Identification and characterization of a nonbiological small-molecular mimic of a Zika virus conformational neutralizing epitope.

Antigenic similarities between Zika virus (ZIKV) and other flaviviruses pose challenges to the development of virus-specific diagnostic tools and effective vaccines. Starting with a DNA-encoded one-bead-one-compound combinatorial library of 508,032 synthetic, non-natural oligomers, we selected and characterized small molecules that mimic ZIKV epitopes. High-throughput fluorescence-activated cell sorter-based bead screening was used to select molecules that bound IgG from ZIKV-immune but not from dengue-immune sera. Deep sequencing of the DNA from the "Zika-only" beads identified 40 candidate molecular structures. A lead candidate small molecule "CZV1-1" was selected that correctly identifies serum specimens from Zika-experienced patients with good sensitivity and specificity (85.3% and 98.4%, respectively). Binding competition studies of purified anti-CZV1-1 IgG against known ZIKV-specific monoclonal antibodies (mAbs) showed that CZV1-1 mimics a nonlinear, neutralizing conformational epitope in the domain III of the ZIKV envelope. Purified anti-CZV1-1 IgG neutralized infection of ZIKV in cell cultures with potencies comparable to highly specific ZIKV-neutralizing mAbs. This study demonstrates an innovative approach for identification of synthetic non-natural molecular mimics of conformational virus epitopes. Such molecular mimics may have value in the development of accurate diagnostic assays for Zika, as well as for other viruses.

Protective Efficacy of Gastrointestinal SARS-CoV-2 Delivery against Intranasal and Intratracheal SARS-CoV-2 Challenge in Rhesus Macaques.

Live oral vaccines have been explored for their protective efficacy against respiratory viruses, particularly for adenovirus serotypes 4 and 7. The potential of a live oral vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), however, remains unclear. In this study, we assessed the immunogenicity of live SARS-CoV-2 delivered to the gastrointestinal tract in rhesus macaques and its protective efficacy against intranasal and intratracheal SARS-CoV-2 challenge. Postpyloric administration of SARS-CoV-2 by esophagogastroduodenoscopy resulted in limited virus replication in the gastrointestinal tract and minimal to no induction of mucosal antibody titers in rectal swabs, nasal swabs, and bronchoalveolar lavage fluid. Low levels of serum neutralizing antibodies were induced and correlated with modestly diminished viral loads in nasal swabs and bronchoalveolar lavage fluid following intranasal and intratracheal SARS-CoV-2 challenge. Overall, our data show that postpyloric inoculation of live SARS-CoV-2 is weakly immunogenic and confers partial protection against respiratory SARS-CoV-2 challenge in rhesus macaques. IMPORTANCE SARS-CoV-2 remains a global threat, despite the rapid deployment but limited coverage of multiple vaccines. Alternative vaccine strategies that have favorable manufacturing timelines, greater ease of distribution, and improved coverage may offer significant public health benefits, especially in resource-limited settings. Live oral vaccines have the potential to address some of these limitations; however, no studies have yet been conducted to assess the immunogenicity and protective efficacy of a live oral vaccine against SARS-CoV-2. Here, we report that oral administration of live SARS-CoV-2 in nonhuman primates may offer prophylactic benefits, but the formulation and route of administration will require further optimization.

A human judgment approach to epidemiological forecasting.

Infectious diseases impose considerable burden on society, despite significant advances in technology and medicine over the past century. Advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic. Historically, such a capability has lagged for many reasons, including in particular the uncertainty in the current state of the system and in the understanding of the processes that drive epidemic trajectories. Presently we have access to data, models, and computational resources that enable the development of epidemiological forecasting systems. Indeed, several recent challenges hosted by the U.S. government have fostered an open and collaborative environment for the development of these technologies. The primary focus of these challenges has been to develop statistical and computational methods for epidemiological forecasting, but here we consider a serious alternative based on collective human judgment. We created the web-based "Epicast" forecasting system which collects and aggregates epidemic predictions made in real-time by human participants, and with these forecasts we ask two questions: how accurate is human judgment, and how do these forecasts compare to their more computational, data-driven alternatives? To address the former, we assess by a variety of metrics how accurately humans are able to predict influenza and chikungunya trajectories. As for the latter, we show that real-time, combined human predictions of the 2014-2015 and 2015-2016 U.S. flu seasons are often more accurate than the same predictions made by several statistical systems, especially for short-term targets. We conclude that there is valuable predictive power in collective human judgment, and we discuss the benefits and drawbacks of this approach.

