Intervention strategies for an influenza pandemic taking into account secondary bacterial infections

Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review

Influenza A virus (IAV) infections represent a substantial global health challenge and are often accompanied by coinfections involving secondary viruses or bacteria, resulting in increased morbidity and mortality. The clinical impact of coinfections remains poorly understood, with conflicting findings regarding fatality. Isolating the impact of each pathogen and mechanisms of pathogen synergy during coinfections is challenging and further complicated by host and pathogen variability and experimental conditions. Factors such as cytokine dysregulation, immune cell function alterations, mucociliary dysfunction, and changes to the respiratory tract epithelium have been identified as contributors to increased lethality. The relative significance of these factors depends on variables such as pathogen types, infection timing, sequence, and inoculum size. Mathematical biological modeling can play a pivotal role in shedding light on the mechanisms of coinfections. Mathematical modeling enables the quantification of aspects of the intra-host immune response that are difficult to assess experimentally. In this narrative review, we highlight important mechanisms of IAV coinfection with bacterial and viral pathogens and survey mathematical models of coinfection and the insights gained from them. We discuss current challenges and limitations facing coinfection modeling, as well as current trends and future directions toward a complete understanding of coinfection using mathematical modeling and computer simulation.

COVID-19 and diarrhea: putative mechanisms and management

BACKGROUND

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of the coronavirus disease 2019 (COVID-19), has recently posed a threat to global health by spreading at a high rate and taking millions of lives worldwide. Along with the respiratory symptoms, there are gastrointestinal manifestations and one of the most common gastrointestinal symptoms is diarrhea which is seen in a significant percentage of COVID-19 patients.

LITERATURE REVIEW

Several studies have shown the plausible correlation between overexpressed angiotensin converting enzyme 2 (ACE2) in enterocytes and SARS-CoV-2, as ACE2 is the only known receptor for the virus entry. Along with the dysregulated ACE2, there are other contributing factors such as gut microbiome dysbiosis, adverse effects of antiviral and antibiotics for treating infections and inflammatory response to SARS-CoV-2 which bring about increased permeability of gut cells and subsequent occurrence of diarrhea. Few studies found that the SARS-CoV-2 is capable of damaging liver cells too. No single effective treatment option is available.

LIMITATIONS

Confirmed pathophysiology is still unavailable. Studies regarding global population are also insufficient.

CONCLUSION

In this review, based on the previous works and literature, we summarized the putative molecular pathophysiology of COVID-19 associated diarrhea, concomitant complications and the standard practices of management of diarrhea and hepatic manifestations in international setups.

Comparative evaluation of bacterial colonization on removable dental prostheses in patients with COVID-19: A clinical study

STATEMENT OF PROBLEM

In the outbreak of COVID-19, coinfections and even superinfections in the background of SARS-CoV-2 viral infection have been reported. Such bacterial and fungal strains may be colonized in different tissues and organs, including the oral cavity. Whether infection with COVID-19 could increase colonization of different bacterial strains on removable dental prostheses is unclear.

PURPOSE

The purpose of this clinical study was to compare bacterial colonization on removable dental prostheses in patients with COVID-19, before versus after diagnosis.

MATERIAL AND METHODS

Two sex- and age-matched groups of complete-denture-wearing participants (N=60) with and without a positive diagnosis for COVID-19 were enrolled in the study. Swabs were used at 2 different time intervals to sample areas of the dentures, which were then cultured and the colony smears Gram stained. A statistical analysis was conducted by using the Mann-Whitney U test (α=.05).

RESULTS

Streptococcus species (93.3% versus 40.0%, P=.047) and Klebsiella pneumonia (46.7% versus 13.4%, P=.036) were detected more frequently in the COVID-19-positive group.

CONCLUSIONS

Higher rates of bacterial colonization, especially with Streptococcus species and Klebsiella pneumonia, were detected on removable dental prostheses after COVID-19 infection.

