Mapping internal connectivity through human migration in malaria endemic countries

Abstract

Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.

OPEN

SUBJECT CATEGORIES

Malaria

Geography

Public health

Environmental impact

Received: 01 March 2016

Accepted: 01 July 2016

Published: 16 August 2016

Data Descriptor: Mapping internal connectivity through human migration in malaria endemic countries

Alessandro Sorichetta1,2,3, Tom J. Bird1,2, Nick W. Ruktanonchai1,2,Elisabeth zu Erbach-Schoenberg1,2, Carla Pezzulo1,2, Natalia Tejedor1,2,4, Ian C. Waldock4, Jason D. Sadler4, Andres J. Garcia5, Luigi Sedda6 & Andrew J. Tatem1,2

Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration ows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.

Design Type(s) data integration objective observation design

Measurement Type(s) Human Migration

Technology Type(s) digital curation

Factor Type(s)

Homo sapiens anthropogenic habitat Angola Burundi Benin

Burkina Faso Botswana Central African Republic Cote d'Ivoire

Cameroon Democratic Republic of the Congo Republic of Congo

Comoros Djibouti Eritrea Ethiopia Gabon Ghana Guinea

Gambia Guinea-Bissau Equatorial Guinea Kenya Liberia

Madagascar Mali Mozambique Mauritania Malawi Mayotte

Namibia Niger Nigeria Rwanda Sudan Senegal Sierra

Leone Somalia South Sudan Sao Tome and Principe Swaziland

Chad Togo Tanzania Uganda Republic of South Africa y

1WorldPop, Geography and Environment, University of Southampton, Higheld Campus, Southampton SO17 1BJ, UK. 2Flowminder Foundation, Roslagsgatan 17, Stockholm SE-11355, Sweden. 3Institute for Life Sciences, University of Southampton, Higheld Campus, Southampton SO17 1BJ, UK. 4GeoData, University of Southampton, Higheld Campus, Southampton SO17 1BJ, UK. 5Bill and Melinda Gates Foundation, 440 5th Ave N., Seattle, Washington 98109, USA. 6CHICAS, Lancaster Medical School, Lancaster University, Lancaster LA1 4YG, UK. Correspondence and requests for materials should be addressed to A.S. (email: mailto:[e-mail]

Web End =[e-mail] ).

SCIENTIFIC DATA | 3:160066 | DOI: 10.1038/sdata.2016.66 1

Sample Characteristic(s)

Background & Summary

According to the International Organization for Migration1 and The World Bank2, without accounting for seasonal and temporary migrants, more than 1 billion people are currently living outside their places of origin, with about 740 million of them classied as internal migrants. Additionally, in 2014 around 67 million passengers travelled on international and domestic ights every week3 and hundreds of millions are estimated to commute daily by public transport and private vehicles4. Human mobility is expected to continue rising in volume and reach, producing increasing global connectivity that has a range of impacts, including rising numbers of invasive species, the spread of drug resistance, and disease pandemics. In this context, quantifying human mobility across multiple temporal and spatial scales, becomes crucial for quantifying its effects on society57, evaluating its relationship with the environment8,9, better understanding human-related processes such as urbanization and land use change1013, and providing a strong evidence base to support both development1416 and public health1719 applications and policies.

In public health, the role of human mobility in the spread of infectious diseases is exemplied by the presence of HIV/AIDS in areas outside where it rst emerged at the beginning of the twentieth century2022, the 2003 SARS epidemic23, the 2007 Chikungunya outbreaks in Italy and France24,25, the 2009 H1N1 pandemic26, the 2014 Ebola outbreak in Western Africa27, the resurgence of malaria cases in areas where the disease was once eliminated28, and the worldwide spread of drug resistant pathogens29. Consequently, it is clear that to provide better informed guidelines, both at the national and international level, the effects of human mobility and connectivity in driving disease dynamics need to be better understood and accounted for refs 3033.

Local malaria elimination and global malaria eradication are rising up the international agenda3436.

Evidence from the previous global malaria eradication program37, as well as from recent studies, control campaigns, and elimination efforts3841 highlight the importance of accounting for human mobility in designing elimination plans. Infected people may unknowingly transport malaria parasites (potentially including antimalarial-resistant strains42) into new areas. Parasites can be imported either from other countries43 or from other areas within the same country44. Thus, because of the ow of imported cases from high- to low-transmission settings, the latter will face difculties in achieving elimination and maintaining malaria-free status if it is achieved43. Nevertheless, despite the importance of these dynamics being long recognized45,46, attempts to translate human mobility model outputs into malaria policy are still rare47.

As detailed in Tatem7, sources of human mobility data potentially useful for modelling pathogen movements include: air and sea travel data records (including open access modelled versions of them); census migration data; travel history and displacement surveys; GPS tracking data and volunteered geographic information (with the latter including geolocated social media data), and even satellite nighttime light data. In particular, patient travel history data, containing detailed demographic information and travel motivations, are traditionally used to understand malaria parasite importation patterns4850.

