Influenza pandemic intervention planning using InfluSim: pharmaceutical and non- pharmaceutical interventions

Americans' Trust in COVID-19 Information from Governmental Sources in the Trump Era: Individuals' Adoption of Preventive Measures, and Health Implications

This study analyzes differences among Americans in their trust in COVID-19 information from governmental sources and how trust is associated with personal adoption of preventative measures under the Trump administration. Based on our analysis of data from a nationally representative survey conducted in October 2020 (effective sample size after weighting = 2615), we find that Americans in general have more trust in COVID-19 information from state/local governments than from the federal government. Variables such as age, party affiliation, religiosity, and race are significantly associated with Americans' trust or lack of trust in COVID-19 information from governmental sources. During the study period, Republicans had more trust in the federal government as a COVID-19 information source than Democrats did, while Democrats had more trust in state/local governments. African Americans had the least trust in the federal and state/local governments as COVID-19 information sources, while Asian Americans had the most trust in both institutions. Trust in the state/local governments as COVID-19 information sources was positively associated with physical distancing and mask-wearing while trust in the federal government as a COVID-19 information source was negatively associated with physical distancing and mask-wearing, suggesting the distinctive roles that state/local governments and the federal government played in mobilizing Americans to adopt preventive measures.

Critical policies disparity of the first and second waves of COVID-19 in the United Kingdom

Objective

This study aims to compare the differences in COVID-19 prevention and control policies adopted by the United Kingdom (UK) during the first wave (31 January 2020 to 6 September 2020) and the second wave (7 September 2020 to 12 April 2021), and analyze the effectiveness of the policies, so as to provide empirical experience for the prevention and control of COVID-19.

Methods We systematically summarized the pandemic prevention and control policies of the UK from official websites and government documents, collated the epidemiological data from 31 January 2020 to 12 April 2021, and analyzed the effectiveness of the two waves of pandemic prevention and control policies.

Results

The main pandemic prevention and control policies adopted by the UK include surveillance and testing measures, border control measures, community and social measures, blockade measures, health care measures, COVID-19 vaccination measure, and relaxed pandemic prevention measures. The new cases per day curve showed only one peak in the first wave and two peaks in the second wave. The number of new cases per million in the second wave was much higher than that in the first wave, and the curve fluctuated less. The difference between mortality per million was small, and the curve fluctuated widely.

Conclusion

During the first and second waves of COVID-19, the UK implemented three lockdowns and managed to slow the spread of the pandemic. The UK’s experience in mitigating the second wave proves that advancing COVID-19 vaccination needs to be accompanied by ongoing implementation of non-pharmacological interventions to reduce the transmission rate of infection. And a stricter lockdown ensures that the containment effect is maximized during the lockdown period. In addition, these three lockdowns featured distinct mitigation strategies and the UK’s response to COVID-19 is mitigation strategy that reduce new cases in the short term, but with the risk of the pandemic rebound.

The Prevalence of ''Food Addiction'' during the COVID-19 Pandemic Measured Using the Yale Food Addiction Scale 2.0 (YFAS 2.0) among the Adult Population of Poland

The announcement of the coronavirus pandemic by the World Health Organization (WHO), ongoing restrictions and isolation led to a break with the daily routine, and suspension of social contacts, but also imposed new challenges on the population related to maintaining healthy eating habits. The purpose of the study was to assess the prevalence of “food addiction” (FA) during the COVID-19 pandemic in Poland in relation to several variables including depression. The method of analysis was a questionnaire containing original questions and the Yale Food Addiction Scale 2.0 (YFAS). A total of 1022 Polish residents aged 18–75 participated in the study (N = 1022; 93.7% women, 6.3% men). The prevalence of FA during the COVID-19 pandemic measured with the YFAS 2.0 scale was 14.1%. The average weight gain during the pandemic in 39% of respondents was 6.53 kg. Along with the increase in the value of the BMI index, the intensity of “food addiction” increased in the study group. People with depression had statistically significantly more FA symptoms than healthy people. This work may motivate future research to evaluate the association and potential overlap of “food addiction” and problem eating behaviors during the pandemic and the obesity problem.

