CoChat
Sign Up
Cited by 23

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania

Runge, Manuela, Molteni, Fabrizio, Mandike, Renata, Snow, Robert W., Lengeler, Christian, Mohamed, Ally, Pothin, Emilie·2020·Malaria Journal
DOI 10.1186/s12936-020-03173-0PMC PMC7053121
Open this paper in CoChat — free account

Abstract

Background: More than ever, it is crucial to make the best use of existing country data, and analytical tools for developing malaria control strategies as the heterogeneity in malaria risk within countries is increasing, and the available malaria control tools are expanding while large funding gaps exist. Global and local policymakers, as well as funders, increasingly recognize the value of mathematical modelling as a strategic tool to support decision making. This case study article describes the long-term use of modelling in close collaboration with the National Malaria Control Programme (NMCP) in Tanzania, the challenges encountered and lessons learned. Case description: In Tanzania, a recent rebound in prevalence led to the revision of the national malaria strategic plan with interventions targeted to the malaria risk at the sub-regional level. As part of the revision, a mathematical malaria modelling framework for setting specific predictions was developed and used between 2016 and 2019 to (1) reproduce setting specific historical malaria trends, and (2) to simulate in silico the impact of future interventions. Throughout the project, multiple stakeholder workshops were attended and the use of mathematical modelling interactively discussed. Evaluation: In Tanzania, the model application created an interdisciplinary and multisectoral dialogue platform between modellers, NMCP and partners and contributed to the revision of the national malaria strategic plan by simulating strategies suggested by the NMCP. The uptake of the modelling outputs and sustained interest by the NMCP were critically associated with following factors: (1) effective sensitization to the NMCP, (2) regular and intense communication, (3) invitation for the modellers to participate in the strategic plan process, and (4) model application tailored to the local context. Conclusion: Empirical data analysis and its use for strategic thinking remain the cornerstone for evidence-based decision-making. Mathematical impact modelling can support the process both by unifying all stakeholders in one strategic process and by adding new key evidence required for optimized decision-making. However, without a long-standing partnership, it will be much more challenging to sensibilize programmes to the usefulness and sustained use of modelling and local resources within the programme or collaborating research institutions need to be mobilized.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12936-020-03173-0