Multi-drug resistant bacteria isolates from lymphatic filariasis patients in the Ahanta West District, Ghana

Background

Antimicrobial resistance is associated with increased morbidity in secondary infections and is a global threat owning to the ubiquitous nature of resistance genes in the environment. Recent estimate put the deaths associated with bacterial antimicrobial resistance in 2019 at 4.95 million worldwide. Lymphatic filariasis (LF), a Neglected Tropical Disease (NTD), is associated with the poor living in the tropical regions of the world. LF patients are prone to developing acute dermatolymphangioadenitis (ADLA), a condition that puts them at risk of developing secondary bacterial infections due to skin peeling. ADLA particularly worsens the prognosis of patients leading to usage of antibiotics as a therapeutic intervention. This may result in inappropriate usage of antibiotics due to self-medication and non-compliance; exacerbating antimicrobial resistance in LF patients. In this perspective, we assessed the possibilities of antimicrobial resistance in LF patients. We focused on antibiotic usage, antibiotic resistance in Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa isolates and looked at genes (mecA and Extended-spectrum beta-lactamase [blaCTX-M, blaSHV and blaTEM]) coding for resistance in multi-drug resistant (MDR) bacterial isolates.

Results

Of the sixty (60) participants, fifty-four (n = 54, 90%) were within 31–60 years of age, twenty (n = 20, 33.33%) were unemployed and thirty-eight (n = 38, 50.67%) had wounds aged (in months) seven (7) months and above. Amoxicillin (54%) and chloramphenicol (22%) were the most frequently used antibiotics for self-medication. Staphylococcus aureus isolates (n = 26) were mostly resistant to penicillin (n = 23, 88.46%) and least resistant to erythromycin (n = 2, 7.69%). Escherichia coli isolates (n = 5) were resistant to tetracycline (n = 5, 100%) and ampicillin (n = 5, 100%) but were sensitive to meropenem (n = 5, 100%). Pseudomonas aeruginosa isolates (n = 8) were most resistant to meropenem (n = 3, 37.50%) and to a lesser ciprofloxacin (n = 2, 25%), gentamicin (n = 2, 25%) and ceftazidime (n = 2, 25%). Multi-drug resistant methicillin resistant Staphylococcus aureus (MRSA), cephalosporin resistant Escherichia coli. and carbapenem resistant Pseudomonas aeruginosa were four (n = 4, 15.38%), two (n = 2, 40%) and two (n = 2, 25%) respectively. ESBL (blaCTX-M) and mecA genes were implicated in the resistance mechanism of Escherichia coli and MRSA, respectively.

Conclusion

The findings show presence of MDR isolates from LF patients presenting with chronic wounds; thus, the need to prioritize resistance of MDR bacteria into treatment strategies optimizing morbidity management protocols. This could guide antibiotic selection for treating LF patients presenting with ADLA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-022-02624-9.

Identification of Antibiotic Resistance Proteins via MiCId's Augmented Workflow. A Mass Spectrometry-Based Proteomics Approach

Fast and accurate identifications of pathogenic bacteria along with their associated antibiotic resistance proteins are of paramount importance for patient treatments and public health. To meet this goal from the mass spectrometry aspect, we have augmented the previously published Microorganism Classification and Identification (MiCId) workflow for this capability. To evaluate the performance of this augmented workflow, we have used MS/MS datafiles from samples of 10 antibiotic resistance bacterial strains belonging to three different species: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The evaluation shows that MiCId's workflow has a sensitivity value around 85% (with a lower bound at about 72%) and a precision greater than 95% in identifying antibiotic resistance proteins. In addition to having high sensitivity and precision, MiCId's workflow is fast and portable, making it a valuable tool for rapid identifications of bacteria as well as detection of their antibiotic resistance proteins. It performs microorganismal identifications, protein identifications, sample biomass estimates, and antibiotic resistance protein identifications in 6-17 min per MS/MS sample using computing resources that are available in most desktop and laptop computers. We have also demonstrated other use of MiCId's workflow. Using MS/MS data sets from samples of two bacterial clonal isolates, one being antibiotic-sensitive while the other being multidrug-resistant, we applied MiCId's workflow to investigate possible mechanisms of antibiotic resistance in these pathogenic bacteria; the results showed that MiCId's conclusions agree with the published study. The new version of MiCId (v.07.01.2021) is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

Major Drugs Used in COVID-19 Treatment: Molecular Mechanisms, Validation and Current Progress in Trials

Background:

Currently, the present world is facing a new deadly challenge against a pandemic disease called COVID-19, which is caused by a coronavirus, named SARS-CoV-2. To date, there is no drug or vaccine that can treat COVID-19 completely, but some drugs have been used primarily, and they are in different stages of clinical trials. This review article discussed and compared those drugs which are running ahead in COVID-19 treatments.

Methods:

We have explored PUBMED, SCOPUS, WEB OF SCIENCE, as well as press release of WHO, NIH and FDA for articles about COVID-19, and reviewed them.

