72 research outputs found
Accounting for regional transmission variability and the impact of malaria control interventions in Ghana : a population level mathematical modelling approach
No full textCITATION: Awine, T. & Silal, S. P. 2020. Accounting for regional transmission variability and the impact of malaria control interventions in Ghana : a population level mathematical modelling approach. Malaria Journal, 19:423, doi:10.1186/s12936-020-03496-y.The original publication is available at https://malariajournal.biomedcentral.comBackground: This paper investigates the impact of malaria preventive interventions in Ghana and the prospects of
achieving programme goals using mathematical models based on regionally diverse climatic zones of the country.
Methods: Using data from the District Health Information Management System of the Ghana Health Service from
2008 to 2017, and historical intervention coverage levels, ordinary non-linear differential equations models were
developed. These models incorporated transitions amongst various disease compartments for the three main ecological
zones in Ghana. The Approximate Bayesian Computational sampling approach, with a distance based rejection
criteria, was adopted for calibration. A leave-one-out approach was used to validate model parameters and the most
sensitive parameters were evaluated using a multivariate regression analysis. The impact of insecticide-treated bed
nets and their usage, and indoor residual spraying, as well as their protective efficacy on the incidence of malaria, was
simulated at various levels of coverage and protective effectiveness in each ecological zone to investigate the prospects
of achieving goals of the Ghana malaria control strategy for 2014–2020.
Results: Increasing the coverage levels of both long-lasting insecticide-treated bed nets and indoor residual spraying
activities, without a corresponding increase in their recommended utilization, does not impact highly on averting
predicted incidence of malaria. Improving proper usage of long-lasting insecticide-treated bed nets could lead to
substantial reductions in the predicted incidence of malaria. Similar results were obtained with indoor residual spraying
across all ecological zones of Ghana.
Conclusions: Projected goals set in the national strategic plan for malaria control 2014–2020, as well as World Health
Organization targets for malaria pre-elimination by 2030, are only likely to be achieved if a substantial improvement in
treated bed net usage is achieved, coupled with targeted deployment of indoor residual spraying with high community
acceptability and efficacy.https://malariajournal.biomedcentral.com/articles/10.1186/s12936-020-03496-yPublisher's versio
Assessing the effectiveness of malaria interventions at the regional level in Ghana using a mathematical modelling application
No full textSupporting malaria control with interfaced applications of mathematical models that enables investigating effectiveness of various interventions as well as their cost implications could be useful. Through their usage for planning, these applications may improve the prospects of attaining various set targets such as those of the National Strategic Plan policies for malaria control in Ghana. A malaria model was adapted and used for simulating the incidence of malaria in various regions of Ghana. The model and its application were developed by the Modelling and Simulation Hub Africa and calibrated using district level data in Ghana from 2012 to 2018. Average monthly rainfall at the zonal level was fitted to trigonometric functions for each ecological zone using least squares approach. These zonal functions were then used as forcing functions. Subsequently, various intervention packages were investigated to observe their impact on averting malaria incidence by 2030. Increased usage of bednets but not only coverage levels, predicted a significant proportion of cases of malaria averted in all regions. Whereas, improvements in the health system by way of health seeking, testing and treatment predicted a decline in incidence largely in all regions. With an increased coverage of SMC, to include higher age groups, a modest proportion of cases could be averted in populations of the Guinea savannah. Indoor residual spraying could also benefit populations of the Transitional forest and Coastal savannah as its impact is significant in averting incidence. Enhancing bednet usage to at least a doubling of the current usage levels and deployed in combination with various interventions across regions predicted significant reductions, in malaria incidence. Regions of the Transitional forest and Coastal savannah could also benefit from a drastic decline in incidence following a gradual introduction of indoor residual spraying on a sustained basis
Changes to input sliders for amplitude parameters of the Transitional forest and Coastal savannah model codes.
No full textChanges to input sliders for amplitude parameters of the Transitional forest and Coastal savannah model codes.</p
Fig 7 -
No full textPredictions for malaria incidence for various intervention packages, singly (a1-c1) for Community Health Worker (CHW), Health System Strengthening (HSS), Indoor Residual Spraying (IRS), Insecticide Treated bednets (ITN) and Seasonal Malaria Chemoprevention (SMC) and in combination (a2-c2) for CHW/CHPS + ITN, HSS + ITN, ITN + IRS and ITN + SMC for the Upper East (a1 and a2) Upper West (b1 and b2) and Northern (c1 and c2) Regions respectively.</p
Fig 4 -
No full textPanels (a), (b), (c) and (d) represent the application cosine output curve, fitted rainfall data for the Guinea savannah, Transitional forest and Coastal savannah zones respectively.</p
Interface for setting values of each intervention scenario to be tested.
No full textInterface for setting values of each intervention scenario to be tested.</p
Fig 9 -
No full textPredictions for malaria incidence for various intervention packages, singly (a1-c1) for Community Health Worker (CHW), Health System Strengthening (HSS), Indoor Residual Spraying (IRS), and Insecticide Treated bednets (ITN) and in combination (a2-c2) for CHW/CHPS + ITN, HSS + ITN and ITN + IRS for the Central (a1 and a2), Greater Accra (b1 and b2) and Western (c1 and c2) Regions respectively.</p
Fig 3 -
No full textPatterns of malaria incidence by month of year (grey bars), monthly average rainfall (blue line) and average monthly temperature (red line) by ecological zone [(a) Guinea savannah, (b) Transitional forest and (c) Coastal savannah].</p
- …
