Giuseppe
Emeritus
Scientific Advances ? Modelling pandemic influenza and antiviral drug resistance - ECDC Health Content
Scientific Advances ? Modelling pandemic influenza and antiviral drug resistance
The recent emergence of antiviral resistance against oseltamivir has led to a spate of exercises using modelling techniques to suggest how transmission of antiviral resistant viruses would develop in a pandemic under different circumstances, especially use of antivirals.
Here ECDC considers and comments on three recent examples.
Brockmann, S. O., M. Schwehm, et al. (2008). "Modeling the effects of drug resistant influenza virus in a pandemic." Virol J 5: 133.
Description:
The study uses a freely available computer program InfluSim [1] to make calculations. Without drug resistance the model predicts that a drug sensitive pandemic strain lead to a cumulative number of 19500 (19.5%) outpatients and around 260 hospitalisations per 100 000 inhabitants. Imported sensitive strains increase these numbers to 20700 (20.7%) and around 310 hospitalisations. When including only ?de novo? resistance the number rises to 22,700 (22.7%) and around 420. The model assumes a basic reproductive number (the average number of infected when an infectious person enters a totally susceptible population) R0 of 2.5. Unspecified social distancing is suggested to decrease contacts by 10% and isolation of cases reduces the number of contacts by between 10% and 30%. One third of those infected are assumed to be asymptomatic, one third become moderately sick and one third seek medical help (these would be offered antiviral treatment). The model is initiated at day 0 when one infectious person arrives to the population; on day 21, one other infectious person arrives - these can be either drug sensitive or resistant.
ECDC comment (10/02/2009):
The use of a readily available computer program has ?pros? but more ?cons?. The program is fast and easy to use but even after reading the documentation the assumptions in the model remain unclear which therefore remains rather a ?black box?. The model is intended for Swiss conditions and other considerations may apply elsewhere. There are no estimations on the uncertainty of the products estimations and little or no validation. For example the assumption of one third of cases being asymptomatic would be challenged by some. The assumption of reduction of contacts by social distancing would benefit of more scientific input than only an assumption of 10%. This article would have benefited from more statistical methodology and more explanation of parameters and assumptions. In its current state it is of rather low value for the scientific community.
Arino, J., C. S. Bowman, et al. (2009). "Antiviral resistance during pandemic influenza: implications for stockpiling and drug use." BMC Infect Dis 9(1): 8.
Description:
The authors have developed a deterministic (a model with no random effects) mathematical model. Modelling is undertaken for situations with and without a supply of antivirals. The authors estimate that for a pandemic with the features of 1918/9 an antiviral stock of 20% of the population is needed. The paper assumes there are three strains of pathogens: drug-sensitive, drug-resistant with low transmission ability (LTFitness) and drug-resistant with high transmission ability (HTFitness). It is assumed that individuals hosting resistance with LTFitness make no contribution to the spread of the resistant virus. Individuals infected are assumed to be symptomatic (two thirds) or asymptomatic (one third). Values of R0 between 1.5 and 2.5 are investigated. The model is seeded by one index case arriving on day 0. The rate of de novo resistance that generates mutants with LTF is assumed a value of 0.018 per day. The simulation is looking at both an adequate (20%) and a 12% supply of antivirals for the population in the illustrations.
ECDC comment (10/02/2009):
The article makes for difficult reading as there are very many numbers. Different values of input values have been used to see their effects of these on the final outcome (sensitivity analysis). The authors have only changed the value of one value and not checked what happens when a number of different values are changed. What is not investigated in the model are the random properties of resistance development, and how these can occur. Also the values of ?de novo? resistance refer to earlier modelling studies by the same researchers [2] and not to any other references. What is neither addressed is the effect of random distribution on the length of the infectious time period and how the infectivity during the infectious time is varying due to the type of infection. The results claim that a 20% stockpile of antivirals is necessary, but it is not illustrated in the article, as this result also does depend on the rates for resistance development and how treatment is given within a certain country. In addition, it is demonstrated that the emergence of highly transmissible resistant strain has no significant impact, on the use of available stockpiles if treatment is maintained at low level. It is not specified the policy for how the treatments are distributed, to all symptomatic cases seeking hospitalisation or how the stockpile distribution policy is changing during the outbreak.
