scholarly journals A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead

2021 ◽  
Author(s):  
Catherine A. A. Beauchemin ◽  
Andreas Handel
Keyword(s):  
Public Health ◽  
Cell Culture ◽  
Mathematical Models ◽  
Influenza A ◽  
Influenza Infection ◽  
Population Level ◽  
Host Population ◽  
Lessons Learned ◽  
Infection Dynamics ◽  
Individual Host

Most mathematical models used to study the dynamics of influenza A have thus far focused on the between-host population level, with the aim to inform public health decisions regarding issues such as drug and social distancing intervention strategies, antiviral stockpiling or vaccine distribution. Here, we investigate mathematical modeling of influenza infection spread at a different scale; namely that occurring within an individual host or a cell culture. We review the models that have been developed in the last decades and discuss their contributions to our understanding of the dynamics of influenza infections. We review kinetic parameters (e.g., viral clearance rate, lifespan of infected cells) and values obtained through fitting mathematical models, and contrast them with values obtained directly from experiments. We explore the symbiotic role of mathematical models and experimental assays in improving our quantitative understanding of influenza infection dynamics. We also discuss the challenges in developing better, more comprehensive models for the course of influenza infections within a host or cell culture. Finally, we explain the contributions of such modeling efforts to important public health issues, and suggest future modeling studies that can help to address additional questions relevant to public health.

scholarly journals A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead

2021 ◽  
Author(s):  
Catherine A. A. Beauchemin ◽  
Andreas Handel
Keyword(s):  
Public Health ◽  
Cell Culture ◽  
Mathematical Models ◽  
Influenza A ◽  
Influenza Infection ◽  
Population Level ◽  
Host Population ◽  
Lessons Learned ◽  
Infection Dynamics ◽  
Individual Host

Most mathematical models used to study the dynamics of influenza A have thus far focused on the between-host population level, with the aim to inform public health decisions regarding issues such as drug and social distancing intervention strategies, antiviral stockpiling or vaccine distribution. Here, we investigate mathematical modeling of influenza infection spread at a different scale; namely that occurring within an individual host or a cell culture. We review the models that have been developed in the last decades and discuss their contributions to our understanding of the dynamics of influenza infections. We review kinetic parameters (e.g., viral clearance rate, lifespan of infected cells) and values obtained through fitting mathematical models, and contrast them with values obtained directly from experiments. We explore the symbiotic role of mathematical models and experimental assays in improving our quantitative understanding of influenza infection dynamics. We also discuss the challenges in developing better, more comprehensive models for the course of influenza infections within a host or cell culture. Finally, we explain the contributions of such modeling efforts to important public health issues, and suggest future modeling studies that can help to address additional questions relevant to public health.

scholarly journals Resource fluctuations inhibit the reproduction and virulence of the human parasite Schistosoma mansoni in its snail intermediate host

2020 ◽  
Vol 287 (1919) ◽  
pp. 20192446
Author(s):  
David J. Civitello ◽  
Lucy H. Baker ◽  
Selvaganesh Maduraiveeran ◽  
Rachel B. Hartman
Keyword(s):  
Schistosoma Mansoni ◽  
Intermediate Host ◽  
Population Level ◽  
Host Population ◽  
Environmental Drivers ◽  
Bioenergetic Model ◽  
Snail Intermediate Host ◽  
Human Parasite ◽  
Infection Dynamics ◽  
Periodic Starvation

Resource availability can powerfully influence host–parasite interactions. However, we currently lack a mechanistic framework to predict how resource fluctuations alter individual infection dynamics. We address this gap with experiments manipulating resource supply and starvation for a human parasite, Schistosoma mansoni , and its snail intermediate host to test a hypothesis derived from mechanistic energy budget theory: resource fluctuations should reduce schistosome reproduction and virulence by inhibiting parasite ingestion of host biomass. Low resource supply caused hosts to remain small, reproduce less and produce fewer human-infectious cercariae. Periodic starvation also inhibited cercarial production and prevented infection-induced castration. The periodic starvation experiment also revealed substantial differences in fit between two bioenergetic model variants, which differ in their representation of host starvation. Simulations using the best-fit parameters of the winning model suggest that schistosome performance substantially declines with resource fluctuations with periods greater than 7 days. These experiments strengthen mechanistic theory, which can be readily scaled up to the population level to understand key feedbacks between resources, host population dynamics, parasitism and control interventions. Integrating resources with other environmental drivers of disease in an explicit bioenergetic framework could ultimately yield mechanistic predictions for many disease systems.

