The following case study is part of the “COVID-19 in Slums & Informal Settlements: Guidelines & Responses” course developed by faculty and students from MIT’s Department of Urban Studies and Planning. Sign up for the updated free online course, which is self-paced over four weeks. Below we include text and visuals from the case study.
About the course
Learn how COVID-19 has impacted the world’s most vulnerable populations across the Global South and understand the varied responses and guidelines in self-organized, self-built urban poor communities.
Case Study: From data to policy — Research to inform Covid-19 response in Sierra Leone
This case study includes data from MIT research partnerships in Sierra Leone, with a focus on results in the capital city of Freetown. MIT Governance Lab (MIT GOV/LAB) and the Civic Data Design Lab (CDDL) at MIT collaborated with the Institute of Governance Reform, Sierra Leone’s Directorate of Science, Technology and Innovation (DSTI), the Ministry of Finance’s Research and Delivery Division, and Statistics Sierra Leone to launch a nationally-representative survey and analyze Call Detail Record (CDR) data. Based on these data, we gathered critical information to measure people’s understanding of the coronavirus, perceptions of how to prevent spread, prevalence of misinformation across every district in the country, and mobility patterns during the pandemic. The goal of this research collaboration is to produce data to inform government policies that can potentially lessen the pandemic’s impacts on the most vulnerable populations.
This case study was developed by the MIT Governance Lab (MIT GOV/LAB) in partnership with the Civic Data Design Lab at MIT (http://civicdatadesignlab.mit.edu/); Institute for Governance Reform (http://igrsl.org/); Directorate of Science, Technology and Innovation (DSTI) (https://www.dsti.gov.sl/); Ministry of Finance’s Research and Delivery Division (MoF-RDD) (https://mof.gov.sl/); Statistics Sierra Leone (Stats-SL) (https://www.statistics.sl/).
In the beginning of the pandemic, amidst high levels of uncertainty, governments in West Africa were preparing emergency responses to the coronavirus. In Sierra Leone, the government took measures as early as January 2020 to quarantine all travelers from countries reporting Covid-19 cases and travelers with high temperatures. With limited public health infrastructure, slowing the spread has been critical to protecting their population of nearly 8 million. In late March, Sierra Leone confirmed the first case in-country, put in place a temporary lockdown, and convened development partners to support Covid-19 preparedness and response.
MIT GOV/LAB collaborated with partners in Sierra Leone to launch two nationally representative surveys, designed with input from multiple government agencies to ensure utility and maximize uptake of the results for decision-making. The study in Sierra Leone is a collaboration with the Institute for Governance Reform, in partnership with Sierra Leone’s Directorate of Science, Technology and Innovation (DSTI) and Ministry of Finance’s Research and Delivery Division (MoF-RDD). The surveys took place in April and July/August 2020 and included around 2,400 respondents from across all 16 districts in Sierra Leone.
A successful response to Covid-19 requires not only financial and technical resources, but also the efficiency and insights that real-time data bring to the process. Our collaboration in Sierra Leone builds on strong local partnerships, as well as our previous experience conducting research on the Ebola epidemic in West Africa.
Applying lesson learned from Ebola to Covid-19
In low-income countries, like Sierra Leone, a combination of factors, including low hospital and health care system capacity, high urban density, poor access to water, and tenuous food security, means that response policies must be carefully designed to consider a wide range of economic and social impacts. The global crisis is also testing the capacity of our institutions and governance systems to respond quickly and effectively.
With governments rushing to put in place policies and guidelines to stem the spread of Covid-19, it’s important to look to the past to inform the present. And we don’t have to look far. Building on MIT GOV/LAB’s experience during the 2014-2015 Ebola epidemic in West Africa, we want to advance understanding of what the role of trust is in ensuring that people comply with government recommendations, and how leaders can build trust and buy-in both during and before a crisis. Some popular articles summarizing key findings from research during Ebola:
- [Psychology Today] How Building Trust Can Save Lives https://www.psychologytoday.com/us/blog/difference-opinion/202009/how-building-trust-can-save-lives
- [MIT News] How Door-to-Door Canvassing Slowed an Epidemic: http://news.mit.edu/2020/how-door-to-door-canvassing-slowed-epidemic-ebola-0227
- [No Jargon – Podcast] Learning from Ebola: https://scholars.org/podcast/learning-ebola
Questions for consideration: How can lessons from Ebola inform Covid response in Sierra Leone? What is similar or different about the current situation? How can these lessons apply to the urban context of Freetown or other major cities?
Priority Policy Questions
Together with our partners, we identified the following policy priorities, including a wide range of topics related to Covid-19, to inform decision-makers.
- What do citizens already know about Coronavirus?
- What public health messages need to be communicated as a priority?
- Which districts need targeted messaging for Coronavirus response?
- How long should the lockdown be?
- Which districts are most at risk of going hungry during a lockdown?
- Which districts are most vulnerable to the impacts of lockdown?
- What is the capacity for community-led coronavirus response by district?
- Which district mobility data show compliance with lockdown measures?
Below we include seven key findings from the research to demonstrate how data can support decision-making, including descriptive statistics, heat maps, and visualizations. As you review the information consider what policy priorities and actions you would recommend for government decision-makers.
#1. National-level results
The infographic below presents a summary of the national-level results.
#2. Citizen Survey Results in Freetown (Western Area Urban)
The district brief below provides a snapshot of performance across key indicators, as compared to the national averages and similarly performing districts. Yellow, at the top of the chart, indicates a perfect performance (100%) and dark blue, bottom of the chart, is low performance (0%).