Recently, mobile phone call detail records (CDRs) have been increasingly used for measuring short-term human movements51,52 and thus, either alone38,53,54 or in combination with travel history data55 and malaria case data, for supporting malaria control and elimination strategic planning.

However, because of difculties in sharing and accessing CDRs (mostly due to commercial and privacy concerns)7,56,57, alternative datasets are required in order to quantify and map internal connectivity across continental scales. To this end, using CDRs, Wesolowski et al.56 and Ruktanonchai et al.58 demonstrated that widely-available and easy-to-obtain census-derived internal migration ow data can serve as reliable proxies for the relative strength of within-country human connectivity across multiple temporal scales.

Within the framework of the WorldPop Project (http://www.worldpop.org

Web End =www.worldpop.org), and following the approaches described in Henry et al.59 and Garcia et al.60 (Fig. 1), internal census-based migration microdata available through the online IPUMS-International (IPUMSI) database61, along with a number of other ancillary datasets, were assembled and processed to produce an open access archive of estimated 5-year (20052010) internal human migration ows for every Plasmodium falciparum and Plasmodium vivax (hereafter simply referred as Pf and Pv, respectively) endemic country62,63 (Supplementary Table 1).

Methods

Estimating internal migration ows between administrative units

Following Garcia et al.60 a gravity model-based approach was used to estimate the total number of people migrating from one administrative unit to any other administrative unit, between 2005 and 2010, within each malaria endemic country located in Africa, Asia, Latin America and the Caribbean62,63

(Supplementary Table 1).

The simplest gravity-type spatial interaction model, proposed by Zipf64, considers the total population in a location of origin i and in a location of destination j (henceforth simply indicated as i and j), and the distance between the two locations to predict the migration ow (MIGij) between them. Thus, migration

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ows between administrative units can be estimated using the following function:

MIGij

PiPj

dij 1

where Pi and Pj represent the populations in the location of origin i and of destination j, respectively, and dij represents the distance between i and j; with , , and being parameters, used to indicate the magnitude of the effect for each covariate, that are typically estimated in the statistical modelling framework.

In this study, following the notation from Henry et al.59 and Garcia et al.60, the basic gravity-type spatial interaction in equation (1) was extended in order to include additional geographical and socioeconomic factors described in detail in the Data collection and preparation subsection below. Since the census-based migration microdata extracted from the IPUMSI database61 represent only a sample of the total census, a logistic regression was used to model the proportion of people migrating between administrative units65. In particular, the logistic regression was used to model the proportion of people residing in j in the census year who were in i n years prior to the census. Thus, the proportion of migrants in j in the census year that were previously residing in i was estimated using the following logistic regression function:

pij

e01Pi2Pj - 3dij

1 e

0 1Pi2Pj - 3dij 2

where pij MIGij=TOTj; with MIGij and TOTj representing the number of people residing in j in the

census year that were in i n years prior to the census and the total population residing in j in the census year, respectively.

Initially, a separate vector = (0, 1, , n) of coefcients was used in the linear predictor of the gravity model for each country (including malaria non-endemic countries), in Africa, Asia, Latin America and the Caribbean, for which migration data were available in the IPUMSI database61 (hereafter referred as IPUMSI countries; Table 1).

However, since the main aim of this study was to estimate internal human migration ows for malaria endemic countries for which migration data are not available, ultimately, models where the linear

Figure 1. Schematic overview of the approach used to estimate the 5-year (20052010) internal human migration ows for every Pf and Pv endemic country. The preparation of the response variable and covariates is described in the yellow and orange panels, respectively. The modelling steps are outlined in the green panels and the estimation of the 5-year internal migration ows is described in the blue panel.

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predictors were common across all countries located in the same continent were constructed (under the assumption of homogeneity of the process along the space). To investigate possible nonlinear relationships, models where linear predictors were replaced by additive predictors, using a Generalized Additive Modelling (GAM) framework66, were also explored.

GAM is a type of regression that, while preserving the functionality of using linear terms, allows covariates to have different and possibly opposite effects on the response variable by incorporating regression coefcients with smooth nonlinear form (Fig. 2).

Thus, all possible combinations of covariates (listed in Table 2 and Supplementary Table 2) were tested in a logistic regression model and then only the linear predictors of all continuous covariates of the best predictive logistic regression model were also modelled using a GAM.