What Elephant? Pedagogical Approaches to Addressing Stigma Toward Mental Disorders in Undergraduate Public Health Education

2020 ◽  
pp. 237337992092287
Author(s):  
Briana Mezuk ◽  
Belinda Needham ◽  
Kevin Joiner ◽  
Daphne Watkins ◽  
Sarah Stoddard ◽  
...  
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Mental Disorders ◽  
Mental Health Problems ◽  
Public Mental Health ◽  
Population Level ◽  
Public Health Education ◽  
Lessons Learned ◽  
Undergraduate Public Health ◽  
Pedagogical Approaches

In the past decade, the number of undergraduate public health programs has increased exponentially. This growth provides a unique opportunity to explore concepts and issues relevant to understanding the determinants of health at a population level using new pedagogical approaches. One of these issues is stigma toward mental disorders. Stigma is a concept that refers to a feature or characteristic that reduces, devalues, and disempowers a person. Given the prevalence of mental and substance use disorders among college students, undergraduate education is an important setting for attempting to address stigmatizing attitudes both for society at large and for faculty, staff, and students, including those experiencing mental health problems. This article describes an effort to develop an undergraduate course in public mental health that explicitly addresses the ways stigma shapes student understanding of this topic and discusses lessons learned from this experience.

scholarly journals 2751. Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED): Methods

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S969-S969 ◽  
Author(s):  
Timothy Burgess ◽  
Stephanie Richard ◽  
Limone Collins ◽  
Rhonda Colombo ◽  
Anuradha Ganesan ◽  
...  
Keyword(s):  
Cell Culture ◽  
Influenza A ◽  
Influenza Season ◽  
Controlled Trial ◽  
Text Message ◽  
Lessons Learned ◽  
Influenza Vaccines ◽  
Military Bases ◽  
Military Recruits ◽  
Military Facilities

Abstract Background Most influenza vaccines come from inactivated virus grown in egg culture, and studies suggest that egg-adapted virus may have decreased immunogenicity in humans for certain influenza A strains. Cell culture-based and recombinant vaccines may be more immunogenic, but comparative studies are lacking. We are conducting a randomized, controlled trial of 3 FDA-licensed influenza vaccines (cell culture, recombinant, and egg culture) to assess differences in immunogenicity and effectiveness in adults. Methods A total of 10,650 eligible adults will be individually randomized 1:1:1 (cell culture, recombinant, or egg-based vaccine) over 2 influenza seasons (2018–2019 and 2019–2020) at military facilities in geographically diverse locations in the US Participants who are not military recruits will report the presence or absence of ILI symptoms on a weekly basis through an automated electronic (text message or email) survey; those who experience ILI symptoms will be scheduled for two in-person visits. Military recruits who experience an ILI report will report directly to clinic and will not receive weekly surveillance reminders (Figure 1). Results Enrollment for year 1 of PAIVED occurred November 7 to December 31, 2018 at 5 military bases. During this season, 1,623 participants were enrolled, among whom 34% were randomized to receive cell culture vaccine, 33% to recombinant vaccine, and 33% to egg-based vaccine. The participants were 61% active military, 19% retired military, and 20% military dependents. One quarter of the participants were women, and the participants were 18–88 years old, median 26 years of age. Among the 1,559 participants with complete data, 324 (21%) experienced ILI at least once. Blood and swab samples were successfully collected at visit 1 from 93% of the participants with case-defined ILIs. Conclusion The initial phase of PAIVED successfully enrolled and randomized 1,623 participants during the 2018/2019 influenza season. Follow-up of this season’s participants is on-going. PAIVED will apply lessons learned during the 2018/2019 influenza season to the next season’s study implementation, with the goal of enrolling more than 9,000 additional participants through increasing the number of individuals enrolled at some sites and adding new sites to the trial. Disclosures All authors: No reported disclosures.