Western Area Urban, the formal administrative name for the district that encompasses Freetown, performs above the national average on food security and lockdown impacts. Respondents believe the government’s willingness to provide healthcare falls short of its capacity. Priorities for action include improving knowledge of and compliance with Covid prevention measures, including willingness to vaccinate or self-isolate.
#3. Community Mobilization
Takeaways for Map 1: Districts in dark purple show where respondents reported the lowest percentage of active Covid-19 community groups. Community mobilization is low in most places, though there are a few scattered districts — Kambia, Falaba, Kailahun — that report relatively high community mobilization. In Freetown (Western Area Urban), about one third of respondents reported a community mobilization group, which is just below the national average.
Map 1: Percentage of all respondents who report an active Covid-19 community group, by district
#4. Ability to Survive Lockdown
Takeaways for Map 2: Districts in darker colors on the map below are least able to survive a lockdown of more than 3 days at a time would not work. Almost all respondents in Port Loko and Pujehun fell into this category. A cluster of districts in the northwest also had high numbers of respondents in this category. In Freetown (Western Area Urban), 40% of respondents reported that they cannot survive a lockdown for more than 3 days, which is below the national average.
Map 2: Percentage of all respondents reporting that they cannot survive a lockdown for more than 3 days, by district
#5. Food Security
Takeaways for Map 3: Districts in dark purple have higher rates of food insecurity. Insecurity is distributed across the country, although a few districts in the south are less worrisome. In Freetown (Western Area Urban), food insecurity is also high, with nearly one third of respondents (27%) reporting that they had gone without food at some point in the last seven days.
Map 3: Percentage of all respondents reporting that they had gone without food at some point in the last seven days, by district
#6. Trust in Authorities
Trust in authorities is a critical indicator of compliance with public health measures. In both surveys, we measured people’s trust in different authorities including religious leaders, international NGOs, Ministry of Health, traditional leaders, and the police. Trust levels were very high for most authorities, with at least 60% of respondents trusting religious leaders, international NGOs, the Ministry of Health, the government, and traditional leaders (like a local chief) “a lot.” For these five authorities, the percentage of respondents who trusted either “a lot” or “somewhat” ranged from 91% for religious leaders to 73% for traditional leaders. Levels of trust in police were significantly lower, with only 45% of respondents trusting police “a lot,” and 38% trusting police either “just a little” or “not at all.”
Trust levels in the Ministry of Health were high. It is interesting to note that trust levels were higher in July and August than they had been at the beginning of the pandemic.
We also found that people who had responded saying they had a high level of trust in the government during our first survey, were also more willing to give their children vaccinations. Of respondents from the first survey who said they trusted the government “a lot,” 54% said they were willing to give their children these routine vaccinations, compared to only 26% of respondents who said they trusted the government “not at all.”
You can access select findings on trust from the two surveys here.
#6. Mobility data
How did the lockdown impact travel? Using anonymized CDR data obtained from Africell, we were able to measure the change in mobility in Sierra Leone during Covid-19 lockdowns, thereby measuring the efficacy of lockdown impositions. We saw reductions in mobility up to 70% reduction in some districts, showing high compliance with the lockdown. All the districts covered, saw a reduction in mobility.
The chart below, produced in collaboration with the Civic Design Lab at MIT, compares mobility patterns in February with the first lockdown in April to see how the policy impacted inter-district travel (measured by the change in average distance per trip per day). Drops in the average distance traveled per trip can be used as a proxy for high compliance with the lockdown order, which is the case for Freetown.
Daily movements can be interpreted as the percentage change in average distance per trip when compared to the average in early February, which are indexed to the baseline (first two-weeks in February). The shaded yellow area marks the lockdown period, and the trendlines for the national average are also in yellow.
Results from the rapid response survey in Sierra Leone
To dig more into the survey results, see the summary as well as the full research brief:
- [MIT News] Informing Covid-19 preparedness in Sierra Leone: http://news.mit.edu/2020/informing-covid-19-preparedness-sierra-leone-0513
- [Research Brief] Preliminary Results from Rapid Survey to Inform Covid-19 Response in Sierra Leone: https://mitgovlab.org/results/preliminary-results-from-rapid-survey-to-inform-covid-19-response-in-sierra-leone/
- [Blog] Trust as a Prerequisite for Compliance: Assessing Trust in Sierra Leone During Covid-19: https://mitgovlab.org/news/trust-as-a-prerequisite-for-compliance-assessing-trust-in-sierra-leone-during-covid-19/
Questions for consideration: Given the information provided, what policy priorities and actions would you recommend for government decision-makers? What information is missing that could better support an evidence-based decision? How can the results inform urban policies and preparedness for Covid-19?
Initial results from the first survey supported the design of Sierra Leone’s lockdown plans. David Moinina Sengeh, Sierra Leone’s minister of education and chief innovation officer, noted “As part of the evidence-based and data-driven policy making process used by the Presidential Task Force on Covid-19, the results of this survey were used in combination with other evidence across government and from partners to design Sierra Leone’s lockdown framework.”
As the pandemic moves beyond an acute emergency stage, new questions about effects on development outcomes and whether it’s possible to emerge from the pandemic with improved governance and service delivery. To develop effective policy in Freetown and the rest of Sierra Leone, collecting data from citizens— community mobilization, trust in government, and willingness to comply with public health measures— will be important to inform government response and support decision-making grounded in citizen needs.
Considerations moving forward
Questions for consideration:
- Which results are helpful in developing a Covid-19 response? What is missing?
- What changes would you make to the research design or data visualizations?
- What are key takeaways from the case study?