1.18. CONCLUSIONS: The burden of gonorrhea has increased among men and women aged 15 to 39 years since 2013. An increasing proportion of incident infections are among men. Additional biomedical and behavioral interventions are needed to control gonococcal transmission.

Potential for Recurrent Mpox Outbreaks Among Gay, Bisexual, and Other Men Who Have Sex with Men - United States, 2023.

More than 30,000 monkeypox (mpox) cases have been diagnosed in the United States since May 2022, primarily among gay, bisexual, and other men who have sex with men (MSM) (1,2). In recent months, diagnoses have declined to one case per day on average. However, mpox vaccination coverage varies regionally, suggesting variable potential risk for mpox outbreak recurrence (3). CDC simulated dynamic network models representing sexual behavior among MSM to estimate the risk for and potential size of recurrent mpox outbreaks at the jurisdiction level for 2023 and to evaluate the benefits of vaccination for preparedness against mpox reintroduction. The risk for outbreak recurrence after mpox reintroduction is linearly (inversely) related to the proportion of MSM who have some form of protective immunity: the higher the population prevalence of immunity (from vaccination or natural infection), the lower the likelihood of recurrence in that jurisdiction across all immunity levels modeled. In contrast, the size of a potential recurrent outbreak might have thresholds: very small recurrences are predicted for jurisdictions with mpox immunity of 50%-100%; exponentially increasing sizes of recurrences are predicted for jurisdictions with 25%-50% immunity; and linearly increasing sizes of recurrences are predicted for jurisdictions with <25% immunity. Among the 50 jurisdictions examined, 15 are predicted to be at minimal risk for recurrence because of their high levels of population immunity. This analysis underscores the ongoing need for accessible and sustained mpox vaccination to decrease the risk for and potential size of future mpox recurrences.

Within-host priority effects and epidemic timing determine outbreak severity in co-infected populations.

Co-infections of hosts by multiple pathogen species are ubiquitous, but predicting their impact on disease remains challenging. Interactions between co-infecting pathogens within hosts can alter pathogen transmission, with the impact on transmission typically dependent on the relative arrival order of pathogens within hosts (within-host priority effects). However, it is unclear how these within-host priority effects influence multi-pathogen epidemics, particularly when the arrival order of pathogens at the host-population scale varies. Here, we combined models and experiments with zooplankton and their naturally co-occurring fungal and bacterial pathogens to examine how within-host priority effects influence multi-pathogen epidemics. Epidemiological models parametrized with within-host priority effects measured at the single-host scale predicted that advancing the start date of bacterial epidemics relative to fungal epidemics would decrease the mean bacterial prevalence in a multi-pathogen setting, while models without within-host priority effects predicted the opposite effect. We tested these predictions with experimental multi-pathogen epidemics. Empirical dynamics matched predictions from the model including within-host priority effects, providing evidence that within-host priority effects influenced epidemic dynamics. Overall, within-host priority effects may be a key element of predicting multi-pathogen epidemic dynamics in the future, particularly as shifting disease phenology alters the order of infection within hosts.

Within-Host Priority Effects Systematically Alter Pathogen Coexistence.

Coinfection of host populations alters pathogen prevalence, host mortality, and pathogen evolution. Because pathogens compete for limiting resources, whether multiple pathogens can coexist in a host population can depend on their within-host interactions, which, in turn, can depend on the order in which pathogens infect hosts (within-host priority effects). However, the consequences of within-host priority effects for pathogen coexistence have not been tested. Using laboratory studies with a coinfected zooplankton system, we found that pathogens had increased fitness in coinfected hosts when they were the second pathogen to infect a host, compared to when they were the first pathogen to infect a host. With these results, we parameterized a pathogen coexistence model with priority effects, finding that pathogen coexistence (1) decreased when priority effects increased the fitness of the first pathogen to arrive in coinfected hosts and (2) increased when priority effects increased the fitness of the second pathogen to arrive in coinfected hosts. We also identified the natural conditions under which we expect within-host priority effects to foster coexistence in our system. These outcomes were the result of positive or negative frequency dependence created by feedback loops between pathogen prevalence and infection order in coinfected hosts. This suggests that priority effects can systematically alter conditions for pathogen coexistence in host populations, thereby changing pathogen community structure and potentially altering host mortality and pathogen evolution via emergent processes.

Top predators determine how biodiversity is partitioned across time and space.