Results:

Drugs like favipiravir, remdesivir, lopinavir/ritonavir, hydroxychloroquine, azithromycin, ivermectin, corticosteroids and interferons have been found effective in some extents, and partially approved by FDA and WHO to treat COVID-19 at different phases of pandemic. However, some of these drugs have been disapproved later, although clinical trials are going on. In parallel, plasma therapy has been found fruitful in some extents too, and a number of vaccine trails are going on.

Conclusions:

This review article discussed the epidemiologic and mechanistic characteristics of SARS-CoV-2, and how drugs could act on this virus with the comparative discussion on progress and backwards of major drugs used till date, which might be beneficial for choosing therapies against COVID-19 in different countries.

Disaster and Pandemic Management Using Machine Learning: A Survey

This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

Association Between the Use of Antibiotics, Antivirals, and Hospitalizations Among Patients With Laboratory-confirmed Influenza

BACKGROUND

Clinicians may prescribe antibiotics to influenza patients at high risk for bacterial complications. We explored the association between antibiotics, antivirals, and hospitalization among people with influenza.

METHODS

A retrospective cohort study of patients with confirmed influenza with encounters during January 2011-January 2019 was conducted using data from the Veterans Affairs Informatics and Computing Infrastructure (VINCI). We compared inpatient hospitalizations (all-cause and respiratory) within 30 days of influenza diagnosis between 4 patient cohorts: (1) no treatment (n = 4228); (2) antibiotic only (n = 671); (3) antiviral only (n = 6492); and (4) antibiotic plus antiviral (n = 1415). We estimated relative risk for hospitalization using Poisson generalized linear model and robust standard errors.

RESULTS

Among 12 806 influenza cases, most were white men (mean age, 57-60 years). Those with antivirals only, antibiotic plus antiviral, and antibiotics only all had a statistically significant lower risk of all-cause and respiratory hospitalization compared to those without treatment. Comparing the antibiotic plus antiviral cohort to those who were prescribed an antiviral alone, there was a 47% lower risk for respiratory hospitalization (relative risk, 0.53 [95% confidence interval, .31-.94]), and no other statistical differences were detected.

CONCLUSIONS

Those prescribed an antiviral, antibiotic, or both had a lower risk of hospitalization within 30 days compared to those without therapy. Furthermore, intervention with both an antibiotic and antiviral had a lower risk of respiratory hospitalization within 30 days compared to those with an antiviral alone. Importantly, the absolute magnitude of decreased risk with antibiotic plus antiviral therapy is small and must be interpreted within the context of the overall risk of antibiotic usage.

Secondary Bacterial Infections in Patients With Viral Pneumonia

Pulmonary diseases of viral origin are often followed by the manifestation of secondary infections, leading to further clinical complications and negative disease outcomes. Thus, research on secondary infections is essential. Here, we review clinical data of secondary bacterial infections developed after the onset of pulmonary viral infections. We review the most recent clinical data and current knowledge of secondary bacterial infections and their treatment in SARS-CoV-2 positive patients; case reports from SARS-CoV, MERS-CoV, SARS-CoV2 and the best-studied respiratory virus, influenza, are described. We outline treatments used or prophylactic measures employed for secondary bacterial infections. This evaluation includes recent clinical reports of pulmonary viral infections, including those by COVID-19, that reference secondary infections. Where data was provided for COVID-19 patients, a mortality rate of 15.2% due to secondary bacterial infections was observed for patients with pneumonia (41 of 268). Most clinicians treated patients with SARS-CoV-2 infections with prophylactic antibiotics (63.7%, n = 1,901), compared to 73.5% (n = 3,072) in all clinical reports of viral pneumonia included in this review. For all cases of viral pneumonia, a mortality rate of 10.9% due to secondary infections was observed (53 of 482). Most commonly, quinolones, cephalosporins and macrolides were administered, but also the glycopeptide vancomycin. Several bacterial pathogens appear to be prevalent as causative agents of secondary infections, including antibiotic-resistant strains of Staphylococcus aureus and Klebsiella pneumoniae.

Secondary Bacterial Infections During Pulmonary Viral Disease: Phage Therapeutics as Alternatives to Antibiotics?

