Underdetected dispersal and extensive local transmission drove the 2022 mpox epidemic.

The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.

Infodemics: A new challenge for public health.

The COVID-19 information epidemic, or "infodemic," demonstrates how unlimited access to information may confuse and influence behaviors during a health emergency. However, the study of infodemics is relatively new, and little is known about their relationship with epidemics management. Here, we discuss unresolved issues and propose research directions to enhance preparedness for future health crises.

Emergence of an early SARS-CoV-2 epidemic in the United States.

The emergence of the COVID-19 epidemic in the United States (U.S.) went largely undetected due to inadequate testing. New Orleans experienced one of the earliest and fastest accelerating outbreaks, coinciding with Mardi Gras. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large-scale events accelerate transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana had limited diversity compared to other U.S. states and that one introduction of SARS-CoV-2 led to almost all of the early transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras, and the festival dramatically accelerated transmission. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate epidemics.

Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning Epidemic.

The Zika epidemic in the Americas has challenged surveillance and control. As the epidemic appears to be waning, it is unclear whether transmission is still ongoing, which is exacerbated by discrepancies in reporting. To uncover locations with lingering outbreaks, we investigated travel-associated Zika cases to identify transmission not captured by reporting. We uncovered an unreported outbreak in Cuba during 2017, a year after peak transmission in neighboring islands. By sequencing Zika virus, we show that the establishment of the virus was delayed by a year and that the ensuing outbreak was sparked by long-lived lineages of Zika virus from other Caribbean islands. Our data suggest that, although mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, such measures need to be maintained to be effective. Our study highlights how Zika virus may still be "silently" spreading and provides a framework for understanding outbreak dynamics. VIDEO ABSTRACT.

Macrophage expression and prognostic significance of the long pentraxin PTX3 in COVID-19.

Long pentraxin 3 (PTX3) is an essential component of humoral innate immunity, involved in resistance to selected pathogens and in the regulation of inflammation1-3. The present study was designed to assess the presence and significance of PTX3 in Coronavirus Disease 2019 (COVID-19)4-7. RNA-sequencing analysis of peripheral blood mononuclear cells, single-cell bioinformatics analysis and immunohistochemistry of lung autopsy samples revealed that myelomonocytic cells and endothelial cells express high levels of PTX3 in patients with COVID-19. Increased plasma concentrations of PTX3 were detected in 96 patients with COVID-19. PTX3 emerged as a strong independent predictor of 28-d mortality in multivariable analysis, better than conventional markers of inflammation, in hospitalized patients with COVID-19. The prognostic significance of PTX3 abundance for mortality was confirmed in a second independent cohort (54 patients). Thus, circulating and lung myelomonocytic cells and endothelial cells are a major source of PTX3, and PTX3 plasma concentration can serve as an independent strong prognostic indicator of short-term mortality in COVID-19.

Establishment of an African green monkey model for COVID-19 and protection against re-infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for an unprecedented global pandemic of COVID-19. Animal models are urgently needed to study the pathogenesis of COVID-19 and to screen vaccines and treatments. We show that African green monkeys (AGMs) support robust SARS-CoV-2 replication and develop pronounced respiratory disease, which may more accurately reflect human COVID-19 cases than other nonhuman primate species. SARS-CoV-2 was detected in mucosal samples, including rectal swabs, as late as 15 days after exposure. Marked inflammation and coagulopathy in blood and tissues were prominent features. Transcriptome analysis demonstrated stimulation of interferon and interleukin-6 pathways in bronchoalveolar lavage samples and repression of natural killer cell- and T cell-associated transcripts in peripheral blood. Despite a slight waning in antibody titers after primary challenge, enhanced antibody and cellular responses contributed to rapid clearance after re-challenge with an identical strain. These data support the utility of AGM for studying COVID-19 pathogenesis and testing medical countermeasures.

Genomic Insights into Zika Virus Emergence and Spread.