COVID-19 and the Change in Lifestyle: Bodyweight, Time Allocation, and Food Choices

We analyze the dynamic changes in individuals' lifestyle during the COVID-19 outbreak and recovery period through a survey of 1061 Chinese households. Specifically, we are interested in individuals' bodyweight, time allocation and food choices. We find that COVID-19 is associated with weight gain, less time spent on exercise and more time on entertainment. The proportion of online food purchase and snack purchases also shows an upward trend. This study provides useful implications on the impact of COVID-19 and its associated lockdowns on individuals' lifestyle and offers foresights for countries in different stages of the pandemic. It explains how encouraging exercise, managing new food purchase venues, and reducing the intake of unhealthy food such as snacks may also need to be considered in dealing with the aftermath of COVID-19.

Design of non-pharmaceutical intervention strategies for pandemic influenza outbreaks

BACKGROUND

As seen during past pandemic influenza outbreaks, pharmaceutical interventions (PHIs) with vaccines and antivirals are the most effective methods of mitigation. However, availability of PHIs is unlikely to be adequate during the early stages of a pandemic. Hence, for early mitigation and possible containment, non-pharmaceutical interventions (NPIs) offer a viable alternative. Also, NPIs may be the only available interventions for most underdeveloped countries. In this paper we present a comprehensive methodology for design of effective NPI strategies.

METHODS

We develop a statistical ANOVA-based design approach that uses a detailed agent-based simulation as an underlying model. The design approach obtains the marginal effect of the characteristic parameters of NPIs, social behavior, and their interactions on various pandemic outcome measures including total number of contacts, infections, and deaths. We use the marginal effects to establish regression equations for the outcome measures, which are optimized to obtain NPI strategies. Efficacy of the NPI strategies designed using our methodology is demonstrated using simulated pandemic influenza outbreaks with different levels of virus transmissibility.

RESULTS

Our methodology was able to design effective NPI strategies, which were able to contain outbreaks by reducing infection attack rates (IAR) to below 10% in low and medium virus transmissibility scenarios with 33% and 50% IAR, respectively. The level of reduction in the high transmissibility scenario (with 65% IAR) was also significant. As noted in the published literature, we also found school closure to be the single most effective intervention among all NPIs.

CONCLUSIONS

If harnessed effectively, NPIs offer a significant potential for mitigation of pandemic influenza outbreaks. The methodology presented here fills a gap in the literature, which, though replete with models on NPI strategy evaluation, lacks a treatise on optimal strategy design.

Sensitivity Analysis of the Parameters of Korea's Pandemic Influenza Preparedness Plan

OBJECTIVES

Our aim was to evaluate Korea's Pandemic Influenza Preparedness Plan.

METHODS

We conducted a sensitivity analysis on the expected number of outpatients and hospital bed occupancy, with 1,000,000 parameter combinations, in a situation of pandemic influenza, using the mathematical simulation program InfluSim.

RESULTS

Given the available resources in Korea, antiviral treatment and social distancing must be combined to reduce the number of outpatients and hospitalizations sufficiently; any single intervention is not enough. The antiviral stockpile of 4-6% is sufficient for the expected eligible number of cases to be treated. However, the eligible number assumed (30% for severe cases and 26% for extremely severe cases) is very low compared to the corresponding number in European countries, where up to 90% of the population are assumed to be eligible for antiviral treatment.

CONCLUSIONS

A combination of antiviral treatment and social distancing can mitigate a pandemic, but will only bring it under control for the most optimistic parameter combinations.

