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.

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.

Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level.

Dynamic models have been successfully used in producing estimates of HIV epidemics at the national level due to their epidemiological nature and their ability to estimate prevalence, incidence, and mortality rates simultaneously. Recently, HIV interventions and policies have required more information at sub-national levels to support local planning, decision-making and resource allocation. Unfortunately, many areas lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time-consuming. In this article, we propose a simple and innovative way to incorporate hierarchical information into the dynamical systems by using auxiliary data. The proposed method efficiently uses information from multiple areas within each country without increasing the computational burden. As a result, the new model improves predictive ability and uncertainty assessment.

Sociocultural determinants of global mask-wearing behavior.

Behavioral responses influence the trajectories of epidemics. During the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) reduced pathogen transmission and mortality worldwide. However, despite the global pandemic threat, there was substantial cross-country variation in the adoption of protective behaviors that is not explained by disease prevalence alone. In particular, many countries show a pattern of slow initial mask adoption followed by sharp transitions to high acceptance rates. These patterns are characteristic of behaviors that depend on social norms or peer influence. We develop a game-theoretic model of mask wearing where the utility of wearing a mask depends on the perceived risk of infection, social norms, and mandates from formal institutions. In this model, increasing pathogen transmission or policy stringency can trigger social tipping points in collective mask wearing. We show that complex social dynamics can emerge from simple individual interactions and that sociocultural variables and local policies are important for recovering cross-country variation in the speed and breadth of mask adoption. These results have implications for public health policy and data collection.

Economic Analyses for Disease Surveillance Planning and Advocacy.

Public health disease surveillance can guide a range of decisions related to the protection of populations. Economic analysis can be used to assess how surveillance for specific diseases can substitute for or complement other public health interventions and how to structure surveillance most efficiently. Assessing the value and costs of different disease surveillance options as part of broader disease prevention and control efforts is important for both using available resources efficiently to protect populations and communicating the need for additional resources as appropriate.

Bacterial Mutation During Seasonal Epidemics.

Rapidly evolving bacterial pathogens pose a unique challenge for long-term plant disease management. In this study, we investigated the types and rate of mutations in bacterial populations during seasonal disease epidemics. Two phylogenetically distinct strains of the bacterial spot pathogen, Xanthomonas perforans, were marked, released in tomato fields, and recaptured at several time points during the growing season. Genomic variations in recaptured isolates were identified by comparative analysis of their whole-genome sequences. In total, 180 unique variations (116 substitutions, 57 insertions/deletions, and 7 structural variations) were identified from 300 genomes, resulting in the overall host-associated mutation rate of ∼0.3 to 0.9/genome/week. This result serves as a benchmark for bacterial mutation during epidemics in similar pathosystems. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Vaccine-Preventable Disease Outbreaks Among Healthcare Workers: A Scoping Review.

BACKGROUND: Outbreaks of vaccine-preventable diseases (VPDs) in healthcare workers (HCWs) can result in morbidity and mortality and cause significant disruptions to healthcare services, patients, and visitors as well as an added burden on the healthcare system. This scoping review aimed to describe the epidemiology of VPD outbreaks in HCWs caused by diseases that are prevented by the 10 vaccines recommended by the World Health Organization for HCWs. METHODS: In April 2022, CINAHL, MEDLINE, Global Health, and EMBASE were searched for all articles reporting on VPD outbreaks in HCWs since the year 2000. Articles were included regardless of language and study type. Clinical and epidemiological characteristics of VPD outbreaks were described. RESULTS: Our search found 9363 articles, of which 216 met the inclusion criteria. Studies describing 6 of the 10 VPDs were found: influenza, measles, varicella, tuberculosis, pertussis, and rubella. Most articles (93%) were from high- and upper-middle-income countries. While most outbreaks occurred in hospitals, several influenza outbreaks were reported in long-term-care facilities. Based on available data, vaccination rates among HCWs were rarely reported. CONCLUSIONS: We describe several VPD outbreaks in HCWs from 2000 to April 2022. The review emphasizes the need to understand the factors influencing outbreaks in HCWs and highlights the importance of vaccination among HCWs.

