Advancing digital methods in the fight against communicable diseases Guillaume Chabot-Couturea,*, Vincent Y. Seamanb, Jay Wengerb, Bruno Moonenb and Alan Magillb a
Institute for Disease Modeling, Intellectual Ventures, Bellevue, 98005, USA; bBill & Melinda Gates Foundation, Seattle, 98109, USA
COMMENTARY
Int Health 2015; 7: 79–81 doi:10.1093/inthealth/ihv008
*Corresponding author: Tel: +1 425 691 3319; E-mail: [email protected]
Important advances are being made in the fight against communicable diseases by using new digital tools. While they can be a challenge to deploy at-scale, GPS-enabled smartphones, electronic dashboards and computer models have multiple benefits. They can facilitate program operations, lead to new insights about the disease transmission and support strategic planning. Today, tools such as these are used to vaccinate more children against polio in Nigeria, reduce the malaria burden in Zambia and help predict the spread of the Ebola epidemic in West Africa. Keywords: Ebola, Epidemiology, GIS, GPS, Malaria, Polio
Modern digital methods have an essential role to play in enabling disease control and eradication programs. For example, GPSenabled smart phones are increasingly used in the field to collect surveys, collect the location of disease cases and to follow up on patients, while increasing the availability and the accuracy of the data created. In a world where the amount of data available is increasing rapidly, dashboards (a graphical presentation of key performance indicators), maps and computer simulations are quickly showing their usefulness in making wellinformed decisions. Advances in computer modeling of diseases are also being used to inform strategic questions and help make sense of complex modes of transmission. However, these technologies can be difficult to operationalize at a meaningful scale, often requiring new capital equipment, training and routine maintenance. Nonetheless, these tools have already contributed to advances in the fight against communicable diseases—monitoring polio vaccination campaigns in Nigeria, improving distribution of antimalarial test-and-treat in Zambia, and to helping control the spread of Ebola in West Africa. At the data collection stage, digital tools—most often GPSenabled smartphones and tablets—provide a number of advantages over non-digital methods. Paper forms do not need to be printed or carried, minimizing data entry and transcription errors, and useful meta-data, such as date, time and geo-location, can be automatically recorded. Since 2010, the polio eradication program in Nigeria has been pioneering the use of geographic information system (GIS) and GPS technology to reach more children during large scale vaccination campaigns. These efforts are an important part of an ensemble of innovations implemented over the past few years that have led to a significant improvement in the quality of the vaccination efforts, helping bring Nigeria to the verge of poliovirus eradication in 2014.1,2
The scale of house-to-house polio vaccination campaigns makes logistics and monitoring inherently difficult: millions of children must be reached by vaccinators in only a few days often operating in harsh environments. In order to make this possible, a detailed and accurate microplan is required. This microplan includes a map showing where settlements are located, how many children must be vaccinated and also describes how the vaccination work must be divided among multiple teams. A village not on the microplan is often missed entirely and usually repeatedly in ensuing campaigns. The resulting pockets of unvaccinated children can serve as a reservoir for the polio virus and delay (or even prevent) eradication. In order to address these issues on a large scale, the Nigerian program embarked on an ambitious effort in 2012 to locate and map over 100 000 settlements in the 10 northern high-risk states using high-resolution satellite imagery and field GPS data collection. Administrative boundaries, road networks, village names and important points of interest (health facilities, schools, markets, etc.) were collected by survey teams and then layered over the imagery basemap to produce an effective microplanning template. These maps helped to ensure that all settlements were included in the microplans, to determine the required number of teams and to deploy those teams in the most efficient way. As a result, many villages that had previously been missing from the microplans were discovered and visited by vaccination teams for the first time. The Nigerian program then added a second technological innovation: they equipped each vaccination team with a GPS-enabled smartphone that collected a time-stamped position every 2 minutes as they traveled from village to village. At the end of each day, the vaccination paths for all teams were overlaid on the microplan map for review, allowing for corrective actions the
© The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: [email protected].
