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''' Telecom Data''' is a type of [[Secondary Data Sources | secondary data]]. This page introduces '''telecom data''', discusses factors to consider when working with it, and outlines different topics in which researchers have used it for analysis.


==Read First==
* [[Secondary Data Sources | Secondary Data]] is data collected by any party other than the researcher that provides important context for any investigation into a particular intervention.
* '''Telecom data''' is a powerful tool to use for analyses of health, mobility, poverty, and other topics in development.
* Telecom data requires careful [[Research Ethics|ethical handling]] in order to maintain the privacy of individuals.
* When using '''telecom data''' you may face issues pertaining to size and barriers to acquisition.


== Basics ==
==About Telecom Data==


===What is Telecom Data?===
Every time you use your phone to make a call or send a text message, data is recorded by your telecom operator on that transaction. While this '''data''' will not include the specific contents of the call or text, it provides metadata, which includes [[Variable Construction | variables]] like the phone number of the person making the call, the phone number of the person receiving the call, the length of the call, the mobile phone tower associated with the call on either side (caller and received), and the type of mobile phone device used to make the call.  
<onlyinclude>
Every time you use your phone to make a call or send a text message, data is recorded by your telecom operator on that transaction. While this data will not include the specific contents of the call or text, it provides what is known as metadata. It can contain things such as the phone number of the person making the call, the phone number of the person receiving the call, the length of the call, the mobile phone tower associated with the call on either side (caller and received), and the type of mobile phone device used to make the call. While one of the main purposes of this data is to collect the necessary information for an operator to charge customers based on their phone usage, researchers have started to use this type of data for a wide array of uses described in the next section. While the data is always anonymized when provided to researchers, meaning that the actual phone number associated with each record is removed and replaced by an anonymous ID, often the same ID is used to track calls and texts made by the same mobile phone, which allows researchers to infer lots of information about movements, social networks, and general phone usage.</onlyinclude>


===How is Telecom Data Used?===
While one of the main purposes of '''telecom data''' is to collect the necessary information for an operator to charge customers based on their phone usage, researchers have started to use this '''telecom data''' for a wide array of [[Data Analysis | analyses]]. While telecom data is always [[De-identification | de-identified]] and '''anonymized''' when provided to researchers, meaning that the actual phone number associated with each record is removed and replaced by an anonymous ID, often the same ID is used to track calls and texts made by the same mobile phone. This allows researchers to infer a lot of information about individuals’ movements, social networks, and general phone usage.


Research using telecom data has been growing tremendously in recent years. There are lots of areas of research that this data has been used in. These include:
==Considerations for Use==


'''Health'''
===Ethics===


Studying the relationship between population mobility and spread of disease using mobile phone data (Erbach-Schoenberg et al 2016, Tatel et al 2014, Wesolowski 2012, Wesolowski 2015a, Wesolowski 2015b)
'''Telecom data''' is always anonymized when companies share the data with researchers. However, the level of detail within the data can lead to the possibility of [[De-identification | de-anonymization]]. For example, using an '''anonymized dataset''' in which individuals’ hourly locations were specified at the antenna level, [https://www.nature.com/articles/srep01376?ial=1 De Montjoye et al.] uniquely identified 95% of individuals with just four spatio-temporal points per individual. This potential to [[De-identification | de-anonymize data]] and track individuals’ movements raises particularly high concerns in sensitive political situations.


'''Mobility'''
As such, both operators and researchers are working to find ways to use this '''telecom data''' in a manner compatible with personal privacy and high ethical standards. Consider, for example, Orange’s [https://datacollaboratives.org/cases/orange-telecom-data-for-development-challenge-d4d.html Data for Development Challenge] (D4D). In D4D’s second iteration in 2015, Orange created an External Ethics Panel to review proposals and entries through an ethical lens. The panel denied '''telecom data''' access to any proposals that contained questionable ethics and also reviewed ethics in ongoing research. '''D4D''' also carefully selected '''the temporal and spatial granularity''' of the released data in order to prevent '''de-anonymization'''. These types of efforts and careful review are critical to making '''telecom data''' available for research while simultaneously maintaining the privacy of telecom users.