Use of Open-Source Epidemic Intelligence for Infectious Disease Outbreaks, Ukraine, 2022.

Formal infectious disease surveillance in Ukraine has been disrupted by Russia's 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021-February 23, 2022) and during (February 24-July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control.

Rapid Decision Algorithm for Patient Triage during Ebola Outbreaks.

The low specificity of Ebola virus disease clinical signs increases the risk for nosocomial transmission to patients and healthcare workers during outbreaks. Reducing this risk requires identifying patients with a high likelihood of Ebola virus infection. Analyses of retrospective data from patients suspected of having Ebola virus infection identified 13 strong predictors and time from disease onset as constituents of a prediction score for Ebola virus disease. We also noted 4 highly predictive variables that could distinguish patients at high risk for infection, independent of their scores. External validation of this algorithm on retrospective data revealed the probability of infection continuously increased with the score.

Systematic Review of Scales for Measuring Infectious Disease-Related Stigma.

Infectious disease outbreaks are associated with substantial stigma, which can have negative effects on affected persons and communities and on outbreak control. Thus, measuring stigma in a standardized and validated manner early in an outbreak is critical to disease control. We reviewed existing scales used to assess stigma during outbreaks. Our findings show that many different scales have been developed, but few have been used more than once, have been adequately validated, or have been tested in different disease and geographic contexts. We found that scales were usually developed too slowly to be informative early during an outbreak and were published a median of 2 years after the first case of an outbreak. A rigorously developed, transferable stigma scale is needed to assess and direct responses to stigma during infectious disease outbreaks.

Cocirculation and coinfection of multiple respiratory viruses during autumn and winter seasons of 2023 in Beijing, China: A retrospective study.

China experienced severe epidemics of multiple respiratory pathogens in 2023 after lifting "Zero-COVID" policy. The present study aims to investigate the changing circulation and infection patterns of respiratory pathogens in 2023. The 160 436 laboratory results of influenza virus and respiratory syncytial virus (RSV) from February 2020 to December 2023, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from June 2020 to December 2023, Mycoplasma pneumoniae, adenovirus, and human rhinovirus from January 2023 to December 2023 were analyzed. We observed the alternating epidemics of SARS-CoV-2 and influenza A virus (IAV), as well as the out-of-season epidemic of RSV during the spring and summer of 2023. Cocirculation of multiple respiratory pathogens was observed during the autumn and winter of 2023. The susceptible age range of RSV in this winter epidemic (10.5, interquartile range [IQR]: 5-30) was significantly higher than previously (4, IQR: 3-34). The coinfection rate of IAV and RSV in this winter epidemic (0.695%) was significantly higher than that of the last cocirculation period (0.027%) (p < 0.001). Similar trend was also found in the coinfection of IAV and SARS-CoV-2. The present study observed the cocirculation of multiple respiratory pathogens, changing age range of susceptible population, and increasing coinfection rates during the autumn and winter of 2023, in Beijing, China.

Epidemiology and molecular evolution of GI.1 sapovirus in the recent era.

Sapovirus (SaV) infection is increasing worldwide. Herein, we provided evidence of a significant increase in SaV infection in Japan during 2010-2022, primarily due to the considerable (p = 0.0003) rise of the GI.1 genotype. Furthermore, we found that all major and minor SaV outbreaks in Japan, including the largest SaV outbreak in 2021-2022, were caused by the GI.1 genotype. Therefore, to get insight into the underlying molecular mechanism behind this rising trend of the SaV GI.1 type, we selected 15 SaV GI.1 outbreak strains for complete genome analysis through next-generation sequencing. Phylogenetically, our strains remained clustered in different branches in lineages I and II among the GI.1 genotype. We showed all amino acid (aa) substitutions in different open reading frames (ORFs) in these strains. Importantly, we have demonstrated that the strains involved in the largest SaV outbreak in Japan in 2021-2022 belonged to lineage II and possessed the third ORF. We have identified some unique aa mutations in these major outbreak strains in the NS1 and NS6-NS7 regions that are thought to be associated with viral pathogenicity, cell tropism, and epidemiological competence. Thus, in addition to enriching the database of SaV's complete sequences, this study provides insights into its important mutations.