79
Downloaded from http://inthealth.oxfordjournals.org/ at University of North Dakota on June 18, 2015
Received 23 December 2014; revised 23 January 2015; accepted 26 January 2015
G. Chabot-Couture et al.
80
ecology, climate and human movement patterns, testing eradication strategies on the ground across a number of scenarios can be expensive and impractical. By contrast, if these strategies can be tested in a computer first, the least effective strategies can be filtered out. Similarly, in designing novel tools like better drugs or better vaccines to fight malaria, determining their target product profile can help direct research efforts. For example, there is a clear need to design malaria interventions that can both interrupt transmission and clear the latent infection reservoir across high and low transmission conditions.13,14 Incorporating digital tools into a disease control program can be challenging as it requires the acquisition and maintenance of hardware, software and specialized expertise, and the training of field and management staff. In the field, digital devices can suffer accelerated wear-and-tear, internet and cellular connectivity can be an issue, and battery life must be able to support extended use.1,2 At headquarters, the digital system will include a database, a dashboard and possibly GIS software, all of which are likely to require specialized expertise to maintain and troubleshoot. Finding and hiring specialists can be difficult, but some of this need can be met by extending the training traditional data analysts and data managers receive. When it is not possible to hire extra staff, collaborations with universities and non-governmental organizations can help cover for the missing expertise, especially at the pilot stage.9,15 A host of digital and technological advances are providing key support to global disease control and eradication efforts. Their increasing adoption by control and eradication programs has the potential to augment the work of those involved, from field volunteers to professional epidemiologists, and to help accelerate the reduction of the global disease burden. However, in order to fully benefit from these new technologies, programs will need to expand their understanding and ability to operate the specialized data, software and hardware involved.
Author contributions: GCC and VS drafted the manuscript; JW, BM and AM critically revised the manuscript for intellectual content. All authors read and approved the final manuscript. GCC is guarantor of the paper. Funding: None. Competing interests: None. Ethical approval: Not required.
References 1 Gammino VM, Nuhu A, Chenoweth P et al. Using geographic information systems to track polio vaccination team performance: pilot project report. J Infect Dis 2014;210:S98–101. 2 Barau I, Zubairu M, Mwanza M N, Seaman V Y. Improving polio vaccination coverage in Nigeria through the use of geographic information system technology. J Infect Dis 2014;210:S102–10. 3 National Malaria Control Center, Zambia. http://www.nmcc.org.zm [accessed 1 February 2015] 4 Bousema T, Griffin JT, Sauerwein RW et al. Hitting hotspots: spatial targeting of malaria for control and elimination. PLoS Med 2012;9: e1001165.
Downloaded from http://inthealth.oxfordjournals.org/ at University of North Dakota on June 18, 2015
following day. The proportion of settlements visited provided a ‘geographic coverage’ that could be used to assess the quality of the campaign and pinpoint the areas that had been missed. Today, this vaccinator tracking system is operational throughout northern Nigeria, following over 12 000 teams in each campaign and providing feedback both locally and on a web-based dashboard that can be viewed by stakeholders worldwide.2 GIS/GPS technology is also used to support the malaria control program in Zambia. In 2007, 190 000 households were geotagged and targeted during an indoor residual spraying campaign. This information was used to determine the amount of insecticide and the number of field staff that would be needed, by area. The Zambian program also uses GPS-enabled phones during its mass screen-and-treat campaigns. During these campaigns, large populations of people are tested and given a course of anti-malarial drugs if they are infected. GPS-enabled phones also used during active case detection such that the location and characteristics of malaria cases can be reported rapidly.3 These types of efforts have been shown to improve situational awareness, reduce how often clinics run out of medicine and allow the program to plan more iteratively. With data on the spatial location of cases and the geography of the region, it is also possible to follow up on cases, to identify village-level hotspots where higher-than-average transmission takes place4–6 and to study how multiple risk factors combine to drive malaria incidence.7 Remote sensing can also be used to study the geography of disease transmission and uncover at-risk populations. For example, remote sensing imagery of Dar Es Salaam in Tanzania identified populations at high risk for malaria by looking for anopheles breeding sites in areas with a high water table.8 Altogether, these efforts can significantly accelerate the gains made by malaria control programs. The geography of disease (and of control efforts) is part of the large and diverse data sets that disease control programs collect. Making sense of all these data can be difficult. Statistical analysis and modeling can help to sort through them systematically and identify which indicators are the most predictive of future outbreaks. From there, best-in-class risk models can be built to combine these indicators and predict where disease outbreaks are most likely to happen. By combining these predictions with the knowledge and experience of local experts, the vulnerability of areas can be triangulated in a robust way and resources can be distributed where the need is greatest.9 Predicting the spread of disease is hard because the movement of people is not well understood. The increasingly widespread use of cellphones is making available new data on population mobility, both locally and regionally. Whereas the movement of people was mainly reconstructed from surveys, airline routes and the road network, people can now be tracked from origin to destination by studying the cell phone call records. Most relevant for disease transmission is movement from infected areas to disease-free areas. For example, recent work found specific movement patterns contributing to malaria transmission in Kenya,10 high-resolution GPS trackers have been used to map the mobility patterns of inner city children and show how they overlap,11 and most recently human migration models for West Africa were built to help the fight against Ebola.12 Using computer models to study disease transmission can be used to design robust eradication strategies. For a disease like malaria, where disease transmission depends on mosquito
International Health
5 Mosha JF, Sturrock HJ, Greenhouse B et al. Epidemiology of subpatent Plasmodium falciparum infection: implications for detection of hotspots with imperfect diagnostics. Malar J 2013;12:221. 6 Smith DL, Perkins TA, Reiner RC Jr et al. Recasting the theory of mosquito-borne pathogen transmission dynamics and control. Trans R Soc Trop Med Hyg 2014;108:185–97. 7 Riedel N, Vounatsou P, Miller JM et al. Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS). Malar J 2010;9:37. 8 Caldas de Castro M, Yamagata Y, Mtasiwa D et al. Integrated urban malaria control: a case study in Dar es Salaam, Tanzania. Am J Trop Med Hyg 2004;71:103–17.