Handel, A., I. M. Longini, Jr., et al. (2009). "Antiviral resistance and the control of pandemic influenza: the roles of stochasticity, evolution and model details." J Theor Biol 256(1): 117-25
This study aims to study on the random effects of resistance generation and spread and the possibilities for ongoing evolution of the strains during an outbreak. It is suggested that taking into account the ongoing evolution does not increase the probability of resistant emergence but it does increase the number of infected individuals if an outbreak of resistant viruses occurs. The random effects at the beginning of an outbreak have strong effects on rapid and strong control measures, not only in terms of controlling the outbreak but in addition on preventing resistance outbreak from occurring. In the model it is assumed that resistance can emerge during treatment and these can produce new infections that can cause infections that are dominated by the resistant strain. Resistance can emerge in untreated patients, with 100 times less probability compared with a treated patient. In the model is including a factor for the evolution of the resistance strain during the outbreak. The study finds that the attack rate increases by approximately 20% if a resistant strain emerges compared with only a susceptible strain. If containment is impossible the study find that intermediate control is the best solution as the risk of getting a resistant strain is considerably lower. The effects of using a random model compared with a deterministic model for effects of resistance development and using more control strategies at the beginning of the outbreak is used. Previous modelling studies [2] have shown that two separate strains (susceptible and resistant) are evolving, and that resistance leads to rapid generation of resistance and an increased attack rate. The random model can account for the realistic case that strong control can lead quick containment of the outbreak.
The study also compared the results in claiming that the best strategy is little control at the beginning and more when the outbreak increases in size [2]. With the random model they find that little initial control is more likely to lead to a generation of a secondary outbreak, while a strong control might contain not only the sensitive outbreak, but also prevent resistance generation. The results can be used in, if a secondary resistant outbreak is unavoidable. But the study suggests that control measures should be implemented for preventing this scenario.
ECDC comment (10/02/2009):
This is the strongest of the papers as its one of the few articles in the field where the researchers have thought of including random effects. Though the experience of the 2008/8 season and the differences in the national levels of antiviral resistance in A(H1N1) viruses suggests this is very important. Due to the random behavioural of genetic changes, this approach is greatly appreciated. The study compares previous studies using deterministic models and possible effects of having random effects. Of great value is the modelled differences on effects of how using different levels of control during the outbreak. Not everything in the model is of random nature yet, as stated by the authors, for example the randomness of the length of the infectious time period and its effects should be investigated. Also what effects changing the probabilities for developing resistance in treated and non-treated patients would benefit this study. We find the developed model very useful and the results are of good help for pandemic planners.
General Summary:
The three compared articles have shown a broad span on available methodology to model antiviral resistance. The past years outbreaks in Europe show that there is a great variation in these results between the countries.
Most of the papers in general and also in this summary are based on deterministic models, these are models which are easy to develop and the calculations go quickly. Deterministic models are good is some situations, for example to estimate the size of a large outbreak. In this situation the results will be the same as from a random model. But the use has a number of limitations, as showed in these papers there is no estimation on the uncertainty of the estimations. In addition deterministic models do not work very well nor at the beginning or the end of an outbreak. At this stage random effects have a huge effect on the final outcome. This is illustrated in the different results on how the stockpiles should be used during the whole outbreak, the deterministic favours more use during the peak. While random models show that depending on what is needed more control at the beginning of the outbreak has great benefits in terms of containment and reducing resistance. We find this area and difference between the methodologies of great scientific interest and call for more research in this field. There is definitively more need for more statistical random modelling papers in order to make realistic models.
The two papers with deterministic models in our summary are thus of limited use for pandemic planning research. To make a comparison, a large consortium is needed of leading modellers, in Europe the FluModCont [3] project would be a candidate to do an analysis of this.
One surprising difference between the papers is the additional increase in attack rate due to resistance; in these there papers it varies between 10-20% with similar assumptions. This is a huge difference in numbers. Reason for this can depend for example on that model structures are different and some assumptions are not written in the article.
One of the articles is based on a readily available computer program, using a tool like that still needs proper declaration of all assumptions, which is not the case here. The modelling community is still very split on the need of a general tool for modelling pandemics, especially when it comes to hospitalisations. All countries have different heath-care systems and it would be rather cumbersome to develop a general model which would fit all countries? systems.
The modelling studies in the field on resistance modelling have still not been using all available modelling approaches, a new input would be to use results from a field called competing epidemics. This is still a new field but should be applied to this kind of problems. Another field not addressed yet is some operational modelling, in which manner and how quickly it is possible to distribute antivirals within a country. These constrains would most possibly make some modelling results more difficult to achieve.
[1] Eichner, M., M. Schwehm, et al. (2007). "The influenza pandemic preparedness planning tool InfluSim." BMC Infect Dis 7: 17.
[2] Moghadas, S. M., C. S. Bowman, et al. (2008). "Population-wide emergence of antiviral resistance during pandemic influenza." PLoS ONE 3(3): e1839.
