An updated version of the case study os available here.

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 (; Institute for Governance Reform (; Directorate of Science, Technology and Innovation (DSTI) (; Ministry of Finance’s Research and Delivery Division (MoF-RDD) (; Statistics Sierra Leone (Stats-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 a nationally representative survey, which was 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 survey took place 11-18 April 2020 and included 2,395 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:

 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? 

Selected Results

Below we include five 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 in 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. Mobility data

How did the lockdown impact travel? 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 is 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:

 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?


Policy Impacts 

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 policymaking process used by the Presidential Taskforce 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.” 

In August 2020, a second survey was completed by MIT GOV/LAB and IGR using phone and field methods, with results forthcoming. As the pandemic evolves, there seems to be a shift from emergency mode to managing the situation as a new reality, which raises 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?

Photo of Freetown, Sierra Leone by Random Institute on Unsplash