11 Vazquez-Prokopec GM, Bisanzio D, Stoddard ST et al. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS One 2013;8:e58802. 12 Wesolowski A, Stresman G, Eagle N et al. Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones. Sci Rep 2014;4:5678. 13 The MalEra Consultative Group on Modeling. A research agenda for malaria eradication: modeling. PLoS Med 2011;8:e1000403. 14 Wenger EA, Eckhoff PA. A mathematical model of the impact of present and future malaria vaccines. Malar J 2013;12:126. 15 Hartung C et al. Open data kit: Tools to build information services for developing regions. Proc Int Conf Inf Commun Technol Dev 2010:1–11.
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Downloaded from http://inthealth.oxfordjournals.org/ at University of North Dakota on June 18, 2015
9 Upfill-Brown AM, Lyons HM, Pate MA et al. Predictive spatial risk model of poliovirus to aid prioritization and hasten eradication in Nigeria. BMC Med 2014;12:92.
10 Wesolowski A, Eagle N, Tatem AJ et al. Quantifying the impact of human mobility on malaria. Science 2012;338:267–70.
Institute for Disease Modeling, Intellectual Ventures, Bellevue, 98005, USA; bBill & Melinda Gates Foundation, Seattle, 98109, USA
COMMENTARY
Int Health 2015; 7: 79–81 doi:10.1093/inthealth/ihv008
*Corresponding author: Tel: +1 425 691 3319; E-mail: [email protected]
Important advances are being made in the fight against communicable diseases by using new digital tools. While they can be a challenge to deploy at-scale, GPS-enabled smartphones, electronic dashboards and computer models have multiple benefits. They can facilitate program operations, lead to new insights about the disease transmission and support strategic planning. Today, tools such as these are used to vaccinate more children against polio in Nigeria, reduce the malaria burden in Zambia and help predict the spread of the Ebola epidemic in West Africa. Keywords: Ebola, Epidemiology, GIS, GPS, Malaria, Polio
Modern digital methods have an essential role to play in enabling disease control and eradication programs. For example, GPSenabled smart phones are increasingly used in the field to collect surveys, collect the location of disease cases and to follow up on patients, while increasing the availability and the accuracy of the data created. In a world where the amount of data available is increasing rapidly, dashboards (a graphical presentation of key performance indicators), maps and computer simulations are quickly showing their usefulness in making wellinformed decisions. Advances in computer modeling of diseases are also being used to inform strategic questions and help make sense of complex modes of transmission. However, these technologies can be difficult to operationalize at a meaningful scale, often requiring new capital equipment, training and routine maintenance. Nonetheless, these tools have already contributed to advances in the fight against communicable diseases—monitoring polio vaccination campaigns in Nigeria, improving distribution of antimalarial test-and-treat in Zambia, and to helping control the spread of Ebola in West Africa. At the data collection stage, digital tools—most often GPSenabled smartphones and tablets—provide a number of advantages over non-digital methods. Paper forms do not need to be printed or carried, minimizing data entry and transcription errors, and useful meta-data, such as date, time and geo-location, can be automatically recorded. Since 2010, the polio eradication program in Nigeria has been pioneering the use of geographic information system (GIS) and GPS technology to reach more children during large scale vaccination campaigns. These efforts are an important part of an ensemble of innovations implemented over the past few years that have led to a significant improvement in the quality of the vaccination efforts, helping bring Nigeria to the verge of poliovirus eradication in 2014.1,2
