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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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Recorded seasonally varying mosquito densities from published papers and fitted curves.As the sampling methods and other factors vary between the datasets, the units for the data are not comparable between sites and have been omitted. A: Ajura village, Garki, Nigeria; B: Bagamayo, Tanzania; C: Navrongo, Ghana; D: Dakar, Senegal.
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pcbi.1004057.g009: Recorded seasonally varying mosquito densities from published papers and fitted curves.As the sampling methods and other factors vary between the datasets, the units for the data are not comparable between sites and have been omitted. A: Ajura village, Garki, Nigeria; B: Bagamayo, Tanzania; C: Navrongo, Ghana; D: Dakar, Senegal.

Mentions: The parametric form of equation (6) was fitted to four published datasets of seasonally varying mosquito densities. The fitting method was to minimise the sum of squares of the difference between the square root of the data and the square root of the predicted value. For simplicity each dataset is the sum of all Anopheles species reported. As the sampling methods and other factors vary between the datasets, the units for the data are not comparable between sites and have been omitted. The fitted parameters c and d, which determine the shape of the curve, are given in Table 3, and the observed and fitted curves are shown in Fig. 9. c is in general difficult to estimate precisely, but d is more precisely estimated. In the subsequent results, to represent a highly seasonal setting the default values used were 3 months for d and 0.05 for c, with other values also explored.


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

Griffin JT - PLoS Comput. Biol. (2015)

Recorded seasonally varying mosquito densities from published papers and fitted curves.As the sampling methods and other factors vary between the datasets, the units for the data are not comparable between sites and have been omitted. A: Ajura village, Garki, Nigeria; B: Bagamayo, Tanzania; C: Navrongo, Ghana; D: Dakar, Senegal.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004057.g009: Recorded seasonally varying mosquito densities from published papers and fitted curves.As the sampling methods and other factors vary between the datasets, the units for the data are not comparable between sites and have been omitted. A: Ajura village, Garki, Nigeria; B: Bagamayo, Tanzania; C: Navrongo, Ghana; D: Dakar, Senegal.
Mentions: The parametric form of equation (6) was fitted to four published datasets of seasonally varying mosquito densities. The fitting method was to minimise the sum of squares of the difference between the square root of the data and the square root of the predicted value. For simplicity each dataset is the sum of all Anopheles species reported. As the sampling methods and other factors vary between the datasets, the units for the data are not comparable between sites and have been omitted. The fitted parameters c and d, which determine the shape of the curve, are given in Table 3, and the observed and fitted curves are shown in Fig. 9. c is in general difficult to estimate precisely, but d is more precisely estimated. In the subsequent results, to represent a highly seasonal setting the default values used were 3 months for d and 0.05 for c, with other values also explored.

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