Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it

The long-term effectiveness and cost-effectiveness of public health interventions; how can we model behavior? A review

The effectiveness and cost of a public health intervention is dependent on complex human behaviors, yet health economic models typically make simplified assumptions about behavior, based on little theory or evidence. This paper reviews existing methods across disciplines for incorporating behavior within simulation models, to explore what methods could be used within health economic models and to highlight areas for further research. This may lead to better-informed model predictions. The most promising methods identified which could be used to improve modeling of the causal pathways of behavior-change interventions include econometric analyses, structural equation models, data mining and agent-based modeling; the latter of which has the advantage of being able to incorporate the non-linear, dynamic influences on behavior, including social and spatial networks. Twenty-two studies were identified which quantify behavioral theories within simulation models. These studies highlight the importance of combining individual decision making and interactions with the environment and demonstrate the importance of social norms in determining behavior. However, there are many theoretical and practical limitations of quantifying behavioral theory. Further research is needed about the use of agent-based models for health economic modeling, and the potential use of behavior maintenance theories and data mining.

Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden

Introduction

Current influenza vaccines have limited effectiveness. COVID-19 vaccines using mRNA technology have demonstrated very high efficacy, suggesting that mRNA vaccines could be more effective for influenza. Several such influenza vaccines are in development. FRED, an agent-based modeling platform, was used to estimate the impact of more effective influenza vaccines on seasonal influenza burden.

Methods

Simulations were performed using an agent-based model of influenza that included varying levels of vaccination efficacy (40-95 % effective). In some simulations, level of infectiousness and/or length of infectious period in agents with breakthrough infections was also decreased. Impact of increased and decreased levels of vaccine uptake were also modeled. Outcomes included number of symptomatic influenza cases estimated for the US.

Results

Highly effective vaccines significantly reduced estimated influenza cases in the model. When vaccine efficacy was increased from 40 % to a maximum of 95 %, estimated influenza cases in the US decreased by 43 % to > 99 %. The base simulation (40 % efficacy) resulted in ∼ 28 million total yearly cases in the US, while the most effective vaccine modeled (95 % efficacy) decreased estimated cases to ∼ 22,000.

Discussion

Highly effective vaccines could dramatically reduce influenza burden. Model estimates suggest that even modest increases in vaccine efficacy could dramatically reduce seasonal influenza disease burden.

Dynamics of a stochastic hepatitis B virus transmission model with media coverage and a case study of China

Hepatitis B virus (HBV) infection is a global public health problem and there are 257 million people living with chronic HBV infection throughout the world. In this paper, we investigate the dynamics of a stochastic HBV transmission model with media coverage and saturated incidence rate. Firstly, we prove the existence and uniqueness of positive solution for the stochastic model. Then the condition on the extinction of HBV infection is obtained, which implies that media coverage helps to control the disease spread and the noise intensities on the acute and chronic HBV infection play a key role in disease eradication. Furthermore, we verify that the system has a unique stationary distribution under certain conditions, and the disease will prevail from the biological perspective. Numerical simulations are conducted to illustrate our theoretical results intuitively. As a case study, we fit our model to the available hepatitis B data of mainland China from 2005 to 2021.

SEIR Model to address the impact of face masks amid COVID-19 pandemic

Early in the pandemic of coronavirus disease 2019 (COVID-19), face masks were used extensively by the general public in several Asian countries. The lower transmission rate of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Asian countries compared with Western countries suggested that the wider community use of face masks has the potential to decrease transmission of SARS-CoV-2. A risk assessment model named Susceptible, Exposed, Infectious, Recovered (SEIR) model is used to quantitatively evaluate the potential impact of community face masks on SARS-CoV-2 reproduction number (R₀ ) and peak number of infectious persons. For a simulated population of one million, the model showed a reduction in R₀ of 49% and 50% when 60% and 80% of the population wore masks, respectively. Moreover, we present a modified model that considers the effect of mask-wearing after community vaccination. Interestingly mask-wearing still provided a considerable benefit in lowering the number of infectious individuals. The results of this research are expected to help public health officials in making prompt decisions involving resource allocation and crafting legislation.