Secondary bacterial infections manifest during or after a viral infection(s) and can lead to negative outcomes and sometimes fatal clinical complications. Research and development of clinical interventions is largely focused on the primary pathogen, with research on any secondary infection(s) being neglected. Here we highlight the impact of secondary bacterial infections and in particular those caused by antibiotic-resistant strains, on disease outcomes. We describe possible non-antibiotic treatment options, when small molecule drugs have no effect on the bacterial pathogen and explore the potential of phage therapy and phage-derived therapeutic proteins and strategies in treating secondary bacterial infections, including their application in combination with chemical antibiotics.

Population-level mathematical modeling of antimicrobial resistance: a systematic review

BACKGROUND

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

METHODS

We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

RESULTS

We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

CONCLUSIONS

The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

Investing in antibiotics to alleviate future catastrophic outcomes: What is the value of having an effective antibiotic to mitigate pandemic influenza?

Over 95% of post-mortem samples from the 1918 pandemic, which caused 50 to 100 million deaths, showed bacterial infection complications. The introduction of antibiotics in the 1940s has since reduced the risk of bacterial infections, but growing resistance to antibiotics could increase the toll from future influenza pandemics if secondary bacterial infections are as serious as in 1918, or even if they are less severe. We develop a valuation model of the option to withhold wide use of an antibiotic until significant outbreaks such as pandemic influenza or foodborne diseases are identified. Using real options theory, we derive conditions under which withholding wide use is beneficial, and calculate the option value for influenza pandemic scenarios that lead to secondary infections with a resistant Staphylococcus aureus strain. We find that the value of withholding an effective novel oral antibiotic can be positive and significant unless the pandemic is mild and causes few secondary infections with the resistant strain or if most patients can be treated intravenously. Although the option value is sensitive to parameter uncertainty, our results suggest that further analysis on a case-by-case basis could guide investment in novel agents as well as strategies on how to use them.

Intermediate levels of vaccination coverage may minimize seasonal influenza outbreaks

For most pathogens, vaccination reduces the spread of the infection and total number of cases; thus, public policy usually advocates maximizing vaccination coverage. We use simple mathematical models to explore how this may be different for pathogens, such as influenza, which exhibit strain variation. Our models predict that the total number of seasonal influenza infections is minimized at an intermediate (rather than maximal) level of vaccination, and, somewhat counter-intuitively, further increasing the level of the vaccination coverage may lead to higher number of influenza infections and be detrimental to the public interest. This arises due to the combined effects of: competition between multiple co-circulating strains; limited breadth of protection afforded by the vaccine; and short-term strain-transcending immunity following natural infection. The study highlights the need for better quantification of the components of vaccine efficacy and longevity of strain-transcending cross-immunity in order to generate nuanced recommendations for influenza vaccine coverage levels.

Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modelling

Evidence is mounting that influenza virus interacts with other pathogens colonising or infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. This is particularly true for mathematical modelling studies, which have become critical in public health decision-making. Yet models usually focus on influenza virus acquisition and infection alone, thereby making broad oversimplifications of pathogen ecology. Herein, we report evidence of influenza virus interactions with bacteria and viruses and systematically review the modelling studies that have incorporated interactions. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitidis, respiratory syncytial virus (RSV), human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. The notable exception is the pneumococcus-influenza interaction, for which several recent modelling studies demonstrate the power of dynamic modelling as an approach to test biological hypotheses on interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and possible misinterpretation, and we illustrate the impact of interactions on public health surveillance using simple transmission models. We demonstrate that the development of multipathogen models is essential to assessing the true public health burden of influenza and that it is needed to help improve planning and evaluation of control measures. Finally, we identify the public health, surveillance, modelling, and biological challenges and propose avenues of research for the coming years.

An agent-based model simulation of influenza interactions at the host level: insight into the influenza-related burden of pneumococcal infections

BACKGROUND

Host-level influenza virus-respiratory pathogen interactions are often reported. Although the exact biological mechanisms involved remain unelucidated, secondary bacterial infections are known to account for a large part of the influenza-associated burden, during seasonal and pandemic outbreaks. Those interactions probably impact the microorganisms' transmission dynamics and the influenza public health toll. Mathematical models have been widely used to examine influenza epidemics and the public health impact of control measures. However, most influenza models overlooked interaction phenomena and ignored other co-circulating pathogens.

