Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data

Abstract

BackgroundTravel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New ‘big data’ approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures.MethodsWe analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national ‘stay at home’ lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis.ResultsRoutinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15–30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted.ConclusionsThe effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment.

On the predictability of infectious disease outbreaks

“…However, instead of being based on the probability of observing a system in a particular state, it utilizes the frequency of discrete motifs, i.e symbols, associated with the growth, decay, and stasis of a time series. For example, in a binary time series the permutation entropy in two dimensions would count the frequency of the set of possible ordered pairs, {[01], [10]}, and the Shannon entropy, or uniformity, of this distribution is the permutation entropy. In higher dimensions, one can define an alphabet of symbols over all factorial combinations of orderings in a given dimension, e.g., {[0, 1, 2], [2,1,0], [1,0,2], etc.…”

“…However, the predictions for SARS failed to match the data 3,5 . Over the subsequent 15 years, the scientific community developed a rich understanding for how social contact networks, variation in health-care infrastructure, the spatial distribution of prior immunity, etc., drive complex patterns of disease transmission [6][7][8][9][10][11] , and demonstrated that data-driven, dynamic, and or agentbased models can produce actionable forecasts [12][13][14][15][16][17] . Additionally, studies have demonstrated that predicting different components of outbreaks-e.g., the expected number of cases, pace, and tempo of cases needing treatment, demand for prophylactic equipment, importation probability, etc.-is feasible 3,13,[18][19][20][21][22][23][24] .…”

A new twenty-first century science for effective epidemic response

“…For example, a study published in 2019 combined epidemiological surveillance data, travel surveys, parasite genetics and anonymized mobile phone data to measure the spread of malaria parasites in southeast Bangladesh 92 . A retrospective analysis of mobile phone call data in Sierra Leone from 2015 showed how it might have been used to assess the impact of travel restrictions on mobility during the Ebola epidemic 46 .…”

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

“…To date, mobile-derived human mobility, especially using CDRs, have been used to explore the transmission of malaria, 12,31,55 dengue, 29 cholera, 63 measles, 64 rubella, 28 Ebola, 65,66 and HIV infection. 67 Taking malaria as an example, we illustrate how spatiotemporally explicit mobility derived from mobile positioning data has been used to define malaria connectivity and inform interventions.…”