Analysis of multi-strain infection of vaccinated and recovered population through epidemic model: Application to COVID-19

In this work, an innovative multi-strain SV EAIR epidemic model is developed for the study of the spread of a multi-strain infectious disease in a population infected by mutations of the disease. The population is assumed to be completely susceptible to n different variants of the disease, and those who are vaccinated and recovered from a specific strain k (k ≤ n) are immune to previous and present strains j = 1, 2, ⋯, k, but can still be infected by newer emerging strains j = k + 1, k + 2, ⋯, n. The model is designed to simulate the emergence and dissemination of viral strains. All the equilibrium points of the system are calculated and the conditions for existence and global stability of these points are investigated and used to answer the question as to whether it is possible for the population to have an endemic with more than one strain. An interesting result that shows that a strain with a reproduction number greater than one can still die out on the long run if a newer emerging strain has a greater reproduction number is verified numerically. The effect of vaccines on the population is also analyzed and a bound for the herd immunity threshold is calculated. The validity of the work done is verified through numerical simulations by applying the proposed model and strategy to analyze the multi-strains of the COVID-19 virus, in particular, the Delta and the Omicron variants, in the United State.

Impact of Low Rates of Influenza on Next-Season Influenza Infections

INTRODUCTION

Interventions to curb the spread of COVID-19 during the 2020-2021 influenza season essentially eliminated influenza during that season. Given waning antibody titers over time, future residual population immunity against influenza will be reduced. The implication for the subsequent 2021-2022 influenza season is unknown.

METHODS

An agent-based model of influenza implemented in the Framework for Reconstructing Epidemiological Dynamics simulation platform was used to estimate cases and hospitalizations over 2 successive influenza seasons. The impact of reduced residual immunity owing to protective measures in the first season was estimated over varying levels of similarity (cross-immunity) between influenza strains over the seasons.

RESULTS

When cross-immunity between first- and second-season strains was low, a decreased first season had limited impact on second-season cases. High levels of cross-immunity resulted in a greater impact on the second season. This impact was modified by the transmissibility of strains in the 2 seasons. The model estimated a possible increase of 13.52%-46.95% in cases relative to that in a normal season when strains have the same transmissibility and 40%-50% cross-immunity in a season after a very low one.

CONCLUSIONS

Given the light 2020-2021 influenza season, cases may increase by as much as 50% in 2021-2022, although the increase could be much less, depending on cross-immunity from past infection and transmissibility of strains. Enhanced vaccine coverage or continued interventions to reduce transmission could reduce this high season. Young children may have a higher risk in 2021-2022 owing to limited exposure to infection in the previous year.

Catch Me if You Can: Superspreading of COVID-19

While significant insights have been gained concerning COVID-19, superspreading of coronaviruses remains a mystery. The vast majority of cases have been linked to a relatively small portion of infected individuals. Yet, the genetic sequence of the virus, severity of disease, and underlying host parameters, such as age, sex, and health conditions, are not clearly driving the superspreading phenomenon. In this commentary we discuss what is known and what is not known about coronavirus superspreader transmission and explore whether characteristics of the virion, the donor, or the environment contribute to this phenomenon.

Multi-agent system collision model to predict the transmission of seasonal influenza in Tokyo from 2014-2015 to 2018-2019 seasons

The objective of this study was to apply the multi-agent system (MAS) collision model to predict seasonal influenza epidemic in Tokyo for 5 seasons (2014-2015 to 2018-2019 seasons). The MAS collision model assumes each individual as a particle inside a square domain. The particles move within the domain and disease transmission occurs in a certain probability when an infected particle collides a susceptible particle. The probability was determined based on the basic reproduction number calculated using the actual data. The simulation started with 1 infected particle and 999 susceptible particles to correspond to the onset of an influenza epidemic. We performed the simulation for 150 days and the calculation was repeated 500 times for each season. To improve the accuracy of the prediction, we selected simulations which have similar incidence number to the actual data in specific weeks. Analysis including all simulations corresponded good to the actual data in 2014-2015 and 2015-2016 seasons. However, the model failed to predict the sharp peak incidence after the New Year Holidays in 2016-2017, 2017-2018, and 2018-2019 seasons. A model which included simulations selected by the week of peak incidence predicted the week and number of peak incidence better than a model including all simulations in all seasons. The reproduction number was also similar to the actual data in this model. In conclusion, the MAS collision model predicted the epidemic curve with good accuracy by selecting the simulations using the actual data without changing the initial parameters such as the basic reproduction number and infection time.

Influenza spread on context-specific networks lifted from interaction-based diary data

Studying the spread of infections is an important tool in limiting or preventing future outbreaks. A first step in understanding disease dynamics is constructing networks that reproduce features of real-world interactions. In this paper, we generate networks that maintain some features of the partial interaction networks that were recorded in an existing diary-based survey at the University of Warwick. To preserve realistic structure in our artificial networks, we use a context-specific approach. In particular, we propose different algorithms for producing larger home, work and social networks. Our networks are able to maintain much of the interaction structure in the original diary-based survey and provide a means of accounting for the interactions of survey participants with non-participants. Simulating a discrete susceptible-infected-recovered model on the full network produces epidemic behaviour which shares characteristics with previous influenza seasons. Our approach allows us to explore how disease transmission and dynamic responses to infection differ depending on interaction context. We find that, while social interactions may be the first to be reduced after influenza infection, limiting work and school encounters may be significantly more effective in controlling the overall severity of the epidemic.