The emergence and spread of Zika virus in the Americas continues to challenge our disease surveillance systems. Virus genome sequencing during the epidemic uncovered the timescale of Zika virus transmission and spread. Yet, we are only beginning to explore how genomics can enhance our responses to emerging viruses.

Transition to endemicity: Understanding COVID-19.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated disease, coronavirus disease 2019 (COVID-19), has caused a devastating pandemic worldwide. Here, we explain basic concepts underlying the transition from an epidemic to an endemic state, where a pathogen is stably maintained in a population. We discuss how the number of infections and the severity of disease change in the transition from the epidemic to the endemic phase and consider the implications of this transition in the context of COVID-19.

Thinking clearly about social aspects of infectious disease transmission.

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

A plant virus manipulates the long-winged morph of insect vectors.

Wing dimorphism of insect vectors is a determining factor for viral long-distance dispersal and large-area epidemics. Although plant viruses affect the wing plasticity of insect vectors, the potential underlying molecular mechanisms have seldom been investigated. Here, we found that a planthopper-vectored rice virus, rice stripe virus (RSV), specifically induces a long-winged morph in male insects. The analysis of field populations demonstrated that the long-winged ratios of male insects are closely associated with RSV infection regardless of viral titers. A planthopper-specific and testis-highly expressed gene, Encounter, was fortuitously found to play a key role in the RSV-induced long-winged morph. Encounter resembles malate dehydrogenase in the sequence, but it does not have corresponding enzymatic activity. Encounter is upregulated to affect male wing dimorphism at early larval stages. Encounter is closely connected with the insulin/insulin-like growth factor signaling pathway as a downstream factor of Akt, of which the transcriptional level is activated in response to RSV infection, resulting in the elevated expression of Encounter. In addition, an RSV-derived small interfering RNA directly targets Encounter to enhance its expression. Our study reveals an unreported mechanism underlying the direct regulation by a plant virus of wing dimorphism in its insect vectors, providing the potential way for interrupting viral dispersal.

The probability of epidemic burnout in the stochastic SIR model with vital dynamics.

We present an approach to computing the probability of epidemic "burnout," i.e., the probability that a newly emergent pathogen will go extinct after a major epidemic. Our analysis is based on the standard stochastic formulation of the Susceptible-Infectious-Removed (SIR) epidemic model including host demography (births and deaths) and corresponds to the standard SIR ordinary differential equations (ODEs) in the infinite population limit. Exploiting a boundary layer approximation to the ODEs and a birth-death process approximation to the stochastic dynamics within the boundary layer, we derive convenient, fully analytical approximations for the burnout probability. We demonstrate-by comparing with computationally demanding individual-based stochastic simulations and with semi-analytical approximations derived previously-that our fully analytical approximations are highly accurate for biologically plausible parameters. We show that the probability of burnout always decreases with increased mean infectious period. However, for typical biological parameters, there is a relevant local minimum in the probability of persistence as a function of the basic reproduction number [Formula: see text]. For the shortest infectious periods, persistence is least likely if [Formula: see text]; for longer infectious periods, the minimum point decreases to [Formula: see text]. For typical acute immunizing infections in human populations of realistic size, our analysis of the SIR model shows that burnout is almost certain in a well-mixed population, implying that susceptible recruitment through births is insufficient on its own to explain disease persistence.

A simplicial epidemic model for COVID-19 spread analysis.

Networks allow us to describe a wide range of interaction phenomena that occur in complex systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology, finance, and social sciences. Until very recently, the primary focus of network models and tools has been on describing the pairwise relationships between system entities. However, increasingly more studies indicate that polyadic or higher-order group relationships among multiple network entities may be the key toward better understanding of the intrinsic mechanisms behind the functionality of complex systems. Such group interactions can be, in turn, described in a holistic manner by simplicial complexes of graphs. Inspired by these recently emerging results on the utility of the simplicial geometry of complex networks for contagion propagation and armed with a large-scale synthetic social contact network (also known as a digital twin) of the population in the U.S. state of Virginia, in this paper, we aim to glean insights into the role of higher-order social interactions and the associated varying social group determinants on COVID-19 propagation and mitigation measures.