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 49

  1. Maude RJ, Lubell Y, Socheat D, Yeung S, Saralamba S, Pongtavornpinyo W, et al. The role of mathematical modelling in guiding the science and economics of malaria elimination. Int Health. 2010;2:239–46. · DOI
  2. Garnett GP, Cousens S, Hallett TB, Steketee R, Walker N. Mathematical models in the evaluation of health programmes. Lancet. 2011;378:515–25. · DOI
  3. Grassly NC, Fraser C. Mathematical models of infectious disease transmission. Nat Rev Microbiol. 2008;6:477–87. · DOI
  4. Njeuhmeli E, Schnure M, Vazzano A, Gold E, Stegman P, Kripke K, et al. Using mathematical modeling to inform health policy: a case study from voluntary medical male circumcision scale-up in eastern and southern Africa and proposed framework for success. PLoS ONE. 2019;14:e0213605. · DOI
  5. Van Kerkhove MD, Ferguson NM. Epidemic and intervention modelling—a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic. Bull World Health Organ. 2012;90:306–10. · DOI
  6. Fung IC-H. Cholera transmission dynamic models for public health practitioners. Emerg Themes Epidemiol. 2014;11:1. · DOI
  7. Habbema JDF, Alley ES, Plaisier AP, van Oortmarssen GJ, Remme JHF. Epidemiological modelling for onchocerciasis control. Parasitol Today. 1992;8:99–103. · DOI
  8. White LJ, Maude RJ, Pongtavornpinyo W, Saralamba S, Aguas R, Van Effelterre T, et al. The role of simple mathematical models in malaria elimination strategy design. Malar J. 2009;8:212. · DOI
  9. Penny MA, Verity R, Bever CA, Sauboin C, Galactionova K, Flasche S, et al. Public health impact and cost-effectiveness of the RTS, S/AS01 malaria vaccine: a systematic comparison of predictions from four mathematical models. Lancet. 2016;387:367–75. · DOI
  10. Brady OJ, Slater HC, Pemberton-Ross P, Wenger E, Maude RJ, Ghani AC, et al. Role of mass drug administration in elimination of Plasmodium falciparum malaria: a consensus modelling study. Lancet Glob Health. 2017;5:e680–7. · DOI
  11. The malERA Refresh Consultative Panel on Combination Interventions and Modelling. malERA: an updated research agenda for combination interventions and modelling in malaria elimination and eradication. PLoS Med. 2017;14:e1002453. · DOI
  12. WHO. From malaria control to malaria elimination: a manual for elimination scenario planning. Geneva: Global Malaria Programme, World Health Organization; 2014.
  13. Kramer R, Dickinson KL, Anderson RM, Fowler VG, Miranda ML, Mutero CM, et al. Using decision analysis to improve malaria control policy making. Health Policy. 2009;92:133–40. · DOI
  14. Walker PGT, Griffin JT, Ferguson NM, Ghani AC. Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling study. Lancet Glob Health. 2016;4:e474–84. · DOI · Paper page
  15. Scott N, Hussain SA, Martin-Hughes R, Fowkes FJI, Kerr CC, Pearson R, et al. Maximizing the impact of malaria funding through allocative efficiency: using the right interventions in the right locations. Malar J. 2017;16:368. · DOI · Paper page
  16. Hamilton M, Mahiane G, Werst E, Sanders R, Briët O, Smith T, et al. Spectrum-malaria: a user-friendly projection tool for health impact assessment and strategic planning by malaria control programmes in sub-Saharan Africa. Malar J. 2017;16:68. · DOI
  17. Otieno G, Koske JK, Mutiso JM. Cost effectiveness analysis of optimal malaria control strategies in Kenya. Mathematics. 2016;4:14. · DOI
  18. WHO. Global Technical Strategy for malaria 2016–2030. Geneva: World Health Organization; 2015.
  19. Winskill P, Walker PG, Cibulskis RE, Ghani AC. Prioritizing the scale-up of interventions for malaria control and elimination. Malar J. 2019;18:122. · DOI
  20. Korenromp E, Mahiané G, Hamilton M, Pretorius C, Cibulskis R, Lauer J, et al. Malaria intervention scale-up in Africa: effectiveness predictions for health programme planning tools, based on dynamic transmission modelling. Malar J. 2016;15:417. · DOI
  21. Winskill P, Walker PG, Griffin JT, Ghani AC. Modelling the cost-effectiveness of introducing the RTS, S malaria vaccine relative to scaling up other malaria interventions in sub-Saharan Africa. BMJ Glob Health. 2017;2:e000090. · DOI
  22. Oduro F, Okyere G, Azu-Tungmah G. Transmission dynamics of malaria in Ghana. J Math Res. 2012;4:22. · DOI
  23. Awine T, Malm K, Bart-Plange C, Silal SP. Towards malaria control and elimination in Ghana: challenges and decision making tools to guide planning. Glob Health Action. 2017;10:1381471. · DOI · Paper page
  24. Stuckey EM, Stevenson JC, Cooke MK, Owaga C, Marube E, Oando G, et al. Simulation of malaria epidemiology and control in the highlands of western Kenya. Malar J. 2012;11:357. · DOI
  25. Stuckey EM, Stevenson J, Galactionova K, Baidjoe AY, Bousema T, Odongo W, et al. Modeling the cost effectiveness of malaria control interventions in the highlands of western Kenya. PLoS ONE. 2014;9:e107700. · DOI
  26. Silal SP, Little F, Barnes KI, White LJ. Hitting a moving target: a model for malaria elimination in the presence of population movement. PLoS ONE. 2015;10:e0144990. · DOI
  27. Lee BY, Bartsch SM, Stone NTB, Zhang S, Brown ST, Chatterjee C, et al. The economic value of long-lasting insecticidal nets and indoor residual spraying implementation in Mozambique. Am J Trop Med Hyg. 2017;96:1430–40. · DOI