The relationship between cyclonic weather regimes and seasonal influenza over the Eastern Mediterranean

The prediction of the occurrence of infectious diseases is of crucial importance for public health, as clearly seen in the ongoing COVID-19 pandemic. Here, we analyze the relationship between the occurrence of a winter low-pressure weather regime - Cyprus Lows - and the seasonal Influenza in the Eastern Mediterranean. We find that the weekly occurrence of Cyprus Lows is significantly correlated with clinical seasonal Influenza in Israel in recent years (R = 0.91; p < .05). This result remains robust when considering a complementary analysis based on Google Trends data for Israel, the Palestinian Authority and Jordan. The weekly occurrence of Cyprus Lows precedes the onset and maximum of Influenza occurrence by about one to two weeks (R = 0.88; p < .05 for the maximum occurrence), and closely follows their timing in eight out of ten years (2008-2017). Since weather regimes such as Cyprus Lows are more robustly predicted in weather and climate models than individual climate variables, we conclude that the weather regime approach can be used to develop tools for estimating the compatibility of the transmission environment for Influenza occurrence in a warming world. Furthermore, this approach may be applied to other regions and climate sensitive diseases. This study is a new cross-border inter-disciplinary regional collaboration for appropriate adaptation to climate change in the Eastern Mediterranean.

Effectiveness of quadrivalent influenza vaccination in the first year of a funded childhood program in Queensland, Australia, 2018

BACKGROUND

Following high influenza activity in 2017, the state of Queensland, Australia, funded a quadrivalent inactivated influenza vaccination program for children aged 6 months to <5 years in 2018. We calculated influenza vaccine effectiveness (VE) among children eligible for this program.

METHODS

A matched case-control study was conducted. Cases were identified using Queensland 2018 influenza notification data among children age-eligible for funded vaccination. Controls were drawn from Australian Immunisation Register records of Queensland resident children age-eligible for funded influenza vaccine. Up to 10 controls per case were matched for location and birthdate. First dose vaccination was valid if received ≥14 days prior to specimen collection; a second dose was valid if received ≥28 days after first dose receipt. VE was calculated for vaccine doses and adherence to national recommendations for two doses in the first season (schedule completeness) and adjusted (VE_adj) for sex and First Nations status.

RESULTS

There were 1,125 cases and 10,645 matched controls analysed. Overall VE_adj against laboratory-confirmed influenza was 51% (95% confidence interval (CI) 41-60). VE_adj was 60% (95% CI 46-70) for children who received two doses in 2018, and 60% (95% CI 48-69) for children vaccinated appropriately according to schedule completeness. VE increased with age.

CONCLUSIONS

Moderate vaccine effectiveness was observed for children eligible for the funded program in Queensland in 2018, adding to the sparse evidence for influenza vaccine use in Australian children. Adhering to the national first season two dose schedule for influenza vaccine receipt in children ensures maximum protection.

Influenza surveillance: determining the epidemic threshold for influenza by using the Moving Epidemic Method (MEM), Montenegro, 2010/11 to 2017/18 influenza seasons

Background: In 2009, an improved influenza surveillance system was implemented and weekly reporting to the World Health Organization on influenza-like illness (ILI) began. The goals of the surveillance system are to monitor and analyse the intensity of influenza activity, to provide timely information about circulating strains and to help in establishing preventive and control measures. In addition, the system is useful for comparative analysis of influenza data from Montenegro with other countries.

Aim: We aimed to evaluate the performance and usefulness of the Moving Epidemic Method (MEM), for use in the influenza surveillance system in Montenegro.

Methods: Historical ILI data from 2010/11 to 2017/18 influenza seasons were modelled with MEM. Epidemic threshold for Montenegro 2017/18 season was calculated using incidence rates from 2010/11–2016/17 influenza seasons.

Results: Pre-epidemic ILI threshold per 100,000 population was 19.23, while the post-epidemic threshold was 17.55. Using MEM, we identified an epidemic of 10 weeks’ duration. The sensitivity of the MEM epidemic threshold in Montenegro was 89% and the warning signal specificity was 99%.

Conclusions: Our study marks the first attempt to determine the pre/post-epidemic threshold values for the epidemic period in Montenegro. The findings will allow a more detailed examination of the influenza-related epidemiological situation, timely detection of epidemic and contribute to the development of more efficient measures for disease prevention and control aimed at reducing the influenza-associated morbidity and mortality.