For each continent, the overall combinations of covariates and model types were explored using a multi-step approach to identify the model with the greatest predictive power in countries for which migration data were not available. The best model was then selected using a leave-one-out cross-validation approach67 in which the observed proportion of migrants in j previously residing in i for all countries except one were used for tting models, that were subsequently used to predict the proportion of migrants in j previously residing in i in the withheld country. The correlation coefcient (R2) was

selected to measure the variance explained after verifying homoscedasticity and testing overdispersion using a chi-squared test. This process was then repeated through iteratively withholding one country at the time. For each model, the R2 values for all withheld countries were averaged and used to rank each models according to their predictive power averaged across all withheld countries (Fig. 3). The overall best predictive model for each continent (Supplementary Table 3) was then used to predict the proportion of migrants residing in j who were previously residing in i for every malaria endemic country located in the corresponding continent (refer to Supplementary Table 4a,b and c for summary statistics of each best predictive model for Africa, Asia, and Latin America and the Caribbean, respectively).

Finally, in order to estimate the total number of people that migrated from i to j between 2005 and 2010 (Figs 46), for each country the predicted proportion of migrants residing in j was multiplied by the 2010 total population in j; with the latter calculated using either the corresponding WorldPop6870 or the

Gridded Population of the World version 4 (GPWv4)71 dataset adjusted to match United Nations Population Division (UNPD) estimates for 2010 (ref. 72). Refer to the Data collection and preparation subsection section below for a detailed description of how the population datasets mentioned above were identied and used.

Both model selection and prediction were performed using an R73 script contained in the WorldPop-InternalMigration-v1 code74 briey described in the Code availability subsection below.

Data collection and preparation

In most of the countries available through the online IPUMSI database, internal migration variables were recorded by asking respondents either their administrative unit of residence 15, 5, or 1 prior to the census, or their previous residence and the number of years they are residing in the current locality. Considering that 5-year was the temporal interval available for most of the countries in the IPUMSI database and the fact that it has been demonstrated that both 1- and 5-year census-based internal migration data generally align well with shorter-term population movements in terms of relative strength of connections56,58, the 5-year migration data were used in this study. This maximised the amount of data that could be used to t the gravity models subsequently used for predicting internal migration ows for every malaria endemic country. Thus, for each country listed in Table 1, harmonized, census-based 5-year internal migration data were extracted from the most recent census microdata available through the IPUMSI database61, downloaded locally, and eventually uploaded into a PostgreSQL database using a Microsoft Visual Studio 2010 user interface. The IPUMSI data stored in the PostgreSQL database were subsequently queried, using SQL, to quantify the number of people that migrated from each subnational administrative unit i to every other subnational administrative unit j during the 5-year timespan. These numbers were then matched to the corresponding country administrative unit spatial dataset, extracted from either the Global Administrative Areas (GADM)75 or the Global Administrative Unit Layers (GAUL)76 database, in a GIS environment. This was done by manually adding a unique ID to each spatial unit corresponding to the one in the PostgreSQL database (hereafter referred as IPUMSID). In some cases, depending on the country, either the spatial detail of the IPUMSI migration data had to be reduced to match the lower spatial detail of the corresponding administrative unit dataset or spatially contiguous units in the administrative unit dataset had to be merged together to match the lower spatial detail of the IPUMSI migration data. In some other cases, IPUMSIDs had to be edited or spatially contiguous units in the administrative unit dataset had to be merged together to match the reorganisation of the administrative units during the 5 years prior to the census. Finally, before calculating the migration ows between administrative units, another SQL query was used to classify each person in the census sample as either an internal migrant (1) or not (0). Examples of SQL queries used to perform the tasks described above are included in the WorldPop-InternalMigration-v1 code74 briey described in the Code availability subsection below.

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Unit level Census data source Administrative unit data

source

AFRICA CMR 2005 10 58 2 Central Bureau of Census and Population

Studies

Continent ISO code Census Year Census sample

(%)

No. of

units

GADM

AFRICA GHA 2000 10 10 1 Ghana Statistical Services GADM AFRICA GIN 1996 10 34 2 National Statistics Directorate GADM AFRICA MWI 2008 10 31 1 National Statistical Ofce GADM AFRICA MLI 1998 10 47 2 National Directorate of Statistics and

Informatics

GADM

AFRICA SEN 2002 10 34 2 National Agency of Statistics and

Demography

GADM

AFRICA ZAF 2007 2 9 1 Statistics South Africa GADM AFRICA UGA 2002 10 56 1 Bureau of Statistics GADM AFRICA ZMB 2010 10 72 2 Central Statistics Ofce GADM AFRICA EGY 2006 10 27 1 Central Agency for Public Mobilization and

Statistics

GADM

AFRICA MAR 2004 5 18 1 Department of Statistics GADM ASIA ARM 2001 10 11 1 National Statistical Service GADM ASIA KGZ 1999 10 39 2 National Statistical Committee GAUL ASIA IND 1999 0.07 32 1 Ministry of Statistics and Programme