Using mobile phone data to study patterns of internal migration (Blumenstock 2012, Wesolowski 2013) and studying mobility to improve disaster response (Bengtsson et al 2011)
===Telecom Operators===


'''Poverty Mapping'''
As '''telecom data''' is proprietary '''data''' owned by telecom operators, obtaining access to it can be extremely difficult. In general, to obtain access to the '''telecom data''', the provider and the requestor must create and sign lengthy agreements that outline the use of the '''data''' and set conditions for its use. It can be difficult to convince providers to spend time and effort on these requests, thus limiting the use of telecom data in many contexts.


Researchers have looked at how variables that can be created using mobile phone metadata can be correlated with measures of poverty (Steele 2017 and Blumenstock 2015).
Some operators have, however, released snippets of their data, such as in the case of '''D4D'''. In 2013, Orange released data from Cote d'Ivore; in 2015, Orange and Sonatel released data from Senegal. Other initiatives work to make telecom data more accessible to researchers. For example, [https://www.opalproject.org/ The Open Algorithms Project] (OPAL), an initiative launched in 2017, seeks to provide access to statistical information extracted from anonymized, secured and formatted telecom data.  


[https://soundcloud.com/worldbank/between-2-geeks-episode-4-what-can-you-measure-with-cell-phone-metadata?in=worldbank/sets/between-2-geeks This podcast] from the World Bank gives a nice overview.
Its intention is for open algorithms accessed by an '''API''' to run on '''OPAL servers''' of partner telecom companies. The data will thus not leave partner companies, and researchers and policy makers will be able to obtain relevant, aggregated information from the telecom data. OPAL is just one example of the ways in which telecom data can be made more accessible to researchers and policymakers.


===What are Things to Consider?===
===Size of the Data===


'''Ethics'''
As with other types of big data, telecom data can consist of millions and billions of observations, depending on the dataset provided. If the dataset is limited to millions of observations, one can manipulate and analyze it using software like Python or other libraries written for statistical analysis. When data consists of billions of observations, it will often be stored on a [https://hadoop.apache.org/ Hadoop cluster]. In order to analyze data on this cluster, there are several different software options, including [https://hive.apache.org/ Hive], which uses commands very similar to SQL; [https://pig.apache.org/ Pig]; and [https://hortonworks.com/apache/spark/ Spark].


While telecom data is always anonymized when companies share the data with researchers; nevertheless, the level of detail provided can lead to the possibility of de-anonymization. For example, De Montjoye et al 2013 find that with just four spatio-temporal points per individual, they are able to uniquely identify 95% of individuals in an anonymized dataset where the location of individuals at the level of the antenna is specified hourly. This potential for de-anonymization is important because especially in sensitive political situations, the ability to track the movement of particular individuals is very concerning. Nevertheless, the research developed with this type of data is valuable, and both operators and researchers are working to find ways of using the data in a way that is compatible with high ethical standards. One example of this is the [http://www.d4d.orange.com/en/Accueil Data for Development Challenge] led by Orange. In the second iteration of the Challenge in 2015, after having gone through the experience of the first Challenge in 2013, Orange realized the importance of ensuring that research complies with ethical standards. For the Challenge, an External Ethics Panel was created in order to review proposals and ensure that all entries were reviewed from an ethical viewpoint and any proposed projects that contained ethical concerns were not granted access to the data, and any research that along the way raised concerns was reviewed. More information is available on the ethic standards in this [file:///C:/Users/wb504522/Downloads/D4D_Challenge_DEEP_Report_IBE.pdf report]. Additionally, the D4D Challenge carefully selected the granularity of the data (in terms of both time frequency and spatial granularity) that it released in order to ensure that de-anonymization would not be possible. These types of efforts and careful review are necessary to ensure that telecom data can be used for valuable research without jeopardizing the privacy of telecom users.
==Research using Telecom Data==


'''Working with Telecom Operators'''
Research using telecom data has grown tremendously in recent years. The following sections outline key pieces of current literature that use '''mobile data''' in [[Data Analysis | analyses]] related to health, mobility, and poverty.