Potential use of school absenteeism record for disease surveillance in developing countries, case study in rural Cambodia

BACKGROUND

Disease surveillance allows prospective monitoring of patterns in disease incidence in the general community, specific institutions (e.g. hospitals, elderly care homes), and other important population subgroups. Surveillance activities are now routinely conducted in many developed countries and in certain easy-to-reach areas of the developing ones. However due to limited health resources, population in rural area that consisted of the most the vulnerable groups are not under surveillance. Cheaper alternative ways for disease surveillance were needed in resource-limited settings.

METHODS AND FINDINGS

In this study, a syndromic surveillance system using disease specific absenteeism rates was established in 47 pre-schools with 1,417 students 3-6 y of age in a rural area of Kampot province, Cambodia. School absenteeism data were collected via short message service. Data collected between 1st January and 31st December 2012 was used for system evaluation for future potential use in larger scale. The system appeared to be feasible and acceptable in the rural study setting. Moderate correlation was found between rates of school absenteeism due to illness and the reference data on rates of attendance at health centers in persons <16 y (maximum cross-correlation coefficient = 0.231 at lag = -1 week).

CONCLUSIONS

School absenteeism data is pre-existing, easily accessible and requires minimum time and resources after initial development, and our results suggest that this system may be able to provide complementary data for disease surveillance, especially in resource limited settings where there is very little information on illnesses in the community and traditional surveillance systems are difficult to implement. An important next step is to validate the syndromic data with other forms of surveillance including laboratory data.

Optimal strategies of social distancing and vaccination against seasonal influenza

Optimal control strategies for controlling seasonal influenza transmission in the US are of high interest, because of the significant epidemiological and economic burden of influenza. To evaluate optimal strategies of vaccination and social distancing, we used an age-structured dynamic model of seasonal influenza. We applied optimal control theory to identify the best way of reducing morbidity and mortality at a minimal cost. In combination with the Pontryagins maximum principle, we calculated time-dependent optimal policies of vaccination and social distancing to minimize the epidemiological and economic burden associated with seasonal influenza. We computed optimal age-specific intervention strategies and analyze them under various costs of interventions and disease transmissibility. Our results show that combined strategies have a stronger impact on the reduction of the final epidemic size. Our results also suggest that the optimal vaccination can be achieved by allocating most vaccines to preschool-age children (age under five) followed by young adults (age 20-39) and school age children (age 6-19). We find that the optimal vaccination rates for all age groups are highest at the beginning of the outbreak, requiring intense effort at the early phase of an epidemic. On the other hand, optimal social distancing of clinical cases tends to last the entire duration of an outbreak, and its intensity is relatively equal for all age groups. Furthermore, with higher transmissibility of the influenza virus (i.e. higher R0), the optimal control strategy needs to include more efforts to increase vaccination rates rather than efforts to encourage social distancing. Taken together, public health agencies need to consider both the transmissibility of the virus and ways to encourage early vaccination as well as voluntary social distancing of symptomatic cases in order to determine optimal intervention strategies against seasonal influenza.

Response to 2009 pandemic influenza A H1N1 among public schools of Georgia, United States--fall 2009

BACKGROUND

Little is known about the extent of implementation or the effectiveness of the Centers for Disease Control and Prevention's (CDC) recommended non-pharmaceutical interventions (NPIs) in schools to control the spread of 2009 pandemic influenza A H1N1 (pH1N1).

METHODS

A web-based, cross-sectional survey of all public K-12 schools in Georgia, USA was conducted about preparedness and response to pH1N1, and absenteeism and respiratory illness. Schools that reported ≥10% absenteeism and at least two times the normal level of respiratory illness in the same week were designated as having experienced significant respiratory illness and absenteeism (SRIA) during that week.

RESULTS

Of 2248 schools surveyed, 704 (31.3%) provided sufficient data to include in our analysis. Participating schools were spread throughout Georgia, USA and were similar to non-participating schools. Of 704 schools, 160 (22.7%) reported at least 1 week of SRIA. Most schools reported implementing the CDC recommendations for the control of pH1N1, and only two schools reported canceling or postponing activities. Schools that communicated with parents about influenza in the summer, had shorter school days, and were located in urban areas were less likely to experience SRIA.

