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Transmission intensity and drug resistance in malaria population dynamics: implications for climate change.

Artzy-Randrup Y, Alonso D, Pascual M - PLoS ONE (2010)

Bottom Line: We then address the implications of warmer temperatures in an East African highland, where, as in other similar regions at the altitudinal edge of malaria's distribution, there has been a pronounced increase of cases from the 1970s to the 1990s.Climate change and drug resistance can interact and need not be considered as alternative explanations for trends in disease incidence in this region.Non-monotonic patterns of treatment failure with transmission intensity similar to those described as the 'valley phenomenon' for Uganda can result from epidemiological dynamics but under poorly understood assumptions.

View Article: PubMed Central - PubMed

Affiliation: Howard Hughes Medical Institute, Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America. YArtzy@umich.edu

ABSTRACT
Although the spread of drug resistance and the influence of climate change on malaria are most often considered separately, these factors have the potential to interact through altered levels of transmission intensity. The influence of transmission intensity on the evolution of drug resistance has been addressed in theoretical studies from a population genetics' perspective; less is known however on how epidemiological dynamics at the population level modulates this influence. We ask from a theoretical perspective, whether population dynamics can explain non-trivial, non-monotonic, patterns of treatment failure with transmission intensity, and, if so, under what conditions. We then address the implications of warmer temperatures in an East African highland, where, as in other similar regions at the altitudinal edge of malaria's distribution, there has been a pronounced increase of cases from the 1970s to the 1990s. Our theoretical analyses, with a transmission model that includes different levels of immunity, demonstrate that an increase in transmission beyond a threshold can lead to a decrease in drug resistance, as previously shown, but that a second threshold may occur and lead to the re-establishment of drug resistance. Estimates of the increase in transmission intensity from the 1970s to the 1990s for the Kenyan time series, obtained by fitting the two-stage version of the model with an explicit representation of vector dynamics, suggest that warmer temperatures are likely to have moved the system towards the first threshold, and in so doing, to have promoted the faster spread of drug resistance. Climate change and drug resistance can interact and need not be considered as alternative explanations for trends in disease incidence in this region. Non-monotonic patterns of treatment failure with transmission intensity similar to those described as the 'valley phenomenon' for Uganda can result from epidemiological dynamics but under poorly understood assumptions.

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Related in: MedlinePlus

An illustration of the structure of the model.Immunity classes are portrayed in an escalating order from left to right, such that subscript i = 1 represents hosts with the lowest level of immunity, and subscript i = n represents hosts with the highest level of immunity. In each immunity class individuals are either susceptible or infected. If the duration of infection within a given class is relatively short, individuals recover and return to the susceptible state within their immunity class. When the duration of infection is extensive, or alternatively, if individuals are repeatedly re-infected spending large fractions of time in the infected state, it is assumed that this prolonged cumulative exposure leads to a higher level of immunity, moving these individuals to the next immunity class. However, if individuals in an immunity class remain susceptible for a long duration time, without renewed exposure to the disease during this time, they loose their current level of immunity and move back down the previous immunity class. The structure of the model captures the idea that acquired immunity gradually weakens in the absence of exposure to infection, but quickly strengthens itself if additional exposure accurse within a given time frame from the previous infection as discussed by [60]. Black represents susceptibles, blue sensitive wild-type and red resistant.
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pone-0013588-g001: An illustration of the structure of the model.Immunity classes are portrayed in an escalating order from left to right, such that subscript i = 1 represents hosts with the lowest level of immunity, and subscript i = n represents hosts with the highest level of immunity. In each immunity class individuals are either susceptible or infected. If the duration of infection within a given class is relatively short, individuals recover and return to the susceptible state within their immunity class. When the duration of infection is extensive, or alternatively, if individuals are repeatedly re-infected spending large fractions of time in the infected state, it is assumed that this prolonged cumulative exposure leads to a higher level of immunity, moving these individuals to the next immunity class. However, if individuals in an immunity class remain susceptible for a long duration time, without renewed exposure to the disease during this time, they loose their current level of immunity and move back down the previous immunity class. The structure of the model captures the idea that acquired immunity gradually weakens in the absence of exposure to infection, but quickly strengthens itself if additional exposure accurse within a given time frame from the previous infection as discussed by [60]. Black represents susceptibles, blue sensitive wild-type and red resistant.

Mentions: The model is constructed as a multi-class SIS model, with levels of immunity increasing from class to class (see diagram in Figure 1). The acquisition of immunity expresses itself in several ways. When immunity is low, infected individuals usually suffer from severe clinical symptoms, leading to a higher likeliness of drug treatment. However, as immunity is gained, these symptoms become milder and the use of drug treatment declines. It is assumed that as a consequence of the lower levels of parasitaemia that accompanies higher levels of immunity, infectivity to mosquitoes decreases.