METHODS

Herein, we describe a novel agent-based model (ABM) of influenza transmission during interaction with another respiratory pathogen. The interacting microorganism can persist in the population year round (endemic type, e.g. respiratory bacteria) or cause short-term annual outbreaks (epidemic type, e.g. winter respiratory viruses). The agent-based framework enables precise formalization of the pathogens' natural histories and complex within-host phenomena. As a case study, this ABM is applied to the well-known influenza virus-pneumococcus interaction, for which several biological mechanisms have been proposed. Different mechanistic hypotheses of interaction are simulated and the resulting virus-induced pneumococcal infection (PI) burden is assessed.

RESULTS

This ABM generates realistic data for both pathogens in terms of weekly incidences of PI cases, carriage rates, epidemic size and epidemic timing. Notably, distinct interaction hypotheses resulted in different transmission patterns and led to wide variations of the associated PI burden. Interaction strength was also of paramount importance: when influenza increased pneumococcus acquisition, 4-27% of the PI burden during the influenza season was attributable to influenza depending on the interaction strength.

CONCLUSIONS

This open-source ABM provides new opportunities to investigate influenza interactions from a theoretical point of view and could easily be extended to other pathogens. It provides a unique framework to generate in silico data for different scenarios and thereby test mechanistic hypotheses.

Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

The progression of an infection within a host determines the ability of a pathogen to transmit to new hosts and to maintain itself in the population. While the general connection between the infection dynamics within a host and the population-level transmission dynamics of pathogens is widely acknowledged, a comprehensive and quantitative understanding that would allow full integration of the two scales is still lacking. Here, we provide a brief discussion of both models and data that have attempted to provide quantitative mappings from within-host infection dynamics to transmission fitness. We present a conceptual framework and provide examples of studies that have taken first steps towards development of a quantitative framework that scales from within-host infections to population-level fitness of different pathogens. We hope to illustrate some general themes, summarize some of the recent advances and-maybe most importantly-discuss gaps in our ability to bridge these scales, and to stimulate future research on this important topic.

Modeling the potential impact of host population survival on the evolution of M. tuberculosis latency

Tuberculosis (TB) is an infectious disease with a peculiar feature: Upon infection with the causative agent, Mycobacterium Tuberculosis (MTB), most hosts enter a latent state during which no transmission of MTB to new hosts occurs. Only a fraction of latently infected hosts develop TB disease and can potentially infect new hosts. At first glance, this seems like a waste of transmission potential and therefore an evolutionary suboptimal strategy for MTB. It might be that the human immune response keeps MTB in check in most hosts, thereby preventing it from achieving its evolutionary optimum. Another possible explanation is that long latency and progression to disease in only a fraction of hosts are evolutionary beneficial to MTB by allowing it to persist better in small host populations. Given that MTB has co-evolved with human hosts for millenia or longer, it likely encountered small host populations for a large share of its evolutionary history and had to evolve strategies of persistence. Here, we use a mathematical model to show that indeed, MTB persistence is optimal for an intermediate duration of latency and level of activation. The predicted optimal level of activation is above the observed value, suggesting that human co-evolution has lead to host immunity, which keeps MTB below its evolutionary optimum.

Can interactions between timing of vaccine-altered influenza pandemic waves and seasonality in influenza complications lead to more severe outcomes?

Vaccination can delay the peak of a pandemic influenza wave by reducing the number of individuals initially susceptible to influenza infection. Emerging evidence indicates that susceptibility to severe secondary bacterial infections following a primary influenza infection may vary seasonally, with peak susceptibility occurring in winter. Taken together, these two observations suggest that vaccinating to prevent a fall pandemic wave might delay it long enough to inadvertently increase influenza infections in winter, when primary influenza infection is more likely to cause severe outcomes. This could potentially cause a net increase in severe outcomes. Most pandemic models implicitly assume that the probability of severe outcomes does not vary seasonally and hence cannot capture this effect. Here we show that the probability of intensive care unit (ICU) admission per influenza infection in the 2009 H1N1 pandemic followed a seasonal pattern. We combine this with an influenza transmission model to investigate conditions under which a vaccination program could inadvertently shift influenza susceptibility to months where the risk of ICU admission due to influenza is higher. We find that vaccination in advance of a fall pandemic wave can actually increase the number of ICU admissions in situations where antigenic drift is sufficiently rapid or where importation of a cross-reactive strain is possible. Moreover, this effect is stronger for vaccination programs that prevent more primary influenza infections. Sensitivity analysis indicates several mechanisms that may cause this effect. We also find that the predicted number of ICU admissions changes dramatically depending on whether the probability of ICU admission varies seasonally, or whether it is held constant. These results suggest that pandemic planning should explore the potential interactions between seasonally varying susceptibility to severe influenza outcomes and the timing of vaccine-altered pandemic influenza waves.