Natural ecosystems are shaped along two fundamental axes, space and time, but how biodiversity is partitioned along both axes is not well understood. Here, we show that the relationship between temporal and spatial biodiversity patterns can vary predictably according to habitat characteristics. By quantifying seasonal and annual changes in larval dragonfly communities across a natural predation gradient we demonstrate that variation in the identity of top predator species is associated with systematic differences in spatio-temporal ß-diversity patterns, leading to consistent differences in relative partitioning of biodiversity between time and space across habitats. As the size of top predators increased (from invertebrates to fish) habitats showed lower species turnover across sites and years, but relatively larger seasonal turnover within a site, which ultimately shifted the relative partitioning of biodiversity across time and space. These results extend community assembly theory by identifying common mechanisms that link spatial and temporal patterns of ß-diversity.

Updated estimate of the annual direct medical cost of screening and treatment for human papillomavirus associated disease in the United States.

The annual direct medical cost attributable to human papillomavirus (HPV) in the United States over the period 2004-2007 was estimated at $9.36 billion in 2012 (updated to 2020 dollars). The purpose of this report was to update that estimate to account for the impact of HPV vaccination on HPV-attributable disease, reductions in the frequency of cervical cancer screening, and new data on the cost per case of treating HPV-attributable cancers. Based primarily on data from the literature, we estimated the annual direct medical cost burden as the sum of the costs of cervical cancer screening and follow-up and the cost of treating HPV-attributable cancers, anogenital warts, and recurrent respiratory papillomatosis (RRP). We estimated the total direct medical cost of HPV to be $9.01 billion annually over the period 2014-2018 (2020 U.S. dollars). Of this total cost, 55.0% was for routine cervical cancer screening and follow-up, 43.8% was for treatment of HPV-attributable cancer, and less than 2% was for treating anogenital warts and RRP. Although our updated estimate of the direct medical cost of HPV is slightly lower than the previous estimate, it would have been substantially lower had we not incorporated more recent, higher cancer treatment costs.

Virulence evolution during a naturally occurring parasite outbreak.

Virulence, the degree to which a pathogen harms its host, is an important but poorly understood aspect of host-pathogen interactions. Virulence is not static, instead depending on ecological context and potentially evolving rapidly. For instance, at the start of an epidemic, when susceptible hosts are plentiful, pathogens may evolve increased virulence if this maximizes their intrinsic growth rate. However, if host density declines during an epidemic, theory predicts evolution of reduced virulence. Although well-studied theoretically, there is still little empirical evidence for virulence evolution in epidemics, especially in natural settings with native host and pathogen species. Here, we used a combination of field observations and lab assays in the Daphnia-Pasteuria model system to look for evidence of virulence evolution in nature. We monitored a large, naturally occurring outbreak of Pasteuria ramosa in Daphnia dentifera, where infection prevalence peaked at ~ 40% of the population infected and host density declined precipitously during the outbreak. In controlled infections in the lab, lifespan and reproduction of infected hosts was lower than that of unexposed control hosts and of hosts that were exposed but not infected. We did not detect any significant changes in host resistance or parasite infectivity, nor did we find evidence for shifts in parasite virulence (quantified by host lifespan and number of clutches produced by hosts). However, over the epidemic, the parasite evolved to produce significantly fewer spores in infected hosts. While this finding was unexpected, it might reflect previously quantified tradeoffs: parasites in high mortality (e.g., high predation) environments shift from vegetative growth to spore production sooner in infections, reducing spore yield. Future studies that track evolution of parasite spore yield in more populations, and that link those changes with genetic changes and with predation rates, will yield better insight into the drivers of parasite evolution in the wild. Supplementary Information: The online version contains supplementary material available at 10.1007/s10682-022-10169-6.

Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities.