The scale of house-to-house polio vaccination campaigns makes logistics and monitoring inherently difficult: millions of children must be reached by vaccinators in only a few days often operating in harsh environments. In order to make this possible, a detailed and accurate microplan is required. This microplan includes a map showing where settlements are located, how many children must be vaccinated and also describes how the vaccination work must be divided among multiple teams. A village not on the microplan is often missed entirely and usually repeatedly in ensuing campaigns. The resulting pockets of unvaccinated children can serve as a reservoir for the polio virus and delay (or even prevent) eradication. In order to address these issues on a large scale, the Nigerian program embarked on an ambitious effort in 2012 to locate and map over 100 000 settlements in the 10 northern high-risk states using high-resolution satellite imagery and field GPS data collection. Administrative boundaries, road networks, village names and important points of interest (health facilities, schools, markets, etc.) were collected by survey teams and then layered over the imagery basemap to produce an effective microplanning template. These maps helped to ensure that all settlements were included in the microplans, to determine the required number of teams and to deploy those teams in the most efficient way. As a result, many villages that had previously been missing from the microplans were discovered and visited by vaccination teams for the first time. The Nigerian program then added a second technological innovation: they equipped each vaccination team with a GPS-enabled smartphone that collected a time-stamped position every 2 minutes as they traveled from village to village. At the end of each day, the vaccination paths for all teams were overlaid on the microplan map for review, allowing for corrective actions the
© The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: [email protected].
79
Downloaded from http://inthealth.oxfordjournals.org/ at University of North Dakota on June 18, 2015
Received 23 December 2014; revised 23 January 2015; accepted 26 January 2015
G. Chabot-Couture et al.
80
ecology, climate and human movement patterns, testing eradication strategies on the ground across a number of scenarios can be expensive and impractical. By contrast, if these strategies can be tested in a computer first, the least effective strategies can be filtered out. Similarly, in designing novel tools like better drugs or better vaccines to fight malaria, determining their target product profile can help direct research efforts. For example, there is a clear need to design malaria interventions that can both interrupt transmission and clear the latent infection reservoir across high and low transmission conditions.13,14 Incorporating digital tools into a disease control program can be challenging as it requires the acquisition and maintenance of hardware, software and specialized expertise, and the training of field and management staff. In the field, digital devices can suffer accelerated wear-and-tear, internet and cellular connectivity can be an issue, and battery life must be able to support extended use.1,2 At headquarters, the digital system will include a database, a dashboard and possibly GIS software, all of which are likely to require specialized expertise to maintain and troubleshoot. Finding and hiring specialists can be difficult, but some of this need can be met by extending the training traditional data analysts and data managers receive. When it is not possible to hire extra staff, collaborations with universities and non-governmental organizations can help cover for the missing expertise, especially at the pilot stage.9,15 A host of digital and technological advances are providing key support to global disease control and eradication efforts. Their increasing adoption by control and eradication programs has the potential to augment the work of those involved, from field volunteers to professional epidemiologists, and to help accelerate the reduction of the global disease burden. However, in order to fully benefit from these new technologies, programs will need to expand their understanding and ability to operate the specialized data, software and hardware involved.
Author contributions: GCC and VS drafted the manuscript; JW, BM and AM critically revised the manuscript for intellectual content. All authors read and approved the final manuscript. GCC is guarantor of the paper. Funding: None. Competing interests: None. Ethical approval: Not required.
References 1 Gammino VM, Nuhu A, Chenoweth P et al. Using geographic information systems to track polio vaccination team performance: pilot project report. J Infect Dis 2014;210:S98–101. 2 Barau I, Zubairu M, Mwanza M N, Seaman V Y. Improving polio vaccination coverage in Nigeria through the use of geographic information system technology. J Infect Dis 2014;210:S102–10. 3 National Malaria Control Center, Zambia. http://www.nmcc.org.zm [accessed 1 February 2015] 4 Bousema T, Griffin JT, Sauerwein RW et al. Hitting hotspots: spatial targeting of malaria for control and elimination. PLoS Med 2012;9: e1001165.