Influenza A (H3) Outbreak at a Hurricane Harvey Megashelter in Harris County, Texas: Successes and Challenges in Disease Identification and Control Measure Implementation

2019 ◽  
Vol 13 (1) ◽  
pp. 97-101 ◽  
Author(s):  
Leann Liu ◽  
Aisha Haynie ◽  
Sherry Jin ◽  
Ana Zangeneh ◽  
Eric Bakota ◽  
...  
Keyword(s):  
Public Health ◽  
Influenza A ◽  
Control Measure ◽  
Lessons Learned ◽  
Control Measures ◽  
Harris County ◽  
Hygiene Practices ◽  
Disease Identification ◽  
Surveillance And Response ◽  
And Control

ABSTRACTWhen Hurricane Harvey landed along the Texas coast on August 25, 2017, it caused massive flooding and damage and displaced tens of thousands of residents of Harris County, Texas. Between August 29 and September 23, Harris County, along with community partners, operated a megashelter at NRG Center, which housed 3365 residents at its peak. Harris County Public Health conducted comprehensive public health surveillance and response at NRG, which comprised disease identification through daily medical record reviews, nightly “cot-to-cot” resident health surveys, and epidemiological consultations; messaging and communications; and implementation of control measures including stringent isolation and hygiene practices, vaccinations, and treatment. Despite the lengthy operation at the densely populated shelter, an early seasonal influenza A (H3) outbreak of 20 cases was quickly identified and confined. Influenza outbreaks in large evacuation shelters after a disaster pose a significant threat to populations already experiencing severe stressors. A holistic surveillance and response model, which consists of coordinated partnerships with onsite agencies, in-time epidemiological consultations, predesigned survey tools, trained staff, enhanced isolation and hygiene practices, and sufficient vaccines, is essential for effective disease identification and control. The lessons learned and successes achieved from this outbreak may serve for future disaster response settings. (Disaster Med Public Health Preparedness. 2019;13:97-101)

scholarly journals The Role of Type I IFNs in Influenza: Antiviral Superheroes or Immunopathogenic Villains?

2020 ◽  
Vol 12 (6) ◽  
pp. 437-447 ◽  
Author(s):  
Wenxin Wu ◽  
Jordan P. Metcalf
Keyword(s):  
Influenza A ◽  
Cell Function ◽  
Influenza Infection ◽  
Immune Defense ◽  
Type I ◽  
Type I Ifns ◽  
Infection Treatment ◽  
Individual Host ◽  
The Individual

The important role of interferons (IFNs) in antiviral innate immune defense is well established. Although recombinant IFN-α was approved for cancer and chronic viral infection treatment by regulatory agencies in many countries starting in 1986, no IFNs are approved for treatment of influenza A virus (IAV) infection. This is partially due to the complex effects of IFNs in acute influenza infection. IAV attacks the human respiratory system and causes significant morbidity and mortality globally. During influenza infection, depending on the strain of IAV and the individual host, type I IFNs can have protective antiviral effects or can contribute to immunopathology. In the context of virus infection, the immune system has complicated mechanisms regulating the expression and effects of type I IFN to maximize the antiviral response by both activating and enhancing beneficial innate cell function, while limiting immunopathological responses that lead to exaggerated tissue damage. In this review, we summarize the complicated, but important, role of type I IFNs in influenza infections. This includes both protective and harmful effects of these important cytokines during infection.

scholarly journals Insights into the first wave of the COVID-19 pandemic in Bangladesh: Lessons learned from a high-risk country

2020 ◽  
Author(s):  
Md. Hasanul Banna Siam ◽  
Md Mahbub Hasan ◽  
Enayetur Raheem ◽  
Md. Hasinur rahaman Khan ◽  
Mahbubul H Siddiqee ◽  
...  
Keyword(s):  
Public Health ◽  
Case Fatality ◽  
Lessons Learned ◽  
Public Health Preparedness ◽  
Distribution Maps ◽  
Infection Dynamics ◽  
Johns Hopkins University ◽  
Epidemiological Characteristics ◽  
Epidemiological Information ◽  
The Eu

Background South Asian countries including Bangladesh have been struggling to control the COVID-19 pandemic despite imposing months of lockdown and other public health measures (as of June 30, 2020). In-depth epidemiological information from these countries is lacking. From the perspective of Bangladesh, this study aims to understand the epidemiological features and gaps in public health preparedness. Method This study used publicly available data (8 March-30 June 2020) from the respective health departments of Bangladesh and Johns Hopkins University Coronavirus Resource Centre. Descriptive statistics was used to report the incidence, case fatality rates (CFR), and trend analysis. Spatial distribution maps were created using ArcGIS Desktop. Infection dynamics were analyzed via SIR models. Findings In 66 days of nationwide lockdown and other public health efforts, a total of 47,153 cases and 650 deaths were reported. However, the incidence was increased by around 50% within a week after relaxing the lockdown. Males were disproportionately affected in terms of infections (71%) and deaths (77%) than females. The CFR for males was higher than females (1.38% vs 1.01%). Over 50% of infected cases were reported among young adults (20-40-year age group). Geospatial analysis between 7 June 2020 and 20 June 2020 showed that the incidences increased 4 to 10-fold in 12 administrative districts while it decreased in the epicenter. As compared to the EU and USA, trends of the cumulative incidence were slower in South Asia with lower mortality. Conclusion Our findings on gaps in public health preparedness and epidemiological characteristics would contribute to facilitating better public health decisions for managing current and future pandemics like COVID-19 in the settings of developing countries.