Stochastic modeling of influenza spread dynamics with recurrences

We present results of a study of a simple, stochastic, agent-based model of influenza A infection, simulating its dynamics over the course of one flu season. Building on an early work of Bartlett, we define a model with a limited number of parameters and rates that have clear epidemiological interpretation and can be constrained by data. We demonstrate the occurrence of recurrent behavior in the infected number [more than one peak in a season], which is observed in data, in our simulations for populations consisting of cohorts with strong intra- and weak inter-cohort transmissibility. We examine the dependence of the results on epidemiological and population characteristics by investigating their dependence on a range of parameter values. Finally, we study infection with two strains of influenza, inspired by observations, and show a counter-intuitive result for the effect of inoculation against the strain that leads to the first wave of infection.

Misinformation making a disease outbreak worse: outcomes compared for influenza, monkeypox, and norovirus

Health misinformation can exacerbate infectious disease outbreaks. Especially pernicious advice could be classified as "fake news": manufactured with no respect for accuracy and often integrated with emotive or conspiracy-framed narratives. We built an agent-based model that simulated separate but linked circulating contagious disease and sharing of health advice (classified as useful or harmful). Such advice has potential to influence human risk-taking behavior and therefore the risk of acquiring infection, especially as people are more likely in observed social networks to share bad advice. We test strategies proposed in the recent literature for countering misinformation. Reducing harmful advice from 50% to 40% of circulating information, or making at least 20% of the population unable to share or believe harmful advice, mitigated the influence of bad advice in the disease outbreak outcomes. How feasible it is to try to make people "immune" to misinformation or control spread of harmful advice should be explored.

An agent-based model about the effects of fake news on a norovirus outbreak

BACKGROUND

Concern about health misinformation is longstanding, especially on the Internet.

METHODS

Using agent-based models, we considered the effects of such misinformation on a norovirus outbreak, and some methods for countering the possible impacts of "good" and "bad" health advice. The work explicitly models spread of physical disease and information (both online and offline) as two separate but interacting processes. The models have multiple stochastic elements; repeat model runs were made to identify parameter values that most consistently produced the desired target baseline scenario. Next, parameters were found that most consistently led to a scenario when outbreak severity was clearly made worse by circulating poor quality disease prevention advice. Strategies to counter "fake" health news were tested.

RESULTS

Reducing bad advice to 30% of total information or making at least 30% of people fully resistant to believing in and sharing bad health advice were effective thresholds to counteract the negative impacts of bad advice during a norovirus outbreak.

CONCLUSION

How feasible it is to achieve these targets within communication networks (online and offline) should be explored.

Mathematical Analysis of Influenza A Dynamics in the Emergence of Drug Resistance

Every year, influenza causes high morbidity and mortality especially among the immunocompromised persons worldwide. The emergence of drug resistance has been a major challenge in curbing the spread of influenza. In this paper, a mathematical model is formulated and used to analyze the transmission dynamics of influenza A virus having incorporated the aspect of drug resistance. The qualitative analysis of the model is given in terms of the control reproduction number, Rc. The model equilibria are computed and stability analysis carried out. The model is found to exhibit backward bifurcation prompting the need to lower Rc to a critical value Rc∗ for effective disease control. Sensitivity analysis results reveal that vaccine efficacy is the parameter with the most control over the spread of influenza. Numerical simulations reveal that despite vaccination reducing the reproduction number below unity, influenza still persists in the population. Hence, it is essential, in addition to vaccination, to apply other strategies to curb the spread of influenza.

Infection prevention behaviour and infectious disease modelling: a review of the literature and recommendations for the future

Background

Given the importance of person to person transmission in the spread of infectious diseases, it is critically important to ensure that human behaviour with respect to infection prevention is appropriately represented within infectious disease models. This paper presents a large scale scoping review regarding the incorporation of infection prevention behaviour in infectious disease models. The outcomes of this review are contextualised within the psychological literature concerning health behaviour and behaviour change, resulting in a series of key recommendations for the incorporation of human behaviour in future infectious disease models.

Methods

The search strategy focused on terms relating to behaviour, infectious disease and mathematical modelling. The selection criteria were developed iteratively to focus on original research articles that present an infectious disease model with human-human spread, in which individuals’ self-protective health behaviour varied endogenously within the model. Data extracted included: the behaviour that is modelled; how this behaviour is modelled; any theoretical background for the modelling of behaviour, and; any behavioural data used to parameterise the models.