Meningococcal ACWYX Conjugate Vaccine in 2-to-29-Year-Olds in Mali and Gambia.

BACKGROUND: An effective, affordable, multivalent meningococcal conjugate vaccine is needed to prevent epidemic meningitis in the African meningitis belt. Data on the safety and immunogenicity of NmCV-5, a pentavalent vaccine targeting the A, C, W, Y, and X serogroups, have been limited. METHODS: We conducted a phase 3, noninferiority trial involving healthy 2-to-29-year-olds in Mali and Gambia. Participants were randomly assigned in a 2:1 ratio to receive a single intramuscular dose of NmCV-5 or the quadrivalent vaccine MenACWY-D. Immunogenicity was assessed at day 28. The noninferiority of NmCV-5 to MenACWY-D was assessed on the basis of the difference in the percentage of participants with a seroresponse (defined as prespecified changes in titer; margin, lower limit of the 96% confidence interval [CI] above -10 percentage points) or geometric mean titer (GMT) ratios (margin, lower limit of the 98.98% CI >0.5). Serogroup X responses in the NmCV-5 group were compared with the lowest response among the MenACWY-D serogroups. Safety was also assessed. RESULTS: A total of 1800 participants received NmCV-5 or MenACWY-D. In the NmCV-5 group, the percentage of participants with a seroresponse ranged from 70.5% (95% CI, 67.8 to 73.2) for serogroup A to 98.5% (95% CI, 97.6 to 99.2) for serogroup W; the percentage with a serogroup X response was 97.2% (95% CI, 96.0 to 98.1). The overall difference between the two vaccines in seroresponse for the four shared serogroups ranged from 1.2 percentage points (96% CI, -0.3 to 3.1) for serogroup W to 20.5 percentage points (96% CI, 15.4 to 25.6) for serogroup A. The overall GMT ratios for the four shared serogroups ranged from 1.7 (98.98% CI, 1.5 to 1.9) for serogroup A to 2.8 (98.98% CI, 2.3 to 3.5) for serogroup C. The serogroup X component of the NmCV-5 vaccine generated seroresponses and GMTs that met the prespecified noninferiority criteria. The incidence of systemic adverse events was similar in the two groups (11.1% in the NmCV-5 group and 9.2% in the MenACWY-D group). CONCLUSIONS: For all four serotypes in common with the MenACWY-D vaccine, the NmCV-5 vaccine elicited immune responses that were noninferior to those elicited by the MenACWY-D vaccine. NmCV-5 also elicited immune responses to serogroup X. No safety concerns were evident. (Funded by the U.K. Foreign, Commonwealth, and Development Office and others; ClinicalTrials.gov number, NCT03964012.).

Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county.

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

The impact of threshold decision mechanisms of collective behavior on disease spread.

Humans are a hyper-social species, which greatly impacts the spread of infectious diseases. How do social dynamics impact epidemiology and what are the implications for public health policy? Here, we develop a model of disease transmission that incorporates social dynamics and a behavior that reduces the spread of disease, a voluntary nonpharmaceutical intervention (NPI). We use a "tipping-point" dynamic, previously used in the sociological literature, where individuals adopt a behavior given a sufficient prevalence of the behavior in the population. The thresholds at which individuals adopt the NPI behavior are modulated by the perceived risk of infection, i.e., the disease prevalence and transmission rate, costs to adopt the NPI behavior, and the behavior of others. Social conformity creates a type of "stickiness" whereby individuals are resistant to changing their behavior due to the population's inertia. In this model, we observe a nonmonotonicity in the attack rate as a function of various biological and social parameters such as the transmission rate, efficacy of the NPI, costs of the NPI, weight of social consequences of shirking the social norm, and the degree of heterogeneity in the population. We also observe that the attack rate can be highly sensitive to these parameters due to abrupt shifts in the collective behavior of the population. These results highlight the complex interplay between the dynamics of epidemics and norm-driven collective behaviors.