Transmission intensity and drug resistance in malaria population dynamics: implications for climate change.

Artzy-Randrup Y, Alonso D, Pascual M - PLoS ONE (2010)

An illustration of the structure of the model.Immunity classes are portrayed in an escalating order from left to right, such that subscript i = 1 represents hosts with the lowest level of immunity, and subscript i = n represents hosts with the highest level of immunity. In each immunity class individuals are either susceptible or infected. If the duration of infection within a given class is relatively short, individuals recover and return to the susceptible state within their immunity class. When the duration of infection is extensive, or alternatively, if individuals are repeatedly re-infected spending large fractions of time in the infected state, it is assumed that this prolonged cumulative exposure leads to a higher level of immunity, moving these individuals to the next immunity class. However, if individuals in an immunity class remain susceptible for a long duration time, without renewed exposure to the disease during this time, they loose their current level of immunity and move back down the previous immunity class. The structure of the model captures the idea that acquired immunity gradually weakens in the absence of exposure to infection, but quickly strengthens itself if additional exposure accurse within a given time frame from the previous infection as discussed by [60]. Black represents susceptibles, blue sensitive wild-type and red resistant.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2965653&req=5

pone-0013588-g001: An illustration of the structure of the model.Immunity classes are portrayed in an escalating order from left to right, such that subscript i = 1 represents hosts with the lowest level of immunity, and subscript i = n represents hosts with the highest level of immunity. In each immunity class individuals are either susceptible or infected. If the duration of infection within a given class is relatively short, individuals recover and return to the susceptible state within their immunity class. When the duration of infection is extensive, or alternatively, if individuals are repeatedly re-infected spending large fractions of time in the infected state, it is assumed that this prolonged cumulative exposure leads to a higher level of immunity, moving these individuals to the next immunity class. However, if individuals in an immunity class remain susceptible for a long duration time, without renewed exposure to the disease during this time, they loose their current level of immunity and move back down the previous immunity class. The structure of the model captures the idea that acquired immunity gradually weakens in the absence of exposure to infection, but quickly strengthens itself if additional exposure accurse within a given time frame from the previous infection as discussed by [60]. Black represents susceptibles, blue sensitive wild-type and red resistant.
Mentions: The model is constructed as a multi-class SIS model, with levels of immunity increasing from class to class (see diagram in Figure 1). The acquisition of immunity expresses itself in several ways. When immunity is low, infected individuals usually suffer from severe clinical symptoms, leading to a higher likeliness of drug treatment. However, as immunity is gained, these symptoms become milder and the use of drug treatment declines. It is assumed that as a consequence of the lower levels of parasitaemia that accompanies higher levels of immunity, infectivity to mosquitoes decreases.

Bottom Line: We then address the implications of warmer temperatures in an East African highland, where, as in other similar regions at the altitudinal edge of malaria's distribution, there has been a pronounced increase of cases from the 1970s to the 1990s.Climate change and drug resistance can interact and need not be considered as alternative explanations for trends in disease incidence in this region.Non-monotonic patterns of treatment failure with transmission intensity similar to those described as the 'valley phenomenon' for Uganda can result from epidemiological dynamics but under poorly understood assumptions.

View Article: PubMed Central - PubMed

Affiliation: Howard Hughes Medical Institute, Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America. YArtzy@umich.edu

ABSTRACT
Although the spread of drug resistance and the influence of climate change on malaria are most often considered separately, these factors have the potential to interact through altered levels of transmission intensity. The influence of transmission intensity on the evolution of drug resistance has been addressed in theoretical studies from a population genetics' perspective; less is known however on how epidemiological dynamics at the population level modulates this influence. We ask from a theoretical perspective, whether population dynamics can explain non-trivial, non-monotonic, patterns of treatment failure with transmission intensity, and, if so, under what conditions. We then address the implications of warmer temperatures in an East African highland, where, as in other similar regions at the altitudinal edge of malaria's distribution, there has been a pronounced increase of cases from the 1970s to the 1990s. Our theoretical analyses, with a transmission model that includes different levels of immunity, demonstrate that an increase in transmission beyond a threshold can lead to a decrease in drug resistance, as previously shown, but that a second threshold may occur and lead to the re-establishment of drug resistance. Estimates of the increase in transmission intensity from the 1970s to the 1990s for the Kenyan time series, obtained by fitting the two-stage version of the model with an explicit representation of vector dynamics, suggest that warmer temperatures are likely to have moved the system towards the first threshold, and in so doing, to have promoted the faster spread of drug resistance. Climate change and drug resistance can interact and need not be considered as alternative explanations for trends in disease incidence in this region. Non-monotonic patterns of treatment failure with transmission intensity similar to those described as the 'valley phenomenon' for Uganda can result from epidemiological dynamics but under poorly understood assumptions.

Show MeSH
Related in: MedlinePlus