Results

Forty-two papers from an initial total of 2987 were retained for inclusion in the final review. All of these papers were published between 2002 and 2015. Many of the included papers employed a multiple, linked models to incorporate infection prevention behaviour. Both cognitive constructs (e.g., perceived risk) and, to a lesser extent, social constructs (e.g., social norms) were identified in the included papers. However, only five papers made explicit reference to psychological health behaviour change theories. Finally, just under half of the included papers incorporated behavioural data in their modelling.

Conclusions

By contextualising the review outcomes within the psychological literature on health behaviour and behaviour change, three key recommendations for future behavioural modelling are made. First, modellers should consult with the psychological literature on health behaviour/ behaviour change when developing new models. Second, modellers interested in exploring the relationship between behaviour and disease spread should draw on social psychological literature to increase the complexity of the social world represented within infectious disease models. Finally, greater use of context-specific behavioural data (e.g., survey data, observational data) is recommended to parameterise models.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5223-1) contains supplementary material, which is available to authorized users.

Mathematical and Computational Modeling in Complex Biological Systems

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

Influence of Media on Seasonal Influenza Epidemic Curves

BACK GROUND

Theoretical investigations predicting the epidemic curves of seasonal influenza have been demonstrated so far; however, there is little empirical research using ever accumulated epidemic curves. The effects of vaccine coverage and information distribution on influenza epidemics were evaluated.

MATERIALS AND METHODS

Four indices for epidemics (i.e., onset-peak duration, onset-end duration, ratio of the onset-peak duration to onset-end duration and steepness of epidemic curves) were defined, and the correlations between these indices and anti-flu drug prescription dose, vaccine coverage, the volume of media and search trend on influenza through internet were analyzed. Epidemiological data on seasonal influenza epidemics from 2002/2003 to 2013/2014 excluding 2009/2010 season were collected from National Institute of Infectious Diseases of Japan.

RESULTS

The onset-peak duration and its ratio to onset-end duration correlated inversely with the volume of anti-flu drug prescription. Onset-peak duration correlated positively with media information volume on influenza. The steepness of the epidemic curve, and anti-flu drug prescription dose inversely correlated with the volume of media information. Pre-epidemic search trend and media volume on influenza correlated with the vaccine coverage in the season. Vaccine coverage had no strong effect on epidemic curve.

CONCLUSION

Education through media has an effect on the epidemic curve of seasonal influenza.

Relevance of workplace social mixing during influenza pandemics: an experimental modelling study of workplace cultures

Workplaces are one of the most important regular meeting places in society. The aim of this study was to use simulation experiments to examine the impact of different workplace cultures on influenza dissemination during pandemics. The impact is investigated by experiments with defined social-mixing patterns at workplaces using semi-virtual models based on authentic sociodemographic and geographical data from a North European community (population 136 000). A simulated pandemic outbreak was found to affect 33% of the total population in the community with the reference academic-creative workplace culture; virus transmission at the workplace accounted for 10·6% of the cases. A model with a prevailing industrial-administrative workplace culture generated 11% lower incidence than the reference model, while the model with a self-employed workplace culture (also corresponding to a hypothetical scenario with all workplaces closed) produced 20% fewer cases. The model representing an academic-creative workplace culture with restricted workplace interaction generated 12% lower cumulative incidence compared to the reference model. The results display important theoretical associations between workplace social-mixing cultures and community-level incidence rates during influenza pandemics. Social interaction patterns at workplaces should be taken into consideration when analysing virus transmission patterns during influenza pandemics.

A Model for Coupled Outbreaks Contained by Behavior Change

Large epidemics such as the recent Ebola crisis in West Africa occur when local efforts to contain outbreaks fail to overcome the probabilistic onward transmission to new locations. As a result, there may be large differences in total epidemic size from similar initial conditions. This work seeks to determine the extent to which the effects of behavior changes and metapopulation coupling on epidemic size can be characterized. While mathematical models have been developed to study local containment by social distancing, intervention and other behavior changes, their connection to larger-scale transmission is relatively underdeveloped. We make use of the assumption that behavior changes limit local transmission before susceptible depletion to develop a time-varying birth-death process capturing the dynamic decrease of the transmission rate associated with behavior changes. We derive an expression for the mean outbreak size of this model and show that the distribution of outbreak sizes is approximately geometric. This allows a probabilistic extension whereby infected individuals may initiate new outbreaks. From this model we characterize the overall epidemic size as a function of the behavior change rate and the probability that an infected individual starts a new outbreak. We find good agreement between the analytical results and stochastic simulations leading to novel findings including critical learning rates that demarcate large and small epidemic sizes.