Wild animals suppress the spread of socially transmitted misinformation.

Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviors to misinformation epidemics. Transmission of information through groups depends on the rules that individuals use to transform the perceived actions of others into their own behaviors. Because it is often not possible to directly infer decision-making strategies in situ, most studies of behavioral spread assume that individuals make decisions by pooling or averaging the actions or behavioral states of neighbors. However, whether individuals may instead adopt more sophisticated strategies that exploit socially transmitted information, while remaining robust to misinformation, is unknown. Here, we study the relationship between individual decision-making and misinformation spread in groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously through groups. Using automated visual field reconstruction of wild animals, we infer the precise sequences of socially transmitted visual stimuli perceived by individuals during decision-making. Our analysis reveals a feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially transmitted cues. This form of dynamic gain control can be achieved by a simple and biologically widespread decision-making circuit, and it renders individual behavior robust to natural fluctuations in misinformation exposure.

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.

Spreading processes with mutations over multilayer networks.

A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under countermeasures that limit interaction in the population. Most epidemiological models do not consider the role of mutations and heterogeneity in the type of contact events. However, pathogens have the capacity to mutate in response to changing environments, especially caused by the increase in population immunity to existing strains, and the emergence of new pathogen strains poses a continued threat to public health. Further, in the light of differing transmission risks in different congregate settings (e.g., schools and offices), different mitigation strategies may need to be adopted to control the spread of infection. We analyze a multilayer multistrain model by simultaneously accounting for i) pathways for mutations in the pathogen leading to the emergence of new pathogen strains, and ii) differing transmission risks in different settings, modeled as network layers. Assuming complete cross-immunity among strains, namely, recovery from any infection prevents infection with any other (an assumption that will need to be relaxed to deal with COVID-19 or influenza), we derive the key epidemiological parameters for the multilayer multistrain framework. We demonstrate that reductions to existing models that discount heterogeneity in either the strain or the network layers may lead to incorrect predictions. Our results highlight that the impact of imposing/lifting mitigation measures concerning different contact network layers (e.g., school closures or work-from-home policies) should be evaluated in connection with their effect on the likelihood of the emergence of new strains.

Enhanced mosquito vectorial capacity underlies the Cape Verde Zika epidemic.

The explosive emergence of Zika virus (ZIKV) across the Pacific and Americas since 2007 was associated with hundreds of thousands of human cases and severe outcomes, including congenital microcephaly caused by ZIKV infection during pregnancy. Although ZIKV was first isolated in Uganda, Africa has so far been exempt from large-scale ZIKV epidemics, despite widespread susceptibility among African human populations. A possible explanation for this pattern is natural variation among populations of the primary vector of ZIKV, the mosquito Aedes aegypti. Globally invasive populations of Ae. aegypti outside of Africa are considered effective ZIKV vectors because they are human specialists with high intrinsic ZIKV susceptibility, whereas African populations of Ae. aegypti across the species' native range are predominantly generalists with low intrinsic ZIKV susceptibility, making them less likely to spread viruses in the human population. We test this idea by studying a notable exception to the patterns observed across most of Africa: Cape Verde experienced a large ZIKV outbreak in 2015 to 2016. We find that local Ae. aegypti in Cape Verde have substantial human-specialist ancestry, show a robust behavioral preference for human hosts, and exhibit increased susceptibility to ZIKV infection, consistent with a key role for variation among mosquito populations in ZIKV epidemiology. These findings suggest that similar human-specialist populations of Ae. aegypti in the nearby Sahel region of West Africa, which may be expanding in response to rapid urbanization, could serve as effective vectors for ZIKV in the future.