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

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.

A predictive decision-aid methodology for dynamic mitigation of influenza pandemics

In a recent report, the Institute of Medicine has stressed the need for dynamic mitigation strategies for pandemic influenza. In response to the need, we have developed a simulation-based optimization methodology for generating dynamic predictive mitigation strategies for pandemic outbreaks affecting several regions. Our methodology can accommodate varying virus and population dynamics. It progressively allocates a limited budget to procure vaccines and antivirals, capacities for their administration, and resources required to enforce social distancing. The methodology uses measures of morbidity, mortality, and social distancing, which are translated into the costs of lost productivity and medical services. The simulation model was calibrated using historic pandemic data. We illustrate the use of our methodology on a mock outbreak involving over four million people residing in four major population centers in Florida, USA. A sensitivity analysis is presented to estimate the impact of changes in the budget availability and variability of some of the critical parameters of mitigation strategies. The methodology is intended to assist public health policy makers.

Predictive and Reactive Distribution of Vaccines and Antivirals during Cross-Regional Pandemic Outbreaks

As recently pointed out by the Institute of Medicine, the existing pandemic mitigation models lack the dynamic decision support capability. We develop a large-scale simulation-driven optimization model for generating dynamic predictive distribution of vaccines and antivirals over a network of regional pandemic outbreaks. The model incorporates measures of morbidity, mortality, and social distancing, translated into the cost of lost productivity and medical expenses. The performance of the strategy is compared to that of the reactive myopic policy, using a sample outbreak in Fla, USA, with an affected population of over four millions. The comparison is implemented at different levels of vaccine and antiviral availability and administration capacity. Sensitivity analysis is performed to assess the impact of variability of some critical factors on policy performance. The model is intended to support public health policy making for effective distribution of limited mitigation resources.

Prevalence of high-risk indications for influenza vaccine varies by age, race, and income

Estimates of the proportions of the population who are at high risk of influenza complications because of prior health status or who are likely to have decreased vaccination response because of immunocompromising conditions would enhance public health planning and model-based projections. We estimate these proportions and how they vary by population subgroups using national data systems for 2006-2008. The proportion of individuals at increased risk of influenza complications because of health conditions varied 10-fold by age (4.2% of children <2 years to 47% of individuals >64 years). Age-specific prevalence differed substantially by gender, by racial/ethnic groups (with African Americans highest in all age groups) and by income. Individuals living in families with less than 200% of federal poverty level (FPL) were significantly more likely to have at least one of these health conditions, compared to individuals with 400% FPL or more (3-fold greater among <2 and 30% greater among >64 years). Among children, there were significantly elevated proportions in all regions compared to the West. The estimated prevalence of immunocompromising conditions ranged from 0.02% in young children to 6.14% older adults. However, national data on race/ethnicity and income are not available for most immunocompromising conditions, nor is it possible to fully identify the degree of overlap between persons with high-risk health conditions and with immunocompromising conditions. Modifications to current national data collection systems would enhance the value of these data for public health programs and influenza modeling.

Two resource distribution strategies for dynamic mitigation of influenza pandemics

As recently pointed out by the Institute of Medicine, the existing pandemic containment and mitigation models lack the dynamic decision support capabilities. We present two simulation-based optimization models for developing dynamic predictive resource distribution strategies for cross-regional pandemic outbreaks. In both models, the underlying simulation mimics the disease and population dynamics of the affected regions. The quantity-based optimization model generates a progressive allocation of limited quantities of mitigation resources, including vaccines, antiviral, administration capacities, and social distancing enforcement resources. The budget-based optimization model strives instead allocating a total resource budget. Both models seek to minimize the impact of ongoing outbreaks and the expected impact of potential outbreaks. The models incorporate measures of morbidity, mortality, and social distancing, translated into the societal and economic costs of lost productivity and medical expenses. The models were calibrated using historic pandemic data and implemented on a sample outbreak in Florida, with over four million inhabitants. The quantity-based model was found to be inferior to the budget-based model, which was advantageous in its ability to balance the varying relative cost and effectiveness of individual resources. The models are intended to assist public health policy makers in developing effective distribution policies for mitigation of influenza pandemics.