Region-wide synchrony and traveling waves of dengue across eight countries in Southeast Asia.

Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control. Time series decomposition methods enable the isolation and study of temporal epidemic dynamics with a specific periodicity (e.g., annual cycles related to climatic drivers and multiannual cycles caused by dynamics in population immunity). We collected and analyzed up to 18 y of monthly dengue surveillance reports on a total of 3.5 million reported dengue cases from 273 provinces in eight countries in Southeast Asia, covering ∼ 10(7) km(2). We detected strong patterns of synchronous dengue transmission across the entire region, most markedly during a period of high incidence in 1997-1998, which was followed by a period of extremely low incidence in 2001-2002. This synchrony in dengue incidence coincided with elevated temperatures throughout the region in 1997-1998 and the strongest El Niño episode of the century. Multiannual dengue cycles (2-5 y) were highly coherent with the Oceanic Niño Index, and synchrony of these cycles increased with temperature. We also detected localized traveling waves of multiannual dengue epidemic cycles in Thailand, Laos, and the Philippines that were dependent on temperature. This study reveals forcing mechanisms that drive synchronization of dengue epidemics on a continental scale across Southeast Asia.

Persistent Chaos of Measles Epidemics in the Prevaccination United States Caused by a Small Change in Seasonal Transmission Patterns.

Epidemics of infectious diseases often occur in predictable limit cycles. Theory suggests these cycles can be disrupted by high amplitude seasonal fluctuations in transmission rates, resulting in deterministic chaos. However, persistent deterministic chaos has never been observed, in part because sufficiently large oscillations in transmission rates are uncommon. Where they do occur, the resulting deep epidemic troughs break the chain of transmission, leading to epidemic extinction, even in large cities. Here we demonstrate a new path to locally persistent chaotic epidemics via subtle shifts in seasonal patterns of transmission, rather than through high-amplitude fluctuations in transmission rates. We base our analysis on a comparison of measles incidence in 80 major cities in the prevaccination era United States and United Kingdom. Unlike the regular limit cycles seen in the UK, measles cycles in US cities consistently exhibit spontaneous shifts in epidemic periodicity resulting in chaotic patterns. We show that these patterns were driven by small systematic differences between countries in the duration of the summer period of low transmission. This example demonstrates empirically that small perturbations in disease transmission patterns can fundamentally alter the regularity and spatiotemporal coherence of epidemics.

Patterns and trends in accidental poisoning death rates in the US, 1979-2014.

OBJECTIVES: The purpose of this study was to examine US accidental poisoning death rates by demographic and geographic factors from 1979 to 2014, including High Intensity Drug Trafficking Areas. METHODS: Crude and age-adjusted death rates were formed for age group, race, sex, and county for accidental poisonings (ICD 9th revision: E850-E869; ICD 10th revision: X40-X49) from 1979 to 2014 using the Mortality and Population Data System housed at the University of Pittsburgh. Rate ratios were calculated comparing rates from 2014 to 1979, overall, by sex, age group, race, and county. Joinpoint regression detected changes in trends and calculated the average annual percentage change (AAPC) as a summary measure of trend. RESULTS: Drug poisoning mortality rates have risen an average of 6% per year since 1979. Increases are occurring in all ages 15+, and in all race-sex groups. HIDTA counties with the highest mortality rates were in Appalachia and New Mexico. Many of the HIDTA border counties had lower rates of mortality. CONCLUSIONS: The drug poisoning mortality epidemic is continuing to grow. While HIDTA resources are appropriately targeted at many areas in the US most affected, rates are also rapidly rising in some non-HIDTA areas.

SIVagm infection in wild African green monkeys from South Africa: epidemiology, natural history, and evolutionary considerations.