Downloaded from http://inthealth.oxfordjournals.org/ at University of North Dakota on June 18, 2015
following day. The proportion of settlements visited provided a ‘geographic coverage’ that could be used to assess the quality of the campaign and pinpoint the areas that had been missed. Today, this vaccinator tracking system is operational throughout northern Nigeria, following over 12 000 teams in each campaign and providing feedback both locally and on a web-based dashboard that can be viewed by stakeholders worldwide.2 GIS/GPS technology is also used to support the malaria control program in Zambia. In 2007, 190 000 households were geotagged and targeted during an indoor residual spraying campaign. This information was used to determine the amount of insecticide and the number of field staff that would be needed, by area. The Zambian program also uses GPS-enabled phones during its mass screen-and-treat campaigns. During these campaigns, large populations of people are tested and given a course of anti-malarial drugs if they are infected. GPS-enabled phones also used during active case detection such that the location and characteristics of malaria cases can be reported rapidly.3 These types of efforts have been shown to improve situational awareness, reduce how often clinics run out of medicine and allow the program to plan more iteratively. With data on the spatial location of cases and the geography of the region, it is also possible to follow up on cases, to identify village-level hotspots where higher-than-average transmission takes place4–6 and to study how multiple risk factors combine to drive malaria incidence.7 Remote sensing can also be used to study the geography of disease transmission and uncover at-risk populations. For example, remote sensing imagery of Dar Es Salaam in Tanzania identified populations at high risk for malaria by looking for anopheles breeding sites in areas with a high water table.8 Altogether, these efforts can significantly accelerate the gains made by malaria control programs. The geography of disease (and of control efforts) is part of the large and diverse data sets that disease control programs collect. Making sense of all these data can be difficult. Statistical analysis and modeling can help to sort through them systematically and identify which indicators are the most predictive of future outbreaks. From there, best-in-class risk models can be built to combine these indicators and predict where disease outbreaks are most likely to happen. By combining these predictions with the knowledge and experience of local experts, the vulnerability of areas can be triangulated in a robust way and resources can be distributed where the need is greatest.9 Predicting the spread of disease is hard because the movement of people is not well understood. The increasingly widespread use of cellphones is making available new data on population mobility, both locally and regionally. Whereas the movement of people was mainly reconstructed from surveys, airline routes and the road network, people can now be tracked from origin to destination by studying the cell phone call records. Most relevant for disease transmission is movement from infected areas to disease-free areas. For example, recent work found specific movement patterns contributing to malaria transmission in Kenya,10 high-resolution GPS trackers have been used to map the mobility patterns of inner city children and show how they overlap,11 and most recently human migration models for West Africa were built to help the fight against Ebola.12 Using computer models to study disease transmission can be used to design robust eradication strategies. For a disease like malaria, where disease transmission depends on mosquito
International Health
5 Mosha JF, Sturrock HJ, Greenhouse B et al. Epidemiology of subpatent Plasmodium falciparum infection: implications for detection of hotspots with imperfect diagnostics. Malar J 2013;12:221. 6 Smith DL, Perkins TA, Reiner RC Jr et al. Recasting the theory of mosquito-borne pathogen transmission dynamics and control. Trans R Soc Trop Med Hyg 2014;108:185–97. 7 Riedel N, Vounatsou P, Miller JM et al. Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS). Malar J 2010;9:37. 8 Caldas de Castro M, Yamagata Y, Mtasiwa D et al. Integrated urban malaria control: a case study in Dar es Salaam, Tanzania. Am J Trop Med Hyg 2004;71:103–17.
11 Vazquez-Prokopec GM, Bisanzio D, Stoddard ST et al. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS One 2013;8:e58802. 12 Wesolowski A, Stresman G, Eagle N et al. Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones. Sci Rep 2014;4:5678. 13 The MalEra Consultative Group on Modeling. A research agenda for malaria eradication: modeling. PLoS Med 2011;8:e1000403. 14 Wenger EA, Eckhoff PA. A mathematical model of the impact of present and future malaria vaccines. Malar J 2013;12:126. 15 Hartung C et al. Open data kit: Tools to build information services for developing regions. Proc Int Conf Inf Commun Technol Dev 2010:1–11.
81
Downloaded from http://inthealth.oxfordjournals.org/ at University of North Dakota on June 18, 2015
9 Upfill-Brown AM, Lyons HM, Pate MA et al. Predictive spatial risk model of poliovirus to aid prioritization and hasten eradication in Nigeria. BMC Med 2014;12:92.
10 Wesolowski A, Eagle N, Tatem AJ et al. Quantifying the impact of human mobility on malaria. Science 2012;338:267–70.