[3] http://www.flumodcont.eu
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<cite cite="http://ecdc.europa.eu/en/health_content/sciadv/090219_sciadv.aspx">ECDC Health Content</cite>The recent emergence of antiviral resistance against oseltamivir has led to a spate of exercises using modelling techniques to suggest how transmission of antiviral resistant viruses would develop in a pandemic under different circumstances, especially use of antivirals.
Here ECDC considers and comments on three recent examples.
Brockmann, S. O., M. Schwehm, et al. (2008). "Modeling the effects of drug resistant influenza virus in a pandemic." Virol J 5: 133.
Description:
The study uses a freely available computer program InfluSim [1] to make calculations. Without drug resistance the model predicts that a drug sensitive pandemic strain lead to a cumulative number of 19500 (19.5%) outpatients and around 260 hospitalisations per 100 000 inhabitants. Imported sensitive strains increase these numbers to 20700 (20.7%) and around 310 hospitalisations. When including only ?de novo? resistance the number rises to 22,700 (22.7%) and around 420. The model assumes a basic reproductive number (the average number of infected when an infectious person enters a totally susceptible population) R0 of 2.5. Unspecified social distancing is suggested to decrease contacts by 10% and isolation of cases reduces the number of contacts by between 10% and 30%. One third of those infected are assumed to be asymptomatic, one third become moderately sick and one third seek medical help (these would be offered antiviral treatment). The model is initiated at day 0 when one infectious person arrives to the population; on day 21, one other infectious person arrives - these can be either drug sensitive or resistant.
ECDC comment (10/02/2009):
The use of a readily available computer program has ?pros? but more ?cons?. The program is fast and easy to use but even after reading the documentation the assumptions in the model remain unclear which therefore remains rather a ?black box?. The model is intended for Swiss conditions and other considerations may apply elsewhere. There are no estimations on the uncertainty of the products estimations and little or no validation. For example the assumption of one third of cases being asymptomatic would be challenged by some. The assumption of reduction of contacts by social distancing would benefit of more scientific input than only an assumption of 10%. This article would have benefited from more statistical methodology and more explanation of parameters and assumptions. In its current state it is of rather low value for the scientific community.
Arino, J., C. S. Bowman, et al. (2009). "Antiviral resistance during pandemic influenza: implications for stockpiling and drug use." BMC Infect Dis 9(1): 8.
Description:
The authors have developed a deterministic (a model with no random effects) mathematical model. Modelling is undertaken for situations with and without a supply of antivirals. The authors estimate that for a pandemic with the features of 1918/9 an antiviral stock of 20% of the population is needed. The paper assumes there are three strains of pathogens: drug-sensitive, drug-resistant with low transmission ability (LTFitness) and drug-resistant with high transmission ability (HTFitness). It is assumed that individuals hosting resistance with LTFitness make no contribution to the spread of the resistant virus. Individuals infected are assumed to be symptomatic (two thirds) or asymptomatic (one third). Values of R0 between 1.5 and 2.5 are investigated. The model is seeded by one index case arriving on day 0. The rate of de novo resistance that generates mutants with LTF is assumed a value of 0.018 per day. The simulation is looking at both an adequate (20%) and a 12% supply of antivirals for the population in the illustrations.
ECDC comment (10/02/2009):
The article makes for difficult reading as there are very many numbers. Different values of input values have been used to see their effects of these on the final outcome (sensitivity analysis). The authors have only changed the value of one value and not checked what happens when a number of different values are changed. What is not investigated in the model are the random properties of resistance development, and how these can occur. Also the values of ?de novo? resistance refer to earlier modelling studies by the same researchers [2] and not to any other references. What is neither addressed is the effect of random distribution on the length of the infectious time period and how the infectivity during the infectious time is varying due to the type of infection. The results claim that a 20% stockpile of antivirals is necessary, but it is not illustrated in the article, as this result also does depend on the rates for resistance development and how treatment is given within a certain country. In addition, it is demonstrated that the emergence of highly transmissible resistant strain has no significant impact, on the use of available stockpiles if treatment is maintained at low level. It is not specified the policy for how the treatments are distributed, to all symptomatic cases seeking hospitalisation or how the stockpile distribution policy is changing during the outbreak.