scholarly journals Exploring the Effect of Collective Cultural Attributes on Covid-19-Related Public Health Outcomes

2021 ◽  
Vol 12 ◽  
Author(s):  
Aysegul Erman ◽  
Mike Medeiros
Keyword(s):  
Public Health ◽  
Health Outcomes ◽  
Health System ◽  
Population Level ◽  
System Capacity ◽  
Sensitivity Analyses ◽  
Infection Dynamics ◽  
Behavioral Attributes ◽  
Political Economic ◽  
High Degree

Infections and deaths associated with COVID-19 show a high degree of heterogeneity across different populations. A thorough understanding of population-level predictors of such outcomes is crucial for devising better-targeted and more appropriate public health preparedness measures. While demographic, economic, and health-system capacity have featured prominently in recent work, cultural, and behavioral characteristics have largely been overlooked. However, cultural differences shape both the public policy response and individuals' behavioral responses to the crisis in ways that can impact infection dynamics and key health outcomes. To address this gap, we used meta-analytic methods to explore the global variability of three public health outcomes (i.e., crude test positivity, case/infection fatality, and mortality risk) during the first wave of the pandemic. This set of analyses identified several cultural/behavioral attributes (e.g., uncertainty avoidance and long-term vs. short-term normative orientation) as independent predictors of public health outcomes after adjusting for key demographic, political, economic, and health-system-related predictors; which were robust in sensitivity analyses. In conclusion, this study clearly demonstrates that cultural attributes do in fact account for some of the global disparities in COVID-19-attributed health outcomes. As a consequence, policymakers should more explicitly consider a society's cultural attributes alongside other important parameters such as demographic characteristics and health system constraints in order to develop better tailored and more effective policy responses.

scholarly journals Coinfection of semi-infectious particles can contribute substantially to influenza infection dynamics

2019 ◽  
Author(s):  
Alex Farrell ◽  
Christopher Brooke ◽  
Katia Koelle ◽  
Ruian Ke
Keyword(s):  
Influenza A ◽  
Rna Virus ◽  
Influenza Infection ◽  
Large Fraction ◽  
Viral Fitness ◽  
Productive Infection ◽  
Target Cells ◽  
Public Health Burden ◽  
Infection Dynamics ◽  
A Genome

AbstractInfluenza is an RNA virus with a genome comprised of eight gene segments. Recent experiments show that the vast majority of virions fail to express one or more gene segments and thus cannot cause a productive infection on their own. These particles, called semi-infectious particles (SIPs), can induce virion production through complementation when multiple SIPs are present in an infected cell. Previous within-host influenza models ignore the potential effects of coinfection and SIPs during virus infection. Here, to investigate the extent that SIPs and coinfection impact viral dynamics, we constructed two within-host models that explicitly keep track of SIPs and coinfection, and fitted the models to clinical data published previously. We found that the model making a more realistic assumption that viruses can only reach a limited number of target cells allows for frequent co-infection during early viral exponential growth and predicts that SIPs contribute substantially to viral load. Furthermore, the model provides a new interpretation of the determinants of viral growth and predicts that the virus within-host growth rate (a measure of viral fitness) is relatively insensitive to the fraction of virions being SIPs, consistent with biological observations. Our results highlight the important role that cellular co-infection can play in regulating infection dynamics and provide a potential explanation for why SIP production is not highly deleterious. More broadly, the model can be used as a general framework to understand coinfection/superinfection in other viral infections.Author SummaryInfluenza A viruses (IAVs) represent a large public health burden across the world. Currently, our understanding of their infection dynamics is incomplete, which hinders the development of effective vaccines and treatment strategies. Recently, it was shown that a large fraction of virions, called semi-infectious particles, do not cause productive infection on their own; however, coinfection of these particles leads to productive infection. The extent that semi-infectious particles and, more broadly, coinfection contribute to overall influenza infection dynamics is not clear. To address this question, we constructed mathematical models explicitly keeping track of semi-infectious particles and coinfection. We show that coinfection can be frequent over the course of infection and that SIPs play an important role in regulating infection dynamics. Our results have implications towards developing effective therapeutics.

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