Implementation

GADM

ASIA IDN 2010 10 27 1 BPS Statistics Indonesia GADM ASIA THA 2000 1 76 1 National Statistical Ofce GADM ASIA KHM 2008 10 24 1 National Institute of Statistics GADM ASIA CHN 1990 1 30 1 National Bureau of Statistics GADM ASIA MYS 2000 2 15 1 Department of Statistics GAUL ASIA PHL 2000 10 77 1 National Statistics Ofce GADM ASIA VNM 2009 15 63 2 General Statistics Ofce GADM ASIA MNG 2000 10 21 1 National Statistical Ofce GADM ASIA FIJ 2007 10 8 2 Bureau of Statistics GADM LAC ARG 2001 10 24 1 National Institute of Statistics and Censuses GADM LAC BOL 2001 10 35 1 National Institute of Statistics GAUL LAC BRA 2010 5 27 1 Institute of Geography and Statistics GADM LAC COL 2005 10 35 1 National Administrative Department of

Statistics

GADM

LAC CRI 2000 10 7 1 National Institute of Statistics and Censuses GADM LAC DOM 2010 10 32 1 National Statistics Ofce GADM LAC ECU 2010 10 23 1 National Institute of Statistics and Censuses GADM LAC SLV 2007 10 14 1 Department of Statistics and Censuses GADM LAC HTI 2003 10 10 1 Institute of Statistics and Informatics GADM LAC MEX 2010 10 32 1 National Institute of Statistics, Geography,

and Informatics

GADM

LAC NIC 2005 10 15 1 National Institute of Information

Development

GADM

LAC PER 2007 10 25 1 National Institute of Statistics and

Informatics

GADM

LAC VEN 2001 10 23 1 National Institute of Statistics GADM LAC CUB 2002 10 15 1 Ofce of National Statistics GADM LAC JAM 2001 10 14 1 Statistical Institute GADM LAC URY 2011 10 19 1 National Institute of Statistics GADM

Table 1. Summary information about the edited IPUMSI 5-year internal migration microdata and the administrative unit datasets used to estimate the 5-year (20052010) internal human migration ows for every malaria endemic country In the 1st column, LAC stands for Latin America and the Caribbean. In the 2nd column, countries are indicated using their ISO three letter country codes94 (refer to http://www.nationsonline.org/oneworld/country_code_list.htm

Web End =http://www. http://www.nationsonline.org/oneworld/country_code_list.htm

Web End =nationsonline.org/oneworld/country_code_list.htm for a list of all world countries and their ISO codes).

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Response variable and covariatesFor each country, the response variable, or the proportion of migrants residing in j in the census year that were residing in i 5 years prior to the census, was obtained by dividing the number of migrants residing in j in the census year that were residing in i 5 years prior the census by the total population residing in j in the census year; with both numbers based only on the information contained in IPUMSI census samples.

The administrative units spatially matching the IPUMSI migration microdata were used to calculate the distance between each pair of administrative units, their area, total population, and proportion of urban population. These main covariates (Table 2), along with other covariates derived from them (Supplementary Table 2), represent the pull and push migration factors, known to inuence internal migration59,60,77, that were used to extend the basic gravity model proposed by Zipf64.

Other factors, including environmental factors59,60, and country-specic factors, such as literacy and percentage of male population59 or infrastructure and transportation78, were not used because (i) the factors listed in the previous paragraph alone proved to be able to explain most of the variance in the gravity models of Garcia et al.59, and (ii) only globally available datasets were explored in order to consistently model internal migration across all countries.

Figure 2. Variation of the effect of the distance between administrative units (dij) on the predicted proportion of migrants in j in the census year that were previously residing in i (solid line) and 0.95 condence intervals (dashed lines) as estimated by using a GAM. The rug plot (i.e., the vertical lines along the x axis) represents the distribution of the observed dij values. This example shows the result obtained using data for all countries located in Latin America and the Caribbean.

Dataset Temporal

coverage

Format Type Resolution Source Main covariate

Subnational administrative

unit boundaries

Distance (DISTIJ) and contiguity (CONTIJ)

between administrative units and their area

(AREAI and AREAJ)

Vector

Categorical

GADM

Subnational administrative

unit boundaries

Distance (DISTIJ) and contiguity (CONTIJ)

between administrative units and their area

(AREAI and AREAJ)

Vector

Categorical

GAUL

Population count (adjusted

to match UNPD estimates)

2010 Raster Continuous 3 arc seconds WorldPop Data

Citation 1

Total population (POPI and POPJ) in each

administrative unit

Population count (adjusted

to match UNPD estimates)

2010 Raster Continuous 30 arc seconds GPWv4 Total population (POPI and POPJ) in each

administrative unit

MODIS 500 m Global Urban

Extent

2000/2001

Raster

Categorical

(binary)

Proportion of urban population

(URBANPROPI and URBAN PROPJ) in each

administrative unit

15 arc seconds

Schneider et al.

Table 2. Summary information about the source datasets and the main covariates tested in the spatial gravity models and used to derive additional covariates (Supplementary Table 2) for improving the predictive power of the models.