CONCLUSIONS

Most Georgia schools in the United States adopted the CDC recommendations for pH1N1 mitigation and few disruptions of school activities were reported. Early and timely communication with parents, as well as shorter school days, may have been effective in limiting the effect of pH1N1 on schools.

Evaluating temporal factors in combined interventions of workforce shift and school closure for mitigating the spread of influenza

Background

It is believed that combined interventions may be more effective than individual interventions in mitigating epidemic. However there is a lack of quantitative studies on performance of the combination of individual interventions under different temporal settings.

Methodology/Principal Findings

To better understand the problem, we develop an individual-based simulation model running on top of contact networks based on real-life contact data in Singapore. We model and evaluate the spread of influenza epidemic with intervention strategies of workforce shift and its combination with school closure, and examine the impacts of temporal factors, namely the trigger threshold and the duration of an intervention. By comparing simulation results for intervention scenarios with different temporal factors, we find that combined interventions do not always outperform individual interventions and are more effective only when the duration is longer than 6 weeks or school closure is triggered at the 5% threshold; combined interventions may be more effective if school closure starts first when the duration is less than 4 weeks or workforce shift starts first when the duration is longer than 4 weeks.

Conclusions/Significance

We therefore conclude that identifying the appropriate timing configuration is crucial for achieving optimal or near optimal performance in mitigating the spread of influenza epidemic. The results of this study are useful to policy makers in deliberating and planning individual and combined interventions.

Dynamics of Single-City Influenza with Seasonal Forcing: From Regularity to Chaos

Seasonal and epidemic influenza continue to cause concern, reinforced by connections between human and avian influenza, and H1N1 swine influenza. Models summarize ideas about disease mechanisms, help understand contributions of different processes, and explore interventions. A compartment model of single-city influenza is developed. It is mechanism-based on lower-level studies, rather than focussing on predictions. It is deterministic, without non-disease-status stratification. Categories represented are susceptible, infected, sick, hospitalized, asymptomatic, dead from flu, recovered, and one in which recovered individuals lose immunity. Most categories are represented with sequential pools with first-order kinetics, giving gamma-function progressions with realistic dynamics. A virus compartment allows representation of environmental effects on virus lifetime, thence affecting reproductive ratio. The model's behaviour is explored. It is validated without significant tuning against data on a school outbreak. Seasonal forcing causes a variety of regular and chaotic behaviours, some being typical of seasonal and epidemic flu. It is suggested that models use sequential stages for appropriate disease categories because this is biologically realistic, and authentic dynamics is required if predictions are to be credible. Seasonality is important indicating that control measures might usefully take account of expected weather.

Pandemic influenza and health system resource gaps in Bali: an analysis through a resource transmission dynamics model

The failure to contain pandemic influenza A(H1N1) 2009 in Mexico has shifted global attention from containment to mitigation. Limited surveillance and reporting have, however, prevented detailed assessment of mitigation during the pandemic, particularly in low- and middle-income countries. To assess pandemic influenza case management capabilities in a resource-limited setting, the authors used a health system questionnaire and density-dependent, deterministic transmission model for Bali, Indonesia, determining resource gaps. The majority of health resources were focused in and around the provincial capital, Denpasar; however, gaps are found in every district for nursing staff, surgical masks, and N95 masks. A relatively low pathogenicity pandemic influenza virus would see an overall surplus for physicians, antivirals, and antimicrobials; however, a more pathogenic virus would lead to gaps in every resource except antimicrobials. Resources could be allocated more evenly across Bali. These, however, are in short supply universally and therefore redistribution would not fill resource gaps.

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread.

METHODS

We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns.

RESULTS

The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east.

CONCLUSIONS

We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficial.

Combination strategies for pandemic influenza response - a systematic review of mathematical modeling studies

BACKGROUND

Individual strategies in pandemic preparedness plans may not reduce the impact of an influenza pandemic.