Enterovirus D68 disease burden and epidemiology in hospital-admitted influenza-like illness, Valencia region of Spain, 2014-2020 influenza seasons.

Enterovirus D68 (EV-D68) is an emerging agent for which data on the susceptible adult population is scarce. We performed a 6-year analysis of respiratory samples from influenza-like illness (ILI) admitted during 2014-2020 in 4-10 hospitals in the Valencia Region, Spain. EV-D68 was identified in 68 (3.1%) among 2210 Enterovirus (EV)/Rhinovirus (HRV) positive samples. Phylogeny of 59 VP1 sequences showed isolates from 2014 clustering in B2 (6/12), B1 (5/12), and A2/D1 (1/12) subclades; those from 2015 (n = 1) and 2016 (n = 1) in B3 and A2/D1, respectively; and isolates from 2018 in A2/D3 (42/45), and B3 (3/45). B1 and B2 viruses were mainly detected in children (80% and 67%, respectively); B3 were equally distributed between children and adults; whereas A2/D1 and A2/D3 were observed only in adults. B3 viruses showed up to 16 amino acid changes at predicted antigenic sites. In conclusion, two EV-D68 epidemics linked to ILI hospitalized cases occurred in the Valencia Region in 2014 and 2018, with three fatal outcomes and one ICU admission. A2/D3 strains from 2018 were associated with severe respiratory infection in adults. Because of the significant impact of non-polio enteroviruses in ILI and the potential neurotropism, year-round surveillance in respiratory samples should be pursued.

Wheat genetic resources have avoided disease pandemics, improved food security, and reduced environmental footprints: A review of historical impacts and future opportunities.

The use of plant genetic resources (PGR)-wild relatives, landraces, and isolated breeding gene pools-has had substantial impacts on wheat breeding for resistance to biotic and abiotic stresses, while increasing nutritional value, end-use quality, and grain yield. In the Global South, post-Green Revolution genetic yield gains are generally achieved with minimal additional inputs. As a result, production has increased, and millions of hectares of natural ecosystems have been spared. Without PGR-derived disease resistance, fungicide use would have easily doubled, massively increasing selection pressure for fungicide resistance. It is estimated that in wheat, a billion liters of fungicide application have been avoided just since 2000. This review presents examples of successful use of PGR including the relentless battle against wheat rust epidemics/pandemics, defending against diseases that jump species barriers like blast, biofortification giving nutrient-dense varieties and the use of novel genetic variation for improving polygenic traits like climate resilience. Crop breeding genepools urgently need to be diversified to increase yields across a range of environments (>200 Mha globally), under less predictable weather and biotic stress pressure, while increasing input use efficiency. Given that the ~0.8 m PGR in wheat collections worldwide are relatively untapped and massive impacts of the tiny fraction studied, larger scale screenings and introgression promise solutions to emerging challenges, facilitated by advanced phenomic and genomic tools. The first translocations in wheat to modify rhizosphere microbiome interaction (reducing biological nitrification, reducing greenhouse gases, and increasing nitrogen use efficiency) is a landmark proof of concept. Phenomics and next-generation sequencing have already elucidated exotic haplotypes associated with biotic and complex abiotic traits now mainstreamed in breeding. Big data from decades of global yield trials can elucidate the benefits of PGR across environments. This kind of impact cannot be achieved without widescale sharing of germplasm and other breeding technologies through networks and public-private partnerships in a pre-competitive space.