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Figure 3. Boxplots showing the distribution of all R2-values, for each withheld country, for all logistic regression (a,c,e) and GAM (b,d,f) models explored for Africa (a,b), Asia (c,d) and Latin America and the Caribbean (e,f). The red lines represent the best averaged R2 values used to select the best predictive model for each continent (Supplementary Table 3) while the red dots represent the R2 values, for all withheld countries, calculated using the best predictive model referring to the continent in which they are located.

Calculating response variable and covariates

For each country, the total population in each administrative unit was calculated using the corresponding WorldPop79 (Data Citation 1) or GPWv4 (ref. 80) population count raster dataset adjusted to match UNPD estimates for 201072. The GPWv4 datasets were resampled to the spatial resolution of the WorlpPop datasets and used only for countries for which the WorldPop datasets were not available (Supplementary Table 1).

The area of each unit was calculated using each country vector administrative unit dataset projected to the most appropriate country-specic projected coordinate system, in order to minimize areal distortion, and ultimately reprojected to GCS WGS84.

The proportion of people in urbanized areas in each unit was calculated using the MODIS 500 m Global Urban Extent raster dataset81,82. The latter was converted to vector polygons, using the ArcGIS Raster to Polygon tool83, and intersected with the reprojected country vector administrative unit dataset using the ArcGIS Intersect tool83. Then, both the intersect output (containing polygons representing the total urban area within each unit uniquely identied by its IPUMSID) and the country vector administrative unit dataset were rasterized, at the resolution of the corresponding raster population dataset (i.e., 3 arc seconds 3 arc equals to approximately 100 m at the equator), and co-registered with it.

The two raster outputs, along with the population count raster dataset, were then input to the ArcGIS Zonal Statistics as Table tool83 to generate two tables containing the total population and urban

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100'0"W

500'0"E

00'0"

100'0"E

200'0"E

300'0"E

400'0"E

300'0"N 200'0"N 100'0"N 00'0" 100'0"S 200'0"S 300'0"S

400'0''S

Togo

1:16,000,000

Kenya

Estimated migration flow

Highest

Lowest

0 1,000 2,000

Km

1:16,500,000

Figure 4. Estimated internal human migration ows between subnational administrative units for every malaria endemic country in Africa (Supplementary Table 1). Coordinates for all three panels refer to GCS WGS 1984. For illustrative purposes, subnational unit boundaries are shown only in the insets and the colour ranges used to represent the ows are country-specic (refer to Supplementary Fig. 1 for additional close-up views of internal migration ows in Africa).

population in each unit (with the rasterized administrative units and thus their IPUMSIDs used to dene the zones). Subsequently, both tables were joined to the attribute table of the vector administrative unit dataset, using the IPUMSID eld to perform the join operation, and the proportion of urban population in each unit was calculated simply dividing its urban population by its total population.

The geodesic distance between each pair of administrative units, with the latter represented by their centroids, was calculated using the ArcGIS Generate Near Table (Analysis) tool83. The IN_FID and NEAR_FID elds (identifying the administrative unit of origin and destination, respectively) in the output distance table were then used for joining twice the centroid attribute table using the centroid ID eld to perform the join operation. Since the centroid attribute table contains the attributes of each

SCIENTIFIC DATA | 3:160066 | DOI: 10.1038/sdata.2016.66 8

400'0"E

800'0"E

600'0"E

0 1,500 3,000

1600'0"E

Estimated migration flowHighest

Lowest

1000'0"E

1400'0"E

1200'0"E

1800'0"

Malaysia

Figure 5. Estimated internal human migration ows between subnational administrative units for every malaria endemic country in Asia (Supplementary Table 1). Coordinates for all three panels refer to GCS WGS 1984. For illustrative purposes, subnational unit boundaries are shown only in the insets and the colour ranges used to represent the ows are country-specic (refer to Supplementary Fig. 2a,b for additional close-up views of internal migration ows in Asia).

administrative unit represented by the corresponding centroid, the join operation allowed to generate a distance table containing all pairs of origin and destination administrative units along with their IPUMSIDs and attributes including the units area, total population, and proportion of urban population. Origin and destination IPUMSID elds were then renamed NODEI and NODEJ, respectively.

A contiguity table containing information about spatial contiguity of administrative units (dened based on polygons sharing an edge) was generated using the ArcGIS Generate Spatial Weights Matrix tool83 and subsequently joined with the distance table to obtain a new table containing all main covariates, listed in Table 2, calculated at the unit level. This join operation (based on both the NODEI and NODEJ eld the in the distance table and the corresponding IPUMSID and NID eld in the contiguity table) was performed through two different R scripts depending on whether the country is an IPUMSI or a non-IPUMSI countries. In particular, the R script for the IPUMSI countries added to the new table a MIGIJ eld containing the number of people that migrated from each NODEI to each other NODEJ according to the IPUMSI migration microdata and calculated the response variable.