METHODS

We searched modeling publications through PubMed and associated references from 1990 to 30 September 2009. Inclusion criteria were modeling papers quantifying the effectiveness of combination strategies, both pharmaceutical and non-pharmaceutical.

RESULTS

Nineteen modeling papers on combination strategies were selected. Four studies examined combination strategies on a global scale, 14 on single countries, and one on a small community. Stochastic individual-based modeling was used in nine studies, stochastic meta-population modeling in five, and deterministic compartmental modeling in another five. As part of combination strategies, vaccination was explored in eight studies, antiviral prophylaxis and/or treatment in 16, area or household quarantine in eight, case isolation in six, social distancing measures in 10 and air travel restriction in six studies. Two studies suggested a high probability of successful influenza epicenter containment with combination strategies under favorable conditions. During a pandemic, combination strategies delayed spread, reduced overall number of cases, and delayed and reduced peak attack rate more than individual strategies. Combination strategies remained effective at high reproductive numbers compared with single strategy. Global cooperative strategies, including redistribution of antiviral drugs, were effective in reducing the global impact and attack rates of pandemic influenza.

CONCLUSION

Combination strategies increase the effectiveness of individual strategies. They include pharmaceutical (antiviral agents, antibiotics and vaccines) and non-pharmaceutical interventions (case isolation, quarantine, personal hygiene measures, social distancing and travel restriction). Local epidemiological and modeling studies are needed to validate efficacy and feasibility.

Adaptive vaccination strategies to mitigate pandemic influenza: Mexico as a case study

BACKGROUND

We explore vaccination strategies against pandemic influenza in Mexico using an age-structured transmission model calibrated against local epidemiological data from the Spring 2009 A(H1N1) pandemic.

METHODS AND FINDINGS

In the context of limited vaccine supplies, we evaluate age-targeted allocation strategies that either prioritize youngest children and persons over 65 years of age, as for seasonal influenza, or adaptively prioritize age groups based on the age patterns of hospitalization and death monitored in real-time during the early stages of the pandemic. Overall the adaptive vaccination strategy outperformed the seasonal influenza vaccination allocation strategy for a wide range of disease and vaccine coverage parameters.

CONCLUSIONS

This modeling approach could inform policies for Mexico and other countries with similar demographic features and vaccine resources issues, with regard to the mitigation of the S-OIV pandemic. We also discuss logistical issues associated with the implementation of adaptive vaccination strategies in the context of past and future influenza pandemics.

Impact of tabletop exercises on participants' knowledge of and confidence in legal authorities for infectious disease emergencies

OBJECTIVE

Legal preparedness is a critical component of comprehensive public health preparedness for public health emergencies. The scope of this study was to assess the usefulness of combining didactic sessions with a tabletop exercise as educational tools in legal preparedness, to assess the impact of the exercise on the participants' level of confidence about the legal preparedness of a public health system, and to identify legal issue areas in need of further improvement.

METHODS

The exercise scenario and the pre- and postexercise evaluation were designed to assess knowledge gained and level of confidence in declaration of emergencies, isolation and quarantine, restrictions (including curfew) on the movement of people, closure of public places, and mass prophylaxis, and to identify legal preparedness areas most in need of further improvement at the system level. Fisher exact test and paired t test were performed to compare pre- and postexercise results.

RESULTS

Our analysis shows that a combination of didactic teaching and experiential learning through a tabletop exercise regarding legal preparedness for infectious disease emergencies can be effective in both imparting perceived knowledge to participants and gathering information about sufficiency of authorities and existence of gaps.

CONCLUSIONS

The exercise provided a valuable forum to judge the adequacy of legal authorities, policies, and procedures for dealing with pandemic influenza at the state and local levels in Massachusetts. In general, participants were more confident about the availability and sufficiency of legal authorities than they were about policies and procedures for implementing them. Participants were also more likely to report the need for improvement in authorities, policies, and procedures in the private sector and at the local level than at the state level.

