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The interaction between seasonality and pulsed interventions against malaria in their effects on the reproduction number.

Griffin JT - PLoS Comput. Biol. (2015)

Bottom Line: The relative change in R0 due to an intervention is referred to as the effect size.However malaria and other diseases are often highly seasonal and some interventions have time-varying effects, meaning that simple reproduction number formulae cannot be used.The optimal time of year for drug administration is in the low season, whereas the best time for indoor residual spraying or a vaccine which reduces infection rates is just before the high season.

View Article: PubMed Central - PubMed

Affiliation: MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, London, United Kingdom.

ABSTRACT
The basic reproduction number (R0) is an important quantity summarising the dynamics of an infectious disease, as it quantifies how much effort is needed to control transmission. The relative change in R0 due to an intervention is referred to as the effect size. However malaria and other diseases are often highly seasonal and some interventions have time-varying effects, meaning that simple reproduction number formulae cannot be used. Methods have recently been developed for calculating R0 for diseases with seasonally varying transmission. I extend those methods to calculate the effect size of repeated rounds of mass drug administration, indoor residual spraying and other interventions against Plasmodium falciparum malaria in seasonal settings in Africa. I show that if an intervention reduces transmission from one host to another by a constant factor, then its effect size is the same in a seasonal as in a non-seasonal setting. The optimal time of year for drug administration is in the low season, whereas the best time for indoor residual spraying or a vaccine which reduces infection rates is just before the high season. In general, the impact of time-varying interventions increases with increasing seasonality, if carried out at the optimal time of year. The effect of combinations of interventions that act at different stages of the transmission cycle is roughly the product of the separate effects. However for individual time-varying interventions, it is necessary to use methods such as those developed here rather than inserting the average efficacy into a simple formula.

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Reduction in R0 due to seasonality.A: Human to mosquito generation time distribution for three models. B: R0 relative to a non-seasonal model with the same mean mosquito density for different transmission models, with sinusoidal seasonal variation. The last three lines all have a mosquito latent period, and correspond to the generation times in A. The thin solid lines are the approximate formulae in equations (1) and (2). C: Seasonal mosquito density with varying duration of high season, and c = 0.05. D: R0 relative to a non-seasonal model with the same mean mosquito density. Colours for the duration of the high season are the same in C and D.
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pcbi.1004057.g002: Reduction in R0 due to seasonality.A: Human to mosquito generation time distribution for three models. B: R0 relative to a non-seasonal model with the same mean mosquito density for different transmission models, with sinusoidal seasonal variation. The last three lines all have a mosquito latent period, and correspond to the generation times in A. The thin solid lines are the approximate formulae in equations (1) and (2). C: Seasonal mosquito density with varying duration of high season, and c = 0.05. D: R0 relative to a non-seasonal model with the same mean mosquito density. Colours for the duration of the high season are the same in C and D.

Mentions: Fig. 2A shows the probability distribution for the human to mosquito infection generation time for this model and two simpler models, an exponential infectious period with mean 70 days, or a latent period with mean 10 days then an infectious period with mean 60 days, both exponential.


The interaction between seasonality and pulsed interventions against malaria in their effects on the reproduction number.

Griffin JT - PLoS Comput. Biol. (2015)

Reduction in R0 due to seasonality.A: Human to mosquito generation time distribution for three models. B: R0 relative to a non-seasonal model with the same mean mosquito density for different transmission models, with sinusoidal seasonal variation. The last three lines all have a mosquito latent period, and correspond to the generation times in A. The thin solid lines are the approximate formulae in equations (1) and (2). C: Seasonal mosquito density with varying duration of high season, and c = 0.05. D: R0 relative to a non-seasonal model with the same mean mosquito density. Colours for the duration of the high season are the same in C and D.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4295870&req=5

pcbi.1004057.g002: Reduction in R0 due to seasonality.A: Human to mosquito generation time distribution for three models. B: R0 relative to a non-seasonal model with the same mean mosquito density for different transmission models, with sinusoidal seasonal variation. The last three lines all have a mosquito latent period, and correspond to the generation times in A. The thin solid lines are the approximate formulae in equations (1) and (2). C: Seasonal mosquito density with varying duration of high season, and c = 0.05. D: R0 relative to a non-seasonal model with the same mean mosquito density. Colours for the duration of the high season are the same in C and D.
Mentions: Fig. 2A shows the probability distribution for the human to mosquito infection generation time for this model and two simpler models, an exponential infectious period with mean 70 days, or a latent period with mean 10 days then an infectious period with mean 60 days, both exponential.

Bottom Line: The relative change in R0 due to an intervention is referred to as the effect size.However malaria and other diseases are often highly seasonal and some interventions have time-varying effects, meaning that simple reproduction number formulae cannot be used.The optimal time of year for drug administration is in the low season, whereas the best time for indoor residual spraying or a vaccine which reduces infection rates is just before the high season.

View Article: PubMed Central - PubMed

Affiliation: MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, London, United Kingdom.

ABSTRACT
The basic reproduction number (R0) is an important quantity summarising the dynamics of an infectious disease, as it quantifies how much effort is needed to control transmission. The relative change in R0 due to an intervention is referred to as the effect size. However malaria and other diseases are often highly seasonal and some interventions have time-varying effects, meaning that simple reproduction number formulae cannot be used. Methods have recently been developed for calculating R0 for diseases with seasonally varying transmission. I extend those methods to calculate the effect size of repeated rounds of mass drug administration, indoor residual spraying and other interventions against Plasmodium falciparum malaria in seasonal settings in Africa. I show that if an intervention reduces transmission from one host to another by a constant factor, then its effect size is the same in a seasonal as in a non-seasonal setting. The optimal time of year for drug administration is in the low season, whereas the best time for indoor residual spraying or a vaccine which reduces infection rates is just before the high season. In general, the impact of time-varying interventions increases with increasing seasonality, if carried out at the optimal time of year. The effect of combinations of interventions that act at different stages of the transmission cycle is roughly the product of the separate effects. However for individual time-varying interventions, it is necessary to use methods such as those developed here rather than inserting the average efficacy into a simple formula.

Show MeSH
Related in: MedlinePlus