One of the toughest aspects of working with telecom data is that it is proprietary data owned by telecom operators; therefore, obtaining access to this data can be extremely difficult. Some operators have released snippets of their data through mediums, such as the Data for Development Challenge. In 2013, Orange released data from Cote d'Ivore and in 2015 they released data from Senegal in collaboration with Sonatel. In general, to obtain access to the data, it is necessary to create and sign lengthy agreements that outline the use of the data and set conditions for its use. Yet it can be difficult to convince providers to spend time and effort on these requests, which has limited the use of this type of data in many contexts.
===Health===


There is a new project currently under development, the [http://www.opalproject.org/ Open Algorithms (OPAL) Project], which is being developed by a group of partners to provide access to statistical information extracted from anonymized, secured and formatted telecom data. The idea is that open algorithms accessed by an API will run on OPAL servers of partner telecom companies. In this way, the data will not leave partner companies, yet it will still be possible for researchers and policy makers to obtain relevant information from the telecom data in an aggregated manner. This is a new project, but is one example of how accessing this type of data in the future could be much easier and available to a much larger group of researchers and policy makers.
An array of researchers have used telecom data to study the relationship between population mobility and the spread of disease. [https://www.ncbi.nlm.nih.gov/pubmed/27777514 Erbach-Schoenberg et al.] study the impact of seasonally varying population numbers on disease incidence estimates, while [https://science.sciencemag.org/content/338/6104/267 Wesolowski et al.] quantify the impact of human mobility on malaria. [https://www.pnas.org/content/112/38/11887.short Wesolowski et al.] look at the impact of human mobility on the emergence of dengue epidemics in Pakistan and [https://www.pnas.org/content/112/35/11114.short Wesolowski et al.] quantify seasonal population fluxes driving rubella transmission. Finally, [https://www.sciencedirect.com/science/article/pii/S138650561400015X Turner-McGrievy and Tate] take a closer look at mobile-based health solutions in a study on remotely-delivered weight-loss interventions.  


'''Size of the Data'''
===Mobility===
[https://www.jblumenstock.com/files/papers/jblumenstock_itd2012.pdf Blumenstock] and [https://royalsocietypublishing.org/doi/full/10.1098/rsif.2012.0986 Wesolowski et al.] use mobile phone data to study patterns of internal migration, while [https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001083 Bengtsson et al.] use it to track post-earthquake population movements in Haiti and sculpt better responses to disaster.


As with other types of Big Data, telecom data can consist of millions and billions of observations, depending on the dataset provided. If the dataset is limited to millions of observations, it is possible to manipulate and analyze using software such as Python and the various libraries that have been written for statistical analysis. When data consists of billions of observations, it will often be stored on a [http://hadoop.apache.org/ Hadoop cluster]. In order to analyze data on this cluster, there are several different software options. These include:
===Poverty===


[https://hive.apache.org/ Hive], which uses commands very similar to SQL
[https://royalsocietypublishing.org/doi/full/10.1098/rsif.2016.0690 Blumenstock et al.] and
 
[https://science.sciencemag.org/content/350/6264/1073 Steele et al.] use mobile phone data to map and predict poverty, considering to what extent certain variables created by telecom data are correlated with measures of poverty.
[https://pig.apache.org/ Pig]
 
[https://hortonworks.com/apache/spark/ Spark]
 
== Back to Parent ==
This article is part of the topic [[Secondary Data Sources]]


== Related Pages ==
[[Special:WhatLinksHere/Telecom_Data|Click here to see pages related to this topic.]]


== Additional Resources ==
== Additional Resources ==


Lots of information can be found on the website of [http://netmob.org/ NetMob], the main conference on the analysis of mobile phone datasets.  
* [https://netmob.org NetMob], the main conference on the analysis of mobile phone datasets
 
* World Bank, [https://pubdocs.worldbank.org/en/233361500582117345/2-Milusheva-Telecom-Presentation-Cross-Cutting-Session.pdf Using Telecom Data to Track Movement at High Spatial and Temporal Frequencies]
'''Scientific Papers'''
* World Bank, [https://soundcloud.com/worldbank/between-2-geeks-episode-4-what-can-you-measure-with-cell-phone-metadata?in=worldbank/sets/between-2-geeks Podcast on telecom data, and implications for development research]
 