Handel, A., I. M. Longini, Jr., et al. (2009). "Antiviral resistance and the control of pandemic influenza: the roles of stochasticity, evolution and model details." J Theor Biol 256(1): 117-25
This study aims to study on the random effects of resistance generation and spread and the possibilities for ongoing evolution of the strains during an outbreak. It is suggested that taking into account the ongoing evolution does not increase the probability of resistant emergence but it does increase the number of infected individuals if an outbreak of resistant viruses occurs. The random effects at the beginning of an outbreak have strong effects on rapid and strong control measures, not only in terms of controlling the outbreak but in addition on preventing resistance outbreak from occurring. In the model it is assumed that resistance can emerge during treatment and these can produce new infections that can cause infections that are dominated by the resistant strain. Resistance can emerge in untreated patients, with 100 times less probability compared with a treated patient. In the model is including a factor for the evolution of the resistance strain during the outbreak. The study finds that the attack rate increases by approximately 20% if a resistant strain emerges compared with only a susceptible strain. If containment is impossible the study find that intermediate control is the best solution as the risk of getting a resistant strain is considerably lower. The effects of using a random model compared with a deterministic model for effects of resistance development and using more control strategies at the beginning of the outbreak is used. Previous modelling studies [2] have shown that two separate strains (susceptible and resistant) are evolving, and that resistance leads to rapid generation of resistance and an increased attack rate. The random model can account for the realistic case that strong control can lead quick containment of the outbreak.
The study also compared the results in claiming that the best strategy is little control at the beginning and more when the outbreak increases in size [2]. With the random model they find that little initial control is more likely to lead to a generation of a secondary outbreak, while a strong control might contain not only the sensitive outbreak, but also prevent resistance generation. The results can be used in, if a secondary resistant outbreak is unavoidable. But the study suggests that control measures should be implemented for preventing this scenario.
ECDC comment (10/02/2009):
This is the strongest of the papers as its one of the few articles in the field where the researchers have thought of including random effects. Though the experience of the 2008/8 season and the differences in the national levels of antiviral resistance in A(H1N1) viruses suggests this is very important. Due to the random behavioural of genetic changes, this approach is greatly appreciated. The study compares previous studies using deterministic models and possible effects of having random effects. Of great value is the modelled differences on effects of how using different levels of control during the outbreak. Not everything in the model is of random nature yet, as stated by the authors, for example the randomness of the length of the infectious time period and its effects should be investigated. Also what effects changing the probabilities for developing resistance in treated and non-treated patients would benefit this study. We find the developed model very useful and the results are of good help for pandemic planners.
General Summary:
The three compared articles have shown a broad span on available methodology to model antiviral resistance. The past years outbreaks in Europe show that there is a great variation in these results between the countries.
Most of the papers in general and also in this summary are based on deterministic models, these are models which are easy to develop and the calculations go quickly. Deterministic models are good is some situations, for example to estimate the size of a large outbreak. In this situation the results will be the same as from a random model. But the use has a number of limitations, as showed in these papers there is no estimation on the uncertainty of the estimations. In addition deterministic models do not work very well nor at the beginning or the end of an outbreak. At this stage random effects have a huge effect on the final outcome. This is illustrated in the different results on how the stockpiles should be used during the whole outbreak, the deterministic favours more use during the peak. While random models show that depending on what is needed more control at the beginning of the outbreak has great benefits in terms of containment and reducing resistance. We find this area and difference between the methodologies of great scientific interest and call for more research in this field. There is definitively more need for more statistical random modelling papers in order to make realistic models.
The two papers with deterministic models in our summary are thus of limited use for pandemic planning research. To make a comparison, a large consortium is needed of leading modellers, in Europe the FluModCont [3] project would be a candidate to do an analysis of this.
One surprising difference between the papers is the additional increase in attack rate due to resistance; in these there papers it varies between 10-20% with similar assumptions. This is a huge difference in numbers. Reason for this can depend for example on that model structures are different and some assumptions are not written in the article.
One of the articles is based on a readily available computer program, using a tool like that still needs proper declaration of all assumptions, which is not the case here. The modelling community is still very split on the need of a general tool for modelling pandemics, especially when it comes to hospitalisations. All countries have different heath-care systems and it would be rather cumbersome to develop a general model which would fit all countries? systems.
The modelling studies in the field on resistance modelling have still not been using all available modelling approaches, a new input would be to use results from a field called competing epidemics. This is still a new field but should be applied to this kind of problems. Another field not addressed yet is some operational modelling, in which manner and how quickly it is possible to distribute antivirals within a country. These constrains would most possibly make some modelling results more difficult to achieve.
[1] Eichner, M., M. Schwehm, et al. (2007). "The influenza pandemic preparedness planning tool InfluSim." BMC Infect Dis 7: 17.
[2] Moghadas, S. M., C. S. Bowman, et al. (2008). "Population-wide emergence of antiviral resistance during pandemic influenza." PLoS ONE 3(3): e1839.
[3] http://www.flumodcont.eu
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