Finally, on a continent basis, all IPUMSI country tables were merged together and input to an R73

script that generated the additional covariates listed in Supplementary Table 2, identied the best predictive model for each continent, as described in the previous section, and was used to estimate the 5-year (20052010) internal human migration ows for every malaria endemic country using the best predictive model selected for the corresponding continent.

All operations described above, excluding the reprojection of the vector administrative unit datasets and the calculation of their surface areas, for all IPUMSI and non-IPUMSI countries, were performed using the WorldPop-InternalMigration-v1 code74 briey described in the Code availability subsection below.

Code availability

The WorldPop-InternalMigration-v1 code74, used to produce the open access archive of estimated 5-year (20052010) internal human migration ows described in this article, is publicly available through Figshare. It consists of 1) a Microsoft Visual Studio 2010 user interface allowing users to upload the IPUMSI census microdata to a PostgreSQL database; 2) example SQL queries that were used to match the spatial detail of the IPUMSI migration data to spatial detail of the corresponding administrative unit dataset and to identify internal migrants within the IPUMSI census samples 3) an ArcToolbox geoprocessing tool82 that assigns a unique ID to each administrative unit and calculates the corresponding total population and proportion of urban population; 4) a Python84/ArcPy83 script that

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500'0"N 300'0"N 100'0"N 00'0" 100'0"S

Km

1:36,000,000

Yemen

1:27,000,000

1200'0"W

300'0"N 200'0"N 100'0"N 00'0" 100'0"S 200'0"S 300'0"S 400'0"S 500'0"S

1100'0"W

1000'0"W

900'0"W

800'0"W

700'0"W

600'0"W

500'0"W

400'0"W

Dominican Republic

1:9,000,000

Belize

1:7,000,000

Colombia

Estimated migration flowHighest

Lowest

0 1,000 2,000 Km

Figure 6. Estimated internal human migration ows between subnational administrative units for every malaria endemic country in Latin America and the Caribbean (Supplementary Table 1). Coordinates for all three panels refer to GCS WGS 1984. For illustrative purposes, subnational unit boundaries are shown only in the insets and the colour ranges used to represent the ows are country-specic (refer to Supplementary Fig. 3 for additional close-up views of internal migration ows in Latin America and Caribbean).

1:22,000,000

creates two tables, one containing spatial contiguity information between each pair of administrative units (contiguity.csv) and another one containing the ISO country code, the continent in which the country is located, the distance between each pair of administrative units, their total population, proportion of urban population, surface area, and the geographic coordinates (GCS WGS84) of their centroid (distance.csv); 5) two R73 scripts, one for the IPUMSI countries used to query the IPUMSI migration microdata loaded in the PostgreSQL database, calculate the response variable, and join the query result with the two output tables of the python script, and another one for the non-IPUMSI countries used just to join together the two output tables of the python script; and 6) an R73 script that performs the model selection and estimates the 5-year (20052010) internal human migration ows between subnational administrative units.

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Name Description Format ISO_5yr_InternalMigFlows_2010 Estimated 5-year (20052010) internal human migration ows between subnational administrative units. CSV ISO_AdminUnit_Centroids Centroids representing the subnational administrative units used to estimate the 5-year internal human migration

ows (with centroid IPUMSIDs matching polygon NODEIs and NODEJs in the corresponding

ISO_5yr_InternalMigFlows_2010.csv dataset).

ISO_AdminUnit_Edits_README Description of the edits needed to match the spatial detail of the GADM/GAUL subnational administrative units to

the spatial detail of the IPUMSI census-based migration microdata.

Table 3. Name (ISO represent the country the dataset refers to), description, and format of all les available for each county listed in Supplementary Table 1 Readme les are distributed along with the other two datasets only if the administrative unit dataset has been edited, to match the spatial resolution of the IPUMSI migration microdata, before extracting the centroids.

All available sets of code are named progressively and must be run sequentially according to the order in which they are presented above. They are also internally documented in order to both briey explain their purpose and, when required, guide the user through their customization.

Data Records

All datasets described in this article, referring to all Pf and Pv endemic countries listed in Supplementary Table 1, are publicly and freely available both through the WorldPop Dataverse Repository (Data Citation 2) and the WorldPop website (http://www.worldpop.org.uk/data/data_sources/

Web End =http://www.worldpop.org.uk/data/data_sources/). However, it is important to note that while the datasets stored in the Dataverse Repository represent the datasets produced at the time of writing, and will be preserved in their published form, the datasets stored on the WorldPop website may be updated as more recent IPUMSI migration data for the countries listed in Table 1, become available. Similarly, the datasets stored on the WorldPop website may be updated as IPUMSI census-based migration microdata become available for additional malaria endemic and non-endemic countries located in Africa, Asia, Latin America and the Caribbean. Indeed, the availability of migration data for additional countries may enable further improvements of the predictive power of the gravity models used to estimate the internal migration ows. For each county, the corresponding internal migration dataset, along with a point dataset showing the nodes of the migration network, (Table 3) can be obtained by downloading the corresponding zipped archive associated with the continent in which the country of interest is located.