* Scientific Papers :
Bengtsson, Linus et al. (2011). “Improved response to disasters and outbreaks by tracking
** Bengtsson, Linus et al. (2011). “Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti”. PLoS Med 8.8, e1001083.
population movements with mobile phone network data: a post-earthquake geospatial
** Blumenstock, Joshua E (2012). “Inferring patterns of internal migration from mobile phone call records: Evidence from Rwanda”. Information Technology for Development 18.2, pp. 107–125.
study in Haiti”. PLoS Med 8.8, e1001083.
** Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. (2015). "Predicting poverty and wealth from mobile phone metadata." Science 350.6264, pp. 1073-1076.
 
** Blumenstock, Joshua E., Nathan Eagle, and Marcel Fafchamps. (2016). "Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters." Journal of Development Economics 120, pp. 157-181.
Blumenstock, Joshua E (2012). “Inferring patterns of internal migration from mobile phone
** De Montjoye, Yves-Alexandre, et al. (2013). "Unique in the crowd: The privacy bounds of human mobility." Scientific reports 3: pp. 1376.
call records: Evidence from Rwanda”. Information Technology for Development 18.2,
** Erbach-Schoenberg, Elisabeth Zu et al. (2016). “Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates”. Population health Metrics 14.1, pp. 35.
pp. 107–125.
** Le Menach, Arnaud et al. (2011). “Travel risk, malaria importation and malaria transmission in Zanzibar”. In: Scientific reports 1.
 
** Ruktanonchai, Nick W et al. (2016). “Identifying malaria transmission foci for elimination using human mobility data”. In: PLoS Comput Biol 12.4, e1004846.
Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. "Predicting poverty and wealth from mobile phone metadata." Science 350.6264 (2015): 1073-1076.
 
Blumenstock, Joshua E., Nathan Eagle, and Marcel Fafchamps. "Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters." Journal of Development Economics 120 (2016): 157-181.
 
De Montjoye, Yves-Alexandre, et al. "Unique in the crowd: The privacy bounds of human mobility." Scientific reports 3 (2013): 1376.
 
Erbach-Schoenberg, Elisabeth zu et al. (2016). “Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates”. Population health
metrics 14.1, p. 35.
 
Le Menach, Arnaud et al. (2011). “Travel risk, malaria importation and malaria
transmission in Zanzibar”. In: Scientific reports 1 (2011).
 
Ruktanonchai, Nick W et al. (2016). “Identifying malaria transmission foci for elimi-
nation using human mobility data”. In: PLoS Comput Biol 12.4 (2016), e1004846.
 
Steele, Jessica E., et al. "Mapping poverty using mobile phone and satellite data." Journal of The Royal Society Interface 14.127 (2017): 20160690.
 
Tatem, Andrew J, Youliang Qiu, et al. (2009). “The use of mobile phone data for
the estimation of the travel patterns and imported Plasmodium falciparum rates
among Zanzibar residents”. In: Malar J 8 (2009), p. 287.
 
Tatem, Andrew J et al. (2014). “Integrating rapid risk mapping and mobile phone call record
data for strategic malaria elimination planning”. Malaria journal 13.1, p. 52.
 
Wesolowski, Amy et al. (2012). “Quantifying the impact of human mobility on malaria”.
Science 338.6104, pp. 267–270.
 
Wesolowski, Amy et al. (2013). “The use of census migration data to approximate human
movement patterns across temporal scales”. PloS one 8.1, e52971.
 
Wesolowski, Amy et al. (2015a). “Impact of human mobility on the emergence of dengue epidemics in Pakistan”. Proceedings of the National Academy of Sciences 112.38, pp. 11887–11892.