Adaptive vaccination strategies to mitigate pandemic influenza: Mexico as a case study

In this modeling work, we explore the effectiveness of various age-targeted vaccination strategies to mitigate hospitalization and mortality from pandemic influenza, assuming limited vaccine supplies. We propose a novel adaptive vaccination strategy in which vaccination is initiated during the outbreak and priority groups are identified based on real-time epidemiological data monitoring age-specific risk of hospitalization and death. We apply this strategy to detailed epidemiological and demographic data collected during the recent swine A/H1N1 outbreak in Mexico. We show that the adaptive strategy targeting age groups 6-59 years is the most effective in reducing hospitalizations and deaths, as compared with a more traditional strategy used in the control of seasonal influenza and targeting children under 5 and seniors over 65. Results are robust to a number of assumptions and could provide guidance to many nations facing a recrudescence of A/H1N1v pandemic activity in the fall and likely vaccine shortages.

Lay perceptions of the pandemic influenza threat

A national survey on the public perception of the pandemic threat was conducted in France during the summer of 2008. Although the majority of the respondents displayed beliefs and attitudes toward the pandemic threat that could be considered as adaptive in the face of an outbreak, our results suggest that there are identifiable needs for public information about the transmission and prevention of the disease.

Antiviral prophylaxis during pandemic influenza may increase drug resistance

BACKGROUND

Neuraminidase inhibitors (NI) and social distancing play a major role in plans to mitigate future influenza pandemics.

METHODS

Using the freely available program InfluSim, the authors examine to what extent NI-treatment and prophylaxis promote the occurrence and transmission of a NI resistant strain.

RESULTS

Under a basic reproduction number of R0 = 2.5, a NI resistant strain can only spread if its transmissibility (fitness) is at least 40% of the fitness of the drug-sensitive strain. Although NI drug resistance may emerge in treated patients in such a late state of their disease that passing on the newly developed resistant viruses is unlikely, resistant strains quickly become highly prevalent in the population if their fitness is high. Antiviral prophylaxis further increases the pressure on the drug-sensitive strain and favors the spread of resistant infections. The authors show scenarios where pre-exposure antiviral prophylaxis even increases the number of influenza cases and deaths.

CONCLUSION

If the fitness of a NI resistant pandemic strain is high, any use of prophylaxis may increase the number of hospitalizations and deaths in the population. The use of neuraminidase inhibitors should be restricted to the treatment of cases whereas prophylaxis should be reduced to an absolute minimum in that case.

Modeling the effects of drug resistant influenza virus in a pandemic

Neuraminidase inhibitors (NI) play a major role in plans to mitigate future influenza pandemics. Modeling studies suggested that a pandemic may be contained at the source by early treatment and prophylaxis with antiviral drugs. Here, we examine the influence of NI resistant influenza strains on an influenza pandemic. We extend the freely available deterministic simulation program InfluSim to incorporate importations of resistant infections and the emergence of de novo resistance. The epidemic with the fully drug sensitive strain leads to a cumulative number of 19,500 outpatients and 258 hospitalizations, respectively, per 100,000 inhabitants. Development of de novo resistance alone increases the total number of outpatients by about 6% and hospitalizations by about 21%. If a resistant infection is introduced into the population after three weeks, the outcome dramatically deteriorates. Wide-spread use of NI treatment makes it highly likely that the resistant strain will spread if its fitness is high. This situation is further aggravated if a resistant virus is imported into a country in the early phase of an outbreak. As NI-resistant influenza infections with high fitness and pathogenicity have just been observed, the emergence of drug resistance in treated populations and the transmission of drug resistant strains is an important public health concern for seasonal and pandemic influenza.