Technical Validation

Goodness of t and error p-value

All countries available in the IPUMSI database were used to assess the accuracy of the predicted proportion of migrants in j in the census year that were previously residing in i 5 years prior to the census by comparing them with the corresponding observed values from the IPUMSI migration microdata. For each country, the goodness of t (R2) between predicted and observed values and the corresponding error P-value, representing the average probability that predicted migration values lay outside the distribution of the observed values, are reported in Table 4 below. Both metrics were derived using (i) the observed IPUMSI migration ows from each i to any other j and (ii) the predicted IPUMSI-based migration ows calculated by multiplying the predicted proportion of migrants residing in j in the census year by the IPUMSI-based total number of people residing in j in the census year.

Usage Notes

The estimated internal human migration ows between subnational administrative units can be used to support a range of applications from planning interventions, to measuring progress, designing strategies, and predicting response variables that are intrinsically dependent on migration ows and internal connectivity.

Ongoing work involves the integration of these datasets with malaria prevalence raster datasets8587

in order to inform local elimination and global eradication planning by identifying subnational communities of malaria movement and sources and sinks of transmission within them36,43,58. Similarly,

these datasets could be used to better model the spread and improve understanding of the drivers of the distributions of other infectious diseases, such as West Nile Virus, schistosomiasis, river blindness, and yellow fever, which are endemic in some of the countries listed in Supplementary Table 1. Additionally there are many uses of these data beyond infectious disease dynamics, in the elds of trade, demography, transportation and economics, for example.

There are a number of limitations, caveats, and assumptions inherent in the approach that should be considered when using the datasets outlined here. For consistency, internal migration ows were estimated using a xed set of pull and push factors common to all countries and thus only a limited number of covariates were used to t the gravity-type spatial interaction models and to create predictions. For this reason, as is a trade-off in the production of generalizable models, the model t varied between countries and for some of them, such as Malawi, China, Cambodia, India, and Venezuela (Table 4), poor

SCIENTIFIC DATA | 3:160066 | DOI: 10.1038/sdata.2016.66 11

SHP

TXT

Continent ISO code R Error P-value AFRICA CMR 0.60 0.07 AFRICA EGY 0.21 0.20 AFRICA GHA 0.68 0.21 AFRICA GIN 0.39 0.09 AFRICA MAR 0.52 0.14 AFRICA MLI 0.51 0.14 AFRICA MWI 0.02 0.06 AFRICA SEN 0.54 0.12 AFRICA UGA 0.50 0.11 AFRICA ZAF 0.49 0.23 AFRICA ZMB 0.37 0.22 ASIA ARM 0.11 0.16 ASIA CHN 0.08 0.19 ASIA FJI 0.16 0.28 ASIA KGZ 0.23 0.08 ASIA IND 0.11 0.15 ASIA IDN 0.70 0.06 ASIA THA 0.27 0.09 ASIA KHM 0.15 0.11 ASIA MYS 0.76 0.14 ASIA PHL 0.35 0.06 ASIA VNM 0.23 0.13 ASIA MNG 0.61 0.14 LAC ARG 0.82 0.05 LAC BOL 0.62 0.07 LAC BRA 0.54 0.16 LAC COL 0.85 0.07 LAC CRI 0.57 0.17 LAC CUB 0.36 0.20 LAC DOM 0.71 0.08 LAC ECU 0.68 0.11 LAC SLV 0.77 0.08 LAC HTI 0.40 0.14 LAC JAM 0.52 0.12 LAC MEX 0.76 0.08 LAC NIC 0.46 0.15 LAC PER 0.66 0.10 LAC URY 0.84 0.04 LAC VEN 0.12 0.13

Table 4. Prediction accuracy of the best predictive models listed in Supplementary Table 3. The goodness of t (R2) and error P-value are provided for all IPUMSI countries (including those that are not malaria endemic) listed in Table 1. Error P-value is here dened as the average probability that predicted migration values do not belong to the observed migration dataset.

ts could be improved by considering additional, locally-specic migration drivers that could help to increase the percentage of variance explained60,78. Other limitations are the fact that migration models were tted using only a small sample (ranging between 0.07 and 10%) of the full census for each country, and that in each sample a small number of households were swapped across administrative units. Moreover, the spatial detail at which migration is captured and summarized varies by country. Because of this, for some countries, the modelled role of some of the pull and push factors, may not have been captured at the spatial level at which they inuence migration as recorded in the census. It is also important to consider that the underlying migration data are based only on permanent movements

SCIENTIFIC DATA | 3:160066 | DOI: 10.1038/sdata.2016.66 12

captured by the census and other types of migrations, such as seasonal movements and forced

displacements, may be not captured by the model8890.