Wastewater-borne viruses and bacteria, surveillance and biosensors at the interface of academia and field deployment.

Wastewater is a complex, but an ideal, matrix for disease monitoring and surveillance as it represents the entire load of enteric pathogens from a local catchment area. It captures both clinical and community disease burdens. Global interest in wastewater surveillance has been growing rapidly for infectious diseases monitoring and for providing an early warning of potential outbreaks. Although molecular detection methods show high sensitivity and specificity in pathogen monitoring from wastewater, they are strongly limited by challenges, including expensive laboratory settings and prolonged sample processing and analysis. Alternatively, biosensors exhibit a wide range of practical utility in real-time monitoring of biological and chemical markers. However, field deployment of biosensors is primarily challenged by prolonged sample processing and pathogen concentration steps due to complex wastewater matrices. This review summarizes the role of wastewater surveillance and provides an overview of infectious viral and bacterial pathogens with cutting-edge technologies for their detection. It emphasizes the practical utility of biosensors in pathogen monitoring and the major bottlenecks for wastewater surveillance of pathogens, and overcoming approaches to field deployment of biosensors for real-time pathogen detection. Furthermore, the promising potential of novel machine learning algorithms to resolve uncertainties in wastewater data is discussed.

The epidemiological profile of incidence and mortality from epidemics in complex humanitarian emergencies from 1990 to 2022 - A scoping review.

AIM: This study aimed to investigate the impact of communicable diseases with epidemic potential in complex emergency (CE) situations, focusing on the epidemiological profile of incidence and mortality and exploring underlying factors contributing to increased epidemic risks. METHODS: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines, we conducted a scoping review of articles published between 1990 and 2022. The search included terms related to complex emergencies, communicable diseases, outbreaks, and epidemics. We identified 92 epidemics related to CE occurring in 32 different countries. RESULTS: Communicable diseases like Shigellosis, Cholera, Measles, Meningococcal meningitis, Yellow Fever, and Malaria caused significant morbidity and mortality. Diarrhoeal diseases, particularly Cholera and Shigellosis, had the highest incidence rates. Shigella specifically had an incidence of 241.0 per 1000 (people at risk), with a mortality rate of 11.7 per 1000, while Cholera's incidence was 13.0 per 1000, with a mortality rate of 0.22 per 1000. Measles followed, with an incidence of 25.0 per 1000 and a mortality rate of 0.76 per 1000. Meningococcal Meningitis had an incidence rate of 1.3 per 1000 and a mortality rate of 0.13 per 1000. Despite their lower incidences, yellow fever at 0.8 per 1000 and malaria at 0.4 per 1000, their high case fatality rates of 20.1% and 0.4% remained concerning in CE. The qualitative synthesis reveals that factors such as water, sanitation, and hygiene, shelter and settlements, food and nutrition, and public health and healthcare in complex emergencies affect the risk of epidemics. CONCLUSION: Epidemics during complex emergencies could potentially lead to a public health crisis. Between 1990 and 2022, there have been no statistically significant changes in the trend of incidence, mortality, or fatality rates of epidemic diseases in CE. It is crucial to understand that all epidemics identified in CE are fundamentally preventable.

Multiple pandemic waves vs multi-period/multi-phasic epidemics: Global shape of the COVID-19 pandemic.

The overall course of the COVID-19 pandemic in Western countries has been characterized by complex sequences of phases. In the period before the arrival of vaccines, these phases were mainly due to the alternation between the strengthening/lifting of social distancing measures, with the aim to balance the protection of health and that of the society as a whole. After the arrival of vaccines, this multi-phasic character was further emphasized by the complicated deployment of vaccination campaigns and the onset of virus' variants. To cope with this multi-phasic character, we propose a theoretical approach to the modeling of overall pandemic courses, that we term multi-period/multi-phasic, based on a specific definition of phase. This allows a unified and parsimonious representation of complex epidemic courses even when vaccination and virus' variants are considered, through sequences of weak ergodic renewal equations that become fully ergodic when appropriate conditions are met. Specific hypotheses on epidemiological and intervention parameters allow reduction to simple models. The framework suggest a simple, theory driven, approach to data explanation that allows an accurate reproduction of the overall course of the COVID-19 epidemic in Italy since its beginning (February 2020) up to omicron onset, confirming the validity of the concept.

