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Quantifying the impact of decay in bed-net efficacy on malaria transmission.

Ngonghala CN, Del Valle SY, Zhao R, Mohammed-Awel J - J. Theor. Biol. (2014)

Bottom Line: The potential impact of ITNs on reducing malaria transmission is limited due to inconsistent or improper use, as well as physical decay in effectiveness.We develop a model for malaria spread that captures the decrease in ITN effectiveness due to physical and chemical decay, as well as human behavior as a function of time.These analyses show that the basic reproduction number R0, and the infectious human population are most sensitive to bed-net coverage and the biting rate of mosquitoes.

View Article: PubMed Central - PubMed

Affiliation: Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA 02115, USA; National Institute for Mathematical and Biological Synthesis, Knoxville, TN 37996-1527, USA. Electronic address: Calistus_Ngonghala@hms.harvard.edu.

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PRCCs depicting how sensitive the infectious human population, Ih is to the parameters of system (2.4) on day 365. The larger the magnitude of the PRCC, the more significant the parameter is in generating uncertainty or variability in Ih. The sign of the PRCC indicates whether an increase in a parameter will lead to more infections (+) or less infections (−). Clearly, uncertainty or variability in b, βmax, νh, γ̃h, α, and pvh influence the magnitude of the infectious class, Ih the most.
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Figure 10: PRCCs depicting how sensitive the infectious human population, Ih is to the parameters of system (2.4) on day 365. The larger the magnitude of the PRCC, the more significant the parameter is in generating uncertainty or variability in Ih. The sign of the PRCC indicates whether an increase in a parameter will lead to more infections (+) or less infections (−). Clearly, uncertainty or variability in b, βmax, νh, γ̃h, α, and pvh influence the magnitude of the infectious class, Ih the most.

Mentions: We use the Latin Hyper-cube Sampling (LHS) and Partial Rank Correlation Coefficient (PRCC) techniques (Marino et al., 2008) to perform a global uncertainty and sensitivity analyses of our non-autonomous model. Specifically, we sample the twenty parameters of the non-autonomous model system and measure their statistical influence on Ih. As in Moore et al. (2012), we assign the baseline parameters in Table 1 to the mean values of the corresponding parameter ranges and set the respective lower and upper bounds of each parameter range to 67% and 133% of the baseline (or mean) value. We then assume that each parameter follows a uniform distribution and partition each parameter range into 1000 equiprobable subintervals. This gives a 1000×20 matrix whose rows consist of unique collections of parameters randomly drawn from the uniform pdf without replacement and each row of the matrix is used to integrate system (2.4). Fig. 10 shows the PRCCs for all the parameters of system (2.4). Our analyses indicate that variability or uncertainty in bed-net coverage b0, the maximum mosquito biting rate βmax, and the rate at which exposed humans become infectious νh, have the most significant impact on the infectious human population Ih. The public health interpretation of this result is that reducing contacts between humans and mosquitoes is important in controlling the size of the infectious human population. The PRCC for b0 is the largest and negative, which indicates that shielding humans from mosquito bites, through the use of ITNs, can reduce human infections. The positive and large value of βmax indicates that implementing control measures such as ITNs, which protect mosquitoes from biting humans, will cause a decrease in malaria prevalence. The human recovery rate γ̃h, the proportion of infectious humans who recover without acquiring immunity α, the infectivity of infectious mosquitoes pvh, the natural mosquito mortality rate μv0, and the infectivity of infectious humans phv, also impact Ih significantly.


Quantifying the impact of decay in bed-net efficacy on malaria transmission.

Ngonghala CN, Del Valle SY, Zhao R, Mohammed-Awel J - J. Theor. Biol. (2014)

PRCCs depicting how sensitive the infectious human population, Ih is to the parameters of system (2.4) on day 365. The larger the magnitude of the PRCC, the more significant the parameter is in generating uncertainty or variability in Ih. The sign of the PRCC indicates whether an increase in a parameter will lead to more infections (+) or less infections (−). Clearly, uncertainty or variability in b, βmax, νh, γ̃h, α, and pvh influence the magnitude of the infectious class, Ih the most.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4374367&req=5

Figure 10: PRCCs depicting how sensitive the infectious human population, Ih is to the parameters of system (2.4) on day 365. The larger the magnitude of the PRCC, the more significant the parameter is in generating uncertainty or variability in Ih. The sign of the PRCC indicates whether an increase in a parameter will lead to more infections (+) or less infections (−). Clearly, uncertainty or variability in b, βmax, νh, γ̃h, α, and pvh influence the magnitude of the infectious class, Ih the most.
Mentions: We use the Latin Hyper-cube Sampling (LHS) and Partial Rank Correlation Coefficient (PRCC) techniques (Marino et al., 2008) to perform a global uncertainty and sensitivity analyses of our non-autonomous model. Specifically, we sample the twenty parameters of the non-autonomous model system and measure their statistical influence on Ih. As in Moore et al. (2012), we assign the baseline parameters in Table 1 to the mean values of the corresponding parameter ranges and set the respective lower and upper bounds of each parameter range to 67% and 133% of the baseline (or mean) value. We then assume that each parameter follows a uniform distribution and partition each parameter range into 1000 equiprobable subintervals. This gives a 1000×20 matrix whose rows consist of unique collections of parameters randomly drawn from the uniform pdf without replacement and each row of the matrix is used to integrate system (2.4). Fig. 10 shows the PRCCs for all the parameters of system (2.4). Our analyses indicate that variability or uncertainty in bed-net coverage b0, the maximum mosquito biting rate βmax, and the rate at which exposed humans become infectious νh, have the most significant impact on the infectious human population Ih. The public health interpretation of this result is that reducing contacts between humans and mosquitoes is important in controlling the size of the infectious human population. The PRCC for b0 is the largest and negative, which indicates that shielding humans from mosquito bites, through the use of ITNs, can reduce human infections. The positive and large value of βmax indicates that implementing control measures such as ITNs, which protect mosquitoes from biting humans, will cause a decrease in malaria prevalence. The human recovery rate γ̃h, the proportion of infectious humans who recover without acquiring immunity α, the infectivity of infectious mosquitoes pvh, the natural mosquito mortality rate μv0, and the infectivity of infectious humans phv, also impact Ih significantly.

Bottom Line: The potential impact of ITNs on reducing malaria transmission is limited due to inconsistent or improper use, as well as physical decay in effectiveness.We develop a model for malaria spread that captures the decrease in ITN effectiveness due to physical and chemical decay, as well as human behavior as a function of time.These analyses show that the basic reproduction number R0, and the infectious human population are most sensitive to bed-net coverage and the biting rate of mosquitoes.

View Article: PubMed Central - PubMed

Affiliation: Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA 02115, USA; National Institute for Mathematical and Biological Synthesis, Knoxville, TN 37996-1527, USA. Electronic address: Calistus_Ngonghala@hms.harvard.edu.

Show MeSH
Related in: MedlinePlus