Wesolowski, Amy et al. (2015b). “Quantifying seasonal population fluxes driving rubella
** Steele, Jessica E., et al. (2017). "Mapping poverty using mobile phone and satellite data." Journal of The Royal Society Interface 14.127: 20160690.
transmission dynamics using mobile phone data”. Proceedings of the National Academy
** Tatem, Andrew J, Youliang Qiu, et al. (2009). “The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents”. In: Malar J 8, pp. 287.
of Sciences 112.35, pp. 11114–11119.
** Tatem, Andrew J et al. (2014). “Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning”. Malaria Journal 13.1, pp. 52.
** Wesolowski, Amy et al. (2012). “Quantifying the impact of human mobility on malaria”. Science 338.6104, pp. 267–270.
** Wesolowski, Amy et al. (2013). “The use of census migration data to approximate human movement patterns across temporal scales”. PloS one 8.1, e52971.
** Wesolowski, Amy et al. (2015a). “Impact of human mobility on the emergence of dengue epidemics in Pakistan”. Proceedings of the National Academy of Sciences 112.38, pp. 11887–11892.
** Wesolowski, Amy et al. (2015b). “Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data”. Proceedings of the National Academy of Sciences 112.35, pp. 11114–11119.


[[Category: Secondary Data Sources]]
[[Category: Secondary Data Sources]]

Latest revision as of 19:44, 12 July 2022

Telecom Data is a type of secondary data. This page introduces telecom data, discusses factors to consider when working with it, and outlines different topics in which researchers have used it for analysis.

Read First

  • Secondary Data is data collected by any party other than the researcher that provides important context for any investigation into a particular intervention.
  • Telecom data is a powerful tool to use for analyses of health, mobility, poverty, and other topics in development.
  • Telecom data requires careful ethical handling in order to maintain the privacy of individuals.
  • When using telecom data you may face issues pertaining to size and barriers to acquisition.

About Telecom Data

Every time you use your phone to make a call or send a text message, data is recorded by your telecom operator on that transaction. While this data will not include the specific contents of the call or text, it provides metadata, which includes variables like the phone number of the person making the call, the phone number of the person receiving the call, the length of the call, the mobile phone tower associated with the call on either side (caller and received), and the type of mobile phone device used to make the call.

While one of the main purposes of telecom data is to collect the necessary information for an operator to charge customers based on their phone usage, researchers have started to use this telecom data for a wide array of analyses. While telecom data is always de-identified and anonymized when provided to researchers, meaning that the actual phone number associated with each record is removed and replaced by an anonymous ID, often the same ID is used to track calls and texts made by the same mobile phone. This allows researchers to infer a lot of information about individuals’ movements, social networks, and general phone usage.

Considerations for Use

Ethics

Telecom data is always anonymized when companies share the data with researchers. However, the level of detail within the data can lead to the possibility of de-anonymization. For example, using an anonymized dataset in which individuals’ hourly locations were specified at the antenna level, De Montjoye et al. uniquely identified 95% of individuals with just four spatio-temporal points per individual. This potential to de-anonymize data and track individuals’ movements raises particularly high concerns in sensitive political situations.

As such, both operators and researchers are working to find ways to use this telecom data in a manner compatible with personal privacy and high ethical standards. Consider, for example, Orange’s Data for Development Challenge (D4D). In D4D’s second iteration in 2015, Orange created an External Ethics Panel to review proposals and entries through an ethical lens. The panel denied telecom data access to any proposals that contained questionable ethics and also reviewed ethics in ongoing research. D4D also carefully selected the temporal and spatial granularity of the released data in order to prevent de-anonymization. These types of efforts and careful review are critical to making telecom data available for research while simultaneously maintaining the privacy of telecom users.

Telecom Operators

As telecom data is proprietary data owned by telecom operators, obtaining access to it can be extremely difficult. In general, to obtain access to the telecom data, the provider and the requestor must create and sign lengthy agreements that outline the use of the data and set conditions for its use. It can be difficult to convince providers to spend time and effort on these requests, thus limiting the use of telecom data in many contexts.

Some operators have, however, released snippets of their data, such as in the case of D4D. In 2013, Orange released data from Cote d'Ivore; in 2015, Orange and Sonatel released data from Senegal. Other initiatives work to make telecom data more accessible to researchers. For example, The Open Algorithms Project (OPAL), an initiative launched in 2017, seeks to provide access to statistical information extracted from anonymized, secured and formatted telecom data.

Its intention is for open algorithms accessed by an API to run on OPAL servers of partner telecom companies. The data will thus not leave partner companies, and researchers and policy makers will be able to obtain relevant, aggregated information from the telecom data. OPAL is just one example of the ways in which telecom data can be made more accessible to researchers and policymakers.

