Best practices in engaging with national malaria control programs to build trusting relationships and strengthen capacity.

1. COLLABORATIVE SPIRIT – Be humble, open to listening and supportive

• Seek out conversation with the NMCP to learn about their needs and to provide an overview of malaria modeling and how it could bring value to improve decision processes on identified needs
• Be there to help and support the program to identify questions to be addressed via modeling; be patient and willing to step away in case there is no immediate need or interest in modeling 
• A genuine interest in the success of the program (not your own) is a prerequisite for building long-term and trusting relationships
• Where possible, support the generation of needed data or underlying systems required to generate data
• Foreign modeling partners: reinforce existing or build new in-country modeling capacity and seek out exchanges with other modelers or researchers that have or are working with the country program

2. CONTEXT – Do your homework, understand the malaria policy and epidemiological context 

• Get familiar with the malaria history, processes and constraints in malaria policy and strategies as well as future plans in the country
• Understand the funding landscape and opportunities for the program staff and modelers 
• Plan for in country visits that fit into the schedule of the program; consider smart and sustainable travel options and a considerate mix of in-person and virtual meetings
• Be aware of local stakeholders from the government, health sector, private sector and communities among others that are contributing to malaria policy or control
• Go beyond and learn about the local culture, beliefs, and language as much as possible; speaking the same language and showing respect for the culture will lead to a trusting and strong relationship

3. EXPECTATIONS – Manage expectations on scope, processes, and result sharing early on 

• Set clear expectations of what modeling can and cannot do
• Discuss potential additional objectives, such as other uses of modeling results, improved understanding of data, improved understanding and encouragement of modeling use by country programs
• Discuss requirements on data availability and access as soon as possible
• Seek agreement with NMCPs when presenting or using any of the collaborative modeling work 
• Clarify expectations on duration and iterations on the proposed collaboration
• Establish appropriate channels for communication (to inform, to engage, to discuss) and share expectations on the frequency of communication

4. DATA SHARING – Be proactive in identifying data availability, access, and requirements

• Explore and seek out available data in country
• Take on leadership, be efficient in getting institutions to formulate and agree to data agreements 
• Consider that if not stated otherwise, data shared by NMCPs remain property of the NMCP and modeling analyses based on these data may be confidential
• Understand the personal or proprietary nature of certain data 
• Ask NMCPs about the context of collected data and data cleaning steps
• Assess data quality and report back identified issues, cleaning methods along with sharing cleaned datasets

5. COMMUNICATION – Ensure clear, accurate, easy-to-understand communication 

• Aim not only to communicate the results of your analysis but also to provide key learnings from your approach
• Avoid unnecessary complexity in high-level model descriptions; when explaining more complex model features, be precise and patient
• Be clear about the model’s limitations and different sources of uncertainty that are most relevant for the modeling question and interpretation of results
• Be open and transparent on assumptions, methodologies, and data used to parameterize the model; provide a supplementary report with the methodological details
• Implement thorough testing and validation, ensure building tangible credibility for your approach, be prepared and keen to discuss and validate your results with other groups
• Solicit feedback, comments, and ideas from the NMCP and partners on format and content of shared results and methods; consider written feedback via questionnaires or other forms to provide opportunity for everyone to share their thoughts
• Allow time to acknowledge each other’s efforts and address suggestions for improvement
• Foreign modelers: if possible, provide key recommendations and reports in the local language

6. VALUE – Be value-oriented in the results and information shared with the program

• Frame modeling results around the problem to address; highlight their contribution to the solution
• Ask NMCP what values and objectives should be focused on; it is likely that the NMCP already has near-term goals such as lowering treatment failures nationwide or eliminating malaria in a particular set of districts
• Greatest value may not always be quantitative modeling results or technical details but can also be of qualitative nature. The value of the process can sometimes be as important as the outputs themselves. 
• Support evidence-informed decision making by synthesizing relevant literature and information and putting them into context to be communicated along with the modeling results

7. INTEGRATION & SUSTAINABILITY – Work towards integration, iteration, and sustainability of the modeling process

• Be proactive and frequently offer to share your skills with the program and interested research partners 
• Integrate modeling into existing policy processes where possible, rather than to add new ones on top
• Be methodical and practical, allow your analysis to be tractable, reproducible, and extendable by yourself and partners
• Foreign modelers: aim to replace external expertise with national expertise through your own collaborative efforts to develop and strengthen modeling capacity

These best practices are based on AMMnet members’ experiences as well as information extracted from key documents.^1,2,3,4They do not provide any warranties.

^[1]Silal, S. Bardsley, C, Menon, R. Abullahi, L and White, L (2022). Epidemiological modelling for public health decision-making in sub-Saharan Africa: A strategic plan for capacity strengthening. London: Foreign, Commonwealth and Development Office.

^[2] Noor, A.(2019). Potential role of modeling in malaria progress analysis, policies and operational strategies. Using Models to Guide Public Health Policy: Community-Defined Standards. Presented at University of Washington, Seattle, WA.

^[3] Guidance for country-level TB modelling. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA 3.0 IGO.

^[4] Runge et al. Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania. Malar J. 2020 Mar 2;19(1):101. doi: 10.1186/s12936-020-03173-0.