Pathogenesis studies of SIV infection have not been performed to date in wild monkeys due to difficulty in collecting and storing samples on site and the lack of analytical reagents covering the extensive SIV diversity. We performed a large scale study of molecular epidemiology and natural history of SIVagm infection in 225 free-ranging AGMs from multiple locations in South Africa. SIV prevalence (established by sequencing pol, env, and gag) varied dramatically between infant/juvenile (7%) and adult animals (68%) (p<0.0001), and between adult females (78%) and males (57%). Phylogenetic analyses revealed an extensive genetic diversity, including frequent recombination events. Some AGMs harbored epidemiologically linked viruses. Viruses infecting AGMs in the Free State, which are separated from those on the coastal side by the Drakensberg Mountains, formed a separate cluster in the phylogenetic trees; this observation supports a long standing presence of SIV in AGMs, at least from the time of their speciation to their Plio-Pleistocene migration. Specific primers/probes were synthesized based on the pol sequence data and viral loads (VLs) were quantified. VLs were of 10(4)-10(6) RNA copies/ml, in the range of those observed in experimentally-infected monkeys, validating the experimental approaches in natural hosts. VLs were significantly higher (10(7)-10(8) RNA copies/ml) in 10 AGMs diagnosed as acutely infected based on SIV seronegativity (Fiebig II), which suggests a very active transmission of SIVagm in the wild. Neither cytokine levels (as biomarkers of immune activation) nor sCD14 levels (a biomarker of microbial translocation) were different between SIV-infected and SIV-uninfected monkeys. This complex algorithm combining sequencing and phylogeny, VL quantification, serology, and testing of surrogate markers of microbial translocation and immune activation permits a systematic investigation of the epidemiology, viral diversity and natural history of SIV infection in wild African natural hosts.

Revealing the microscale spatial signature of dengue transmission and immunity in an urban population.

It is well-known that the distribution of immunity in a population dictates the future incidence of infectious disease, but this process is generally understood at individual or macroscales. For example, herd immunity to multiple pathogens has been observed at national and city levels. However, the effects of population immunity have not previously been shown at scales smaller than the city (e.g., neighborhoods). In particular, no study has shown long-term effects of population immunity at scales consistent with the spatial scale of person-to-person transmission. Here, we use the location of dengue patients' homes in Bangkok with the serotype of the infecting pathogen to investigate the spatiotemporal distribution of disease risk at small spatial scales over a 5-y period. We find evidence for localized transmission at distances of under 1 km. We also observe patterns of spatiotemporal dependence consistent with the expected impacts of homotypic immunity, heterotypic immunity, and immune enhancement of disease at these distances. Our observations indicate that immunological memory of dengue serotypes occurs at the neighborhood level in this large urban setting. These methods have broad applications to studying the spatiotemporal structure of disease risk where pathogen serotype or genetic information is known.

A large-scale immuno-epidemiological simulation of influenza A epidemics.

BACKGROUND: Agent based models (ABM) are useful to explore population-level scenarios of disease spread and containment, but typically characterize infected individuals using simplified models of infection and symptoms dynamics. Adding more realistic models of individual infections and symptoms may help to create more realistic population level epidemic dynamics. METHODS: Using an equation-based, host-level mathematical model of influenza A virus infection, we develop a function that expresses the dependence of infectivity and symptoms of an infected individual on initial viral load, age, and viral strain phenotype. We incorporate this response function in a population-scale agent-based model of influenza A epidemic to create a hybrid multiscale modeling framework that reflects both population dynamics and individualized host response to infection. RESULTS: At the host level, we estimate parameter ranges using experimental data of H1N1 viral titers and symptoms measured in humans. By linearization of symptoms responses of the host-level model we obtain a map of the parameters of the model that characterizes clinical phenotypes of influenza infection and immune response variability over the population. At the population-level model, we analyze the effect of individualizing viral response in agent-based model by simulating epidemics across Allegheny County, Pennsylvania under both age-specific and age-independent severity assumptions. CONCLUSIONS: We present a framework for multi-scale simulations of influenza epidemics that enables the study of population-level effects of individual differences in infections and symptoms, with minimal additional computational cost compared to the existing population-level simulations.

A systematic review of barriers to data sharing in public health.

BACKGROUND: In the current information age, the use of data has become essential for decision making in public health at the local, national, and global level. Despite a global commitment to the use and sharing of public health data, this can be challenging in reality. No systematic framework or global operational guidelines have been created for data sharing in public health. Barriers at different levels have limited data sharing but have only been anecdotally discussed or in the context of specific case studies. Incomplete systematic evidence on the scope and variety of these barriers has limited opportunities to maximize the value and use of public health data for science and policy. METHODS: We conducted a systematic literature review of potential barriers to public health data sharing. Documents that described barriers to sharing of routinely collected public health data were eligible for inclusion and reviewed independently by a team of experts. We grouped identified barriers in a taxonomy for a focused international dialogue on solutions. RESULTS: Twenty potential barriers were identified and classified in six categories: technical, motivational, economic, political, legal and ethical. The first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing. CONCLUSIONS: The simultaneous effect of multiple interacting barriers ranging from technical to intangible issues has greatly complicated advances in public health data sharing. A systematic framework of barriers to data sharing in public health will be essential to accelerate the use of valuable information for the global good.