Size of the Data

As with other types of big data, telecom data can consist of millions and billions of observations, depending on the dataset provided. If the dataset is limited to millions of observations, one can manipulate and analyze it using software like Python or other libraries written for statistical analysis. When data consists of billions of observations, it will often be stored on a Hadoop cluster. In order to analyze data on this cluster, there are several different software options, including Hive, which uses commands very similar to SQL; Pig; and Spark.

Research using Telecom Data

Research using telecom data has grown tremendously in recent years. The following sections outline key pieces of current literature that use mobile data in analyses related to health, mobility, and poverty.

Health

An array of researchers have used telecom data to study the relationship between population mobility and the spread of disease. Erbach-Schoenberg et al. study the impact of seasonally varying population numbers on disease incidence estimates, while Wesolowski et al. quantify the impact of human mobility on malaria. Wesolowski et al. look at the impact of human mobility on the emergence of dengue epidemics in Pakistan and Wesolowski et al. quantify seasonal population fluxes driving rubella transmission. Finally, Turner-McGrievy and Tate take a closer look at mobile-based health solutions in a study on remotely-delivered weight-loss interventions.

Mobility

Blumenstock and Wesolowski et al. use mobile phone data to study patterns of internal migration, while Bengtsson et al. use it to track post-earthquake population movements in Haiti and sculpt better responses to disaster.

Poverty

Blumenstock et al. and Steele et al. use mobile phone data to map and predict poverty, considering to what extent certain variables created by telecom data are correlated with measures of poverty.

Related Pages

Click here to see pages related to this topic.

Additional Resources

  • NetMob, the main conference on the analysis of mobile phone datasets
  • World Bank, Using Telecom Data to Track Movement at High Spatial and Temporal Frequencies
  • World Bank, Podcast on telecom data, and implications for development research
  • Scientific Papers :
    • Bengtsson, Linus et al. (2011). “Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti”. PLoS Med 8.8, e1001083.
    • Blumenstock, Joshua E (2012). “Inferring patterns of internal migration from mobile phone call records: Evidence from Rwanda”. Information Technology for Development 18.2, pp. 107–125.
    • Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. (2015). "Predicting poverty and wealth from mobile phone metadata." Science 350.6264, pp. 1073-1076.
    • Blumenstock, Joshua E., Nathan Eagle, and Marcel Fafchamps. (2016). "Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters." Journal of Development Economics 120, pp. 157-181.
    • De Montjoye, Yves-Alexandre, et al. (2013). "Unique in the crowd: The privacy bounds of human mobility." Scientific reports 3: pp. 1376.
    • Erbach-Schoenberg, Elisabeth Zu et al. (2016). “Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates”. Population health Metrics 14.1, pp. 35.
    • Le Menach, Arnaud et al. (2011). “Travel risk, malaria importation and malaria transmission in Zanzibar”. In: Scientific reports 1.
    • Ruktanonchai, Nick W et al. (2016). “Identifying malaria transmission foci for elimination using human mobility data”. In: PLoS Comput Biol 12.4, e1004846.
    • Steele, Jessica E., et al. (2017). "Mapping poverty using mobile phone and satellite data." Journal of The Royal Society Interface 14.127: 20160690.
    • Tatem, Andrew J, Youliang Qiu, et al. (2009). “The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents”. In: Malar J 8, pp. 287.
    • Tatem, Andrew J et al. (2014). “Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning”. Malaria Journal 13.1, pp. 52.
    • Wesolowski, Amy et al. (2012). “Quantifying the impact of human mobility on malaria”. Science 338.6104, pp. 267–270.
    • Wesolowski, Amy et al. (2013). “The use of census migration data to approximate human movement patterns across temporal scales”. PloS one 8.1, e52971.
    • Wesolowski, Amy et al. (2015a). “Impact of human mobility on the emergence of dengue epidemics in Pakistan”. Proceedings of the National Academy of Sciences 112.38, pp. 11887–11892.
    • Wesolowski, Amy et al. (2015b). “Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data”. Proceedings of the National Academy of Sciences 112.35, pp. 11114–11119.