Antiviral resistance and the control of pandemic influenza: the roles of stochasticity, evolution and model details

Antiviral drugs, most notably the neuraminidase inhibitors, are an important component of control strategies aimed to prevent or limit any future influenza pandemic. The potential large-scale use of antiviral drugs brings with it the danger of drug resistance evolution. A number of recent studies have shown that the emergence of drug-resistant influenza could undermine the usefulness of antiviral drugs for the control of an epidemic or pandemic outbreak. While these studies have provided important insights, the inherently stochastic nature of resistance generation and spread, as well as the potential for ongoing evolution of the resistant strain have not been fully addressed. Here, we study a stochastic model of drug resistance emergence and consecutive evolution of the resistant strain in response to antiviral control during an influenza pandemic. We find that taking into consideration the ongoing evolution of the resistant strain does not increase the probability of resistance emergence; however, it increases the total number of infecteds if a resistant outbreak occurs. Our study further shows that taking stochasticity into account leads to results that can differ from deterministic models. Specifically, we find that rapid and strong control cannot only contain a drug sensitive outbreak, it can also prevent a resistant outbreak from occurring. We find that the best control strategy is early intervention heavily based on prophylaxis at a level that leads to outbreak containment. If containment is not possible, mitigation works best at intermediate levels of antiviral control. Finally, we show that the results are not very sensitive to the way resistance generation is modeled.

Epidemic models with heterogeneous mixing and treatment

We consider a two-group epidemic model with treatment and establish a final size relation that gives the extent of the epidemic. This relation can be established with arbitrary mixing between the groups even though it may not be feasible to determine the reproduction number for the model. If the mixing of the two groups is proportionate, there is an explicit expression for the reproductive number and the final size relation is expressible in terms of the components of the reproduction number. We also extend the results to a two-group influenza model with proportionate mixing. Some numerical simulations suggest that (i) the assumption of no disease deaths is a good approximation if the disease death rate is small and (ii) a one-group model is a close approximation to a two-group model but a two-group model is necessary for comparing targeted treatment strategies.

Impact of public health interventions in controlling the spread of SARS: modelling of intervention scenarios

A variety of intervention measures exist to prevent and control diseases with pandemic potential like SARS or pandemic influenza. They differ in their approach and effectiveness in reducing the number of cases getting infected. The effects of different intervention measures were investigated by a mathematical modelling approach, with comparisons based on the effective reproduction number (R(e)). The analysis showed that early case detection followed by strict isolation could control a SARS outbreak. Tracing close contacts of cases and contacts of exposed health care workers additionally reduces the R(e). Tracing casual contacts and measures aiming to decrease social interaction were less effective in reducing the number of SARS cases. The study emphasizes the importance of early identification and isolation of SARS cases to reduce the number of people getting infected. However, doing so transfers cases to health care facilities, making infection control measures in hospitals essential to avoid nosocomial spread. The modelling approach applied in this study is useful for analysing interactions of different intervention measures for reducing the R(e) of SARS.

Quantifying the transmission potential of pandemic influenza

This article reviews quantitative methods to estimate the basic reproduction number of pandemic influenza, a key threshold quantity to help determine the intensity of interventions required to control the disease. Although it is difficult to assess the transmission potential of a probable future pandemic, historical epidemiologic data is readily available from previous pandemics, and as a reference quantity for future pandemic planning, mathematical and statistical analyses of historical data are crucial. In particular, because many historical records tend to document only the temporal distribution of cases or deaths (i.e. epidemic curve), our review focuses on methods to maximize the utility of time-evolution data and to clarify the detailed mechanisms of the spread of influenza.

First, we highlight structured epidemic models and their parameter estimation method which can quantify the detailed disease dynamics including those we cannot observe directly. Duration-structured epidemic systems are subsequently presented, offering firm understanding of the definition of the basic and effective reproduction numbers. When the initial growth phase of an epidemic is investigated, the distribution of the generation time is key statistical information to appropriately estimate the transmission potential using the intrinsic growth rate. Applications of stochastic processes are also highlighted to estimate the transmission potential using similar data. Critically important characteristics of influenza data are subsequently summarized, followed by our conclusions to suggest potential future methodological improvements.