Origins of the problematic E in SEIR epidemic models.

During the COVID-19 pandemic, over one thousand papers were published on "Susceptible-Exposed-Infectious-Removed" (SEIR) epidemic computational models. The English word "exposed" in its vernacular and public health usage means a state of having been in contact with an infectious individual, but not necessarily infected. In contrast, the term "exposed" in SEIR modeling usage typically stands for a state of already being infected but not yet being infectious to others, a state more properly termed "latently infected." In public health language, "exposed" means possibly infected, yet in SEIR modeling language, "exposed" means already infected. This paper retraces the conceptual and mathematical origins of this terminological disconnect and concludes that epidemic modelers should consider using the "SLIR" notational short-hand (L for Latent) instead of SEIR.

Assessment of serosurveys for H5N1.

BACKGROUND: It has been suggested that the true case-fatality rate of human H5N1 influenza infection is appreciably less than the figure of approximately 60% that is based on official World Health Organization (WHO)-confirmed case reports because asymptomatic cases may have been missed. A number of seroepidemiologic studies have been conducted in an attempt to identify such missed cases. METHODS: We conducted a comprehensive literature review of all English-language H5N1 human serology surveys with detailed attention to laboratory methodology used (including whether investigators used criteria set by the WHO to define positive cases), laboratory controls used, and the clades/genotypes involved. RESULTS: Twenty-nine studies were included in the analysis. Few reported using unexposed control groups and one-third did not apply WHO criteria. Of studies that used WHO criteria, only 4 found any seropositive results to clades/genotypes of H5N1 that are currently circulating. No studies reported seropositive results to the clade 2/genotype Z viruses that have spread throughout Eurasia and Africa. CONCLUSIONS: This review suggests that the frequency of positive H5 serology results is likely to be low; therefore, it is essential that future studies adhere to WHO criteria and include unexposed controls in their laboratory assays to limit the likelihood of false-positive results.

Policies to reduce influenza in the workplace: impact assessments using an agent-based model.

OBJECTIVES: We examined the impact of access to paid sick days (PSDs) and stay-at-home behavior on the influenza attack rate in workplaces. METHODS: We used an agent-based model of Allegheny County, Pennsylvania, with PSD data from the US Bureau of Labor Statistics, standard influenza epidemic parameters, and the probability of staying home when ill. We compared the influenza attack rate among employees resulting from workplace transmission, focusing on the effects of presenteeism (going to work when ill). RESULTS: In a simulated influenza epidemic (R0 = 1.4), the attack rate among employees owing to workplace transmission was 11.54%. A large proportion (72.00%) of this attack rate resulted from exposure to employees engaging in presenteeism. Universal PSDs reduced workplace infections by 5.86%. Providing 1 or 2 "flu days"-allowing employees with influenza to stay home-reduced workplace infections by 25.33% and 39.22%, respectively. CONCLUSIONS: PSDs reduce influenza transmission owing to presenteeism and, hence, the burden of influenza illness in workplaces.

FRED (a Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations.

BACKGROUND: Mathematical and computational models provide valuable tools that help public health planners to evaluate competing health interventions, especially for novel circumstances that cannot be examined through observational or controlled studies, such as pandemic influenza. The spread of diseases like influenza depends on the mixing patterns within the population, and these mixing patterns depend in part on local factors including the spatial distribution and age structure of the population, the distribution of size and composition of households, employment status and commuting patterns of adults, and the size and age structure of schools. Finally, public health planners must take into account the health behavior patterns of the population, patterns that often vary according to socioeconomic factors such as race, household income, and education levels. RESULTS: FRED (a Framework for Reconstructing Epidemic Dynamics) is a freely available open-source agent-based modeling system based closely on models used in previously published studies of pandemic influenza. This version of FRED uses open-access census-based synthetic populations that capture the demographic and geographic heterogeneities of the population, including realistic household, school, and workplace social networks. FRED epidemic models are currently available for every state and county in the United States, and for selected international locations. CONCLUSIONS: State and county public health planners can use FRED to explore the effects of possible influenza epidemics in specific geographic regions of interest and to help evaluate the effect of interventions such as vaccination programs and school closure policies. FRED is available under a free open source license in order to contribute to the development of better modeling tools and to encourage open discussion of modeling tools being used to evaluate public health policies. We also welcome participation by other researchers in the further development of FRED.