The two main assumptions behind the approach presented here are that for each country (i) the census samples are considered to be representative at the administrative unit level at which migration was recorded and (ii) the percentage of people migrating between administrative units is considered to be constant over time. Regarding the second assumption, it is important to highlight that the use of census data from many years ago for some countries may have generated inaccurate estimates for the period considered in this study (i.e., 20052010), for example because of major changes in the countries socioeconomic conditions from the time period covered by the census (e.g., the rapid economic development and urbanization that has occurred in China during the last two decades91,92). Similarly, in some other countries, either the presence of conicts93 or the occurrence of natural disasters88,89 during the specic time period covered by the census may have produced uctuations in the number of internal migrants and consequently biased results for the period considered in this study.

Finally, the estimated internal ows represent modelling outputs generated using ancillary covariate datasets, and thus, to avoid circularity they should not be used to make predictions or explore relationships with any of these ancillary datasets. It is also important to note that these ancillary datasets are modelling outputs in themselves and thus they have a degree of uncertainty that will carry over into the migration estimates.

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Acknowledgements

The authors wishes to acknowledge the statistical ofces that provided the underlying data making this research possible: Central Bureau of Census and Population Studies, Cameroon; Ghana Statistical Services; National Statistics Directorate, Guinea; National Statistical Ofce, Malawi; National Directorate of Statistics and Informatics, Mali; National Agency of Statistics and Demography, Senegal; Statistics South Africa; Bureau of Statistics, Uganda; Central Statistics Ofce, Zambia; Central Agency for Public Mobilization and Statistics, Egypt; Department of Statistics, Morocco; National Statistical Service, Armenia; National Statistical Committee, Kyrgyzstan; Ministry of Statistics and Programme Implementation, India; BPS Statistics Indonesia; National Statistical Ofce, Thailand; National Institute of Statistics, Cambodia; National Bureau of Statistics, China; Department of Statistics, Malaysia; National Statistics Ofce, Philippines; General Statistics Ofce, Vietnam; National Statistical Ofce, Mongolia; Bureau of Statistics, Fiji; National Institute of Statistics and Censuses, Argentina; National Institute of Statistics, Bolivia; Institute of Geography and Statistics, Brazil; National Administrative Department of Statistics, Colombia; National Institute of Statistics and Censuses, Costa Rica; National Statistics Ofce, Dominican Republic; National Institute of Statistics and Censuses, Ecuador; Department of Statistics and Censuses, El Salvador; Institute of Statistics and Informatics, Haiti; National Institute of Statistics, Geography, and Informatics, Mexico; National Institute of Information Development, Nicaragua; National Institute of Statistics and Informatics, Peru; National Institute of Statistics, Venezuela; Ofce of National Statistics, Cuba; Statistical Institute, Jamaica; National Institute of Statistics, Uruguay. A.S. is supported by funding from the Bill & Melinda Gates Foundation (OPP1106427, 1032350). A.J.T. is supported by funding from NIH/NIAID (U19AI089674), the Bill & Melinda Gates Foundation (OPP1106427, 1032350, 1134076), the Clinton Health Access Initiative, National Institutes of Health, and a Wellcome Trust Sustaining Health Grant (106866/Z/15/Z). This work forms part of the outputs of WorldPop (http://www.worldpop.org

Web End =www.worldpop.org) and the Flowminder Foundation (http://www.flowminder.org

Web End =www.owminder.org ). The funders had no role in study design, data collection and analysis, decision to publish, and preparation of the manuscript.

Author Contributions

A.S. coordinated the study, undertook data collection and assembly, and drafted the manuscript. A.S. and T.J.B. undertook data analyses, generated the internal migration datasets, and prepared the tables and gures. A.S. and L.S. performed the technical validation of the internal migration datasets. A.S., T.J.B., N.R., and A.J.T. conceptualized the study. A.S., T.J.B., N.R., E.z.E.S., C.P., N.T., I.C.W., J.D.S., A.J.G., and

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L.S. developed the WorldPop-InternalMigration-v1 code. T.J.B. implemented the R script used for model selection and prediction. C.P., A.J.G., I.C.W., and J.D.S. aided with data assembly. A.J.T. aided drafting the manuscript and conceived the study. All authors read and approved the nal version of the manuscript.

Additional Information

Supplementary Information accompanies this paper at http://www.nature.com/scidata

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Competing nancial interests: The authors declare no competing nancial interests.

How to cite this article: Sorichetta, A. et al. Mapping internal connectivity through human migration in malaria endemic countries. Sci. Data 3:160066 doi: 10.1038/sdata.2016.66 (2016).

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Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/

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The Author(s) 2016

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