Adaptive sequential surveillance with network and temporal dependence.

Strategic test allocation is important for control of both emerging and existing pandemics (eg, COVID-19, HIV). It supports effective epidemic control by (1) reducing transmission via identifying cases and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest (positive infection status) is often a latent variable. In addition, presence of both network and temporal dependence reduces data to a single observation. In this work, we study an adaptive sequential design, which allows for unspecified dependence among individuals and across time. Our causal parameter is the mean latent outcome we would have obtained, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. The key strength of the method is that we do not have to model network and time dependence: a short-term performance Online Super Learner is used to select among dependence models and randomization schemes. The proposed strategy learns the optimal choice of testing over time while adapting to the current state of the outbreak and learning across samples, through time, or both. We demonstrate the superior performance of the proposed strategy in an agent-based simulation modeling a residential university environment during the COVID-19 pandemic.

Dynamics of viral disease outbreaks: A hundred years (1918/19-2019/20) in retrospect - Loses, lessons and emerging issues.

Infectious diseases continue to be the leading cause of morbidity and mortality, and a formidable obstacle to the development and well-being of people worldwide. Viruses account for more than half of infectious disease outbreaks that have plagued the world. The past century (1918/19-2019/20) has witnessed some of the worst viral disease outbreaks the world has recorded, with overwhelming impact especially in low- and middle-income countries (LMIC). The frequency of viral disease outbreak appears to be increasing. Generally, although infectious diseases have afflicted the world for centuries and humankind has had opportunities to examine the nature of their emergence and mode of spread, almost every new outbreak poses a formidable challenge to humankind, beating the existing pandemic preparedness systems, if any, and causing significant losses. These underscore inadequacy in our understanding of the dynamics and preparedness against viral disease outbreaks that lead to epidemics and pandemics. Despite these challenges, the past 100 years of increasing frequencies of viral disease outbreaks have engendered significant improvements in response to epidemics and pandemics, and offered lessons to inform preparedness. Hence, the increasing frequency of emergence of viral outbreaks and the challenges these outbreaks pose to humankind, call for the continued search for effective ways to tackle viral disease outbreaks in real time. Through a PRISMA-based approach, this systematic review examines the outbreak of viral diseases in retrospect to decipher the outbreak patterns, losses inflicted on humanity and highlights lessons these offer for meaningful preparation against future viral disease outbreaks and pandemics.

Surveillance of SARS-CoV-2 prevalence from repeated pooled testing: application to Swiss routine data.

Surveillance of SARS-CoV-2 through reported positive RT-PCR tests is biased due to non-random testing. Prevalence estimation in population-based samples corrects for this bias. Within this context, the pooled testing design offers many advantages, but several challenges remain with regards to the analysis of such data. We developed a Bayesian model aimed at estimating the prevalence of infection from repeated pooled testing data while (i) correcting for test sensitivity; (ii) propagating the uncertainty in test sensitivity; and (iii) including correlation over time and space. We validated the model in simulated scenarios, showing that the model is reliable when the sample size is at least 500, the pool size below 20, and the true prevalence below 5%. We applied the model to 1.49 million pooled tests collected in Switzerland in 2021-2022 in schools, care centres, and workplaces. We identified similar dynamics in all three settings, with prevalence peaking at 4-5% during winter 2022. We also identified differences across regions. Prevalence estimates in schools were correlated with reported cases, hospitalizations, and deaths (coefficient 0.84 to 0.90). We conclude that in many practical situations, the pooled test design is a reliable and affordable alternative for the surveillance of SARS-CoV-2 and other viruses.