  28. Tompkins AM, Colón-González FJ, Giuseppe FD, Namanya DB. Dynamical malaria forecasts are skillful at regional and local scales in Uganda up to 4 months ahead. GeoHealth. 2019;3:58–66. · DOI
  29. Silal SP, Little F, Barnes KI, White LJ. Towards malaria elimination in Mpumalanga, South Africa: a population-level mathematical modelling approach. Malar J. 2014;13:297. · DOI
  30. Stuckey EM, Miller JM, Littrell M, Chitnis N, Steketee R. Operational strategies of anti-malarial drug campaigns for malaria elimination in Zambia’s southern province: a simulation study. Malar J. 2016;15:148. · DOI
  31. Nikolov M, Bever CA, Upfill-Brown A, Hamainza B, Miller JM, Eckhoff PA, et al. Malaria elimination campaigns in the Lake Kariba Region of Zambia: a spatial dynamical model. PLoS Comput Biol. 2016;12:e1005192. · DOI
  32. Gerardin J, Bever CA, Bridenbecker D, Hamainza B, Silumbe K, Miller JM, et al. Effectiveness of reactive case detection for malaria elimination in three archetypical transmission settings: a modelling study. Malar J. 2017;16:248. · DOI
  33. Celhay OJ, Silal SP, Maude RJ, Gran Mercado CE, Shretta R, White LJ. An interactive application for malaria elimination transmission and costing in the Asia-Pacific. Wellcome Open Res. 2019;4:61. · DOI
  34. Silal SP, Shretta R, Celhay OJ, Gran Mercado CE, Saralamba S, Maude RJ, et al. Malaria elimination transmission and costing in the Asia-Pacific: a multi-species dynamic transmission model. Wellcome Open Res. 2019;4:62. · DOI
  35. Brown Z, Kramer R, Mutero C, Kim D, Miranda ML, Ameneshewa B, et al. Stakeholder development of the malaria decision analysis support tool (MDAST). Malar J. 2012;11:P15. · DOI
  36. The Global Fund. The Global Fund’s New Funding Model The Global Fund to Fight AIDS, tuberculosis and malaria: fourth replenishment (2014–2016). Geneva: The Global Fund; 2013.
  37. Runge M, Snow RW, Molteni F, Thawer S, Mohamed A, Mandike R, et al. Simulating the council-specific impact of anti-malaria interventions: a tool to support malaria strategic planning in Tanzania. PLoS ONE. 2020;15:e0228469. · DOI
  38. Programme National Malaria Control. Supplementary malaria midterm strategic plan 2018–2020. Dar Es Salaam: Ministry of Health Community Development Gender Elderly & Children; 2019.
  39. Renggli S, Mandike R, Kramer K, Patrick F, Brown NJ, McElroy PD, et al. Design, implementation and evaluation of a national campaign to deliver 18 million free long-lasting insecticidal nets to uncovered sleeping spaces in Tanzania. Malar J. 2013;12:85. · DOI
  40. Lungo JH. The reliability and usability of district health information software: case studies from Tanzania. Tanzan J Health Res. 2008;10:39–45. · DOI
  41. Kabula B, Derua YA, Tungui P, Massue DJ, Sambu E, Stanley G, et al. Malaria entomological profile in Tanzania from 1950 to 2010: a review of mosquito distribution, vectorial capacity and insecticide resistance. Tanzan J Health Res. 2011;13:319–31. · DOI
  42. Kisinza W, Nkya T, Msangi S, Mbilu T, Batengana B, Lyimo E, et al. Detection and monitoring of insecticide resistance in malaria vectors in Tanzania Mainland. Dar es Salaam: National Institute for Medical Research; 2015.
  43. Chacky F, Runge M, Rumisha SF, Machafuko P, Chaki P, Massaga JJ, et al. Nationwide school malaria parasitaemia survey in public primary schools, the United Republic of Tanzania. Malar J. 2018;17:452. · DOI · Paper page
  44. White RG, Charalambous S, Cardenas V, Hippner P, Sumner T, Bozzani F, et al. Evidence-informed policy making at country level: lessons learned from the South African tuberculosis think tank. Int J Tuberc Lung Dis. 2018;22:606–13. · DOI
  45. Solter S, Solter C. Providing technical assistance to ministries of health: lessons learned over 30 years. Glob Health Sci Pract. 2013;1:302–7. · DOI
  46. Bowen S, Erickson T, Martens PJ, Crockett S. More than “using research”: the real challenges in promoting evidence-informed decision-making. Healthc Policy. 2009;4:87–102.
  47. The MacKenzie D, Trough Certainty. In: Williams R, Faulkner W, Fleck J, editors. Exploring expertise: issues and perspectives. London: Palgrave Macmillan; 1998. p. 325–9. · DOI · Paper page
  48. Bilcke J, Beutels P, Brisson M, Jit M. Accounting for methodological, structural, and parameter uncertainty in decision-analytic models: a practical guide. Med Decis Making. 2011;31:675–92. · DOI
  49. The malERA Consultative Group on Modeling. A research agenda for malaria eradication: modeling. PLoS Med. 2011;8:e1000403. · DOI

Cited by

Sorted by citations

Metadata

Authors
Runge, M., Molteni, F., Mandike, R., Snow, R. W., Lengeler, C., Mohamed, A., & Pothin, E.
Venue
Malaria Journal
Publisher
Springer Science and Business Media LLC
Published
2020-03-02
Volume
19
Issue
1
ISSN
1475-2875
DOI
10.1186/s12936-020-03173-0
PMC
PMC7053121
Cited by
23
References
1
Language
en
Licence
Funders
  • Global Fund to Fight AIDS, Tuberculosis and Malaria
  • Swiss Agency for Development and Cooperation
  • Wellcome Trust (079080, 103602, 212176)
  • Department for International Development (203155)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review