A game dynamic model for vaccine skeptics and vaccine believers: measles as an example.

Widespread avoidance of Measles-Mumps-Rubella vaccination (MMR), with a consequent increase in the incidence of major measles outbreaks, demonstrates that the effectiveness of vaccination programs can be thwarted by the public misperceptions of vaccine risk. By coupling game theory and epidemic models, we examine vaccination choice among populations stratified into two behavioral groups: vaccine skeptics and vaccine believers. The two behavioral groups are assumed to be heterogeneous with respect to their perceptions of vaccine and infection risks. We demonstrate that the pursuit of self-interest among vaccine skeptics often leads to vaccination levels that are suboptimal for a population, even if complete coverage is achieved among vaccine believers. The demand for measles vaccine across populations driven by individual self-interest was found to be more sensitive to the proportion of vaccine skeptics than to the extent to which vaccine skeptics misperceive the risk of vaccine. Furthermore, as the number of vaccine skeptics increases, the probability of infection among vaccine skeptics increases initially, but it decreases once the vaccine skeptics begin receiving the vaccination, if both behavioral groups are vaccinated according to individual self-interest. Our results show that the discrepancy between the coverages of measles vaccine that are driven by self-interest and those driven by population interest becomes larger when the cost of vaccination increases. This research illustrates the importance of public education on vaccine safety and infection risk in order to maintain vaccination levels that are sufficient to maintain herd immunity.

Impact of introducing the pneumococcal and rotavirus vaccines into the routine immunization program in Niger.

OBJECTIVES: We investigated whether introducing the rotavirus and pneumococcal vaccines, which are greatly needed in West Africa, would overwhelm existing supply chains (i.e., the series of steps required to get a vaccine from the manufacturers to the target population) in Niger. METHODS: As part of the Bill and Melinda Gates Foundation-funded Vaccine Modeling Initiative, we developed a computational model to determine the impact of introducing these new vaccines to Niger's Expanded Program on Immunization vaccine supply chain. RESULTS: Introducing either the rotavirus vaccine or the 7-valent pneumococcal conjugate vaccine could overwhelm available storage and transport refrigerator space, creating bottlenecks that would prevent the flow of vaccines down to the clinics. As a result, the availability of all World Health Organization Expanded Program on Immunization vaccines to patients might decrease from an average of 69% to 28.2% (range = 10%-51%). Addition of refrigerator and transport capacity could alleviate this bottleneck. CONCLUSIONS: Our results suggest that the effects on the vaccine supply chain should be considered when introducing a new vaccine and that computational models can help assess evolving needs and prevent problems with vaccine delivery.

Strategies for mitigating an influenza pandemic.

Development of strategies for mitigating the severity of a new influenza pandemic is now a top global public health priority. Influenza prevention and containment strategies can be considered under the broad categories of antiviral, vaccine and non-pharmaceutical (case isolation, household quarantine, school or workplace closure, restrictions on travel) measures. Mathematical models are powerful tools for exploring this complex landscape of intervention strategies and quantifying the potential costs and benefits of different options. Here we use a large-scale epidemic simulation to examine intervention options should initial containment of a novel influenza outbreak fail, using Great Britain and the United States as examples. We find that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2-3 weeks unless more than 99% effective. School closure during the peak of a pandemic can reduce peak attack rates by up to 40%, but has little impact on overall attack rates, whereas case isolation or household quarantine could have a significant impact, if feasible. Treatment of clinical cases can reduce transmission, but only if antivirals are given within a day of symptoms starting. Given enough drugs for 50% of the population, household-based prophylaxis coupled with reactive school closure could reduce clinical attack rates by 40-50%. More widespread prophylaxis would be even more logistically challenging but might reduce attack rates by over 75%. Vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy. Estimates of policy effectiveness will change if the characteristics of a future pandemic strain differ substantially from those seen in past pandemics.