In non-pharmaceutical management of COVID-19, occupancy of intensive care units (ICU) is often used as an indicator to inform when to intensify mitigation and thus reduce SARS-CoV-2 transmission, strain on ICUs, and deaths. However, ICU occupancy thresholds at which action should be taken are often selected arbitrarily. We propose a quantitative approach using mathematical modeling to identify ICU occupancy thresholds at which mitigation should be triggered to avoid exceeding the ICU capacity available for COVID-19 patients and demonstrate this approach for the United States city of Chicago. We used a stochastic compartmental model to simulate SARS-CoV-2 transmission and disease progression, including critical cases that would require intensive care. We calibrated the model using daily COVID-19 ICU and hospital census data between March and August 2020. We projected various possible ICU occupancy trajectories from September 2020 to May 2021 with two possible levels of transmission increase and uncertainty in core model parameters. The effect of combined mitigation measures was modeled as a decrease in the transmission rate that took effect when projected ICU occupancy reached a specified threshold. We found that mitigation did not immediately eliminate the risk of exceeding ICU capacity. Delaying action by 7 days increased the probability of exceeding ICU capacity by 10-60% and this increase could not be counteracted by stronger mitigation. Even under modest transmission increase, a threshold occupancy no higher than 60% was required when mitigation reduced the reproductive number Rt to just below 1. At higher transmission increase, a threshold of at most 40% was required with mitigation that reduced Rt below 0.75 within the first two weeks after mitigation. Our analysis demonstrates a quantitative approach for the selection of ICU occupancy thresholds that considers parameter uncertainty and compares relevant mitigation and transmission scenarios. An appropriate threshold will depend on the location, number of ICU beds available for COVID-19, available mitigation options, feasible mitigation strengths, and tolerated durations of intensified mitigation.

Dose relationships can exacerbate, mute, or reverse the impact of heterospecific host density on infection prevalence.

The likelihood an individual becomes infected depends on the community in which it is embedded. For environmentally transmitted parasites, host community composition can alter host density, the density of parasites that hosts encounter in the environment, and the dose to which hosts are subsequently exposed. While some multi-host theory incorporates some of these factors (e.g., competition among hosts), it does not currently consider the nonlinear relationships between parasite exposure dose and per-propagule infectivity (dose-infectivity relationships), between exposure dose and infected host mortality (dose-mortality relationships), and between exposure dose and parasite propagule excretion (dose-excretion relationships). This makes it difficult to predict the impact of host species on one another's likelihood of infection. To understand the implications of these nonlinear dose relationships for multi-host communities, we first performed a meta-analysis on published dose-infectivity experiments to quantify the proportion of accelerating, linear, or decelerating dose-infectivity relationships; we found that most experiments demonstrated decelerating dose-infectivity relationships. We then explored how dose-infectivity, dose-mortality, and dose-excretion relationships might alter the impact of heterospecific host density on infectious propagule density, infection prevalence, and density of a focal host using two-host, one-parasite models. We found that dose relationships either decreased the magnitude of the impact of heterospecific host density on propagule density and infection prevalence via negative feedback loops (decelerating dose-infectivity relationships, positive dose-mortality relationships, and negative dose-excretion relationships), or increased the magnitude of the impact of heterospecific host density on infection prevalence via positive feedback loops (accelerating dose-infectivity relationships and positive dose-excretion relationships). Further, positive dose-mortality relationships resulted in hosts that traditionally decrease disease (e.g., low competence, strong competitors) increasing infection prevalence, and vice versa. Finally, we found that dose relationships can create positive feedback loops that facilitate friendly competition (i.e., increased heterospecific density has a positive effect on focal host density because the reduction in disease outweighs the negative effects of interspecific competition). This suggests that without taking dose relationships into account, we may incorrectly predict the effect of heterospecific host interactions, and thus host community composition, on environmentally transmitted parasites.

How parasite interaction strategies alter virulence evolution in multi-parasite communities.

The majority of organisms host multiple parasite species, each of which can interact with hosts and competitors through a diverse range of direct and indirect mechanisms. These within-host interactions can directly alter the mortality rate of coinfected hosts and alter the evolution of virulence (parasite-induced host mortality). Yet we still know little about how within-host interactions affect the evolution of parasite virulence in multi-parasite communities. Here, we modeled the virulence evolution of two coinfecting parasites in a host population in which parasites interacted through cross immunity, immune suppression, immunopathology, or spite. We show (1) that these within-host interactions have different effects on virulence evolution when all parasites interact with each other in the same way versus when coinfecting parasites have unique interaction strategies, (2) that these interactions cause the evolution of lower virulence in some hosts, and higher virulence in other hosts, depending on the hosts infection status, and (3) that for cross immunity and spite, whether parasites increase or decrease the evolutionarily stable virulence in coinfected hosts depended on interaction strength. These results improve our understanding of virulence evolution in complex parasite communities, and show that virulence evolution must be understood at the community scale.